Is climate change driving urbanization in Africa?

Transcription

1 I climate change driving urbanization in Africa? J. Vernon Henderon, London School of Economic Adam Storeygard, Tuft Univerity Uwe Deichmann, World Bank Augut 30, 2014 Abtract Thi paper document a ignificant impact of climate variation on urbanization in ub-saharan Africa, primarily in more indutrialized and arid area. By lowering farm income, reduced moiture availability encourage migration to nearby citie, while wetter condition low it. Rural-urban income linkage are alo important. In region with a larger indutrial bae, reduced moiture hrink the agricultural ector and raie total income in nearby citie. However, if local citie depend entirely on ervicing agriculture, reduced moiture tend to reduce local urban income. Finally, the paper how that climate alo induce employment change within the rural ector itelf. Drier condition induce a hift out of farm activitie, epecially for women, into non-farm activitie, and epecially out of the work force. Overall, thee finding imply a trong link between climate and urbanization in Africa. JEL Code: O10, O55, R12 Key word: Africa, Urbanization, Climate Change The author thank Nat Tharnpanich, Marine Gaier, Kofi Acquah, Kevin Proulx, and Louie Bernard for reearch aitance, and Deborah Balk, Ram Fihman, Margaret McMillan, Mark Robert and eminar participant at Barcelona, Columbia, CUNY Intitute for Demographic Reearch, GWU, LSE, LSU, Tuft, William, and the Population Aociation of America and Urban Economic Aociation meeting for helpful comment and uggetion. We are indebted to Alexander Moradi for pointing u to the Oxford Regional Economic Atla, Africa (1965) for data on earlier indutrialization in Africa, a well a for helpful comment on an earlier verion of the paper. Financial upport from the World Bank Knowledge for Change Program i gratefully acknowledged. The finding, interpretation, and concluion expreed in thi paper are entirely thoe of the author. They do not necearily repreent the view of the International Bank for Recontruction and Development/World Bank and it affiliated organization, or thoe of the Executive Director of the World Bank or the government they repreent. 1

2 I climate change driving urbanization in Africa? J. Vernon Henderon, Adam Storeygard, and Uwe Deichmann 1. Introduction Sub-Saharan Africa (hereafter Africa) i urbanizing quickly, with citie and town growing at an annual rate of cloe to four percent over the lat 20 year. It urban population of 335 million now exceed the total population of the United State. Neverthele, almot two-third of Africa population till live in rural area. How urbanization evolve in Africa over the next decade will determine where people and job locate and where public ervice hould be delivered. A longtanding debate in the global development literature about the relative importance of puh veru pull factor in urbanization ha focued recently on Africa. Paper have aeed the contribution of pull factor including tructural tranformation driven by human capital accumulation and trade hock (e.g., Fay and Opal 2000; Henderon, Robert and Storeygard 2013) and of reource rent windfall pent in citie (Jedwab, 2011; Gollin, Jedwab and Vollrath 2013). Other paper examine puh factor including civil war (Fay and Opal 2000), poor rural infratructure (Collier, Conway and Venable 2008), and our focu, climate variability and change (Barrio, Bertinelli and Strobl 2006). Thi paper analyze the conequence of climate variability and change for African urbanization and the tranformation of the rural ector. Over the lat 50 year much of Africa ha experienced a decline in moiture availability. Figure 1 how average moiture for different area of Africa in the 1950 and 1960, where moiture i meaured by an index combining precipitation and potential evapotranpiration (which i a function of temperature). A moiture level under 1 indicate that there i le rainfall available than would evaporate given prevailing temperature. Thi i the cut-off we ue to define arid area. 1 A Figure 2 how, much of the tronget (10-50%) decline in moiture over the ubequent forty year occurred in part of Africa that were initially relatively dry (moiture under 0.65 or between 0.65 and 1.0 in Figure 1), increaing the vulnerability of thee already vulnerable area. Thi decline in moiture ha urely affected agricultural productivity. We addre three related quetion. The firt quetion i whether advere change in climate puh people out of rural area becaue of reduced agricultural productivity. We find trong evidence of thi, but only in particular and limited circumtance. The econd inter-related quetion i whether that puh increae the total income of local citie. We find evidence upporting thi hypothei, but again 1 We ue arid a horthand for area that alo include dry-ubhumid, emi-arid and hyper-arid climate (ee UNEP 1992). 2

3 only in certain limited circumtance. Our final quetion i whether advere climate change alo alter occupation choice within the rural ector itelf, puhing people away from farming. We find more general evidence of thi. We find conitent pattern when analyzing thee iue over different time and patial cale. Specifically, firt we look at local, within-ditrict urbanization for an unbalanced 50-year panel of cenu data for 369 ditrict in 29 African countrie. Typical interval between cenue in the panel are year. Two type of heterogeneity are critical to our analyi and define the limited circumtance in which climate change affect urbanization. The firt i whether the ditrict i likely to produce manufacture for export outide the ditrict, and the econd i whether the ditrict i arid. Our model implie there are climate effect on urbanization only in ditrict that have ome indutry, not in ditrict producing agriculture almot excluively. When the local agricultural ector i competing for labor with an urban ector engaged in production of good for export outide the ditrict, decline in moiture encourage urbanization by offering alternative employment for farmer. If, however, local town exit only to erve agriculture with local ervice not traded acro ditrict, then a decline in moiture ha little or no effect on city population becaue the two ector are not in competition for labor for export activity. About 20-25% of ditrict in our ample how evidence of an indutrial bae. Among the approximately half of thee indutrialized ditrict that are in moit area, we expect weaker climate effect, ince reduced moiture may be le harmful to farmer. For an arid indutrialized ditrict, a one tandard deviation increae in a ditrict annualized moiture growth rate lower the annualized growth rate of it urban hare by about 57% of the mean growth rate. Moreover, acro the range of annualized growth in moiture, moving from the lowet to highet moiture growth rate (in a lightly trimmed ample) lower the annualized growth in urban hare by over 250% of the mean, a huge effect. We next conider whether advere change in climate raie total urban income and timulate the development of the urban ector. The anwer i theoretically ambiguou and again depend critically on the initial tate of the urban ector. When the local agricultural ector i competing for labor with urban production of good for export outide the ditrict, total city population and alo total income rie with a decline of moiture. However if citie only exit to erve agriculture, then a decline in moiture generally lead to a decline in total city income. Our empirical analyi i baed on much more recent, annual data for 1992 to 2008 on city and town income growth and rainfall in their immediate agricultural hinterland. City income growth i proxied by growth in night light (Henderon, Storeygard, and Weil 2012). For the citie mot likely to have an export bae in arid region, the point etimate of 3

4 the elaticity of light with repect to rainfall i at leat However, when citie are likely to jut provide ervice to farmer, the point etimate of the elaticity i poitive, although mall. Finally, we ak how moiture change affect a related margin of adaptation: occupational choice in the rural ector. Thi quetion i motivated by the little-noticed tranformation of the rural ector over the lat 20 year in many African countrie, ignified by a large hift into non-farm occupation. 2 For example, data for Benin, Malawi, and Niger in the period all howed between 85 and 91% of the rural male labor force working in agriculture. Thi low proportion of rural worker in non-farm activity contrat with countrie like India or China, even 25 year ago. However Africa i now tranforming. By 2006 to 2008, only 57-72% of the rural male labor force in thee countrie remained in agriculture. 3 Ha climate played a role in thi tranformation? Baed on individual-level obervation from the Demographic and Health Survey (DHS), we how that decreae in moiture decreae the probability of working in agriculture. For women, a one tandard deviation (level) decreae in moiture decreae the probability of working in farm activitie by about 0.03 from a mean of 0.44, a 7% decreae, motly through increaed probability of not working (0.027). Decreaing moiture acro it full range lower the probability of working on the farm by 0.18, a 40% decreae. For men, for a one tandard deviation decreae in moiture, there i a imilar (0.034) decreae in the probability of working on the farm but it come at the expene of off-farm work (0.028). When moiture decline, women are more likely to drop out of the meaured rural labor force altogether, while men are likely to hift into non-farm activitie. 4 While our analyi necearily focue on the impact of pat climate variability, the pecter of future climate change i a trong motivation. The combination of an already difficult climate, ignificant projected climate change and limited adaptation capacity ha led ome oberver to tate that Africa will be more affected than other region by climate change (e.g., Collier, Conway and Venable 2008). Barrio, Bertinelli and Strobl (2010) argue that unfavorable rainfall trend may have already contributed to Africa poor growth performance over the lat 40 year, explaining between 15 and 40 percent of today gap in African countrie GDP relative to other developing countrie. While the precie pattern of future change for individual region i highly uncertain, further drying i the mot common prediction for part of Africa. Overall, our reult ugget that if future climate change will have the negative 2 Concurrent work by McMillan and Harttgen (2014) ha alo noted thi. 3 We are comparing the 1996 and 2006 DHS urvey in Benin, the 1992 and 2006 DHS in Niger, and the 1987 and 2008 cenue of Malawi. 4 While we acknowledge the difficulty of defining labor force participation in thi context, we are imply comparing anwer to the ame quetion aked to ucceeding cohort. 4

