rafmacalaba/datause-encoder-data huggingface dataset:
Moving beyond the example there is a more fundamental objection to the RCT approach if outcomes depend not only on _P_ but also on _X_ , as in (1). If the RCT involved randomization over actual program officers then it is unlikely that randomization can also be achieved in terms of all the confounding _X_ variables since program officers will not have been posted randomly across space. This introduces a correlation between _X_ and characteristics of the program officers and hence a correlation between _P_ and _X_ . The two groups of program officers (“treatment” and “control”) will therefore differ systematically so that internal validity is lost.<sup>15</sup> The proposed approach, by contrast, collects data at the level of beneficiaries and can therefore control for differences in _X_ .

In summary, estimating the TPE on the basis of group averages from RCTs becomes problematic when _β_ and _P_ are correlated as a result of targeting on the basis of unobservables. If one randomizes at the level of beneficiaries the TPE estimator will be biased because the correlation is not taken into account. If one randomizes at the level of program officers the estimator is inefficient and, if confounders are important, may become inconsistent.

# **_V. An Empirical Example: Estimating the Total Program Effect for a Health Insurance_**

# **_Program in Vietnam_**

To illustrate how the total program effect can deviate from a naïve approach to calculating the effect of a program a study of the impact of a health insurance program in Vietnam (Wagstaff and Pradhan, 2005) is reconsidered. Health insurance was introduced between the 1992-93 and the 1997-98 rounds of the Vietnam Household Living Standards Survey (General Statistics Office of Vietnam, 1993 and 1998). To account for possible treatment heterogeneity Wagstaff and Pradhan match households on propensity scores and then compare changes in health 13
propensity score estimates with difference in difference helps address the possibility that the assumption of time independent selection does not hold (Ravallion, 2008).

Several studies have used both approaches in order to evaluate the degree of selection bias to which observational studies may be subjected. For example, Lalonde (1986) finds that there are often significant differences between prospective and retrospective empirical approaches for the evaluation of programs as does Glewwe et al. (2004).<sup>5</sup> The current paper aims to contribute to this discussion by employing alternative approaches using the same dataset.

# **4. Empirical Analysis**

# <u>4.1. Measures of program success used</u>

The main focus of the evaluation of the current nutrition intervention is to assess the impact of the set of services offered on the nutritional status of children. When preparing the intervention, the outcome measure of weight-for-age z-score was determined as the original indicator to determine the sample size of the program as well as the indicator for tracking success of the program on the part of the organizations financing the PRN<sup>6</sup> .

The outcome of interest reflects a package of services which are valued not only for their impact on weight but also as indicators of the functioning of a community health program in general. For this reason, we also examine the availability of health care measures before and after birth such as micronutrient supplementation or malaria bednets. The analysis of these measures can provide supportive evidence of program

> 5 For overviews see Duflo, Glennerster, and Kremer (2006) and Ravallion (2008).

> 6 Apart from weight-for-age, we also investigated the impact of the program on height-for-age, another frequently used anthropometric measure that captures more long-term impacts. The results obtained for this measure are consistent with the ones for weight-for-age and are therefore not presented to reduce the number of output tables.  However, they have fewer observations since recumbent length – notoriously difficult to measure – was not collected on younger children.  The omitted tables are available from the authors.

