UNIVERSITY OF WAIKATO. Hamilton New Zealand. The Effect of Infrastructure Access and Quality on Non-Farm Enterprises in Rural Indonesia

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1 UNIVERSITY OF WAIKATO Hamlton New Zealand The Effect of Infrastructure Access and Qualty on Non-Farm Enterprses n Rural Indonesa John Gbson Unversty of Wakato and Susan Olva Unversty of Calforna, Davs Department of Economcs Workng Paper n Economcs 17/08 December 2008 Correspondng Author John Gbson Department of Economcs Unversty of Wakato, Prvate Bag 3105, Hamlton, New Zealand Fax: +64 (7) Emal: jkgbson@wakato.ac.nz

2 Abstract There s growng nterest n the rural non-farm sector n developng countres as a contrbutor to economc growth, employment generaton, lvelhood dversfcaton and poverty reducton. Access to nfrastructure s dentfed n some studes as a factor that affects nonfarm rural employment and ncome but less attenton has been pad to the constrants mposed by poor qualty nfrastructure. In ths paper we use data from 4000 households n rural Indonesa to show that the qualty of two key types of nfrastructure roads and electrcty affects both employment n and ncome from non-farm enterprses. It appears that there would be gans from development strateges that mprove both the access to and the qualty of rural nfrastructure. Keywords nfrastructure non-farm rural economy Indonesa JEL Classfcaton H54, O17 Acknowledgements We are grateful to Nel McCulloch, Danang Parkest and audences at the Indonesan Rural Investment Clmate Assessment workshops for ther helpful suggestons. 2

3 I. Introducton The rural non-farm economy (RNFE) s emergng as a key contrbutor to economc growth, employment generaton, lvelhood dversfcaton and poverty reducton n developng countres. The combnaton of off-farm wage work, rural non-farm self-employment and remttances contrbutes 30-50% of rural household ncome n sub-saharan Afrca (Reardon, 1997) and about one-thrd of ncome n Asa (Haggblade, Hazell and Reardon, 2007). Ths growng mportance of the RNFE s reflected n several recent studes of the determnants of non-farm rural employment and ncome (Lanjouw, 1999; Berdegué, Ramrez, Reardon, and Escobar, 2001; Corral and Reardon, 2001; Escobal, 2001; Lanjouw, 2001; Isgut, 2004; Zhu and Luo, 2006). But polcy makers who seek gudance from ths lterature may detect ambvalence about at least one key nterventon -- mprovng rural nfrastructure (Reardon, Berdegué, and Escobar, 2001). Investments n roads, electrcty and telecommuncatons are often cted as nterventons that can assst the rural non-farm economy by reducng transactons costs (Zhu and Luo, 2006; Reardon, Stamouls and Pngal, 2007). Yet these same nvestments mght also harm the non-farm rural economy, as Start (2001, p.502) ponts out: The rony of the RNFE s that the same nfrastructure that wll open up rural areas and speed ther development wll also allow urban goods to compete away the RNFE, as the protecton of ther non-tradablty s eroded. For example, Berdegué et al. (2001) fnd rural Chlean households served by the worst type of drt roads earn hgher ncome from non-farm self-employment than households wth access to better paved roads, partly due to the protecton aganst effcent urban compettors gven by the bad roads. In vew of ths ambvalence we reexamne the relatonshp between rural nfrastructure and non-farm self-employment and ncome. At least three features dstngush the current study from much of the rapdly growng lterature on the RNFE. Frst, we concentrate on an Asan developng country, Indonesa, whle pror studes are mostly from Afrca and Latn Amerca. Relatve to those regons, more of non-farm employment n rural Asa s n manufacturng and servces (Haggblade et al. 2007). In addton, the hgher populaton densty n Asa may make nfrastructural constrants bnd dfferently than n other regons. Second, we consder both the accessblty and the qualty of rural nfrastructure. Ths dstncton makes sense from a polcy pont of vew because there may be tradeoffs between buldng new nfrastructure to mprove accessblty, and upgradng the qualty of exstng nfrastructure. 1 Accountng for qualty dfferences also makes sense from an econometrc pont of vew because the estmated effect of nfrastructure access on the RNFE may be based f relevant qualty attrbutes are gnored. Heterogeneous nfrastructure qualty mples 3