5 impact on agriculture in Africa that many climate cientit and agronomit expect, there will be an increaed pace of urbanization in elected part of Africa. Where town have tarted to indutrialize, total town population and income will likely grow, but we have no evidence about per capita income, and the tranition may be more problematic in le indutrialized region. Tranformation of the rural ector may alo continue, a people move out of farming into non-farm rural production. The following ection review the literature on predicted impact of climate change in Africa and on the link between climate and development outcome including urbanization. Section 3 develop a model of how change in climate will affect (a) the diviion of population between the urban and rural ector and (b) urban income. Section 4 decribe the contruction of the core climate and urbanization indicator ued in the main analyi in Section 5. Other data et ued are decribed in the relevant empirical ection. Section 5 preent the analyi of the impact of change in moiture availability on local urbanization. Section 6 examine the effect on urban income. Section 7 analyze work activity repone within the rural ector. Section 8 conclude. 2. Literature on climate change and it impact in Africa 2.1 Urbanization, local city growth and climate The key paper on climate change and urbanization in Africa i Barrio, Bertinelli and Strobl (2006), who etimate an increae in the national urban hare of 0.45 percent with a reduction in national rainfall of 1 percent. Henderon, Robert and Storeygard (2013) reviit the quetion and find an imprecie effect of rainfall after controlling for agricultural price indice. Both paper have two limitation we overcome in the preent work. Firt, they ue national data, when there i ignificant within-country variation in climate change and mot migration in Africa i local (Jonon, 2001). We exploit within-country heterogeneity for a more nuanced and precie analyi of the effect of climate change on urbanization. Second, thoe paper examine national urbanization uing population data at regular 5- or 10-year interval. Such data rely heavily on interpolation, epecially in Africa where many cenue are infrequent and irregularly timed. We contruct a new data et of urban growth for ub-national region baed on actual cenu data, not interpolation. Related tudie ue micro data to tudy the effect of rainfall on migration per e, rather than urbanization. They are very informative and examine iue not covered in our approach, including movement acro rural area, between countrie, and from rural area to citie (ee Henry, Schoumaker, and Beauchemin 2004 on Burkina Fao) and temporary or circular movement (Parnell and Walawege 5

6 2011). 5 Thee tudie typically interview rural reident about their migration hitory, thereby omitting permanent move to citie, though the Demographic and Health Survey could be ueful for that purpoe (Young, 2013). We limit our cope to net effect on urbanization within ditrict over long time period of climate change. Thi approach allow u to conider a broad wath of African countrie. Two other paper indirectly conider how climate change might affect African urban income. Jedwab (2011) hitorical tudy of Ghana and Cote d Ivoire ugget that condition in agriculture have a trong effect on nearby market town that erve them. Gollin, Jedwab, and Vollrath (2013) explore how natural reource income affect urban development, extending the imple two-ector model of the rural-urban divide to include multiple urban economic ector that may be differentially affected. We will model the effect of climate change on ditrict urban income uing inight from thee two paper. 2.2 Climate change in general Like other large world region, ub-saharan Africa ha a highly divere and variable climate. Moiture availability range from the hyperarid Sahara and Kalahari deert to the humid tropic of Central Africa. In place like the Wet African Sahel, long drought have followed extended wet period. Africa climate i haped by the intertropical convergence zone, eaonal monoon in Eat and Wet Africa, and the multi-year El Nino/La Nina Southern Ocillation (ENSO) phenomenon in which change in Pacific Ocean temperature indirectly affect African weather (Conway 2009). Thee procee influence temperature and precipitation acro the continent including extreme event like meteorological drought, epecially in the Sahel, the Horn of Africa and the Southern African dryland, a well a evere flood, uch a in Kenya in Climate record indicate a warming trend over Africa during the 20 th Century, continuing at a lightly fater pace in the firt decade of the 21 t Century, independently of ENSO impact (Collin 2011; Nicholon et al. 2013; ee alo Giannini, Saravanan, and Chang 2003 and Held et al. 2005). 5 We have focued in the text on paper of immediate relevance. We note that migration may be affected by the development of network in detination (Munhi, 2003). Recorded urban veru rural population growth may be affected by differential fertility rate and by the claification of what i urban (McGranahan, Mitlin, Satterthwaite, Tacoli, and Turok 2009). Recent macro-level tudie have invetigated the role of climate factor in African migration including international migration (e.g., Naudé 2010 and Marchiori, Maytadt, and Schumacher 2012). Marchiori et al. (2012) divide driver of migration into thoe related to (di-)amenitie (potential pread of dieae; rik of flood or heat wave) and economic geography (mot importantly, agricultural performance). They find both channel to be important, etimating that temperature and rainfall anomalie have triggered 5 million migration epiode between 1960 and There ha been much le conideration of year-to-year climatic variability in uch model, depite evidence that the length of growing period, for intance, varie coniderably in much of Africa (Vrieling, de Beur and Brown 2011; Vrieling, de Leeuw and Said 2013). An exception i Marchiori, Maytadt and Schumacher (2013) who ugget that environmentally induced income level proxied by per capita GDP may be more important for migration deciion than variability. 6

7 Climate reearcher predict future climate change uing variou emiion cenario a input to everal different aement model. The underlying cenario range from aggreive mitigation of greenhoue gae to a continuation of current trend. While there i fairly broad conenu about global average temperature trend, regional cenario of temperature and particularly of precipitation pattern remain quite uncertain. Reearcher from the Potdam Intitute for Climate Impact Reearch recently reviewed the prediction of a number of credible climate model for regional climate change in Africa (World Bank 2013). In general, average ummer temperature i expected to increae by 1.5 C by 2050 in Africa under an optimitic (2 C) global warming cenario. The area expoed to heat extreme i expected to expand to 45 percent of the region by Under a more peimitic (4 C) global cenario, thee trend would be exacerbated. Falling precipitation and riing temperature would likely woren agricultural growing condition in large part of Africa, epecially in coatal Wet African countrie and in Southern Africa. Agriculture worldwide will feel the effect of climate change more directly than any other ector, but extreme climate condition on the continent mean that many African farming ytem operate in fairly marginal condition even in the bet of time. 7 A ignificant literature on climate change and African agriculture i emerging and help inform and motivate ome of our pecification. The majority of tudie predict yield loe for important taple and traded crop of 8 to 15 percent by midcentury, with much higher loe of more than 20 percent and up to 47 percent by 2090 for individual crop (epecially wheat) under more peimitic climate cenario (Kurukulauriya, Mendelohn, Haan, et al. 2006, Kurukulauriya and Mendelohn 2008; Lobell, Burke, Tebaldi, et al. 2008; Schlenker and Lobell 2010; Thornton, Jone, Ericken and Challinor 2011; Calzadilla, Zhu, Rehdanz, Tol and Ringler 2013; the meta-analye by Piguet 2010; Roudier, Sultan, Quirion and Berg 2011; and Knox, He, Daccache and Wheeler 2012). 8 Aeing potential effect ha been challenging in part becaue adaptation in the agricultural ector appear to be more difficult in Africa. Fertilizer ue, for intance, ha tagnated in Africa at low level ince 1980, while it ha rien tenfold in Aia and Latin America (Cooper, Stern, Noguer and Gathenya 2013), and only 4 percent of agricultural land i irrigated 6 The report define heat extreme a 3-igma event with repect to the local ditribution. 7 A number of tudie have etimated the impact on the value of crop and livetock production under variou cenario, with a focu on the United State (Mendelohn, Nordhau and Shaw 1994, Schlenker, Hanemann and Fiher 2006, Dechêne and Greentone 2007). 8 Some tudie find modet or even poitive impact under optimitic cenario of limited climate change and ucceful adaptation (Kurukulauriya, Mendelohn, Haan, et al. 2006, Kurukulauriya and Mendelohn 2008; Calzadilla, Zhu, Rehdanz, Tol and Ringler 2013). 7