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|**Table**|**8: Change in Ex**|**ports from Banglad**|**esh to India**||
|---|---|---|---|---|
||**Share in Base**<br>**Year Export to**<br>**India (percent)**|**India–Bangladesh**<br>**Full FTA (%**<br>**Change)**|**Bangladesh Gets**<br>**DFQF Market**<br>**Access in India (%**<br>**Change)**|**India–Bangladesh**<br>**Full FTA +**<br>**Improved**<br>**Connectivity (%**<br>**Change)**|
|Chemical, rubber, and plastic<br>products<br>|36.46<br>|130.49<br>|90.16<br>|228.17<br>|
|Textiles|17.27|240.01|188.84|392.37|
|Plant-based fibres|12.38|165.23|102.54|226.99|
|Fishing|8.59|32.89|21.66|56.52|
|Metalproducts|3.17|244.63|202.71|368.19|
|Leatherproducts|2.72|195.07|158.95|365.44|
|Business services nec|2.65|31.48|1.08|61.19|
|Wearingapparel|2.45|278.51|199.61|359.31|
|Public<br>admin/defence/health/education|2.11|30.78|0.9|50.71|
|Foodproducts nec|2.05|202.69|152.56|402.46|
|Ferrous metals|1.62|261.34|201.76|370.40|
|Metals nec|1.10|335.8|245.22|513.15|
|Machineryand equipment nec|0.99|307.58|213.46|494.88|
|Petroleum and coalproducts|0.93|123.92|82.47|177.26|
|Crops nec|0.74|2002.23|1746.15|3461.86|
|Sea transport|0.72|33.43|1.33|48.26|
|Woodproducts|0.61|248.35|165.63|375.78|
|Communication|0.6|25.99|0|45.97|
|Vegetables,fruits,and nuts|0.54|1659.19|1280.7|2678.03|
|Mineralproducts nec|0.31|127.95|75|169.23|
|Minerals nec|0.27|56.93|25|116.25|
|Beverages and tobaccoproducts|0.27|137.86|85.71|222.93|
|Financial services nec|0.22|29.65|4.35|51.64|
|Transport nec|0.19|28.78|0|42.96|
|Manufactures nec|0.16|295.5|205.88|510.92|
|Animalproducts nec|0.14|78.38|40|126.75|
|Vegetable oils and fats|0.10|1765.26|1580|3174.33|
|Sugar|0.10|185.08|136.36|276.26|
|Electronic equipment|0.10|309.98|220|495.62|
|Transport equipment nec|0.07|301.17|242.86|502.39|
|Recreation and other services|0.07|30.53|0|49.36|
|Paperproducts andpublishing|0.06|179.57|133.33|312.72|
|Motor vehicles andparts|0.06|3505.94|2916.67|5233.18|
|Insurance|0.06|32.27|0|52.28|
|Forestry|0.04|381.17|250|567.42|
|Construction|0.04|27.62|25|46.58|
|Air transport|0.04|30.23|0|45.13|
|Processed rice|0.01|29.65|0|51.27|
|Trade|0.01|28.49|0|42.88|
|**Total **|**100.00**|**182.17**|**133.53**|**297.04 **|

_Source_ : GTAP simulation results.

16
here). Both studies find robust evidence of a positive association between changes in education and economic growth. Even more recently, Ciccone and Papaioannou (2005) find strong support for both the human capital level and the human capital accumulation effects using cross-country industry-level data that allow them to control for country-specific and industry-specific effects.

When we add the change in years of schooling over 1960-2000 to the specification depicted in Figure 3.1 and similar specifications, it never turns significant with the sole exception of the sample of 23 OECD countries.<sup>36</sup> (Because of the possibly substantial amount of mismeasurement in the education data, though, the results on the first-differenced data on changes in education may well suffer).

Thus, while recent research tends to find a positive effect of schooling quantity on economic growth, it seems beyond the scope of current data to draw strong conclusions about the relative importance of different mechanisms by which schooling quantity may affect economic growth. However, several recent studies suggest that education is important both as an investment in human capital and in facilitating research and development and the diffusion of technologies. With respect to the relative importance of the latter two mechanisms, Vandenbussche, Aghion, and Meghir (2006) suggest that innovation is more important relative to imitation for countries close to the technological frontier. As a consequence, the composition of human capital between basic and higher education may be important, with initial phases of education being more important for imitation and higher education being more important for innovation. Vandenbussche, Aghion, and Meghir (2006) provide evidence from a panel of OECD countries in line with this argument, which – when applied to developing countries – might suggest that a focus on basic skills seems warranted for developing countries. We will come back to the issue of the relative importance of basic versus advanced skills in more detail in Section 4.4 below.