4 that smply measurng quanttes, such as spendng on roads or the length of roads, may not be suffcent. For example, n Chna a one Yuan nvestment n the lowest level rural roads rases non-farm rural GDP by fve Yuan, but a smlar nvestment n expressways has no sgnfcant effect (Fan and Chan-Kang, 2005) so gnorng ths heterogenety could bas estmates of the effect of nfrastructure spendng. Thrd, we corroborate the cross-sectonal results wth a lmted panel analyss of the effect that changes n nfrastructure over tme have on partcpaton n rural non-farm selfemployment. Ths panel analyss may mtgate two econometrc problems affectng crosssectonal estmates: reverse causaton and omsson of unobserved productve factors. Reverse causaton occurs f rcher areas wth more non-farm actvty attract more nfrastructure nvestment, so that the correlaton between nfrastructure and the level of RNFE s not a causal relatonshp (Gbson and Rozelle, 2003). Omtted varables bas may occur f rural nfrastructure s systematcally located n places wth hgher unobserved (to the econometrcan) productvty (Jacoby, 2000). A soluton to these problems s to use panel data so that tme-nvarant unobserved productvty can be controlled for by estmatng the relatonshp between changes n nfrastructure and changes n the RNFE. Such analyss s somewhat lmted compared wth panel studes n other areas (e.g. labor economcs) because most nfrastructure changes only slowly over tme. For ths reason, and to mantan comparablty wth the lterature, we manly concentrate on the cross-sectonal results. Our analyss s based on 4000 rural households n the Indonesa Famly Lfe Survey (IFLS). The cross-secton s from wave 3, whch took place n md-2000, and the panel compares wth wave 1 (1993). Three measures from the IFLS are used to ndcate the mportance of the RNFE: the share of household ncome from the net revenue of non-farm enterprses, whether any household member worked n a self- or famly-owned non-farm enterprse wthn the prevous 12 months, and the total number of non-farm enterprses operated by the household. We prefer these measures to others such as off-farm wage work because they clearly relate to local non-farm economc actvty whch can then be related to nformaton on the accessblty and qualty of local nfrastructure. Whle there have been prevous studes of both ncome shares and partcpaton decsons, the number of enterprses that households operate has been less commonly studed. However, t s plausble that the more enterprses the household s engaged n, the greater the dversfcaton of ther ncome. 2 II. The Non-farm Economy and Infrastructure n Rural Indonesa Rapd economc growth pror to the 1997 fnancal crss created many opportuntes for rural Indonesan households to be nvolved n a range of non-farm sales and servce actvtes, partcularly n Java (Effend and Mannng, 1994: ). 3 But only lmted attenton was pad to rural non-farm employment because the hgh growth rate obtaned va outwardlookng ndustralzaton allowed urban enterprses to absorb much of the excess labor 4

5 (Krstansen, 2003). But snce the fnancal crss, RNFE actvtes have receved renewed attenton, due to ther potental to stablze ncomes of the rural poor (Tambunan, 2000) and because small-scale rural enterprses performed better durng the crss than larger scale enterprses (Hll, 2001). Over one-thrd of rural employment n Indonesa s outsde of the prmary sector (Table 1). The major non-farm sectors are manufacturng, trade and servces. The non-farm sector may be even more mportant as an ncome source; accordng to the data n the second part of Table 1, over two-thrds of rural ncome n Java may come from non-farm sources, 4 although the share of non-farm ncome s lkely to be lower n the outer slands. The major non-farm ncome sources are wage ncome and self-employment ncome. It s also notable that the average share of non-farm ncome s lower than the contrbuton of these ncome sources to aggregate ncome, reflectng a somewhat skewed contrbuton from these ncome sources. Table 1. The Importance of Rural Non-Farm Economy n Indonesa Total (000) Share Employment by sector Agrculture, Forestry and Fshery 36, % Manufacturng ndustry 4, % Wholesale/Retal Trade, Restaurants, Hotels 7, % Publc Servces 3, % Others 5, % All Non-Farm 20, % Composton of rural ncome (Java, 1999) Rupah per household per year Average share of household ncome Farm ncome 127, % Non Farm Income - Self employment ncome 111, % - Wage ncome 147, % - Remttances and transfers 39, % - Rent/nterest/penson ncome 58, % - Other ncome 35, % Source: World Bank (2006) (derved from SAKERNAS 2004) and authors calculaton based on SUSENAS One drawback of the evdence presented n Table 1 s that t does not ndcate the sectors where non-farm self-employment s concentrated. Snce the average sze of enterprses dffers by sector, employee numbers may not be a good proxy for enterprse numbers. To provde more focused evdence, Table 2 reports on the range of non-farm actvtes the rural populaton s engaged n. Nearly 60% of rural non-farm enterprses are engaged n trade (both food and non-food sales). The remanng actvtes are largely n manufacturng and servce sectors. In addton to rapd economc growth creatng non-farm opportuntes, mprovements n nfrastructure n rural Indonesa also lkely contrbuted to the rse of the non-farm economy. The percentage of the rural populaton usng electrcty ncreased from below 10% n 1980 to 5

