rafmacalaba/datause-encoder-data huggingface dataset:
41 C. Availability of Supportina Infrastructure, Proper lanagemnt and

# Oversight

Similar to Agricultural Development Banks and other rural financial institutions, credit cooperatives have often suffered from inadequate leadership, a lack of well defined managerial responsibilities and insufficient accounting and controlling facilities. The ability to track financial performance is a prerequisite for sound management and overall performance cf any credit institution. Loan collection and denial of access to new loans before outstanding debts are settled depends on adequate record keeping. Yet instances where records of loan collection have been inaccurate or nonexistent are not uncommon. For example, a survey of credit cooperatives in Niger found that less than half of the local leaders were in possession of a record indicating who was eligible for a loan and less than a quarter had records indicating the amounts received by each farmer. Such information was believed to be kept in memorized form by most leaders. (Cuevas et.al., 1988).

One reason for ineffective cooperative management is lack of adequate training, or the frequent focus of training efforts on the national rather than the local level. This danger especially exists when cooperatives are the result of a top-down intervention. In Nigeria, for example, cooperative training was solely directed towards government cooperative officials with the result that conflicts between higher cooperative management, local staff and members seriously affected cooperative performance (Rochin et.al., 1988). This is not to say that training and a strong management at the regional or national level are not essential. In fact, well trained people at this level are crucial if the
Policy Research Working Paper 8299

# **Abstract**

Sustainable Development Goal 16 is explicitly committed to measuring aspects of corruption over time, and the identification of robust indicators to do so is an important endeavor. This paper critically reviews the strengths and weaknesses of various objective and subjective indicators of corruption, using the standard criteria of validity and reliability to identify indicators most salient to measuring Sustainable Development Goal 16. Consistent with the large literature in the field, the paper finds that the aggregate survey-based indicators of corruption, especially the Corruption Perceptions Index and the World Bank’s Control of Corruption indicator, despite some important reservations and limitations, are the most valid measures of the magnitude of overall corruption in many country contexts. However, in every case, the initial results using one indicator should be cross checked with the use of the other indicator, as there are some minor differences between how the two indicators are constructed, and in practice it is difficult to establish a priori which indicator is marginally more efficient. Furthermore, whenever possible, subjective indicators should be cross checked with objective indicators, even when the latter may be of a more narrow scope and time limited availability.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at chammer@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Political Regimes, Trade and Labor Policies

# in Developing Countries

## **_Anrp Banerji and Hafez Ghanem_**

We are grateful to Claudio Montenegro for excellent research assistance and to Joseph Ryan for providing us with the Freedom House data on political and civil liberties. We benefited from discussions with other members of the core team for World Development Report 1995: Michael Walton, Alejandra Cox Edwards, Ishac Diwan, David Lindauer, Ana Revenga and Michal Rutkowsld.
TABLE 1: Balance in baseline characteristics between treatment and control groups

