rafmacalaba/datause-encoder-data huggingface dataset:
measured, which is 2011, 2012, or 2013 (depending on the data source).<sup>11</sup> Village controls and fixed effects are not necessary for identification but improve the efficiency of the estimation. The coefficient _β_ 1 captures the effect of a new road on the outcome variable. The optimal bandwidth according to the method of Imbens and Kalyanaraman (2012) is 84.<sup>12</sup> We use a triangular kernel which places the most weight on observations close to the threshold, as in Dell (2015). Results are highly similar with different fixed effects or controls, a rectangular kernel, or alternate bandwidths.

Regression discontinuity estimates can be interpreted causally if baseline covariates and the density of the running variable are balanced across the treatment threshold. Table 1 presents the mean values for various village baseline characteristics, including the set of controls that we use in all regressions. While there are average differences between villages above and below the population threshold (Columns 2 and 3), in part because many village characteristics are correlated with size, we find no significant differences when we use the RD specification to test for discontinuous changes at the threshold. Figure 2 shows the graphical version of the balance test, plotting means of baseline variables in population bins, residual of fixed effects and controls. Baseline village characteristics are continuous at the treatment threshold. Figure 3 shows that the density of the village population distribution is also continuous across the treatment threshold; the McCrary test statistic is -0.01 (s.e. 0.05) (McCrary, 2008).<sup>13</sup>

> 11Our primary outcomes are measured in 2011 (Population Census), 2012 (SECC), and 2013 (Economic Census). These were not particularly unusual years for the Indian economy. GDP growth these years was 6.6%, 5.5% and 6.4%, slightly below the 2008-16 average of 7.1%. Rainfall for the main growing season (June-September) was neither particularly high or low: 901, 824 and 937 mm, compared to the 2000-2014 average of 848 mm.

> 12The optimal bandwidth according to the method of Calonico et al. (2014) is 78.

> 13Note that the density function of habitation population as reported in the internal PMGSY records exhibits notable discontinuities above the treatment thresholds, indicating that some habitation were able to misreport population to gain eligibility (Figure A1). For this reason, we use village population from the 2001 Population Census as the running variable. The Population Census was collected before PMGSY implementation began to scale up, and was done so by a government agency considered to be apolitical and impartial.

14
|Uma ampliação da jornada<br>escolar melhora o esempenho<br>acadêmico dos estudantes?|De Aquino (201|1)<br>Brazil|No impact on proficiency, grade<br>advancement, or math. Small effect on<br>language (significant)|Propensity Score<br>Matching|
|---|---|---|---|---|
|Does lengthening the school day<br>increase students’ academic<br>achievement? Results from a<br>natural experiment in Chile|Bellei (2009)|Chile|Positive impact. Math: 0.07 (secondary<br>school-level); Language: 0.05-0.07<br>(secondary school-level)|Natural<br>experiment/diff in diff|
|Partial Evaluation of a Big|||||
|Reform in the Chilean Education<br>System: From a Half Day to a<br>Full Day Schooling|<br>Valenzuela<br>(2005)|Chile|Positive impact. Language: 0.1-0.2 (public<br>schools); Language: 0.4 (voucher schools)|Propensity Score<br>Matching|
|Evaluación del Impacto de la<br>Jornada Escolar Completa|Garcia (2006)|Chile|Positive impact. Language: 0.07 (urban<br>public schools with copayment); Language:<br>0.14 (urban voucher schools)|<br>Propensity Score<br>Matching|
||||Insignificant positive impact for those||
|Impacto de la Jornada Escolar|||having four years (2005-2009). Math: 1||
|Completa en el Desempeño de<br>los Alumnos, medido con la|Arzola (2010)|Chile|point increase (secondary school-level);<br>Language: 1 point increase (secondary|Panel, difference-in-<br>differences|
|Evolución en sus Pruebas Simce|||school-level). No effect for those having at<br>least one year.||

30
# **2 Descriptive Analysis**

An important focus of this paper is to ascertain what part of firms’ exporting behavior can be explained by the effect of agglomeration, in other words, if spillovers between firms can lower the sunk costs of export entry. In fact, whilst this study will also focus on the effect of various infrastructure and institutions within a location, at this stage it is pertinent to establish if there is any evidence of clustering. At a later stage, the analysis will focus on trying to disentangle the effects of second-nature effects from the natural geography and the more general sources of business-oriented advantages.