6 above 80% by The man electrcty company, PLN (Pelayanan Lstrk Negara) reached 82% of rural households n the IFLS-3 survey. However, qualty of electrcty vares consderably, wth outer regons sufferng regular black-outs (Reuters, 2008 and World Bank, 2008). Indonesa s roads system also expanded rapdly, wth the offcal road network ncreasng n length by 19.5% snce As wth electrcty, however, there s consderable varaton across regons n both the extent of access and the qualty of nfrastructure. Densely populated Java, wth only 6.8% of Indonesa s land area but 61.9% of ts populaton, accounts for 26.8% of the classfed road network (World Bank, 2006). Only about 50% of dstrct roads are paved wth asphalt and about 40% are classfed as ether damaged or serously damaged (Table 3). Table 2. Sector of non-farm enterprses for rural households n Indonesa Sector % of rural non-farm enterprses Agrculture, Forestry and Fshery 3.85 Mnng and Quarryng 1.04 Electrcty, Gas and Water 0.30 Constructon 1.93 Transportaton and Communcaton 2.96 Fnance, Insurance, Real Estate Restaurant, food sales Sales: non food Trade Food Processng Clothng Other Industry 8.55 Manufacturng Servces Source: Authors calculatons from IFLS3 data. Table 3. Condton and Surface Type of Dstrct Road Network n 2003 Sumatra Java Bal & Nusatenggara Kalmantan Sulawes Maluku & Papua Total Km km Km Km km km km % Surface Type Asphalt 41,814 61,948 12,389 9,537 23,718 3, , Gravel/Stone 15,580 10,409 4,128 4,417 7, , Earth 25,875 10,099 8,142 9,925 9,411 8,917 72, Other 6,963 1,487 1,041 4,357 4,202 6,510 24, Condton Good 29,779 36,183 9,217 7,183 18,357 5, , Moderate 22,215 22,433 5,456 5,684 9,557 12,386 77, Damaged 21,815 18,283 7,295 8,818 6, , Serously damaged 16,423 7,044 3,732 6,551 9,753 1,589 45, Source: Parkest (2006). 6

7 III. Data and Methods The data we use come manly from the thrd wave of the Indonesan Famly Lfe Survey, conducted n md-2000 (Strauss et al., 2004). The frst wave of ths survey (n 1993) orgnally ntervewed 7,200 households from 130 rural vllages and 180 census enumeraton areas n urban locatons. The sample had grown to 10,400 households by wave 3, because the IFLS tracks and ntervews households who splt off from the orgnal sampled households. However, the detaled questons on rural nfrastructure are only collected n the orgnal IFLS vllages so our analyss s restrcted to 3,951 households n the orgnal 130 rural vllages. For the IFLS sample of rural households, the share of total ncome from non-farm enterprses (NFE) s 3.5% (8.4% amongst those households wth an enterprse), wth 40% households havng at least one member nvolved n a NFE and a mean of 1.22 NFEs per household (Table 4). Households that have non-farm enterprses are larger, and more lkely to be headed by a younger male who s marred, and s Islamc, and have hgher ncome. In terms of locatonal and nfrastructure characterstcs, the data shows that households wth NFEs tend to lve n communtes n whch a hgher percentage of households have electrcty. Table 4. Comparson of Mean Characterstcs of Partcpant and Non-partcpant Households Households wth All Households Households wthout non-farm enterprses non-farm enterprses F-stat for sgnfcant dfference Varable Mean Std.Dev. Mean Std.Dev. Mean Std. Dev. Importance of NFE Share of HH ncome from NFE n.a. n.a n.a. # of non-farm enterprses (NFE) n.a. n.a n.a. Has NFE (=1 f yes, else 0) n.a. n.a n.a. Household Characterstcs Household sze ** Female HH head * Age of HH head Marred HH head ** % kds 0 6 yrs % kds 7 14 yrs ** % adults yrs * Prmary < Gr Completed prmary Secondary & above Per capta farm sze (ha) Islamc HH ** HH speaks Chnese Total ncome (Rp 000 per HH) 6,842 27,800 4,881 10,700 9,778 25, ** Locaton and nfrastructure characterstcs Log dstance to Prov captal a Log average road speed b Drt road (=1, 0 otherwse) % of HH wth electrcty ** Vllage never has blackouts Total observaton 3,951 2,369 1,582 Notes: + denotes sgnfcant at 10% level, * at 5% level; ** at 1% level; a = klometer, b = klometre/hour. 7

8 In addton to these dfferences between households wth and wthout NFEs, there also appear to be relatonshps between nfrastructure qualty and the share of household ncome from non-farm enterprses. For the IFLS rural sample, NFE ncome shares are hgher for households that are connected to the publc electrcty network relatve to households wthout access to electrcty and for those n a vllage wth asphalt or concrete roads rather than drt roads (Fgure 1). Smlarly, blackouts occurrng at least once a week are assocated wth a 60% lower NFE ncome share compared wth lvng n vllages wth no blackouts. Fgure 1. Infrastructure and the Extent of NFE Actvtes n Indonesa Drt road Asphalt or concrete road HH wthout electrcty HH wth electrcty More than weekly blackouts Less than weekly blackouts No blackouts 0% 1% 2% 3% 4% 5% Percentage of household ncome from NFE Emprcal Methods We frst model the determnants of how much rural household ncome comes from the net revenue of non-farm enterprses. Snce many households do not report any ncome from nonfarm enterprses we use the Tobt regresson model: y = xβ+ u f xβ+ u> 0 * = 0 f xβ+ u 0 = 1,2,..., N (1) where N s the number of observatons, y * s the dependent varable, whch s a latent varable only observed for ncome shares above a threshold ( u > X β ) and s otherwse zero, x s a vector of ndependent varables whch ncludes attrbutes of the household and the household head, and communty and nfrastructure characterstcs, β s a vector of unknown coeffcents, and u s an ndependently dstrbuted error term assumed to be normal 2 wth zero mean and constant varance σ. 8

