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33
# SURVEYING SURVEYS AND QUESTIONING QUESTIONS: LEARNING FROM THE WORLD BANK EXPERIENCE'

**Francesca Recanatini, Scott J. Wallsten, and Lixin Colin Xu**<sup>**2**</sup>

We would like to thank Philip Keefer, Howard Pack, Mary Shirley and seminar participants at the World Bank for their coniments. Any remaining errors are our own.

**2** The World Bank, 1818 H Street, NW Washington DC, 20433; and the Stanford Institute for Economic Policy Research, 579 Serra Mall at Galvez Street, Stanford, CA, 94305; email: frecanatini@worldbank.org; wallsten@,leland.stanford.edu; Lxul@worldbank.org.

1
activities or transactions that they would have undertaken under secure property rights. In other words, the economic outcomes of the failure of contracting institutions tend to be less extreme than those of the failure of property rights institutions.

Following the contribution of Fernandes and Kraay to World Bank (2006b), we will think of corruption as a proxy for the quality of property rights institution. As Fernandes and Kraay (2006) argue, corruption is ultimately the use of political authority in order to make private economic gain (in the form bribes). Its incidence should therefore be higher where property rights institutions are weaker. On the other hand we treat labor regulation, and the financial system as they would be categorized in Acemoglu and Johnson (2005), that is, as contracting institutions. If the Acemoglu-Johnson hypothesis of the primacy of property rights institution over contracting institutions is correct, and we are right in using corruption as a proxy for the first type of institutions, then one would expect the investment effects of labor regulation, access to finance and power shortages all to depend on the incidence of corruption. Specifically, we would expect weak contracting institutions to be a binding constraint on rate of fixed investment only where property rights institutions are not. One way of testing this is to estimate the effects of the first three conditional on the incidence of corruption and compare the results with those of unconditional (on corruption) estimates of effects, which is what we have done in this paper. We have also estimated all specifications of our investment equation on the allIndia sample as well as for ‘lagging regions’ and for ‘advanced states’ separately.

It turns out that the effects of labor regulation, power shortages and access to finance on the rate business investment all differ significantly between the two groups of states. More significantly, they also depend crucially on the incidence of corruption. Over the full sample, the rate of fixed investment is significantly lower where power shortages are more severe or labor regulation is stronger. Moreover, each of these impacts is greater for businesses self-reportedly affected by corruption. While access to finance does not seem to influence the rate of investment for most firms, there is evidence that investment decisions are constrained by cash flow in enterprises that are unaffected by corruption or by power shortages. There are nuances to this story as we look deeper to take into account regional specificity, but the key result always holds that labor regulation, power shortages and access to finance influence the rate of fixed investment in ways that crucially depend on the incidence of corruption.

The rest of the paper is organized as follows. We describe our data in section 2. We discuss specification, identification, and estimation issues in section 3. Details of estimation results are presented in section 4. Section 5 concludes.

4
# **Tables**

**Table 1. Scale of Employment Fund Programs and AGEI Sub‐group**

|All EF programs|2010|2011|2012|
|---|---|---|---|
|Total T&E providers|21|32|35|
|Total Events|598|645|711|
|Total trained|11750|12869|14255|
|AGEI only||||
|T&E providers working with AGEI|11|13|13|
|Total Events|110|218|246|
|Total trained|808|1664|1936|

**Table 2. Summary of Sample of Events for Baseline Surveys**

||2010|2011|2012|
|---|---|---|---|
|Total # events conducted by EF in Jan‐Apr|110|142|143|
|# events randomly sampled|N/A|182|112|
|# events included in baseline survey|65|69|85|
|# districts covered|30|34|29|
|# T&E providers covered|18|26|28|

Notes: More events were sampled than conducted in Jan‐Apr 2011 because some events that were scheduled for Jan‐Apr were delayed and did not start on time.

**Table 3. Summary of Sample of Individuals for 2010, 2011 and 2012 Baseline Surveys**

||**2010**|**2011**|**2012**|**Pooled**|
|---|---|---|---|---|
|_Treatment group_|||||
|Treatment‐ Male|478|447|458|1383|
|Treatment‐ Female|706|790|586|2082|
|Treatment‐ all|1184|1237|1044|3465|
|_Control group_|||||
|Control‐ Male|138|139|203|480|
|Control‐ Female|234|210|288|732|
|Control‐ all|372|349|491|1212|
|_Pooled (treatment an_|_d control)_||||
|Male|616|586|661|1863|
|Female|940|1000|874|2814|
|**TOTAL**|1556|1586|1535|4677|