8 compared to 18 percent globally (You, Ringler, Nelon, et al. 2010). Thee tudie motivate ome of the pecification we tet below A Model of the impact of climate variability on local urbanization We model movement between an urban and a rural ector which together comprie a ditrict. While migration acro ditrict boundarie, for example to capital citie, clearly play a role in thi context, our focu i on local migration, which i very important in many African countrie (Jonon, 2010). Our goal i to model the effect of a change in moiture in a ditrict on the urban-rural diviion of population and on city total income. We will how that if, a we have modeled, citie have an exporting indutrial ector in addition to a ervice ector trading with local agriculture, a decline in moiture will lead to increaed urbanization and increaed total city income. The model doe not addre occupational choice a conidered in our final empirical exercie. 3.1 The baic model Urban ector The urban ector (city) produce ervice and manufacturing. Output per unit labor i b in ervice and cl ε m in manufacturing, where Lm i total labor unit in manufacturing andε > 1. Service, produced with contant return to cale, repreent non-agricultural item produced and old locally, but not traded outide the ditrict. Scale economie in manufacturing, repreented by ε, can come from information pillover or from diverity of local intermediate input in a monopolitic competition framework. 10 Final output of manufacture i tradable nationally or internationally at fixed price to the city. Given thee two ector, the wage rate per unit labor in the city i where w = p b = cl ε (1) p i the price of ervice and manufacturing i the numeraire. m 9 Beide urbanization and local city development, an emerging literature i finding broader impact of variation in temperature and rainfall on a variety of human capital, economic, and political outcome. Thee include birth weight effect with long term conequence (Dechêne, Greentone and Guryan 2009), childhood effect on health, chooling and ocioeconomic tatu (Maccini and Yang 2009), later childhood effect on chooling (Shah and Steinberg 2013), and effect on the rik of conflict in Africa (Burke, Dykema, Lobell, Miguel and Satyanath 2009; Hiang, Meng and Cane 2011; O Loughlin, Witmer, Linke, et al. 2012). n 1+ ε 10 1/(1 + ε ) In the latter context, output of any final good firm i m = z( h) dh where output of any intermediate 0 input producer employing lh ( ) worker i zh ( ) = γlh ( ) λ and n i the number of local intermediate input producer a city can upport. Solving the monopolitic competition problem, the equilibrium wage of a worker in the manufacturing ector ha the form c L ε m. 8

9 Following tandard urban model (Duranton and Puga, 2004), worker live in a city where they mut commute to work in the city center. Each worker i endowed with 1 unit of labor and commuting reduce time pent working at a rate of 4t per unit ditance commuted. Thoe living far from the city center pend le on land rent to compenate for their higher commuting cot, or lot labor earning. City land rent are reditributed to urban worker. Per worker net income, after commuting and land rent are paid and land rent income i reditributed, i where where N U i city population. 11 y = w(1 tn ) = p b(1 tn ) (2) U U City effective total labor upply net of time pent commuting, L, i L + L = L = N (1 tn ) (3) m U U L i the labor force in ervice The rural ector and equilibrium condition for the ditrict The other part of the ditrict i the rural ector producing agricultural product, old at a fixed price in international market. Per worker income in the agricultural ector i given by The rural (agricultural) population i p f( N, R), f < 0, f > 0. (4) a A 1 2 N A and the total land area i hared equally among that population. Per worker output (either marginal or average output depending on how agricultural rent are ditributed) i declining in total farm worker and increaing in moiture or rainfall, R. Migration arbitrage between the urban and rural ector equalize income and there i full employment in the ditrict o that pa f ( N A, R) pb(1 tnu) = 0 (5) N = N N (6) U A p a 11 Following Duranton and Puga (2004), in a linear city, where each worker i endowed with 1 unit of time and working time i 1 4tu where u i ditance from the city center and 4t unit commuting cot, it i eay to derive expreion for city labor force L a a function of population N U (by integrating over the two halve of the city each of length N U /2 ), for the city rent gradient (equating rent plu commuting cot for a peron at u with that of a peron at the city edge where rent are 0, o they are equally well off in equilibrium) and for total rent. Thee have form repectively: 2 L = NU(1 tnu); R(u) = wt(2nu 4 u); total rent= wtnu where w i the wage rate. A peron living at the city edge and paying zero rent earn in net w(1 2 tn U ), with the dieconomy ariing from increaing commuting ditance reducing time available to work. After getting a hare in urban rent income their net income i y = w(1 tn U ). 9

10 N i ditrict total population. The model i cloed by noting that the untraded ervice market mut clear. Total production i (,, ) a bl and total demand i (,, ) D y p p. Thu we know uing (2) and (5) that bl N D( p f ( N, R), p, p ) a A a ND yp p for the individual demand function = (7) 3.2 Comparative Static when the local urban ector export manufacturing. We eek the effect of moiture change on city (or converely agricultural) population and total city income. That i, we want to olve for dn / dr and d( y N ) / dr Change in urbanization A Firt we olve for the effect on the population allocation. We differentiate (1), (7), (3) and (5), having ued (6) to ubtitute for U a N U. We define income and own-price elaticitie of demand for ervice, η > 0, η < 0 in the uual fahion. The reult are y p dp p m m 1 2 y A y p p (8 a) dl f f dp = η dn + η dr + η, η < 0 (8 b) L f f p dl = ε L dl + dl = [1 2 t( N N )] dn (8 c) m A A f1 f2 dp t dn A + dr dn A= 0 (8 d) f f p 1 tn ( N) Uing (8a) and (8b) to ubtitute for dlm and A dl in (8c) and olving for dp / f 1 2 t(n N ) + Lη dp L L f p L L L f 1 A y 1 f 2 = ε [1 + ε ηp ] dn A + ηy m m m We ubtitute (9) into (8d) to get dn ( ) A f L 2 m + εl ηy + ηp = dr f Z dr f t [ ( )] ( ) [1 2 ( )] 1 Z Lm + εl ηy + ηp L m + εlηp + ε t N N A f 1 tn ( NA) p we get (9) (10) 10

11 To ign thi expreion we firt need to ign Z. Stability of migration between the urban and rural ector require that the differential in (5) be decreaing in N A, and therefore that the expreion in (8d) divided by dn i negative when dr = 0. Thi reduce to A Z L + εlη <. (11) 1 ( m p ) 0 A long a the local urban manufacturing ector i not negligible (i.e. L / L i not too mall) then ( L + εlη ) > 0,. For example if η = 1, we require that L / L > ε. Given the literature believe m p p ε < 0.08 for example, then a long a the local city ha a modicum of manufacturing, ( L + εlη ) >0, m m m p and tability implie Z < 0. We focu on thi cae here, and the oppoite cae in ection 3.3. Returning to (10), given ( L + εlη ) > 0 and therefore Z < 0, dn / dr > 0 follow directly. m p The magnitude of repone depend on the magnitude of f / 2 A f. Of coure, a moiture change all variable change, but we can ay that a f 2 approache zero, o doe the repone. f / 2 important role in the empirical formulation in Section Change in city income Next we turn to the effect of moiture on city income. Total city income i y( N N ) = p f( N, R) ( N N ). Thu A a A A f play an dy( N N A) 1 1 = pa fz 2 [1 tn ( NA)] * M (12) dr where M [ L + εl ( η + η )][1 2 t( N N )] + t( N N ) εlη + ( N N ) ε[1 2 t( N N )][1 t( N N )] m y p A A y A A A Under the current aumption that ( L + εlη ) > 0, Z < 0. If we further require that city earned m p income ([1 2 tn ( N A )] ) be poitive, M mut be poitive. Given that Z i negative, dy( N N A)/ dr i negative. In um we have the following propoition relevant to our empirical work: Propoition 1. If the city ha a tradable manufacturing ector that i not too mall relative to it local ervice ector o that ( L + εlη ) > 0, a decline in moiture will lead to an increae in urban m p population and total city income. 11