9 To model partcpaton of household members n ther non-farm enterprse we use a Probt model, whch takes the followng form: Pr ( p = 1x ) = Φ( x β ) (2) where p s the outcome of the 0-1 varable for the th observaton, Φ s the standard cumulatve normal, x s the vector of explanatory varables for observaton and β s the vector of coeffcents to be estmated. These probt coeffcents are not drectly nterpretable, but margnal effects for contnuous varables can be calculated (at the mean) as: Φ( xb) x x = x = φ( xb) b where b s the vector of estmated coeffcents and φ s the normal densty. For dummy varables, the dscrete change n probablty when the dummy varable swtches from zero to one s calculated as Φ ( x1b ) Φ( x0b) where x 1 = x 0 = x except that the th elements of x 1 and x 0 are set to one and zero, respectvely. (3) The model of the number of non-farm enterprses that the household operates s estmated wth a Posson regresson model, where the observed count for each household, y s assumed to be drawn from a Posson dstrbuton wth mean μ, where μ s estmated from observed characterstcs: μ = E( x ) = exp( x β ). (4) y The exponental of x β s taken to ensure that μ s postve, whch s needed snce counts can only be zero or postve. The vector of characterstcs n x ncludes attrbutes of the household and the household head, and communty and nfrastructure characterstcs. IV. Cross-Sectonal Results Table 5 contans results of Tobt regressons for the share of rural household s total ncome that comes from the net revenue of ther non-farm enterprses. The explanatory varables are dvded nto three groups: characterstcs of the household, ncludng demographcs, man language spoken, relgon and land ownershp; characterstcs of the household head, ncludng age, gender and educaton; and characterstcs of ther communty, ncludng provnce fxed effects, dstance of the communty from the provncal captal and local nfrastructure. Rural households appear to have hgher ncome shares from non-farm enterprses when older chldren and (less conclusvely) prme age adults are a bgger share of the household populaton. The ncome shares are also hgher for Islamc households and lower when the household manly speaks Chnese. The only characterstc of the household head that appears to matter s whether they have educaton at a secondary school level or above. 9

10 Table 5. Determnants of Rural Household s Share of Total Income from Nonfarm Enterprses (1) (2) (3) (4) (5) Household characterstcs Household sze (0.95) (1.04) (0.85) (1.04) (1.08) % kds 0-6 yrs (0.18) (0.28) (0.23) (0.12) (0.23) % kds 7-14 yrs (1.89)+ (2.01)* (1.93)+ (1.98)* (2.05)* % adults yrs (1.64) (1.95)+ (1.52) (1.43) (1.71)+ Per capta land area (0.71) (0.82) (0.74) (0.76) (0.87) Islamc HH (2.20)* (2.07)* (2.19)* (2.30)* (2.14)* HH manly speaks Chnese (2.01)* (2.61)** (2.05)* (2.57)* (2.87)** Characterstcs of the household head Age of HH head (0.36) (0.54) (0.37) (0.58) (0.70) Female HH head (0.84) (0.73) (0.88) (0.60) (0.54) Incomplete prmary school (0.55) (0.76) (0.58) (0.67) (0.86) Completed prmary school (0.01) (0.40) (0.08) (0.15) (0.42) Has secondary schoolng (1.84)+ (1.75)+ (1.66)+ (1.57) (1.52) Locaton and nfrastructure characterstcs Log dstance to Prov captal (3.46)** (4.37)** (3.42)** (4.43)** (4.42)** Log average road speed (2.10)* (1.03) Drt road (=1, 0 otherwse) (1.71)+ (1.80)+ HH connected to electrcty (1.51) (1.37) (1.33) Vllage never has blackouts (3.36)** (3.32)** Constant (2.27)* (2.68)** (2.49)* (2.39)* (2.24)* Provnce fxed effects Yes Yes Yes Yes Yes χ 2 test all slopes= ** 232.7** 224.1** 233.4** 246.5** χ 2 test access varables=0 a 11.9** 19.1** 13.4** 20.7** 20.1** χ 2 test qualty varables=0 b n.a. 7.6* n.a. 11.3** 18.5** Note: Coeffcents are robust Tobt estmates from IFLS n year 2000, for N=3913 rural households. The dependent varable s the share of total household ncome n the form of net revenue from non-farm busnesses, wth 451 uncensored observatons and 3462 left censored observatons. Robust z-statstcs n ( ) are adjusted for clusterng by communty. + sgnfcant at 10%; *at 5%; **at 1%. a Access varables are log dstance to provncal captal and whether the household s connected to electrcty. b Qualty varables are the log average road speed, whether manly a drt road and prevalence of blackouts. 10