There are two phenomena that would indicate that agglomeration and exporting go hand-in-hand – if exporters were drawn to other exporters, and/or if exporters were drawn to industrial activity more generally. Different methods can be used to ascertain whether firms are uniformly distributed across various locations in the country, or if they show patterns of spatial concentration. Clustering in its simplest form can be shown through a bird’s eye view of where economic activity is located. In this paper I look at the location of firms at the level of the district<sup>3</sup> . In particular, I compute (1) all firms, exporting and non-exporting, as a percentage of the population<sup>4</sup> and (2) firms that export as a percentage of the population. It should be kept in mind that the sample of firms contains mostly medium and large-scale manufacturing units. In other words, I look at clustering of all firms and that of exporters after having controlled for the size of the district.

I find that there is much concordance between the districts hosting general and exportoriented economic activity. Not only do firms and exporters show evidence of clustering in a few districts, they also seem to cluster in the same districts. Table 1 lists districts in descending order of the economic activity hosted. Thus, while economic activity, whether exporting or not, seems to be located in the same districts, there is evidence of some locations hosting much higher proportions of exportoriented activity.

Having established that there is evidence of clustering of exporters and economic activity in the country, this paper will now examine to what extent the characteristics of the location affect the propensity of firms to start exporting and other attributes of their exporting behavior more generally. The effect of second-nature clustering will be identified separately from that of first-nature geography and the investment climate. The latter are particularly interesting, in so far as public policy makers can directly affect the provision of infrastructure and affect institutional variables within a location. The next section will provide a brief overview of the theoretical literature and outline a few empirical studies of relevance.

> 3 Maps can be made available on request.

> 4 Proportions instead of absolute counts are presented since it could be argued that clustering in these districts is simply a factor of the size of the district.

4
<!-- Start of picture text --> Kerala Kerala<br>Karnataka Karnataka<br>Tamil Nadu Tamil Nadu<br>Rajasthan Northern StatesAndhra Pradesh Andhra Pradesh Rajasthan Northern States<br>R-sq=.05 West Bengal R-sq=.04 West Bengal<br>Orissa Orissa<br>Uttar Pradesh Maharashtra Uttar Pradesh Maharashtra<br>Madhya Pradesh Gujarat Gujarat<br>Madhya Pradesh<br>Seven Sisters Seven Sisters<br>Bihar Bihar<br>-1 -.8 -.6 -.4 -.2 -.4 -.3 -.2 -.1<br>diff. log rel. urb/rur construction share 68-61 diff. log rel. urb/rur services share 68-61<br>Kerala Kerala<br>Karnataka Karnataka<br>Andhra Pradesh Tamil Nadu Tamil Nadu<br>Rajasthan Northern States RajasthanNorthern StatesAndhra Pradesh R-sq=.41<br>West Bengal West Bengal<br>Orissa Orissa<br>Maharashtra Uttar Pradesh Maharashtra Uttar Pradesh<br>Madhya Pradesh Gujarat Madhya Pradesh Gujarat<br>Seven Sisters R-sq=.31 Seven Sisters<br>Bihar Bihar<br>0 .1 .2 .3 .4 0 .2 .4 .6<br>diff. log rel. urb/rur female LFP 68-61 Inter-censal urban pop. share growth<br>DD log-wage uneducated fem. urb/rural-68/61 Fitted values<br>Figure 16: Drivers of changes in urban vs. rural wages for females.<br>.05 .05<br>0 0<br>-.05 -.05<br>-.1 -.1<br>.05 .05<br>0 0<br>-.05 -.05<br>-.1 -.1<br><!-- End of picture text --> For males, the construction sector stands out as the key _relative_ wage driver, with higher construction growth strongly associated with higher wage growth (R<sup>2</sup> =0.34), whereas for females the corresponding association is actually negative, albeit weak (R<sup>2</sup> =0.05). Relative growth in the service sector, by contrast, bears little relationship to relative wage changes for either males or females. As for female LFP, we again see a strong correlation with wage growth. In states where women have withdrawn from the labor force faster in the countryside than in cities, rural wages of females have risen faster than urban wages (R<sup>2</sup> =0.31), a pattern essentially absent with respect to male wages (R<sup>2</sup> =0.04).