27
There is vast fiscal literature on the factors leading to the deterioration of state finances in the late 1990s. Factors considered critical to the fiscal deterioration include the impact of the wage revisions; inability to contain wasteful expenditure, including subsidies; reluctance to raise additional resources; and competitive reduction in taxes. Mohan (2000) pointed to the increasing debt service payments and inadequate returns on government spending as important factors behind the deterioration in states‘ fiscal conditions. Acharya (2001) and Rao (2002) attributed the worsening of revenue (current) balance during this period to the implementation of the Fifth Pay Commission recommendations.<sup>22</sup> The RBI Study of State Budgets, 2002–03, while drawing attention to the growing fiscal and revenue deficit and high debt levels of states, pointed to the following causes of the deterioration in states‘ fiscal condition: (a) an inadequate increase in tax receipts, (b) negative or negligible returns from public investments due to losses in public sector undertakings, (c) large subsidy payments, (d) increased expenditure on salaries due to pay revisions, and (e) higher pension outgo. Another study, by Prasad, Goyal, and Prakash (2004), concludes that interest payments played a prominent role in the deterioration of state finances.

Until the mid-1980s, interest rates on government borrowing were highly subsidized, indicative of the degree of financial repression. After the 1980s, the rates on government bonds became progressively aligned with market interest rates; during the 1990s there were increases in both bank deposit rates and policy rates (table 2). During the 1990s, average interest rates rose, and those on state government bonds averaged over 10 percent during the 1990s (RBI). At the same time, the reliance on market borrowing to finance the fiscal deficits increased from 11 percent in the 1980s to 16 percent in the 1990s. Significant changes in the structure and cost of state government debt contributed to a sharp increase of about 60 percent in the repayment burden from the beginning to the end of the 1990s. Interest rates started softening in the mid2000s, and these were taken advantage of in formulating the debt-swap scheme for states (discussed in section 4).

> position. Dholakia, Mohan, and Karan (2004) analyzed what interest burden a state can tolerate as a proportion of its revenue receipts. In 2004, Dholakia, Mohan, and Karan used one-fifth of revenue receipts as a tolerable ratio. The FC also considered the same.

> 22 In India, government wages are reviewed and revised periodically, usually every 10 years.

13
and those who do not. Our main finding are as follows. First non-farm self-employed individuals in rural Rwanda have significantly higher earnings than farm workers and non-farm formal employees. Second, the benefits to non-farm self-employment are much higher among the non-poor than the among the poor. Third, we show that diversified households, those with a farm and a non-farm enterprise, are less likely to be poor. Finally, farm households who do not participate in the market have significantly lower consumption levels than households that do. However, the benefits to market participation appear less palpable for the poor than for the non-poor. We find little difference in expenditures between market participants and non-market participants for comparable households in the bottom 40% of the expenditure distribution.

Following this introduction, section 2 provides background information on the non-farm sector in Rwanda; sections 3 and 4 describe the data used and the methodology implemented while section 5 presents the results of the analysis: first the determinants of non-farm sector participation are discussed and second the size of earnings differentials are examined as we compare outcomes for participants and non-participants in the nonfarm sector, at both the individual and household levels. Section 6 concludes by drawing policy implications to inform any future reform efforts.

# **2. Data description**

For the analysis presented in this paper we use the Rwanda Household Living Conditions Survey (EICV). This survey provides information on a wide range of economic and social issues (e.g., unemployment, health, education, incomes, expenditure, subsistence agriculture, migration and access to services). It is modeled on the World Bank’s Social Dimensions of Adjustment data collection and analysis format.<sup>3</sup> The design was completed in 1997 and piloted in 1998; 6420 households were interviewed between October 1999 and July 2001.

The sample design is a stratified two-stage design with three main strata: (i) Kigali urban, (ii) other prefecture urban centers and (iii) rural prefecture. Stratum (iii) is further stratified into 11 sub-strata corresponding to each rural prefecture. Each stratum is divided into zones (EAs - enumeration areas - or PSUs – primary sampling units). In the first stage, in each stratum, EAs are selected with probability proportional to size (i.e. number of households). The second stage involves the selection of a random sample (by systematic sampling) of a fixed number of households for enumeration.

The survey questionnaire has 12 major sections, each covering a major social or economic topic. The household schedule is divided into two parts: Part A on household characteristics (demographics, education, health, employment, migration, housing, etc.) and Part B on agriculture, expenditure, income, household enterprises, etc. For rural areas, there is a third part on infrastructure access. Annex 2 describes how the variables that we use were constructed.

# **3. The non-farm sector in Rwanda**

> 3 See World Bank (1992).