Two more skeptical studies raise noteworthy caveats, and we will return to each of them below. First, Bils and Klenow (2000) raise the issue of causality, suggesting that reverse causation running from higher economic growth to additional education may be at least as important as the causal effect of education on growth in the cross-country association. We will address the issue of causality in crosscountry regressions in Section 4.2 below. Second, one of the conclusions that Pritchett (2001, 2006) draws from the fragility of the evidence linking changes in education to economic growth is that it is important for economic growth to get other things right as well, in particular the institutional framework of the economy. We will address this issue in Section 4.4 below.

> 36 The positive association between growth in education and economic growth in the OECD sample is sensitive to the inclusion of Korea, though.

24
fairly high site fidelity (e.g., lobster [Acosta 1999]) to those that move extensively as larvae (e.g., shrimp [Calderon-Aguilera et al. 2003]) or adults (e.g., tuna [Schaefer et al. 2007]). This variation allows for an analysis of how cooperatives deal differently with species that vary in their mobility.

Moreover, the region’s terrestrial and marine ecosystems respond dramatically to ENSO (El Niño/Southern Oscillation) events, which occur every several years (Polis et al. 2002; Velarde et al. 2004). During El Niño years, ocean waters warm, upwelling slows, and rainfall increases, with important implications for fisheries species (Velarde et al. 2004; Aburto-Oropeza et al. 2007). While ocean productivity varies temporally—both with ENSO and other sources of climatic variability—and spatially throughout the Gulf region, we find remarkable coherence in the variability of mean concentration of chlorophyll _a_ , a common proxy for marine primary productivity (Mann and Lazier 2005) in the vicinity of the three cooperatives for which we have logbook data (Leslie et al. 2015). ENSO’s significant role enables us to explicitly test the influence of periodic environmental shocks on cooperatives’ decision-making. ENSO may affect species through three channels: recruitment and growth of juveniles, growth of adults, and movement of adults. Here we focus on the recruitment channel.

Fishing cooperatives have had a long history in the Gulf of California—and Mexico more broadly—and continue to be a major factor in the fishing industry today. Under the 1947 Fisheries Law, cooperatives had exclusive concessions to the eight most commercially valuable species and often had rights to bays, estuaries, or lagoons adjacent to their lands (DeWalt 2001; Young 2001). In addition to cooperatives, the fisheries law created two other classes of fishermen: _permisionarios_ , who are private individuals or corporate entities with permits to catch—and sell to the open market—species for which cooperatives do not hold concessions; and _pescadores libres_ , who have rights to fish within cooperatives’ concessions for subsistence only, but are also allowed to fish for _permisionarios_ (Young 2001).

To encourage private investment in fisheries, the 1992 Fisheries Law took exclusive rights for the eight species away from the cooperatives and made it possible for _permisionarios_ to fish and sell them (SEPESCA 1992; Ibarra 1996; Villa 1996; Ibarra et al. 1998; Ibarra et al. 2000; Young 2001). Consequently, in the present-day system, independent fishers (i.e., _permisionarios_ ) are able to fish most species and sell their catch as long as they are able to acquire permits to do so. The acquisition of these permits involves important costs, including the administrative burden of applying for a permit, interactions with government officials, travel to (often distant) administrative offices, and the financial cost of the permit itself.

6
Policy Research Working Paper 6797

# **Abstract**

Financial constraints are widespread in developing countries, where even short-term credit is limited. Finance held by firms as working capital is a substantial proportion of sales revenue, yet the role of working capital is largely neglected by existing models of financial constraints. This paper presents a dynamic model of the firm that incorporates working capital by introducing a delay between factor payments and the receipt of revenue. In contrast with previous models, the working capital model predicts that firms under binding constraints will substitute between labor and capital in response to demand shocks, causing investment to be countercyclical. For firms near the margin of being constrained, constraints bind when positive production opportunities arise. Output growth is therefore constrained in response to positive shocks but not to negative shocks. Simulations suggest that models without working capital may understate the predicted effects of financial constraints on production efficiency, firm profit and growth over time. The predictions are tested with the Bangladesh Panel Survey data for manufacturing firms. Consistent with the theory, there is evidence that constraints bind when output price increases, that investment by constrained firms is countercyclical, and that output response to positive shocks is dampened for firms that are sometimes constrained. The results also are important for policy. In order to maximize growth, efforts to relieve credit constraints should be focused on periods when demand shocks are high.