Next, we ask whether the growth of cities has in and of itself led to changes in SDI at the state level. By far the fastest urbanization over the last decade occurred in Kerala, which is clearly an outlier in the bottom right panels of figures 15 and 16. Nevertheless, even with Kerala excluded, the story is clear. Faster urbanization is associated with greater _urban_ wage growth relative to rural areas for both genders, but especially for females. Moreover, this latter effect is not driven merely by correlation between falling female LFP and urbanization; it survives virtually intact after controlling for the relative change in female LFP. Thus, it appears that in rapidly urbanizing states the demand for female labor, as reflected in their wages, has been growing faster in cities than in the countryside.

21
||Table 4b. Det<br>|ailed sect<br>|ors of jobs an<br>|d enterpris<br>|es, Urban<br>||||
|---|---|---|---|---|---|---|---|---|
||ETHIOPIA|2011-12|MALAWI|2010-11|TANZANIA|2010-11|UGANDA|2010-11|
||Enterprises|Jobs|Enterprises|Jobs|Enterprises|Jobs|Enterprises|Jobs|
|N total (hhs or indivs)|503|672|857|1457|1263|2154|746|1227|
|of which N part. in frm or job|293|206|340|657|730|597|440|435|
|N frms or jobs|370|213|409|701|1011|629|584|471|
|Ag and Primary Prod Share|0.0216|0.0704|0.00489|0.0228|0.0089|0.0461|0.0188|0.0892|
|Mining Share||0.00469|0.00244|0.00143|0.0237|0.0223|0.0137|0.00849|
|Manufacturing Share|0.178|0.0376|0.149|0.0585|0.094|0.0636|0.154|0.0679|
|Electricity, Utilities Share|0.216|0.0235||0.0128|0.00396|0.0143|0.00514|0.0127|
|Construction Share|0.00811|0.0516|0.0122|0.0542|0.0119|0.0715|0.00342|0.0977|
|Commerce Share|0.27|0.0329|0.665|0.0485|0.576|0.205|0.483|0.0764|
|Transport, Storage, Comm. Sh.|0.13|0|0.0562|0.0471|0.0307|0.108|0.0599|0.104|
|Finance, Real Estate Share||0.0329|0.0122|0.03|0.00791|0.035|0.00514|0.00849|
|Services Share|0.119|0.549|0.0978|0.452|0.235|0.421|0.187|0.51|
|Other Industries Share||0.00469||0|0.00198|0.00159||0|
|Missing sector info Share|0.0568|0.192||0.272|0.00593|0.0111|0.0702|0.0255|
Policy Research Working Paper 7786

# **Abstract**

Recent evidence suggests only modest social and economic impacts of microfinance. Favorable cost-benefit ratios then depend on low costs. This paper uses proprietary data on 1,335 microfinance institutions between 2005 and 2009, jointly serving 80.1 million borrowers, to calculate the costs of microfinance and other elements of the microfinance business model. It calculates that on average, subsidies amounted to $132 per borrower, but the distribution is highly skewed. The median microfinance institution used subsidies at a rate of just $26 per borrower, and no subsidy was used by the institution at the 25th percentile. These data suggest that, for some institutions, even modest benefits could yield impressive cost-benefit ratios. At the same time, the data show that the subsidy is large for some institutions. Counter to expectations, the most heavily-subsidized group of borrowers is customers of the most commercialized institutions, with an average of $275 per borrower and a median of $93. Customers of nongovernmental organizations, which focus on the poorest customers and women, receive a far smaller subsidy: the median microfinance nongovernmental organization used subsidy at a rate of $23 per borrower, and subsidy for the nongovernmental organization at the 25th percentile was just $3 per borrower.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. 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 rcull@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
information for the applicants including starting and completion times, renewal day, renewal table number, sex, age, rural status, education level, whether the applicant ever attended a private school, whether the applicant spoke an indigenous language, and neighborhood of residence. We also collected information on indigenous attire for female applicants only, who can easily and objectively be classified across indigenousstatus by simple inspection of their clothing — which is not the case for men.<sup>7</sup> The information on sex, age, rural status, education level, and residence was directly collected from the expired ID card. If another document was used as proof as identity, then this information was directly collected from the applicant. The information about indigenous language and private school status was self-reported by applicants, and the information about indigenous attire was collected from direct observation.