12 For completene, the expreion for the change in city per capita income i: dy 1 t = pafz 2 { ( Lm + εlηp ) + ε[1 2 tn ( N )]} A dr 1 t( N N ) A. In the current ituation, given < 0 Z, Lm + εlηp > 0, and the definition of Z, dy / dr > 0. In our empirical work, total income or expenditure in the city will be meaured by night light data, which are recorded over time period incompatible with the bulk of the population data. Income i nominal in a context where the price of ervice will change, but for a broad cla of utility function, the city um of utilitie i affected in qualitatively the ame way a city income Comparative tatic with minimal local manufacturing. If the local traded good manufacturing ector i very mall o ( L + εlη ) < 0, then the fortune of the m p city are tied to the local agricultural ector, a in Jedwab (2011). 13 Stability thu require Z > 0, and the ign of / A dn dr in eq. (10) i ambiguou. A a imple example, if η η 0 + =, then dn / dr < 0. In y p that cae, a L 0, dn / dr 0. When L = 0, the ign of dn / ign of m η + η. There, if η η 0 y p A y p m A dr depend entirely on the + =, there i no effect of rainfall on the rural-urban population allocation, becaue migration effect only come through change in demand for ervice (and the effect on demand for ervice of reduced price i exactly offet by the effect on demand of reduced per peron income). Ambiguity arie in the general cae in (10), ifη + η < 0. y p Total urban income from (12) i more conitently increaed by rainfall. Given Z > 0, if η + η 0, we can unambiguouly how that dy( N N )/ dr > 0. Increaed rainfall raie local y p farm productivity and all local income. 14 With city population modetly affected, total city income mut rie. However, if η << η, o that city population decline a lot, we cannot rule out the y p poibility that urban income decline a well. A A 12 We examine the um of utilitie baed on a log linear indirect utility function, but it applie to any indirect utility function where doubling income double utility. For V ( y, p) NU = ANUyp σ where σ i the expenditure hare of ervice and differentiating we can how that σ d( N ) [1 2 ( )]( εη ) [1 (1 σ ) ( )] εη σ Uyp C f tn NA Lm p L tn NA yl = p CNUy Z (1 αε ) [1 2 t( N N A)] +. dr f [1 t( N NA)]( N NA) If Z < 0 thi expreion i negative. 13 We decribe thi cae auming the local manufacturing ector exit, but the ituation i analogou in the cae where there i no manufacturing at all and per worker output of the ervice ector i given by bl ε, ε See the expreion for change in per capita income above. 12

13 Propoition 2. If the city ha a traded good manufacturing ector that i tiny or non-exitent o that ( L + εlη ) < 0, the effect of a decline in moiture on city population i ambiguou and tend to zero m p a Lm 0 when η + η = 0. However total city income decline, auming negative. y p η + η i not trongly y p Thi trict difference between the effect of moiture change on local city income depending on whether manufacturing ha a noticeable local preence will inform the empirical work in Section 6. Whether a city ha manufacturing i of coure endogenou. In our tatic framework, an abence of manufacturing implie that the wage the firt worker in manufacturing would receive in the city, c, i le than the equilibrium wage in the ervice ector ( pb). Manufacturing arie if either local (potential) productivity, c, rie with, for example, enhanced education, or if the price of the manufactured good rie relative to the other good. Thi latter cae could be driven by change in international price or change in the cot of tranporting product between the local city and a port. 15 Studying the development of local indutry i beyond the cope of our work and for mot Sub-Saharan African countrie lack of data would make uch a tudy difficult. We tudy whether climate affect urbanization and local income given exiting indutrial compoition, but not whether it contribute to change in indutrial compoition. 4. Data on urbanization, climate, and indutrialization In thi ection we dicu our baic meaure of urbanization, moiture and extent of indutrialization of ditrict, data we need to conduct the firt analyi of the effect of climate on urbanization. We leave the decription of night light and DHS occupational data to the relevant ection. 4.1 Urbanization Scarcity of demographic and economic data hamper empirical reearch on climate effect in Africa. Many countrie carry out cenue only irregularly, and ample urvey uch a the DHS are infrequent and provide little information before While there are now a number of geographically detailed climate data et that are increaingly ued by economit (ee Auffhammer, Hiang, Schlenker, and Sobel 2013), mot tudie have employed national level population and economic data et which are 15 Other work uch a Atkin and Donaldon (2013) and Storeygard (2014) conider the tranport cot tory in Africa directly. 16 The World Fertility Survey of the late 1970 and early 1980 (DHS precuror), are le conitently available to reearcher. 13

14 readily available from the UN and other agencie and which, for African countrie, rely heavily on imputation and interpolation. We collected urban and rural population meaure for ub-national region (province and ditrict) from cenu report. We include countrie with at leat two available cenue with the relevant information for a complete or nearly complete et of ub-national unit, where either ditrict boundarie changed little or common unit over time can be defined. The data were extracted motly from hardcopy cenu publication obtained from the U.S. Cenu Bureau library, the U.S. Library of Congre, the LSE library, and the Britih Library. The collected ample cover 32 countrie but Namibia and Congo-Brazzaville are dropped becaue of problem with urban or ditrict definition. 17 We limit the panel ample to intercenal period (L) of le than 20 year, o Liberia i omitted becaue it two available cenue were 34 year apart. We have information from 2 to 5 cenue between 1960 and 2010 for each remaining country (Figure 3 and Appendix Table A1). Kenya i effectively treated a two countrie, before and after rapid reditricting and urban redefinition of the Each country i divided into a number of ub-national unit we call ditrict. The 369 ditrict ued in panel etimation are hown in Figure 3. The mot notable omiion i Nigeria, Africa mot populou country, becaue of concern over the quality of cenu figure (ee, e.g., Okafor, Adeleke and Oparac 2007). Other Sub-Saharan African countrie are miing becaue either they had no cenue with needed information or in a few cae becaue we were unable to obtain the printed volume. Finally, we do not include South Africa becaue it i more developed, province map were redrawn pot-apartheid, and pre-apartheid migration retriction make it a pecial cae. 4.2 Climate With few exception, mot tudie of climate impact on agriculture focu excluively on precipitation. However, moiture available for plant growth i alo a function of evapotranpiration. Thu, dividing precipitation by potential evapotranpiration (PET), which i a non-linear function of temperature, increaing in the relevant range, i viewed a a better meaure of climatic agricultural potential. Although thi meaure i often called an aridity index and ued to define aridity zone (UNEP 1992), we call it a moiture availability index, becaue larger value indicate relatively greater water availability, with value above one indicating more moiture than would be evaporated given prevailing temperature. Precipitation and temperature data are from the Univerity of Delaware gridded climate 17 For Namibia, the problem i changing ditrict boundarie and urban definition. For Congo mot ditrict were originally drawn to be either wholly urban or wholly rural, making within-ditrict analyi impoible. 14

15 data et (Willmott and Matuura 2012). We etimated monthly PET from 1950 to 2010 uing the Thornthwaite (1948) method baed on temperature, number of day per month and average monthly day length, and ubequently ummed monthly value to obtain annual total (ee, e.g., Willmott, Rowe and Mintz 1985 for detail). 18 Figure 4 how average annual country-level moiture trend for the countrie in our ample, indicating the long term downward trend over the lat 60 year, conitent with Figure 2. It alo how the high inter-annual variability of moiture in thee countrie, even with three-year moothing. The climate data et have a patial reolution of 0.5 degree, which correpond to about 3000 km 2 at the equator. To generate ditrict level climate indicator, we average grid cell value that overlap with the correponding ub-national unit, weighting by area in the cae of cell that cro ditrict boundarie Extent of indutrialization Our model ugget that place with export indutrie will repond differently than other ditrict. Subnational data on indutrialization in African countrie i carce; even data on the hare of GDP in manufacturing at the national level i care before So for the firt analyi of urbanization baed on outcome from 1960 onward, we need a bae from that time period. The Oxford Regional Economic Atla, Africa (Ady 1965) map all indutrie by type and city location in Africa, baed on an in-depth analyi from a variety of document and cenu ource from the late 1950 and early We integrated thee map with our cenu data to locate all place with any of 16 different modern manufacturing indutrie: iron and teel, electrical equipment, general engineering equipment, cement, other building material, rubber, petroleum refining, printing, footwear, four type of textile, chemical paint, gla and pottery. Following Moradi (2005), we call the firt five key indutrie, meaning they provide input to other downtream indutrie, and we conider thee eparately. Figure 5a how the total count of indutrie found in each of our ditrict, where the maximum i 8 of the 16. Only 16% of our ditrict had any of thee indutrie, uggeting that there may be limited cope for the induced indutrialization channel in our model. Figure 5b map all indutrie from Ady (1965), combining the More pecifically, potential evapotranpiration (PET) for month i i calculated a: 0, T i < 0 PET i = N i L 16(10T i /I) α, 0 T i < 26.5, T i 0.43T 2 i, T i 26.5 where T i i the average monthly temperature in degree Celiu, N i i the number of day in the month, L i i day length at the middle of the month, α = ( )I 3 ( )I 2 + ( )I , and the 12 heat index I = T i i= where T i indicate the 12 monthly mean temperature. The Penman method provide a more precie etimate of PET, but require data on atmopheric condition that are not available conitently for the area and time period of thi tudy. 19 In practice, we ue the number of 0.1-degree ub-cell a a weight. 15