11 The further a communty s from the provncal captal, the lower the share of household ncome from NFE. Even after controllng for ths effect of remoteness, two ndcators of the qualty of road nfrastructure are assocated wth varatons n the household relance on the NFE (column 2). The frst ndcator s the (log) average speed of travel between the vllage and the provncal captal (whch averages 52 km/hr). 5 The better the qualty of roads (relatve to the traffc load), the faster the average speed of travel and accordng to the regresson results the greater the mportance of NFE for households. The fnal ndcator of road qualty concerns the predomnant type of road wthn the vllage: when ths s not asphalt or cement (denoted drt roads n the table) there s a sgnfcantly lower share of total ncome comng from NFE. If a household has electrcty t opens up a wder range of actvtes (e.g., mnor constructon or assembly tasks requrng electrcal equpment, food stalls where refrgeraton s requred). However, to the extent that the electrcty supply s unrelable, wth frequent blackouts, a rural household may be less wllng to engage n an electrcty-dependent enterprse, snce they may then ether face the captal cost of buyng ther own generator or put up wth the dsruptons caused by blackouts. To look at both of these effects, the share of rural household ncome from non-farm enterprses s regressed on ndcators for whether the household s connected to electrcty and for the qualty of supply proxed here by a dummy varable for whether the vllage never has blackouts (column 4). The regresson also controls for the (log) dstance from the provncal captal because otherwse the electrfcaton varables may smply be actng as a proxy for overall remoteness. The results show that the presence of electrcty s postve but s not statstcally sgnfcant. However, qualty of electrcty supply has a consderable mpact n affectng households to engage n NFE. Column 4 of Table 5 shows that the share of rural ncome from NFE s 26.9 percentage ponts hgher for households n vllages that never suffer blackouts. The last column of Table 5 shows that even after controllng for access to nfrastructure and dstance from the provncal captal, the better the qualty of roads and electrcty the hgher the ncome share from NFE for rural households. 11

12 Table 6. Determnants of Partcpaton n Nonfarm Enterprses for Rural Households (1) (2) (3) (4) (5) Household characterstcs Household sze (4.14)** (4.20)** (3.78)** (3.93)** (3.98)** % kds 0-6 yrs (1.18) (1.10) (0.95) (0.91) (0.87) % kds 7-14 yrs (1.92)+ (1.98)* (2.00)* (1.92)+ (1.96)+ % adults yrs (3.66)** (3.76)** (3.34)** (3.15)** (3.26)** Per capta land area (0.63) (0.75) (0.70) (0.80) (0.89) Islamc HH (3.86)** (3.58)** (3.77)** (3.35)** (3.15)** HH manly speaks Chnese (1.27) (1.03) (1.16) (0.94) (0.77) Characterstcs of the household head Age of HH head (1.46) (1.47) (1.46) (1.39) (1.42) Female HH head (0.14) (0.28) (0.10) (0.21) (0.32) Incomplete prmary school (0.09) (0.05) (0.05) (0.06) (0.16) Completed prmary school (0.26) (0.08) (0.02) (0.04) (0.30) Has secondary schoolng (2.76)** (2.57)* (2.31)* (2.05)* (1.93)+ Locaton and nfrastructure characterstcs Log dstance to Prov captal (1.00) (1.47) (0.85) (1.07) (1.30) Log average road speed (1.42) (0.89) Drt road (=1, 0 otherwse) (1.74)+ (1.54) HH connected to electrcty (4.54)** (4.63)** (4.41)** Vllage never has blackouts (1.99)* (1.88)+ Provnce fxed effects Yes Yes Yes Yes Yes Pseudo R-squared χ 2 test all slopes= ** 187.9** 192.6** 172.0** 179.4** χ 2 test access varables= ** 23.0** 21.2** χ 2 test qualty varables=0 n.a n.a. 4.0* 7.4+ Note: Robust probt estmates from IFLS n year 2000, for N=3951 rural households. The dependent varable equals one f any household member was employed or self-employed n a non-farm enterprse n the past year (N=1582) and otherwse equals zero (N=2369). Coeffcents show the change n probablty from a unt change n the explanatory varable. All other notes are as reported n Table 5. 12