# **5.2 Administrative data**

The basic socioeconomic and demographic characteristics of police officers were collected from administrative records provided by the Identification Unit. These data included sex, age, rank, years of education, tenure at the Identification Unit, and tenure at the renewal table. The information on technical failures among the digital tables was also collected from administrative records and it was collected on a daily basis.

# **5.3 Subpopulation of analysis**

The subpopulation of analysis in this study are all individuals who officially applied for an ID card renewal, in the city of La Paz, during the data collection time period. This subpopulation of analysis is not representative of the whole population, but rather of those individuals who decide to apply for an ID card renewal. The sample of applicants used in the subsequent analysis excludes all applicants younger than 21 years old and those older than 100 years old.

# **5.4 Descriptive statistics**

The summary statistics of the data used in the study are presented in Table 2. The statistics are estimated separately by completion status and type of renewal process. The type of renewal process for digital tables does not correspond to the original renewal process, but rather to an _ex-post_ measure already adjusted for technical failures. Panel A presents the _ex-post_ average values for the outcome variables considered in the paper, which are measured at the police officer-applicant pair level. Panels B and C report the basic characteristics of applicants and police officers. These are _ex-ante_ characteristics measured at the randomization table. Panel D summarizes technical failures measured at the renewal day-table level. The aim of this summary table is twofold. First is to verify that police officers’ and applicants’ characteristics are well balanced across type of renewal process. Second, is to explore whether _ex-post_ successful and unsuccessful unrelated to the renewal process and just hired to perform this task.

> 7Indigenous women wear their hair in two long braids decorated with tassels, and dress in distinctive skirts over puffy petticoats.

9
# **Introduction**

Monitoring and supervision are intrinsic aspects of the management of any social programs. Monitoring over time how the program performs in the field allows administrators to understand whether the project is implemented according to its design. An essential data tool underlying the monitoring activities is usually referred to as ‘monitoring data’, or MIS (monitoring and information system), which is a subset of the administrative data that is specifically linked to activities of a specific program. The data contained in the monitoring system generally produces simple descriptive statistics that, at different level of aggregation, provide feedback to the implementing agents to address eventual problems. Administrative data have been increasingly used as a tool to evaluate program performance of public assistance programs in developed countries, but there is still very limited evidence to date that uses administrative data to evaluate social programs in developing countries and hardly any that uses monitoring data for this purpose.<sup>1</sup> Why is it the case? The main reason constraining the use monitoring data for evaluation purposes is that, by design, it only collects information on participants, therefore making it impossible to estimate what would have happened in the absence of the program. Evaluating a public program requires data from which to construct participant groups and comparisons groups. Simple _reflexive_ comparisons (before-after) using only data on participants would generally impose a very strong identification assumption (Heckman, Lalonde, Smith 1999): they implicitly assign all the observed change in the outcomes of interest to the program, and assume away the need to construct a counterfactual change in the absence of the program<sup>2</sup> . As such, evaluations using monitoring data would not allow quantifying the parameters of interest generally used in the evaluation literature, such as the average treatment effect or the average effect of treatment on the treated, both of which require information on nonparticipants.

Yet, monitoring data have inherent (but un-explored) features that make them an attractive tool for analytical and evaluation work. First, it covers the entire universe of the participating population, allowing the results based

> 1 A notable exception is Ravallion (2000), who uses administrative data on the geographical allocation of expenditures of a workfare program in Argentina matched with a poverty map to implement a methodology to measure the latent differences in the average allocation of the program between poor and non-poor.

> 2 An interesting variation to using reflexive comparisons is proposed by Piehl et al. (2003), who show that sufficiently long time-series data on participants can be used to test for structural breaks and identifying impacts.