16 modern indutrie with 10 agricultural proceing indutrie: brewing, wine/pirit, tanning, canning, and the proceing/milling/refining of ugar, oil, cotton, grain, tobacco and timber. Twenty-three percent of the ample ha an indutry in thi wider et, with at mot 13 different indutrie in a ingle ditrict. In our empirical work, we try three meaure of 1960 indutrial activity: preence of a key indutry, count of modern indutrie, and count of all indutrie. We alo conider ditance to the coat a a proxy, although it i clear from Figure 5 that the correlation between the extent of indutry at the end of the colonial period and ditance to the coat i not very trong for mot countrie. For the analyi of growth in night light in Section 6, which tart 30 year after thee indutry data, we will rely more on country-level meaure of the extent of indutry to proxy for whether a city i likely to export manufacture. 5. Empirical analyi of the effect of climate on urbanization 5.1 Specification We etimate the effect of growth in moiture on growth in urbanization for a panel of ditrict that i highly unbalanced becaue different countrie conduct cenue in different year. Growth rate are annualized to account for thee intercenal period of different length. The bae pecification i u ijt = βw ijt,mooth + β 0 X ij + β 1 X ij w ijt,mooth + α jt + ε ijt (13) where variable for ditrict i, in country j, in year t, are defined a follow: u ijt i annualized growth of the urban population hare from t L jt to t; w ijt,mooth = lnw ij,t,mooth3 lnw ij,t LJ,mooth3 /L jt ; W ij,mooth3 i average moiture from t 2 to t; L jt i the number of year between year t and the prior cenu; X ij are time invariant control, including initial level of variable; α jt i a country-year fixed effect controlling for time-varying national condition; and ε ijt i an error term clutered by ditrict. In (13), growth in urbanization i a function of growth in moiture. The growth pecification remove the effect of time-invariant ditrict characteritic (ditance to market, oil quality and the like) on urbanization level. Some of thee factor (X ij ) may alo affect the impact of climate change on urban hare growth rate, yielding heterogeneou effect. We control for country-year fixed effect to account 16

17 for national time-varying condition driving urbanization overall in a country. Thi alo control to ome extent for variation between countrie in the definition of urban area, which poe a ignificant problem in cro-country urban analyi. What we are doing i demanding on the data identification of climate effect on urbanization mut come from within-country difference acro ditrict in annualized growth rate of moiture. We mooth the moiture level over three year, on the aumption that potentially permanent deciion are more likely to be baed on average recent experience rather than one good or bad year. A an example of the moothing, the annualized rate of change in urban hare between cenue in 1965 and 1980 i etimated a a function of the annualized rate of change in moiture between the average for 1963, 1964 and 1965 and the average for 1978, 1979 and Although thi moothing period i omewhat arbitrary, our reult are robut to reaonable adjutment a noted later. Our theoretical model ugget two important form of heterogeneity, baed on indutrial capacity and aridity ( L / L and f / 2 m f in equation 10). Our primary meaure of indutrial capacity come from Ady (1965). We try both country and ditrict-level meaure of aridity for We examine thee two dimenion eparately and together. In Section 5.4, we briefly conider heterogeneity baed on everal additional factor: oil quality, irrigation potential, rainfall concentration with the year, variability or noiine in moiture change over our interval, and change in climate variability over time. In Table 1 we preent ummary tatitic on the etimating variable for all countrie and for the more arid one. The average annualized growth rate of moiture i negative, conitent with Figure 2, and the average growth rate in the urban hare i poitive. We are concerned that outlier in thee variable could reflect meaurement problem. For example, an extremely high urban hare growth rate could be due to a poorly meaured low bae. An extremely high or low moiture growth rate could reflect intercenal change in the denity of weather tation, epecially in arid region. We thu trim 0.5% from the top and bottom of the ditribution of growth in both urban hare and in moiture, a total of 16 obervation for the whole ample. We comment on the effect of trimming when preenting reult. 5.2 Identification Our chief identification concern are inufficient within-country variation and omitted variable. In Figure 6a, the growth in moiture variable ha more denity to the left of zero, conitent with drying; and it ha a large pread of poitive and negative value. However, Figure 6b how that pread doe hrink omewhat after factoring out country-year fixed effect. 17

18 With repect to omitted variable, ince change in climatic condition are exogenou and in principle randomized by nature acro ditrict, etimate of reduced form (or net) effect may appear to be unbiaed. We have differenced out time-invariant factor affecting urbanization level. However, it i poible that unobervable affecting growth in urbanization could be correlated with climate change within our limited ample. We thu control for initial urbanization, which might repreent a variety of factor. For example, initial urbanization might be correlated with both growth in urbanization (e.g., mean reverion) and growth in moiture (by chance). Figure 7 how a modet poitive correlation (ignificant at the 10% level) for arid countrie, which are our focu. 5.3 Bae pecification reult Table 2-4 report on three baic pecification of the effect of moiture growth on urbanization. In Table 2, after howing the effect with no allowance for heterogeneity, we explore the effect of allowing for heterogeneity in the likelihood of having indutry. Table 3 explore effect allowing for heterogeneity in initial moiture level, and Table 4 combine the two ource of heterogeneity. We focu on qualitative reult in Table 2 and 3, deferring mot quantitative comparion until Table 4 where both ource are preent. In Table 2, column 1, the effect of moiture growth alone on urbanization i inignificant, uggeting that there are no effect on average. Significant and ditinct effect only arie when heterogeneity i introduced, and thu thee effect apply only to particular ub-ample Likelihood of indutrialization The ret of Table 2 explore heterogeneity baed on the likelihood of having manufacture for export, a oppoed to only agriculture and local ervice. In column 2, we interact the moiture effect with a dummy for whether the ditrict ha no key indutrie in the Oxford Atla, o the bae coefficient applie to area with key indutrie, about 11% of the ample. It i inignificant, but conitent in ign with the ret of the table. In column 3 we ue a proxy for the extent of agriculture, baed on the number of modern (non-agriculture baed) indutrie preent. The meaure ha a value of zero if a ditrict ha the maximal count (8) of thee indutrie and then rie, a the number of indutrie decline, to a maximum of 8 in ditrict with no indutrie (84% of the ample), o the uninteracted moiture coefficient applie directly to the mot indutrial ditrict. Thi continuou meaure i broadly analogou to L / L in equation (10) of our model, repreenting not only the likelihood of indutry but it poible m extent. Column 4 applie an analogou meaure to a broader indutry meaure that include the agricultural proceing indutrie. 77 % of ditrict had no indutry of any type in the early Baed on either modern or all indutrie, point etimate in column 3 and 4 ugget a very large effect for the mot likely indutrialized ditrict of and Here a one tandard deviation 18