13 Table 6 contans a parallel set of analyses to those n Table 5, except ths tme the results are from a Probt model of whether household members worked n ther non-farm enterprse. The results are largely the same as for the ncome shares. The probablty of partcpatng s hgher for households wth a larger proporton of youths aged 7 14 years, and a greater proporton of adults n the household. Muslm households and those where the household head has secondary educaton and above also have a hgher probablty of partcpaton. In terms of locaton and nfrastructure characterstcs, the results are largely the same as for the ncome shares, wth one excepton. After controllng for the qualty of supply, a dummy varable for whether the partcular household uses electrcty remans statstcally sgnfcant (column 4). Accordng to the coeffcent on ths dummy varable, the partcpaton rate n NFE goes up by 13.3 percentage ponts when the household utlzes electrcty. Hence, when a household connects to the electrcty network, t expands the range of actvtes that household members can partcpate n. However, for both NFE partcpaton and NFE ncome shares, the qualty of electrcty supply matters even after controllng for access to electrcty. Table 7 contans the results of Posson regressons for the number of nonfarm enterprses operated by each rural household. Household sze, the percentage of chldren aged 7 14 and of adults n the household, beng a Muslm household and havng a household head wth secondary educaton or above are statstcally sgnfcant n determnng the number of nonfarm enterprses. Controllng for locaton and road qualty (as proxed by average speed of travel), there s a further negatve effect of havng a predomnantly drt road n the vllage whch decreases the expected number of NFEs operated by rural households by 0.78 (ths s the exponental of -0.25). Snce 37% of the rural populaton s n vllages wth drt roads, there s consderable scope for upgradng local road qualty and thereby ncreasng the average number of NFEs. Access to electrcty also seems to play an mportant role havng an electrcty connecton rases the expected number of NFEs operated by each household by 1.5 (the exponental of 0.42). The qualty of electrcty supply also matters, wth households n vllages whch never suffer blackouts havng an average of 1.3 more NFEs, even when controllng for access to electrcty. A consstent pattern n the results for NFE ncome shares, partcpaton n NFE and the number of NFEs operated, s that nfrastructure qualty matters even after controllng for nfrastructure access. Ths s shown formally by the ch-squared values at the foot of Tables 5, 6, and 7 whch are for tests of the hypothess that the nfrastructure qualty varables have no effect, once access to nfrastructure, communty locaton and household and household head characterstcs are controlled for. In all cases, the hypothess that nfrastructure qualty does not matter s rejected. 13

14 Table 7. Determnants of the Number of Nonfarm Enterprses Operated by Rural Households (1) (2) (3) (4) (5) Household characterstcs Household sze (4.92)** (4.99)** (4.57)** (4.74)** (4.83)** % kds 0-6 yrs (1.11) (1.09) (0.94) (0.97) (0.98) % kds 7-14 yrs (2.12)* (2.16)* (2.15)* (2.11)* (2.13)* % adults yrs (3.41)** (3.49)** (3.20)** (3.04)** (3.15)** Per capta land area (0.05) (0.14) (0.11) (0.18) (0.25) Islamc HH (3.07)** (2.81)** (3.03)** (2.88)** (2.66)** HH manly speaks Chnese (1.27) (0.91) (1.12) (0.85) (0.57) Characterstcs of the household head Age of HH head (1.55) (1.74)+ (1.61) (1.69)+ (1.86)+ Female HH head (1.34) (1.49) (1.32) (1.44) (1.56) Incomplete prmary school (0.07) (0.14) (0.09) (0.12) (0.30) Completed prmary school (0.82) (0.42) (0.56) (0.54) (0.22) Has secondary schoolng (2.83)** (2.59)** (2.36)* (2.15)* (2.00)* Locaton and nfrastructure characterstcs Log dstance to Prov captal (1.18) (1.64) (1.00) (1.27) (1.48) Log average road speed (1.59) (0.90) Drt road (=1, 0 otherwse) (2.31)* (2.12)* HH connected to electrcty (4.47)** (4.51)** (4.20)** Vllage never has blackouts (2.10)* (2.16)* Constant (4.01)** (3.75)** (3.91)** (4.15)** (3.77)** Provnce fxed effects Yes Yes Yes Yes Yes χ 2 test all slopes= ** 192.6** 193.1** 181.5** 186.8** χ 2 test access varables= ** 22.4** 19.3** χ 2 test qualty varables=0 n.a. 8.1* n.a. 4.4* 11.2** Note: Posson regresson estmates from IFLS n year 2000, for N=3951 rural households. The dependent varable s the number of non-farm enterprses operated by the household n the past year (expected value=0.48). Exponental of coeffcents shows the change n expected number from a unt change n the explanatory varable. All other notes are as reported n Table 5. 14