||**N**|**Control**|**Treatment**|**P-values**|**P-Values**|
|---|---|---|---|---|---|
|||||with**stratifcation** (T - C)|**Unconditional** (T - C)|
|**Child-specifc characteristics**||||||
|Age at baseline|2,416|2.27|2.32|0.533|0.457|
|Father lived in household at baseline|2,416|0.76|0.75|0.770|0.535|
|Mother lived in household at baseline|2,416|0.96|0.96|0.916|0.969|
|Years education mother|2,414|3.26|3.27|0.468|0.889|
|Years education father|2,291|3.03|3.00|0.634|0.689|
|**Health and Nutrition**||||||
|# Days in bed sick in last month|2,416|0.19|0.22|0.638|0.631|
|Received deworming in last 6 months|2,416|0.39|0.38|0.738|0.769|
|Received vitamins in last 6 months|2,416|0.39|0.38|0.786|0.801|
|# Days in the week the child had vegtables|2,415|1.71|1.88|0.068|0.074|
|# Days in the week the child had fruit|2,415|2.88|3.02|0.240|0.241|
|<br># Days in the week the child had meat|2,415|1.20|1.17|0.702|0.713|
|# Days in the week the child had eggs|2,414|2.69|2.64|0.689|0.653|
|<br># Days in the week the child had breast milk|2,414|2.46|2.45|0.936|0.972|
|# Days in the week the child had coffee|2,414|4.35|4.06|0.042|0.052|
|<br># Days in the week the child had soup|2,415|0.50|0.61|0.073|0.069|
|# Days read to the child in the last week|2,415|0.76|0.67|0.256|0.220|
|<br>Someone read to the child in the last month|2,415|0.22|0.22|0.924|0.844|
|**Household-level characteristics**||||||
|Male household head|2,512|0.86|0.86|0.443|0.827|
|Age household head|2,512|41.39|41.25|0.759|0.825|
|<br>Literate household head|2,512|0.74|0.74|0.756|0.898|
|Number of men in the household|2,512|1.58|1.56|0.914|0.720|
|Number of women in the household|2,512|1.66|1.61|0.311|0.205|
|Number of boys (age 7-14) in the household|2,512|0.57|0.52|0.106|0.101|
|<br>Number of girls (age 7-14) in the household|2,512|0.49|0.47|0.415|0.444|
|Number of young boys (age 0-6) in the hh|2,512|0.66|0.64|0.070|0.466|
|<br>Number of young girls (age 0-6) in the hh|2,512|0.60|0.57|0.284|0.286|
|<br>Number of rooms in house|2,511|1.91|1.84|0.139|0.111|
|**Network**||||||
|Distance (in min) to closest cellphone signal|2,505|32.48|29.65|0.193|0.233|
|<br>Distance (meters) to closest cellphone signal|2,343|2,116.53|1,979.93|0.832|0.478|

Baseline survey characteristics of children and households who were visited at endline. The sample includes all baseline children with cognitive tests at follow-up and all households interviewed at follow-up. Children born after the baseline are not included. P-values are of the test of difference between control and treatment, after controlling for stratification variables (one-to-last column), or without any controls (i.e. unconditional, last column). P-values based on standard errors clustered by community.

35
savings instruments (as shown in table 6) and this is considered through the inclusion of treatment dummies as controls.

We also present LATE regressions using total savings balance, as opposed to only looking at experimental savings. The total savings variable is the sum of balances in informal and formal savings accounts, ROSCA savings, and experimental savings if applicable. We use amount contributed to the ROSCA in the past 30 days (midline) and the past 3 months (whole season) as a proxy for ROSCA balance. The results are presented in table 10, and present a similar story as in table 9. The coefficients are smaller in size, but similarly all statistically insignificant. This further emphasizes that it was the type of savings treatment, as opposed to total savings balance, that affects agricultural investment.

**Table 10: Impact of Total Savings (LATE) on Agricultural Investment**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|||After on|e month<br>Other||Ov|er whole season||
||Total<br>spending|Spending at<br>fair|spending on<br>inputs|Fertilizer<br>per acre|Fertilizer per<br>acre|Land<br>cultivated|Yield|
|Total savings|0.0000|-0.0000|0.0000|0.0000|0.0000|0.0001|-0.0000|
||[0.0000]|[0.0000]|[0.0000]|[0.0000]|[0.0000]|[0.0001]|[0.0000]|
|Burkina Faso|-1.7156|4.1041|-3.0533|-0.0157|0.0452|-2.4343|0.1058|
||[0.8267]**|[0.8664]***|[0.9889]***|[0.7114]|[0.5635]|[1.6519]|[0.1217]|
|Male|0.6745|0.1063|1.1672|0.3521|0.0815|0.6106|0.2305|
||[0.5542]|[0.9400]|[0.7416]|[0.4534]|[0.4356]|[1.2674]|[0.1118]**|
|Constant|9.6516|0.6669|7.9395|1.5363|3.3000|6.5275|-0.1626|
||[0.5393]***|[0.5994]|[0.5924]***|[0.4248]***|[0.3625]***|[1.2121]***|[0.0851]*|
|Observations|603|603|603|588|587|591|603|
|R-squared|-0.1423|0.0855|-0.1536|-0.9410|-0.5835|-0.3855|-0.7570|