19 decreae in the growth rate of moiture increae the growth rate of hare urban by about 0.012, where that growth rate ha a mean of In both of thee column, a the extent of indutry decreae, the effect diminihe at rate of 0.10 and.075, repectively, per indutry lot. Thu for ditrict with no indutry the net marginal effect of moiture growth are an inignificant 0.07 and 0.08 in the two column. Thee reult are conitent with the theory we preented: trong negative effect of moiture growth on urbanization in indutrialized ditrict but little or no effect in agricultural one. In the lat column we ue ditance to the coat a a proxy for indutrial activity and find no bae or interactive effect. All reult o far have impoed moothing over 3 year (0 to 2 before each cenu) and trimming. Appendix Table A2 varie thee aumption, tarting from the Table 2, column 4 pecification. Smoothing over 3-4 period appear to provide optimal variation. Smoothing over 2 period leave more noie and over 5 limit variation. By contrat, trimming i conervative. Without trimming, both the bae effect and the rate of diminution are much tronger; further trimming beyond the 16 obervation we removed ha modet effect on reult Heterogeneity baed on initial aridity Table 3 examine the effect of moiture growth allowing for heterogeneity in jut initial aridity. Column 1 how the effect of allowing for heterogeneity at the country level baed on whether the country overall i moit (moiture index in exce of 1.0). With thi country level ditinction, we have a ignificant negative effect of moiture growth in arid countrie a expected. The net effect for moit countrie i poitive but impreciely meaured. It may eem odd to ue a country level index, when we know moiture by ditrict. The problem i that our identification come from within-country variation in moiture growth. Defining aridity baed on ditrict leave little uch variation: in 11 of 17 arid countrie all ditrict are arid, and in 2 more, 2 or fewer ditrict are non-arid. In eence, for many countrie the country-level deignation applie perfectly or nearly perfectly to all ditrict. We try two alternative to focu on ditrict-level heterogeneity. Firt, in column 2 we draw the country line for moit at 0.75, rather than our preferred 1.0, Thi achieve variation in all but three our countrie. Thi doe not give ignificant reult here, but it i tronger when both ource of heterogeneity are included in Table 4. Second, in column 3, we impoe a linear tructure on heterogeneou effect by interacting moiture change with the initial ( average) level of moiture in a ditrict, a continuou variable. Here reult are reaonably conitent with thoe in column 1 although preciion i limited. An arid ditrict with initial moiture of 0.5 ha a moiture growth elaticity of compared to an overall for arid countrie in column 1. 19

20 5.3.3 Heterogeneity of aridity and indutrialization In Table 4 we combine the two ource of heterogeneity, to ditinguih indutrialization effect in arid veru moit area. All column have all appropriate interaction, but only the key coefficient are hown. In the top row we how the effect of moiture growth in arid place that mot likely have indutry, varying the definition of indutry and arid place acro column. Thee are all large effect. Heterogeneity i more ditinct acro level of indutry likelihood than level of moiture, with differential effect for moit place not being ignificant. However ditinguihing moit place increae and in ome pecification harpen the climate change effect in indutrialized ditrict. Column 1-4 define indutry likelihood analogouly to column 2-5 of Table 2, and aridity at the country level a in Table 3, column 1. In column 1, uing the key indutry dummy, there i a trong negative effect of in indutrial ditrict of arid countrie, and a maller, inignificant negative net effect of for their moit country counterpart. In column 2, the moiture growth effect tart at in the mot indutrial ditrict and decreae to an inignificant net effect of in the mot agricultural ditrict. In column 3, uing the all indutrie meaure, the effect tart at in the mot likely to be indutrialized area (with 13 indutrie in the 1960) and decline at a rate of per indutry, reaching a net effect of in the mot agricultural ditrict. Thi i our main reult. For the mot indutrialized area in an arid country, a one tandard deviation increae in moiture reduce urbanization by 0.018, or 57% of the mean growth rate in hare urban. Moving from the minimum to maximum (trimmed) growth in moiture give a decreae in the urban hare growth rate of 0.116, about 274% in exce of the mean. In column 4, we ue ditance to the coat a the indutry meaure. There i a trong effect at the coat and a rate of diminution that i poitive a expected, but only ignificant at the 10% level. By the maximum ditance in the ample, climate effect are cloe to 0. The effect in column 1-4 do not how ignificant difference for moit countrie. For ditrict that have indutry (23%), about half are in arid countrie and half in moit. It i clear there i limited cell ize to make nuanced ditinction between moit and arid. In column 5 and 6, we ue all indutrie a in column 3, but moiture ditinction done at the ditrict, rather than country level. In both cae, uing a binary cut-off at a 0.75 and a continuou meaure, moit reult are imprecie. In ummary, we can ditinguih effect of moiture growth in ditrict that are more (likely) indutrialized compared to ditrict that have no indutry. However, in our limited ample we ee no trong evidence of a diminution of effect in more moit area once we control for the indutry 20

21 ditinction. The moiture ditinction jut enlarge and in ome cae harpen the indutry ditinction in arid area. 5.4 Other dimenion of heterogeneity The effect of moiture on urbanization may differ along many other dimenion. We focu on ix here, fully interacting each with the Table 4, column 3 pecification. 20 A we thu create quadruple interaction, it i not urpriing that we find no compelling reult for any new dimenion overall, and pecifically, we find no evidence that they affect the marginal effect of moiture growth in indutrialized arid area. The firt three are meaure of agricultural productivity that might influence the effect of moiture change: oil water capacity and total oil uitability from Ramankutty et al. (2002), and evidence of modern irrigation infratructure from Siebert et al. (2007). 21 The other three are meaure of weather variability within and acro year, which might make farmer more or le vulnerable to change. One i a Gini of rainfall acro month within the year to meaure rainfall concentration within the year, uing baeline data. The other two are the tandard error of the linear prediction of rainfall between cenue to meaure noie in the growth in climate variable, 22 and the intercenal change in the tandard deviation of rainfall in the 10 (or 17) year before a cenu. 6. Climate change and city income Having hown evidence of the population effect predicted by our model, we turn to effect on city total income. Our theory indicate that if the local town or city perform an exportable activity, then reduced (increaed) moiture unambiguouly raie (lower) city income. However if the local town exit olely to provide farmer with ervice (or potentially good) that are not traded outide the ditrict, then the fortune of the urban and rural ector are tied. Decreaed moiture i then likely to decreae local city income. Data on income or city product are not conitently available for African citie, o we ue an indirect meaure. Following the approach in Henderon, Storeygard and Weil (2011, 2012), we tet 20 Each new variable i interacted with are moiture, moiture*(8-#indutrie), moiture*1(country moiture>1), moiture*(8-#indutrie)*1(country moiture>1), 1(country moiture>1), (8-#indutrie), (8- #indutrie)*1(country moiture>1). 21 Although oil degradation can change oil condition over the time cale of decade (ee UNEP 1992), data on thee dynamic are not conitently available, o oil quality i time invariant in our analyi. 22 Baed on the annualized growth rate, w ijt,mooth, from equation (13), we can formulate the predicted value for, moiture in any year between cenu interval a ˆ wijt mooth W = W e. From that we form the tandard ijt, mooth3 ijt L j, mooth3 error of prediction: t ˆ 2 ijt = ( ij,mooth 3 ij,mooth 3) / ( j 2) = t Lj SEP W W L. 21

22 whether the intenity of nighttime light emitted by a city i affected by the amount of rainfall within a 30 km radiu around each city in the current or prior year (ee Figure 8). The nighttime light data come from the U.S. Defene Meteorological Satellite Program (DMSP), a weather atellite ytem that capture viible light between about 8:30 p.m. and 10 p.m. We ue annual data from 1992 to 2008 for 30 arc-econd grid cell (0.86 km 2 at the equator). The data product typically ued for ocioeconomic analyi contain only table light after temporary light ource uch a foret or avannah fire have been removed (e.g., Elvidge et al 1997). We further remove ga flare baed on Elvidge et al. (2009). Light intenity for each pixel i expreed a a digital number (DN) linearly caled between 0 and Specification Our analyi include 1,158 citie and town, in 42 countrie (all of mainland ub-saharan Africa except Somalia, plu Madagacar). We define citie a contiguou lit area in the DMSP data et for which a population etimate i available from a comprehenive cenu databae. 23 More pecifically, we overlay lit area for all year and find the outer envelope of light a pictured in Figure 8. The city total amount of light for each year i the um of the digital number (light intenity) over all grid cell that fall within thi outer envelope (maximum extent) of the city light footprint. Rainfall meaure are from the Africa Rainfall Climatology Verion 2 (Novella and Thiaw 2012), which combine weather tation data with atellite information, reulting in a horter time erie but finer patial reolution (0.1 degree) than Wilmott and Matuura (2012). We ue rainfall rather than moiture in thi ection becaue we are unaware of any temperature meaure at uch fine reolution that do not heavily rely on interpolation of pare data. Each city hinterland annual average rainfall i calculated a an average of grid-cell value within 30 km of the ever-lit area. Summary tatitic are in Appendix Table A3. Our pecification i k k ( light + ) = β rain + γ n( ra X + φ + λ + α + ε ln 1 ln l in )* t (14) ict j ic, t j j ic, t j i i t c c ict j= 0 j= 0 where light ict i light DN ummed over all pixel in city i, country c, in year t ; rain ict i average rainfall in millimeter per day within 30 km of city i; X i are country-level indicator for moiture level and agricultural hare, a well a city-level indicator; i and λ t are city and time fixed effect; α i t i a city-pecific linear time trend;