This paper is a product of the Finance and Private Sector Unit, South Asia Region. 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 author may be contacted at rchan@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
I compare the educational attainment of two groups. The first consists of adults whose mandatory schooling was completed before the war started (born 1966-1973). The second group contains individuals who were of school age or relatively young during the war (“exposed” cohort - born 1976-1985). The latter group is then sub-divided into two subgroups where one of the subgroups lived in the areas highly affected by the conflict (the main group of interest) and the second subgroup lived in the lesser affected areas. The inclusion in the exposed cohort is determined by the age of mandatory school attendance, where children in Tajikistan are required to start attending school at age seven and nine years of education is mandatory and free of charge. I assume that a child was exposed to the conflict if he was between ages 7 and 15 during the 1992-1998, and therefore was eligible to be enrolled in a publicly funded school. The study of educational outcomes in 2007 is a follow-up on Shemyakina (2011) who found using the 1999 and 2003 data that in the short- and medium runs, young girls from the households and regions more affected by the conflict, were less likely to be enrolled in school or less likely to complete nine grades (equivalent to basic education level) of schooling by 2003 as a cohort. This analysis explores the long-run effect of conflict on educational outcomes and uses a pooled data from the 2003 and 2007 surveys.

As explained in more detail in section 5.2, the sample for the analysis of labor market outcomes is limited to those who were 22-49 year old in 2007. This age group is the most economically active. In the analysis of labor market outcomes, the “war-affected cohort” is defined as those born between 1970-1985 and thus also includes individuals whose early labor market experiences may have been disrupted by the war. The comparison group is set to those born in 1958-1969.<sup>13</sup>

> 13 Since exposure to conflict could affect all birth cohorts differentially, my initial model specification for women and men included a full set of interactions between 5-year birth cohort dummies and residence in the more affected area. The regression analysis indicated that the estimated coefficients for the younger cohorts of women were close to each other in size (about 11 percentage points increase in employment for those born in 1970-1975 and 1976-1979 (significant at 1%) and about 8.5 percentage points increase for those born in 1980-1985) and were significant for cohorts born in 1970-1975 and 1976-1979. The analysis of employment in this paper uses a catch-all cohort term for those born in 1970-1985 interacted with the conflict exposure variable to evaluate an average effect of the conflict exposure for this cohort. Thus, the use of one cohort term provides us with a more conservative impact of conflict on employment for women.

14
- (c) The household-specific errors are simulated as

in the homoscedastic case. When there is heteroscedasticity, they are simulated as Unlike the original parametric bootstrap procedure in MR (2010) described in Section 3, here the sample errors _e_<sup>_∗_</sup> _ch,s_<sup>are not a subset of the census errors simulated in step 2, although they</sup> are generated from exactly the same model (with the same variance).

5. Fit the model (1) to the newly simulated survey data from step 4. This yields a bootstrap vector of estimated model parameters: 6. Obtain the bootstrap extended CEB estimator _τ_ ˆ _c,b_<sup>_CEB∗_</sup> through MC simulation using the bootstrap parameter estimates _θ_<sup>ˆ</sup><sup>_∗_</sup> from step 5, by the approach described in the previous section.