2
# Fungibility and the Impact of Development Assistance: Evidence from Vietnam’s Health Sector

by

## Adam Wagstaff<sup>a</sup>

a Development Research Group, The World Bank, Washington DC, USA

**Corresponding author and contact details:** Adam Wagstaff, World Bank, 1818 H Street NW, Washington, D.C. 20433, USA. Tel. (202) 473-0566. Fax (202)-522 1153. Email: <u>awagstaff@worldbank.org.</u>

**Keywords:** foreign aid, fungibility, impact evaluation, child mortality.

**Acknowledgements:** My thanks to Kiersten Johnson for answering queries about the DHS, to Joseph Capuno, Martin Rama and Minh Van Nguyen for sharing their provincial expenditure data, and to Misha Lokshin and Zurab Sajaia for answering queries on the use of movestay. The findings, interpretations and conclusions expressed in this paper and any errors are entirely mine; the views expressed do not necessarily represent those of the World Bank, its Executive Directors, or the countries they represent.
**Table 3: Relationship between firm innovation and the sources of funding of fixed assets**

|**Variables**|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|---|---|---|---|---|---|---|
|**% Fixed assets funded by banks**|0.1234***|0.1210***|0.0986***|0.0651**|0.0866**|0.0560*|
||(0.0443)|(0.0419)|(0.0380)|(0.0283)|(0.0377)|(0.0302)|
|**% Fixed assets funded by NBFI**|0.0903|0.1516|0.3100**|0.1957|0.3221**|0.1955|
||(0.0943)|(0.1030)|(0.1326)|(0.1367)|(0.1388)|(0.1467)|
|**% Fixed assets funded by other**|0.0224|0.0514|0.0349|0.0526*|0.0290|0.0460|
||(0.0433)|(0.0408)|(0.0360)|(0.0305)|(0.0375)|(0.0325)|
|**Log (employees)**||0.0317***|0.0337***|0.0333***|0.0331***|0.0338***|
|||(0.0106)|(0.0114)|(0.0096)|(0.0120)|(0.0104)|
|**log (labor productivity)**||0.0218***|0.0342***|0.0239***|0.0308**|0.0248***|
|||(0.0063)|(0.0125)|(0.0088)|(0.0125)|(0.0091)|
|**Age**||0.0004|-0.0006|0.0001|-0.0006|0.0000|
|||(0.0009)|(0.0009)|(0.0007)|(0.0010)|(0.0008)|
|**Percentage of domestic ownership**||-0.1227***|-0.1057***|-0.1193***|-0.1052***|-0.1159***|
|||(0.0346)|(0.0332)|(0.0353)|(0.0350)|(0.0399)|
|**Country FE**|||Yes|Yes|Yes|Yes|
|**Year FE**||||Yes||Yes|
|**Sample**|MRES|MRES|MRES|Full|MRES|Full|
||||||No HInc|No HInc|
|**Observations**|11,335|11,335|11,271|17,267|10,199|15,563|
|**Number of countries**|103|103|103|104|91|92|

Note: This table reports the results of the estimation of Equation (1). The dependent variable is a dummy variable measuring whether a firm innovates (value of 1) or not (value of 0), as described in Table 1. In columns (1), (2), (3) and (5) we include data on the last survey available for each country. In columns (4) and (6) we employ the full sample. In columns (5) and (6) we exclude high-income countries. *, **, and *** represent statistical significance at 10%, 5%, and 1% two-tailed level, respectively. Robust standard errors in parentheses, clustered at strata level.

Using the set of controls employed in Table 3, column 4, we run regressions where the dependent variable is either the adoption dummy or invention. Coefficients and their confidence interval at a 90 percent level are reported in Figure 2. Access to bank funding is strongly and positively associated with both adoption and firm invention. The magnitude of the coefficients denotes a stronger increase in the probability of undertaken invention compared to adoption and overall innovation. This pattern of increase in the strength of association from the innovation score to invention is also observed in the case of financing via other sources of funding. Figure 2 shows also the large variability associated with the estimates of the relationship between the percentage of fixed assets financed by NBFIs and innovation measures, resulting in statistically insignificant coefficients.

13