23 ε ict i an error term, clutered at the city level to capture city-pecific erial correlation. Equation (14) i an annual panel pecification for citie. To identify rainfall effect on light, we control for time-invariant city condition, time effect (to account for annual difference in enor etting acro and within atellite), and city-pecific linear growth trend. The idea in empirical implementation i that each city i on a growth path and rainfall fluctuation in the local area caue it to deviate from that growth path. If climate change are more permanent then the growth path i hifted up or down. The empirical context i different from the urbanization analyi of Section 5 in two important repect. Firt, we are looking at year-to-year fluctuation rather than year change. Thi ugget local migration and income repone may be mall, but empirically we do find effect. Second, becaue night light data are only available after 1991, the period of analyi i horter and tart later. Thi affect how we define likely to be indutrialized. Uing a map from 30 year before our ample period may not give the bet information. A before we ue ditance to the coat which may be more relevant now, well after the colonial era and with the increae in world trade. By 1990 we alo have full data at the national level on the extent of indutrialization. Baed on the national agriculture hare in GDP data for , we ay that ditrict in a country are likely to be jut agricultural if the hare of agriculture in GDP exceed 30%. That leave 25% of the large ample of citie defined a likely to have indutry. 24 We ue the ame moit/arid cutoff of 1.0 at the country level a in mot of Table 4. A in Table 2-4 and the theory, thee ditinction are critical. 6.2 Reult Reult with heterogeneity by likelihood of the city being indutrialized Table 5 how effect with heterogeneity baed on having indutry. A for urbanization, in column 1 the average impact of rainfall on city income (light) overall i zero. However once we iolate the much maller ubample of citie likely to have indutry for export outide the local area, we ee effect. In column 2 where we define thi likelihood baed on national hare of agriculture, the elaticity of light with repect to rainfall for indutrialized area i A one tandard deviation increae in rainfall reduce city light by 4%. Rainfall draw people out of the city and reult in a lo in total city income. For agricultural area the net coefficient i poitive (0.067) but not ignificant. It hint at the idea that increaed rainfall in agricultural area might benefit local town becaue migration effect are mall but all income are larger. 24 We aume that Nigeria agricultural hare (net of reource rent) i higher than 30% baed on the earliet available data, from the 2000, when it i above 50%. 23

24 In column 3 we ue the tronget extent of agriculture meaure from the 1965 map for thi cae: modern indutrie. Covariate have the expected ign but effect are inignificant, conitent with a problem of poor aignment from dated map. In column 4, we turn to the coatal pecification which i compelling. The elaticity of light with repect to rainfall for coatal citie which are the one mot likely to have indutry i , diminihing at a rate of per log point of ditance to the coat (almot ignificant at the 5% level). By 140 kilometer, effect are zero. If we take the reult in column 2 a the main reult with an elaticity of and apply the light-gdp elaticity of about 0.3 from Henderon, Storeygard and Weil (2012), thi implie a rainfall-city product elaticity of about for more indutrialized place. If we ue column 4, with the bae cae being coatal citie, the elaticity almot double Rainfall change effect: Indutrialization and initial moiture heterogeneity In Table 6, we check whether initial area moiture level affect the marginal effect of rainfall variation found in Table 5. Here, baed on both reult from Section 5 and the fact that we don t have aridity defined for thee data at the city level, we focu on the country-level aridity ditinction. In column 1, differentiation of rainfall effect by the moiture dummy produce no ignificant reult. In the remaining column, the moit or not ditinction erve to motly enhance and harpen the differential in rainfall effect between citie likely to be indutrialized and other, jut like in Table 4. In column 2, uing country hare of agriculture in GDP in arid countrie, the elaticity of light with repect to rainfall i now for indutrialized citie and the net effect for agricultural citie i 0.05 (although not ignificant). Column 3 ue modern indutry extent baed on the 1965 map. Now by focuing on arid area, effect are tronger than in Table 5. The citie mot likely to be indutrialized have an elaticity of -0.20, while thoe with maximal agriculture (8) have a zero effect. Finally in the lat column uing coatal ditance, reult for arid area are harper than in Table 5. For citie on the coat in arid area the elaticity of light with repect to rainfall i -0.37, decreaing again to zero at about 370 km, and then becoming poitive. Overall, for the mot indutrialized area in arid region, the elaticity of light with repect to rainfall range from to Uing a a central etimate, a one tandard deviation increae in rainfall reduce light by 8.3%. The diminution for moit area i clearer in thi table than previouly but till inignificant. In either indutrialized or agricultural citie, if one add the relevant coefficient together, net effect in moit area are cloe to zero. Overall the reult are conitent with our model. Rainfall decline raie local city income in total for indutrialized citie, a labor move to the urban ector. But for agricultural citie, rainfall decline have if anything a negative effect on total city income. Thi ugget that local urban area will 24

25 be hurt by any further drying out in the future unle they have an indutrial bae. Unfortunately, only a fraction of African urban area do Lead and Lag In Table 7 we tet for lagged and lead effect of rainfall. While we continue to rely on clutered error at the city level to account for erial correlation, a a robutne check here we alo try impoing an AR[1] tructure. Column 1 and 2 how the reult from Table 6 column 2 with clutering and AR[1]. The pecification with AR[1] ha quantitatively omewhat different coefficient, but the ame qualitative pattern. In column 3 and 4 we look for lagged effect. In column 3 with clutered error, increaed lagged rainfall in indutrialized arid area ha a mall additional negative effect on city light, about half of the contemporaneou effect, which diappear for moit area (but the offet effect i not ignificant). In column 4 with an AR[1] proce, lagged effect are much tronger. Thi difference in effect dependent on pecification and the more modet effect with clutered error led u to decide that trying to teae out longer lag tructure would not produce robut reult; and in general longer lag are not preciely etimated. In column 5 and 6 we tried lead rainfall a a placebo tet. For both clutered error and the AR[1] proce there are no ignificant effect, jut a hould be the cae. For the bae cae, indutrialized citie in arid region, the effect of lead i zero Other conideration In work not reported here, we examined whether effect differ for citie that are likely to be erved by hydro power. Our concern i that light could be affected directly by electricity availability and pricing, which could be affected by climate directly, independently of climate effect on income. However, becaue mot town are erved by national grid with uniform pricing, we don t actually expect differential effect. When we fully interacted our Table 6, column 2 pecification with a meaure of hydropower reliance, we found no differential effect. 7. Occupational choice within rural area Migration, whether temporary or permanent, i not the only poible repone to advere climate fluctuation or long term change in the rural ector. Drier growing condition will lower the return to farming and farmer may top working or witch to non-farm activitie. In thi ection, we find evidence of both, with differential pattern by gender. Thee poible repone mut be een in the overall context of climate change in rural economie. A noted above, if farm income drop, there will be le money in the rural economy in general, o alternative work opportunitie may be carce, muting the expected benefit of witching to a non-farm occupation. We looked only at repone in the rural ector. 25