3
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25
|SYS<br>GMM<br>(8)<br>0.695***<br>(0.0628)<br>0.659**<br>(0.294)<br>0.513*<br>(0.277)<br>0.00753<br>(0.0209)<br>1.393**<br>(0.655)<br>-2.351***<br>(0.591)<br>yes<br>0.000<br>0.646<br>0.788<br>476<br>91<br>86<br>alanced sample<br>es reported for<br>ented for using<br>n (4) shows the<br>nts, the system|
|---|
|SYS<br>GMM<br>(7)<br>0.680***<br>(0.0556)<br>0.662**<br>(0.290)<br>0.513**<br>(0.237)<br>0.00390<br>(0.0166)<br>0.744<br>(0.611)<br>-1.872*<br>(1.021)<br>yes<br>0.000<br>0.555<br>0.836<br>476<br>91<br>90<br>mple is an unb<br>tions. The valu<br>ey are instrum<br>rticular, colum<br> these instrume|
|SYS<br>GMM<br>(6)<br>0.647***<br>(0.0576)<br>0.670**<br>(0.321)<br>0.673***<br>(0.256)<br>0.0329<br>(0.0204)<br>-0.108<br>(0.126)<br>yes<br>0.000<br>0.571<br>0.620<br>476<br>91<br>91<br>mator. The sa<br> serial correla<br>ermined. Th<br>lumns. In pa<br>n addition to|
|SYS<br>GMM<br>(5)<br>0.626***<br>(0.0605)<br>-0.125<br>(0.426)<br>0.304<br>(0.413)<br>-0.00304<br>(0.0201)<br>0.0773**<br>(0.0319)<br>yes<br>0.000<br>0.427<br>0.471<br>423<br>85<br>76<br> GMM esti<br>ond test for <br>d as pre-det<br> the other co<br>ess check. I<br>one period.|
|SYS<br>GMM<br>(4)<br>0.609***<br>(0.0658)<br>0.796**<br>(0.361)<br>0.914***<br>(0.349)<br>0.0485*<br>(0.0284)<br>yes<br>0.000<br>0.553<br>0.302<br>476<br>91<br>62<br>e step system<br> of Arellano-B<br>les are treate<br>nd lags in all <br>gs for robustn<br>ences lagged|
|SYS<br>GMM<br>(3)<br>0.640***<br>(0.0623)<br>0.642*<br>(0.335)<br>0.885***<br>(0.338)<br>0.0478*<br>(0.0273)<br>yes<br>0.000<br>0.542<br>0.351<br>476<br>91<br>74<br>arentheses. On<br>re the p-values <br> All the variab<br>wn ...rst to seco<br>l the second la<br> the ...rst di¤er|
|F.E.<br>OLS<br>(2)<br>0.355***<br>(0.0520)<br>-0.650<br>(0.700)<br>-0.647<br>(0.661)<br>-0.443***<br>(0.158)<br>yes<br>0.247<br>476<br>91<br>y country in p<br> and AR(2) a<br>ment validity. <br>ed for their o<br>struments til<br>y variables in|
|Pooled<br>OLS<br>(1)<br>PRt�5<br>0.721***<br>(0.0381)<br>Human capitalt�5<br>0.678***<br>(0.243)<br>Total emigration ratet�5<br>0.183<br>(0.112)<br>Log populationt�5<br>-0.00865<br>(0.00663)<br>Log GDP per capitat�5<br>Share tertiary ed. migrantst�5<br>Human capitalt�5*Soc. dummy<br>Total emigration ratet�5*Soc. dummy<br>Human capitalt�5*Soc. dummy*d90<br>Total emigration ratet�5*Soc. dummy*d90<br>Time dummies<br>yes<br>R-squared<br>0.611<br>AR(1) test<br>AR(2) test<br>Hansen J test<br>Observations<br>476<br>N. countries<br>N. instr.<br>*** p<0.01, ** p<0.05, * p<0.1. Robust standard errors clustered b<br>comprising data at ...ve year interval between 1980 and 2005. AR(1) <br>the Hansen J test are the p-values for the null hypothesis of instru<br>their own ...rst to third lags in columns 3 and 6. They are instrument<br>same speci...cation as column (3), but now reducing the number of in<br>GMM also uses as instruments for the level equations the explanator|