15 V. Do Improvements n Infrastructure Affect Partcpaton n Non-Farm Enterprses? The cross-sectonal relatonshps reported above are subject to varous nterpretaton problems whch can weaken nferences drawn from the results for the nfrastructure varables. It s possble that more productve areas (due to envronmental and other factors) have both more nfrastructure and more NFE actvty. Alternatvely, NFE actvty may drve demand for nfrastructure, rather than the reverse. Because IFLS s a panel survey t s possble to at least partally deal wth ths problem. If nfrastructure s endogenously placed, then the communtes wth the most favorable attrbutes should receve nfrastructural nvestment before less well-endowed communtes. Hence, nformaton on access to nfrastructure for the same communty n a prevous perod can help to control for some of ths unmeasured productvty attrbutes. Smlarly, there are characterstcs of households (such as educaton, atttudes to rsk and entrepreneurshp etc) whch are lkely to affect ther current partcpaton n NFE actvtes, rrespectve of the nfrastructural constrants that they face. So a regresson of current NFE partcpaton on prevous partcpaton n NFE may control for the other household-level characterstcs affectng choce of economc actvtes. Therefore the strategy n ths secton of the paper s to estmate probt models of whether any household member worked n a non-farm enterprse wthn the prevous 12 months (that s, for the 1999 year, gven that the data were collected n md-2000), condtonng upon the partcpaton of the same household n non-farm enterprses n We also control for nfrastructure access n The key explanatory varables are the change n nfrastructure avalablty at the vllage level between 1993 and Once we have condtoned on prevous household behavor (dd they partcpate n NFE or not?) and prevous nfrastructure access, the coeffcents on the change n nfrastructure should have a stronger causal nterpretaton for the effects of nfrastructure on the mportance of NFE than s possble n cross-sectonal analyss. Note also that t would be possble to do ths analyss n another way, by lookng at changes n NFE ncome shares between 1993 and 2000 but changes n the structure of the ncome module between the 1993 and 2000 waves of the survey would make ths analyss less clear than one based on the smpler partcpaton questons. The results n column (1) of Table 8 suggest that mprovements n vllage nfrastructure, n the form of upgradng from drt roads and connectng to an electrcty network, rase the lkelhood of households havng a NFE, even after condtonng on prevous nfrastructure and prevous household partcpaton. Improvements n vllage access to electrcty and n the predomnant type of local road are postvely correlated (p<0.001) so the results n columns (2) and (3) separate out the effects of roads and electrcty n case multcollnearty s affectng the coeffcents. In both cases the results are largely the same. 15

16 Table 8. Relatonshp Between Changes n Vllage Infrastructure and Whether Anyone n the Household Partcpates n Non-Farm Busness (1) (2) (3) (4) HH partcpated n NFE n 1993? (20.28)** (20.61)** (20.42)** (20.07)** Vllage had drt road n (0.98) (1.85)+ Vllage road mproved snce (2.04)* (2.41)* Vllage had electrcty n (2.95)** (3.23)** Vllage ganed electrcty snce (2.47)* (2.41)* % of HH wth electrcty n (5.59)** Change n % of HH wth electrcty (2.17)* Observatons Source: Authors calculatons from IFLS3 and IFLS1 data, for rural households. Robust z-statstcs n parentheses. * sgnfcant at 5%; ** sgnfcant at 1%; + sgnfcant at 10% The estmates are from a probt model for whether anyone n the household partcpated n non-farm busness n the prevous 12 months. The coeffcents reported are margnal effects. Condtonal on prevous nfrastructure and whether the household prevously engaged n a NFE, upgradng the local road ncreases the lkelhood of a household beng engaged n an NFE by just over four percentage ponts (equvalent to one-tenth of the mean partcpaton rate). Connectng the vllage to the electrcty network rases the lkelhood of NFE partcpaton by 13 percentage ponts, whch s an ncrease equvalent to about one-thrd of the mean. In the fnal column n Table 8 an alternatve measure of mprovements n electrfcaton the change n the share of households wthn the vllage who use electrcty s used. Once agan, the results suggest that mprovements n nfrastructure are assocated wth hgher partcpaton rates n NFE, even after controllng for prevous nfrastructure avalablty. VI. Conclusons The results n ths paper suggest that both lack of access to nfrastructure and poor qualty of nfrastructure constran the non-farm enterprses of rural households n Indonesa. Households are less lkely to have a non-farm enterprse and also have a lower ncome share from NFE f they lve n a locaton that s more remote, has lower qualty roads, lacks access to electrcty, and suffers from frequent electrcty blackouts. Moreover, t appears that mprovements n vllage-level nfrastructure between 1993 and 2000 are assocated wth ncreases n the share of households that have non-farm enterprses. Whle there s some ambvalence n the lterature about whether mprovements n rural nfrastructure ad or harm the rural non-farm economy, the results reported here favor the vew that poor nfrastructure constrans rural non-farm enterprses. Moreover, there s a 16