26 Our data do not provide indutry information to analyze hift between ervice and manufacturing in the urban ector (which in themelve may be econd order effect) nor do they provide relevant migration information. 7.1 Data and pecification We tet whether change in climate have an impact on employment by ector within rural area uing individual-level data from the Demographic and Health Survey (DHS, Macro International) for 18 African countrie, all but two of which are in our urbanization dataet (Appendix Table A4). DHS ue a two-tage ampling deign, firt randomly electing enumeration area in a country and then urveying a cluter of about 30 randomly elected houehold in each. The urvey overample female houehold member ince one of the primary purpoe i to collect data on fertility and reproductive health. We compile DHS data from 2-3 repeated cro-ection for each country. In total we ue 43 urvey from between 1996 and 2011, and only include people in rural location. Our ample i retricted to thoe DHS that record cluter location, whether a repondent worked in the lat year or not, and if o in what occupation. Work need not be paid. Summary tatitic are in Appendix Table A5. Sample ize i 100,788 men and 312,769 women aged While the majority of male and female do report working (paid or unpaid), the percentage are only 82% and 67% repectively for our ample. We don t think of thi a the uual election problem of whether to work or not and, if o, what occupation to chooe baed on wage differential. Working i cloely tied to the farm and the deciion for many may be more whether to work on the farm or to carry out other houehold reponibilitie not conidered work. We thu model a multinomial choice between not working, working in agriculture, and working in a non-agricultural occupation. Thu, an increae in agricultural work may both draw people into the workforce and draw people out of non-agricultural work activitie. We note that a comprehenive tudy of intra-houehold dynamic and choice i beyond the cope of thi paper. Intead, we are etimating the reduced form effect of rainfall on occupation a tated in the urvey. 26 For both men and women, the dominant activity i working in agriculture but thi i epecially true of men, both in term of the choice among the 3 activitie (58 v. 44%) and conditional on working 25 Reducing the ample to the age group to include only repondent who have completed all poible education doe not change reult. 26 Furthermore, we are aware that people in different place may conceptualize work in different way. Thu while we cannot be ure that we are capturing preciely the ame margin in all context, we are identifying local change over time in the way people anwer the ame quetion of whether they are working, and if o in what occupation. 26

27 (71 v. 66%). The average age of repondent i between 28 and 29 for both men and women. Men generally have more education with about 66% reporting at leat primary chool veru 53% for women. Since all DHS ued in our tudy are georeferenced at the cluter level, matching to the Willmott and Matuura (2012) climate data i traightforward. However, different round of the DHS do not urvey the exact ame cluter, and the number of cluter typically increae over time. We created upercluter by matching each cluter to the geographically cloet cluter in the firt urvey in it country. We etimate the multinomial choice of not working, working in agriculture, and working in a non-agricultural occupation. Agricultural work i the reference occupation, o covariate effect on it are a reidual (ince marginal effect mut um to zero acro the three choice). The general pecification i y icjt = αx icjt + βw cj,(t 1,t 3) + d jt + f c + e icjt (15) where y icjt i a choice for individual in ditrict c, in country j and year t (i.e., not work, work in agriculture, work in non-agriculture); x icjt are individual characteritic: age (and age quared) and education dummie; W cj,(t 1,t 3) i average moiture over the three previou year; f c i a upercluter (or province) fixed effect; d jt i a country-year fixed effect; and e icjt i an error term clutered at the upercluter level. We control for predetermined individual characteritic age and education in x icjt, and etimate eparate regreion by gender. We do not include control for marital tatu, number of children or other indicator that could plauibly be affected by climate and intead etimate a reduced form model of climate impact on choice. We again mooth moiture over 3 year to remove noie, but ince urvey timing varie within the calendar year and thi year climate may not yet have an effect at urvey time, we ue year t-3 to t-1. We cluter tandard error by upercluter, a meaured moiture doe not vary within them. Since thee are not individual panel data, we cannot firt- or long-difference them, but upercluter fixed effect perform an analogou role in controlling for time-invariant local effect. Incluion of upercluter fixed effect enure identification i baed on within-cluter variation in 27

28 rainfall. Thi i important. For example, in dry and drying area, non-farm opportunitie may be limited and there may be a low probability of non-farm work per e, o imple correlation might ugget a negative aociation between drying out and non-farm work. Our main pecification i a linear probability model (LPM) with upercluter fixed effect. We alo etimate the model by logit and probit, but with 3,939 upercluter for female and 3,751 for male, upercluter fixed effect are not computationally feaible. In thee nonlinear model we intead include province fixed effect, auming that cluter within (larger) province have imilar condition. We alo control for country-year effect. Multinomial logit and probit marginal effect are almot identical, o we report jut the probit Reult The reult are in Table 8. We focu on the LPM reult in column 1-3 for women in panel A and for men in panel B. The effect for men and women differ. More moiture draw women out of the home and into farming, with no repone in off-farm work. More moiture draw men out of non-farm work into farm work. Thi preumably reflect an average gendered diviion of labor for thi ample. A one tandard deviation increae in moiture (about 0.5) increae the probability of women working in farming by 0.03 from a mean of Increaing moiture acro it full range (3.5) raie the probability of working on the farm by 0.18, a 40% increae. A one tandard deviation increae in moiture reduce the probability of men working off farm by about 3%. The control variable have expected effect: the more educated and younger women are, the le likely they are to work in agriculture. Reult retricting to the firt and lat urvey in each country are imilar (not hown). A noted above, the province fixed effect ued in the probit pecification are a much weaker control for underlying local condition than upercluter fixed effect. Reult for the probit in column 4-6 of Table 8 are different from the LPM. For women probit effect are larger, perhap reflecting identification problem in the probit, or attenuation bia from the upercluter fixed effect in the LPM. One might thu be tempted to think of the LPM etimate a a lower bound and the probit a an upper bound. However for men the probit reult are much maller than the LPM, only marginally different from zero for not working. Reetimating the LPM with jut ditrict fixed effect ugget that mot of thee difference are explained by the difference in fixed effect pecification, not in etimation procedure (not hown). 27 Note that the covariance tructure with cro-choice correlation in error i not identified when there i no variation in covariate acro choice (only acro individual). 28

29 In ummary, baed on OLS etimation with upercluter fixed effect, when climate for farming improve, women are more likely to leave houehold work behind to engage in farming, while men are more likely to leave non-farm work. For men at leat, drying drive movement into non-farm occupation within the rural ector. 8. Concluion With a high dependence on agriculture and an already highly variable and often marginally uitable agro-climate, Africa may be at higher rik from climate change than mot other world region. Agricultural adaptation through improved eed and increaed irrigation may mitigate thi rik. But technological change in Africa ha been low and, depite frequent drought in the pat, irrigation infratructure remain carce. So for many farmer facing advere climatic condition the only option may be to migrate to urban area. Our analyi ugget that agro-climatic condition do indeed influence urbanization rate, with better condition retarding urbanization and unfavorable condition leading to greater urban population growth. However trong effect are confined to about 10-15% of Sub-Saharan African ditrict, in arid local area that have ome degree of indutrialization. A our model predict, decreaed moiture increae total city population and income in ditrict whoe citie are likely to have manufacturing, and are therefore more likely to be able to aborb worker leaving the farm into the urban labor force. Again a theory predict, in the more uual context where local citie are unlikely to have manufacturing and rely on demand from local farmer, we find that reduced moiture lead to reduced or unchanged city income. Finally, we find ome evidence of alternative adaptation trategie. When growing condition are unfavorable, rural female are more likely to report not working and rural male are more likely to move from farm to non-farm work. Thee reult confirm the trong link between climatic condition and urbanization but jut in particular circumtance, adding to the growing economic literature on climate and development. Our reult ugget that more evere and peritent climate change, which will likely increae the challenge faced by Africa farmer, could further accelerate migration to citie, but only in more indutrialized area. Support for agricultural adaptation, and creating condition for urban economic growth, are therefore even more urgent prioritie. 29

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34 Figure 1: Hitorical level of moiture (precipitation / potential evapotranpiration) Note: Map boundarie reflect the ituation during the time period covered by thi tudy. See Appendix Table A1 for detail on the time period ued for each country. Figure 2: Decreaing moiture in Africa in the econd half of the twentieth century 34

35 Figure 3. Cenu data ample Note: Map boundarie reflect the ituation during the time period covered by thi tudy. See Appendix Table A1 for detail on the time period ued for each country. Figure 4. Variability in climate change in Africa 35

36 Figure 5a. Extent of indutry 1965, modern (non-food proceing) indutrie Figure 5b. Extent of indutry 1965, all indutrie 36

37 Figure 6. Spread of Dependent Variable a. Raw data b. Factoring out country fixed effect Figure 7. Initial urbanization and moiture growth: arid countrie 37

38 Figure 8: Spatial data integration to obtain city level light and rain catchment data City light expanion ( ) Merging yearly light to obtain outer envelope Adding population point Creating 30km rain catchment area 38