17
# Table 7

|**Table 8: Transition matrix of occupatio**|**nal chan**|**ges‐youn**|**g female**|**s 1993/2**<br>Occ|**000**<br>upationa|l status|2000||||
|---|---|---|---|---|---|---|---|---|---|---|
||1.|2.|3.|4.|5.|6.|12.|13.|19.|Total|
|Occupationalstatus1993|||||||||||
|1. Self‐employed|**38%**|8%|0%|0%|13%|10%|0%|0%|31%|100.0%|
|_‐Observations_|**15**|**3**|**0**|**0**|**5**|**4**|**0**|**0**|**12**|**39**|
|2. Self‐employed,familyworker|12.5%|**25.0%**|0.0%|0.0%|6.3%|18.8%|0.0%|0.0%|37.5%|100.0%|
|_‐Observations_|2|4|0|0|1|3|0|0|6|16|
|3. Self‐employed, permanent worker|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|
|_‐Observations_|0|0|0|0|0|0|0|0|0|0|
|4.Government worker|0.0%|0.0%|0.0%|**66.7%**|33.3%|0.0%|0.0%|0.0%|0.0%|100.0%|
|_‐Observations_|0|0|0|2|1|0|0|0|0|3|
|5. Private worker|0.0%|0.0%|0.0%|66.7%|**33.3%**|0.0%|0.0%|0.0%|0.0%|100.0%|
|_‐Observations_|4|1|0|0|34|5|0|0|30|74|
|6. Unpaid familyworker|5.4%|1.4%|0.0%|0.0%|45.9%|**6.8%**|0.0%|0.0%|40.5%|100.0%|
|_‐Observations_|7|6|0|0|2|14|0|0|21|50|
|12.Job Search|14.0%|12.0%|0.0%|0.0%|4.0%|28.0%|**0.0%**|0.0%|42.0%|100.0%|
|_‐Observations_|1|0|1|0|1|1|0|0|4|8|
|13. Education|12.5%|0.0%|12.5%|0.0%|12.5%|12.5%|0.0%|**0.0%**|50.0%|100.0%|
|_‐Observations_|5|0|0|5|25|3|5|7|30|80|
|19. Not in labour force/edu|6.3%|0.0%|0.0%|6.3%|31.3%|3.8%|6.3%|8.8%|**37.5%**|100.0%|
|_‐Observations_|49|23|1|1|47|36|1|1|299|458|
|Column total|83|37|2|8|116|66|6|8|402|728|

# Table 8

|**Table 9: Transition matrix of occupatio**|**nal chan**|**ges‐you**|**ng female**|**s 2000/**|Occupat<br>**2007**|ional sta|tus 2007||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||1.|2.|3.|4.|5.|6.|7.|8.|12.|13.|19.|Total|
|Occupational status 2000|||||||||||||
|1. Self‐employed|**15.0%**|8.8%|1.3%|0.0%|8.8%|15.0%|0.0%|1.3%|0.0%|0.0%|50.0%|100.0%|
|_‐Observations_|**12**|7|1|0|7|12|0|1|0|0|40|80|
|2. Self‐employed,familyworker|5.9%|**0.0%**|0.0%|0.0%|5.9%|14.7%|0.0%|2.9%|2.9%|0.0%|67.6%|100.0%|
|_‐Observations_|2|**0**|0|0|2|5|0|1|1|0|23|34|
|3. Self‐employed, permanent worker|50.0%|0.0%|**0.0%**|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|50.0%|100.0%|
|_‐Observations_|1|0|**0**|0|0|0|0|0|0|0|1|2|
|4. Government worker|0.0%|0.0%|0.0%|**56.3%**|25.0%|0.0%|0.0%|0.0%|0.0%|0.0%|18.8%|100.0%|
|_‐Observations_|0|0|0|**9**|4|0|0|0|0|0|3|16|
|5. Private worker|4.0%|3.0%|0.2%|2.0%|**36.3%**|5.0%|**1.2%**|**1.6%**|0.8%|0.2%|45.6%|100.0%|
|_‐Observations_|20|15|1|10|**181**|25|**6**|**8**|4|1|227|498|
|6. Unpaid familyworker|5.5%|3.0%|0.0%|1.0%|6.0%|**27.0%**|2.0%|0.0%|0.5%|0.0%|55.0%|100.0%|
|_‐Observations_|11|6|0|2|12|**54**|4|0|1|0|110|200|
|12. Job Search|0.0%|1.6%|0.0%|4.7%|28.1%|4.7%|0.0%|3.1%|**3.1%**|3.1%|51.6%|100.0%|
|_‐Observations_|0|1|0|3|18|3|0|2|**2**|2|33|64|
|13. Education|3.0%|2.0%|0.1%|7.4%|35.5%|4.1%|0.1%|1.6%|1.2%|**4.1%**|40.8%|100.0%|
|_‐Observations_|23|15|1|56|268|31|1|12|9|**31**|308|755|
|19. Not in labour force/edu|4.4%|2.9%|0.6%|1.0%|9.4%|8.0%|1.2%|1.7%|0.3%|0.8%|**69.5%**|100.0%|
|_‐Observations_|56|37|8|13|119|101|15|22|4|10|**879**|1264|
|Column total|125|81|11|93|611|231|26|46|21|44|1624|2913|

38
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