17 negatve effect of poor qualty nfrastructure on top of prevously examned effects of poor access to nfrastructure. Therefore, gans can be expected from mprovng the qualty of exstng nfrastructure and not just from buldng new nfrastructure to mprove access. References Berdegué, J., Ramrez, E., Reardon, T., and Escobar, G. (2001). Rural nonfarm employment and ncomes n Chle World Development 29(3): Booth, A. (2002). The changng role of non-farm actvtes n agrcultural households n Indonesa: Some nsghts from the agrcultural censuses Bulletn of Indonesan Economc Studes, 38(2): Corral, L. and Reardon, T. (2001). Rural nonfarm ncomes n Ncaragua World Development 29(3): Deaton, A. (1997), The Analyss of Household Surveys Johns Hopkns Unversty Press, Baltmore. Effend, T. and Mannng, C. (1994). Rural development and non-farm employment n Java. In B. Koppel, J. Hawkns and W. James (eds.), Development or Deteroraton: Work n Rural Asa. Boulder: Lynne Renner. Escobal, J. (2001). The determnants of nonfarm ncome dversfcaton n rural Peru World Development 29(3): Fan, S. and Chan-Kang, C. (2005). Road development, economc growth, and poverty reducton n Chna Research Report No. 138, Washngton, D.C.: Internatonal Food Polcy Research Insttute. Gbson, J. and Rozelle, S. (2003). Poverty and access to roads n Papua New Gunea Economc Development and Cultural Change 52(1): Haggblade, S., Hazell, P. and Reardon, T. (2007). Transformng the Rural Nonfarm Economy: Opportuntes and Threats n the Developng World. John Hopkns Unversty Press: Baltmore. Hll, H. (2001). Small and medum enterprses n Indonesa: Old polcy challenges for a new admnstraton Asan Survey, 41 (2): Isgut, A. (2004). Non-farm ncome and employment n rural Honduras: assessng the role of locatonal factors Journal of Development Studes 40(3): Jacoby, H. (2000). Access to markets and the benefts of rural roads The Economc Journal 110(July): Jn, S. and Dennger, K. (2008). Key constrants for rural non-farm actvty n Tanzana: Combnng nvestment clmate and household surveys Journal of Afrcan Economes Advance Access publshed September 1,2008, do: /jae/ejn016. Krstansen, S. (2003). Lnkages and rural non farm employment creaton: Changng challenges and polces n Indonesa ESA Workng Paper No, FAO: Rome. Lanjouw, P. (1999). Rural nonagrcultural employment and poverty n Ecuador Economc Development and Cultural Change 48(1):

18 Lanjouw, P. (2001). Nonfarm employment and poverty n rural El Salvador World Development 29(3): Parkest, D. (2006). Infrastructure support for mprovng rural nvestment clmate mmeo. Background paper for the Indonesa Rural Investment Clmate Assessment. Reardon, T. (1997). Usng evdence of household ncome dversfcaton to nform study of the rural nonfarm labour market n Afrca World Development 25(5): Reardon, T., Berdegué, J., and Escobar, G. (2001). Rural non-farm employment and ncomes n Latn Amerca: Overvew and polcy mplcatons World Development 29(3): Reardon, T., Stamouls, K. and Pngal, P. (2007). Rural non-farm employment n developng countres n an era of globalzaton Agrcultural Economcs 37(s1): Reuters (2008). Creakng Indonesa power grd drags on busness [On-lne] Avalable Start, D. (2001). The rse and fall of the rural non-farm economy: poverty mpacts and polcy optons Development Polcy Revew 19(4): Strauss, J., Beegel, K., Skok, B., Dwyanto, A., Herawat, Y. and Wtoelar, F. (2004). The Thrd Wave of the Indonesa Famly Lfe Survey: Overvew and Feld Report. March WR144/1- NIA/NICHD. Tambunan, T. (2000). The performance of small enterprses durng economc crss: Evdence from Indonesa Journal of Small Busness Management, 38(4): Warr, P. (2005). Road development and poverty reducton: the case of Lao PDR Research Paper No. 64, Manla: Asan Development Bank Insttute. World Bank (2006). Revtalzng the rural economy: An assessment of the nvestment clmate faced by the non-farm enterprses at the dstrct level. World Bank: Jakarta. World Bank (2008). Spendng for development Makng the most of Indonesa s new opportuntes. World Bank: Washngton, D.C. Zhu, N. and Luo, X. (2006). Nonfarm actvty and rural ncome nequalty: A case study of two provnces n Chna World Bank Research Workng Paper No. 3811, The World Bank, Washngton DC. 18

19 Notes For example, n the Lao PDR, roads nvestment between and helped to brng dry weather roads up to a wet weather standard and contrbuted to the poverty reducton that occurred over the perod (Warr, 2005). Another way to reduce poverty mght have been to allocate road nvestment so that those areas wth no road access (contanng 32% of rural households) got to at least a wet weather standard. Evdence from Papua New Gunea shows a sgnfcant declne n the number of ncome-earnng actvtes that household members partcpate n for every one-hour ncrease n travelng tme to the nearest road (Gbson and Rozelle, 2003). These ncluded actvtes such as sellng snacks and gasolne, workng as mnbus and truck drvers and kenek (assstants), and engagng n TV/rado and motorcycle repar actvtes. Those nvolved n these new servce actvtes tended to be better educated than those engaged n tradtonal areas of non-farm work, such as tradtonal healers and masseurs (dukun and tukang pjt), talors and trshaw drvers. Although these results refer to 1999, the same patterns are lkely to hold n other years because of the trend for non-farm ncome to ncrease faster than farm ncome. Accordng to Booth (2002), the growth of off-farm ncome of agrcultural households was 24 percent faster than the growth of ncome from agrcultural holdngs between the 1983 and 1993 Agrcultural Censuses. Ths s derved from two questons on the dstance to the captal cty and the tme taken for a oneway trp. 19