Notes: Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1. The sample is T2, T3 and T4 Other control variables included: ag savings, low commitment savings, low commitment X Burkina Faso We also examine whether the treatments had additional impacts on household welfare, outside of encouraging investments in agriculture. We examine whether non-experimental savings behavior is significantly different across treatments. This may be the case if increased savings in the experiment crowds out savings in other instruments. Very little difference across products is observed. Results are not shown to conserve space. Those in the low commitment savings treatment in Burkina Faso invested more in ROSCAs than those in other treatments, perhaps suggesting that the lower amount of saving in the envelope was compensated for by increased saving in other forms. However after harvest, when the savings products are no longer available, this effect disappears. There were no other significant differences.

Tables 11 and 12 present regression results for a variety of welfare measures in the month after the experiment and at the end line. Self-reported food security is assessed in columns (1) and (3). The number 23
47

# CHAPTER VI: MONITORING AD EVALUATION

6.1 In India the system of evaluation and monitoring often determines the nature of the program. Monitoring of practically all Government programs is based on quantitative data with reference to particular periods, monthly or quarterly. There is heavy reliance on the figures furnished from the Block to the District and then to the State and National levels. This emphasis on rigorous quantitative monitoring often leads to insufficient concern with the qualitative aspects of the program implementation. Also, the reliability of the data furnished from the block can be questionable, since block level functionaries are under constant pressure to meet targets. This is being increasingly brought to the notice of policy implementors, who in recent years, are hiring outside evaluation agencies and institutes of social science research to undertake objective appraisals of various programs. Yet, in neither monitoring nor in most outside evaluation efforts are there effective mechanisms for feedback and program improvement.

6.2 Little attention seems to have been paid to monitoring and evaluation at the time of formulation of WDP document. All it says is that the State IDARA, Institute of Development Studies, Jaipur, (IDS) and the Centers of Women's Development Studies in the universities will be involved to build up a system
An outcome of interest y, such as total household expenditure, is observed for both those offered treatment choice and those not offered treatment choice.

where is a vector of errors. To be clear, all of the elements of X in the first term of the left hand side of equation (1) are identical to the elements of X in the upper sub-matrix of the first term on the left hand side of (2). To keep the notation simple, and because we need only to distinguish between observations that are deterministically zero and observations that are not, the lower sub-matrix of the first term on the left hand side of (2) is also called X because it contains the same variables, but the values of the variables (elements) in the lower sub-matrix correspond to a different set of individuals -- those individuals without credit program choice.

We are interested in estimating the treatment effect parameter δ. Least squares estimation of (2) yields biased estimates of the parameter of interest δ if the errors ε and ν covary. In PK, it is assumed (fairly innocuously) that the joint distribution of the errors is The setup presented above is sufficient to provide a consistent instrumental variable estimate of . This model is identified even though there are no exclusion restrictions of the usual sort; that is, there is no set of exogenous variables Z that affects c but does not affect y conditional on c.<sup>10</sup> The model is identified by the deterministic lack of credit program choice for a subsample of the data. PK estimate the model by maximum likelihood. The lower submatrices in (1) are all zero and thus provide no information to help identify the first-stage parameters πm, and hence are not in the portion of the likelihood identifying those parameters. The likelihood is presented in detail in an appendix to PK.

Roodman and Morduch Stata forgo ML in favor of the packaged 2SLS test statistics obtainable from Stata. The question is whether the model, given in its simplest form in (1) and (2), can actually be estimated by 2SLS packages without doing violence to the model’s setup.<sup>11</sup> The requirements imposed by the packaged software (Stata) used by RM of particular concern are that (1) there must be a vector of identifying instruments Z with at least as many columns as there are endogenous variables, and (2) all the included exogenous variables in the second-stage equation must also appear in the first-stage.

> 10 Note that adding exogenous variables with associated parameter vector βp to equation (2) does not aid identification.

> 11 RM use the _ivreg2_ package of _Stata_ with the “liml” option that estimates linear 2SLS by limited information maximum likelihood.  We will say more about this choice below; however, the “linear LIML” model estimated by _ivreg2_ is essentially two-stage least squares and not the “LIML” estimated by iterating over a nonlinear loglikelihood until convergence as carried out in PK.