7. Repeat steps 2 to 6 a sufficiently large number of times, _B_ . Let _τc_<sup>_∗_(</sup><sup>_b_)</sup> be the true value and _τ_ ˆ _c,b_<sup>_CEB∗_(</sup><sup>_b_)</sup> be the CEB estimator obtained in the bth replicate of the bootstrap procedure. A parametric bootstrap estimator of the MSE is given by:

# **7 Simulation experiments for comparison of methods**

## **7.1 Simulation experiment with poor model**

Here we compare the procedures described in the previous sections for the estimation of poverty rates and gaps (FGT indicators with _α_ = 0 _,_ 1 respectively) and their corresponding error measures. To this aim, we perform simulation experiments under the same setup as in MR (2010), but extended to consider also more realistic scenarios.

Thus, like in MR (2010), a census data set of _N_ = 20 _,_ 000 observations is created, where all observations are uniformly spread among _C_ = 80 areas, labeled from 1 to 80. This means that every area consists of _iid Nc_ = 250 observations. Location effects are simulated as _ηc ∼ N_ �0 _,_ 0 _._ 15<sup>2�</sup> ; note that every observation within a given area will have the same simulated effect. Then, values of two right-hand side binary variables are simulated. The first one, _x_ 1, takes the value 1 if a generated random uniform value between 0 and 1 is less than or equal to 0 _._ 3 + 0 _._ 5 80<sup>_<u>c</u>_.Thismeansthatobservationsinareaswithahigherlabel</sup> are more likely to get value 1. The next one, _x_ 2, is not tied to the area’s label. This variable takes the value 1 if a simulated random uniform value between 0 and 1 is less than or equal to 0.2. The census welfare vectors _yc_ = ( _yc,_ 1 _, . . . , yc,Nc_ )<sup>_T_</sup> for each area _c_ = 1 _, . . . , C_ , are then created as follows: ln( _ych_ ) = 3 + 0 _._ 03 _x_ 1 _,ch −_ 0 _._ 04 _x_ 2 _,ch_ + _ηc_ + _ech,_ _iid_ where household-level errors are generated under the homoscedastic setup, as _ech ∼ N_ �0 _,_ 0 _._ 5<sup>2�</sup> . The poverty line is fixed at _z_ = 12, which is roughly 60 percent of the median welfare of a generated population.

16
**D-ata** Soumc

Most of the cross-nadonal data employed in this study were taken from widely available national account statistics published by the Ur.ited Nations, UNESCO, and the World Bank. The main dependent variable, economic growth, was measured by per capita real gross domestic product (RGDP) in constant 1980 international prices (Kravis, Heston and Summers, 1982; Summers and Heston, 1988).1 This variable, in contrast to the more widely-used measure gross national product (GNP), provides more realistic and comparable estimates of national income levels and purchasing power parities. Since the distribution of per capita RGDP data is positively skewed, like most other measures of economic development, it was logged in all the analyses reported below.

Apart from the main explanatory variables which refer to annual instructional hours in different curricular subjects (to be discussed below), several other independent and control measures were incorporated into the analyses. Two measures of the expansion of mass education were employed -- the primary and secondary enrollment rates, respectively -- which refer to the proportion of the relevant school-age population enrolled in the lower two levels of the educational system (UNESCO, 1970). In addition, three other contro! measures were included since previous crossnational studies have shown them to have significantly effects on economic growth rates. These include: 1) population growth: this was measured by each nation's total fertility rate (UN, 1986) and was expected to have negative effects on economic development; 2) export commodity concentration: this indicator measured one aspect of economic dependence and was expected to negatively affect economic growth (World Bank, 1983); and 3) a "dummy" variable for nations whose economies are highly dependent on oil and mineral exporting, which was thought to have a positive effect on economic growth.

Data on national curricular structure and content came from a wide array of published and unpublished sources. A considerable arnount of curricular information was gathered from reports of international agencies such as UNESCO, comparative educational handbooks and encyclopedia, historical surveys of national and colonial educational systems, contemporary case studies of educational developments in particular countries, and two sets of microfiche from the Intemational

1. Similiar results were obtained, though for a slightly smaller sample, when per capita GNP was employed as the dependent variable.

7