7
depends on tools to reduce these information asymmetries. The informational framework is thus a third fundament for effective financial intermediation. International experience has shown that credit information sharing is important to reduce adverse selection problems and foster competition in the financial system. Credit registries that give easy and reliable access to both negative and positive information about clients can dramatically reduce the time and costs of obtaining such information from individual sources and therefore reduce the total costs of financial intermediation. Credit reporting makes borrower quality more transparent, which benefits good borrowers and increases the cost of defaulting on one’s obligations. Credit registries are especially important for SMEs as their creditworthiness is harder to evaluate and they have less visibility and transparency relative to large enterprises. Consumer rights to access their information and challenge erroneous information have to be effectively protected to make such a system a widely acceptable part of the financial system infrastructure and balance consumer rights with efficiency considerations.

International comparisons have shown that countries with better developed systems of credit information sharing enjoy higher levels of financial development and their firms report lower financing obstacles (Japelli and Pagano, 2002; Love and Mylenko, 2003; Djankov et al., 2005, and Miller, 2003 for an overview). Figure 15 illustrates the positive relationship between the efficiency of credit information sharing and Private Credit.

Bangladesh has a well functioning public Credit Information Bureau (CIB), established in 1992 and housed in the Bangladesh Bank. It collects data from all banks and financial institutions that are regulated and supervised by Bangladesh Bank on a monthly basis on all borrowers, i.e. it collects both positive and negative information. While there is no unique borrower identifier, the wealth of information of individual borrowers seems to allow a relatively 17
# **Table A.11. The district fixed-effect RDD-DD regressions of households’ access to credit using the sample without the program**

# **<u>135 communes</u>**

||||Dependen<br>|t variables<br>|||
|---|---|---|---|---|---|---|
|Explanatory variables|Borrow from<br>formal sources<br>(yes=1, no=0)|Borrow from<br>microcredit<br>sources<br>(yes=1, no=0)|Log of formal<br>loan (the<br>sample of<br>borrowing<br>households)|Log of<br>microcredit<br>(the sample of<br>borrowing<br>households)|Log of formal<br>loan (the<br>sample all<br>households)|Log of<br>microcredit<br>(the sample all<br>households)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Program * Post-program period|0.2078***|0.1529***|-0.1693|-0.1595|1.7207***|1.5428***|
||(0.0717)|(0.0547)|(0.1472)|(0.1138)|(0.5882)|(0.5202)|
|(Poverty rate – 50) * Post-program|-0.0229***|-0.0171**|0.0124|-0.0023|-0.1665**|-0.1732**|
|period|(0.0085)|(0.0074)|(0.0194)|(0.0144)|(0.0715)|(0.0700)|
|Program * (Poverty rate – 50) *|0.0202**|0.0174**|-0.0189|0.0069|0.1452*|0.1775**|
|Post-program period|(0.0089)|(0.0077)|(0.0199)|(0.0148)|(0.0745)|(0.0741)|
|Age of household heads|0.0080***|0.0033**|0.0207***|0.0147***|0.0711***|0.0354**|
||(0.0018)|(0.0014)|(0.0067)|(0.0048)|(0.0144)|(0.0139)|
|A d f hhld hd|-0.0001***|-0.0001***|-0.0002***|-0.0001***|-0.0010***|-0.0006***|
|ge square o ouseo eas|(0.0000)|(0.0000)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|
|Gender of household head (male=1,|0.0245*|0.0027|0.1278***|0.0623**|0.2379**|0.0333|
|female=0)|(0.0129)|(0.0091)|(0.0374)|(0.0276)|(0.1026)|(0.0914)|
||-0.0129|0.0503**|-0.5654***|-0.2406***|-0.3353*|0.4499**|
|Ethnic minorities (yes=1, Kinh=0)|(0.0228)|(0.0198)|(0.0754)|(0.0356)|(0.1794)|(0.1958)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|0.1996***|0.0740*|9.1621***|8.8840***|1.9772***|0.5824|
||(0.0506)|(0.0441)|(0.1536)|(0.1129)|(0.3941)|(0.4371)|
|Observations|21,737|28,920|8,215|8,085|28,920|28,920|
|R-squared|0.116|0.123|0.384|0.335|0.207|0.126|

Note: This table reports district fixed-effect RDD-DD regressions of loans of households using household-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

61
negative impact disappears for the number of hours worked when the first differences are considered as shown in Table 12. However, all the other results obtained with the firstdifferencing models are consistent with the fixed effects results discussed above.<sup>26</sup> The p-values from serial autocorrelation tests in Tables 10 and 11 actually indicate that first-differencing would yield more efficient estimates in comparison to the fixed-effects estimator.

In section 3, we noted our expectation concerning work disincentive effects of income transfers to be prominent in the presence of widespread unemployment, inadequate employment generation, and a high degree of worker discouragement, i.e. in urban areas and among females. This is fully supported by our intensive margin findings, which indicate unequivocal differences in individual labor supply response to household NE receipts. Considering that the effects of NE on household welfare and individual labor supply are both negative and primarily driven by the outcomes in urban areas, it is probable that the negative impact of program participation on household welfare is realized through negative individual labor response to household NE receipts in areas with weak labor demand and abundance of unemployed individuals who are likely to gain initial entry into the labor market at relatively low paid occupations.

**_Extensive Margin Results_** : Thus far, we have looked at the intensive margin results which show how individuals adjust the number of hours and weeks of work they supply upon the receipt of NE, pension and/or other transfers. However, transfer receipts can also influence the decision to work. In Albania, one distinguishes between three types of work (i) work for a nonhousehold member, (ii) work on the household’s farm, and (iii) work on own non-farm business.

> 26 When we estimated either the fixed-effect or the first differencing model of hours and weeks worked for the entire sample with four dummy variables indicating whether a household received any transfers from the four social protection regimes considered, the results were very comparable, in terms of statistical significance and magnitude, to the findings from the individual-level matching that we conducted among work-eligible individuals from ALSMS02 and ALSMS05. Although these results are not reported here, they are available upon request. In any case, this observation may be supportive of the internal validity of the matching estimates.

29
related to both the capability of the owner to exert influence on firm policy and the type of restructuring it can implement once gaining control.

# **II.2 Data**

For the job flow and productivity of job flow analysis, the paper uses annual industrial registries provided by the State Committees for Statistics in Russia (the Goskomstat) and Ukraine (the Derzhkomstat). In Soviet Russia, the data include the universe of civilian industrial enterprises, while after 1991 the coverage is supposed to be all industrial firms with more than 100 employees plus those that are more than 25 percent owned by the state and/or by legal entities that are themselves included in the registry. The firms in the Russian registry accounted for 90.5 percent of officially reported total industrial employment in 1992, while the Ukrainian registry covered 94.1 percent. The coverage rate in relation to official employment declined somewhat thereafter, falling by the year 2000 to 69.8 percent in Russia and 85.2 percent in Ukraine. No doubt the decline is due partially to the entrance of new small firms owned by individuals, since the registries do not include such entities. Our analysis includes cases of entry and exit, probably due in most cases to reorganization rather than genuine startup or shutdown.<sup>15</sup> The worker flow and worker flow relationships with job flows analysis is based on a survey of 530 industrial firms, selected through national probability sampling in 32 Russian _oblasts_ , with a probability proportional to employment size. The data permit us to estimate annual rates of both job flows and worker flows for a consistent set of firms for the period 1990-1999 and to relate these flows to detailed information on firm characteristics.

A 2001 survey of 297 new small enterprises in Romania is used to investigate the determinants of employment growth in the new private sector. The data contain detailed measures of loans, tax breaks, technical assistance, managerial and employee human capital, and the business environment, as well as employment in 1992-2001.

Russian registry data for 1985-2001, Ukrainian registry data for 1989 and 1992-2002, Hungarian registry data for 1986-2001, Romanian registry data for 19922002 are used in the studies of privatization’s effects on productivity and workers. In

> 15 The size and ownership selection criteria for the registry imply that observed entrants are more likely to represent reorganizations of existing assets than startups from scratch.

17