rafmacalaba/datause-encoder-data huggingface dataset:
explained to each association, and each was asked to supply the names and contact details of three members who we could interview. Twenty of the 25 associations agreed to participate, supplying 67 seed names to us (several gave more than 3 names). The associations were asked to inform their members about the survey and obtain their consent. However, many of the individuals appear not to have been informed.

The snowball survey took place from December 5-20, 2006, and experienced two main problems. The first was that some of the households supplied as seeds by the Nikkei associations refused to answer the survey. The second problem was that among households interviewed, most households did not wish to provide referrals to other Nikkei associations. They noted that the length and content of the questionnaire made them reluctant to give the names of friends who could answer it.

In response to these problems, a second phase of the snowballing survey ran from January 22, 2007 to March 23, 2007. More associations were contacted to provide additional seed names (69 more names were obtained), and as with the stratified sample, an adaption of the intercept survey was used when individuals refused to answer the longer questionnaire. A decision was made to continue the snowball process until a target sample size of 100 had been achieved. 75 households received long survey, and 25 the short survey. Of those receiving the long survey, only 39 percent provided at least one referral. The mean number of referrals per referral-providing household was 1.5. As a result, we obtained 0.57 referrals per surveyed household – higher than the rate of one referral per four households reported by Bilsborrow (2006) in his survey of Colombian migrants in Ecuador, but still much lower than hoped for.

Table 9 provides a summary of the households surveyed using the snowball survey. The final sample consists of 60 households who came as seed households from Japanese associations, and 40 households who were chain referrals. The longest chain achieved was 3 links.

28

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Policy Research Working Paper 6508

# **Abstract**

Using a randomized evaluation with 432 Mexican small and medium enterprises, this paper shows that access to management consulting led to better firm performance: one-year results show positive effects on return-on-assets and total factor productivity. Owners also had large increases in “entrepreneurial spirit” (an entrepreneurs’ managerial confidence index). Using Mexican social security data, the analysis finds a large increase in the number of employees and total wage bill several years after the program. The paper documents large heterogeneity in the specific managerial practices that improved as a result of the consulting, but there is no singular mechanism as a panacea for all firms.

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 mbruhn@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
|**_Use of command and control regulation_**: indicates the extent to which government uses coercive (as|
|---|
|opposed to incentive-based) regulation in general and in specific service sectors.|

|**Barriers to entrepreneurship**<br>**: Regulatory and administrative opacity**|
|---|
|**_Licenses and permits systems_**: reflects the use of ‘one-stop shops’ and ‘silence is consent’ rules for<br>getting information on and issuing licenses and permits.|
|**_Communication and simplification of rules and procedures_**: reflects aspects of government’s<br>communication strategy and efforts to reduce and simplify the administrative burden of interacting<br>with government.|
|**Barriers to entrepreneurship**<br>**: Administrative burden on corporations**|
|**_Administrative burdens for corporations_**: measures the administrative burdens on the creation of<br>corporations.<sup>3</sup>|
|**_Administrative burdens for sole proprietors_**: measures the administrative burdens on the creation of<br>sole proprietor firms.<sup>4</sup>|
|**_Sector-specific administrative burdens_**: reflects administrative burdens in the road transport and retail<br>distribution sectors.|

|**OUTWARD-ORIENTEDPOLICIES**|
|---|
|**Barriers to entrepreneurship**<br>**: Barriers to competition**|

**_Legal barriers_** : measures the scope of explicit legal limitations on the number of competitors allowed in a wide range of business sectors.

**_Antitrust exemptions_** : measures the scope of exemptions to competition law for public enterprises.

**<u>Barriers to trade and investment: Explicit barriers</u>** **_Tariffs_** : calculated as the (simple) average of most-favoured-nation tariffs.

**_Foreign Ownership barriers_** : reflects legal restrictions on foreign acquisition of equity in public and private firms and in the telecommunications and airlines sectors.

**_Discriminatory procedures_** : reflects the extent of discrimination against foreign firms at the procedural level.

# **<u>Barriers to trade and investment: Regulatory barriers</u>**

**_Regulatory barriers_** : reflects other barriers to international trade ( _e.g._ international harmonisation, mutual recognition agreements).

_______________________________ Source: reproduced from Conway, Janod and Nicoletti, 2005 The indicators are calculated on the basis of the qualitative and quantitative information obtained from questionnaire answers. Qualitative data are assigned a numerical value that allows ordering each of the possible responses to a given question. Quantitative information is ranked by subdividing it into categories based on a system of thresholds. The coded information is then normalized over a scale of zero to six. These data are then

> 3 Data from Doing Business in 2005 was used to construct this indicator.

> 4 This indicator is not available for Bulgaria as information about it was not included in the adapted questionnaire for Bulgaria.

7
# IMPACT EVALUATION HELPS DELIVER DEVELOPMENT PROJECTS

Arianna Legovini, Vincenzo Di Maro, and Caio Piza<sup>*</sup> JEL No. O01, O12, O19, O02, D04, H43, C93, F61 Keywords: Impact Evaluation, Development Effectiveness, Development Policy, Experiments, Projects

* Arianna Legovini is the Manager of Development Impact Evaluation in the Research Group of the World Bank, and Vincenzo Di Maro is a senior economist and Caio Piza is an economist in the same group. We are especially grateful to Aart Kraay, Guadalupe Bedoya, Florence Kondylis, Daniel Rogger, and Daniel Stein for their detailed comments on an earlier draft. We thank Pavel Luengas and Susumu Yoshida for their assistance in collating project-level data from the World Bank’s databases and providing research assistance.
# **5.  Projecting Baseline Changes in Female Education**

The panel estimation results in Table 5 provide a reasonable basis for projecting the future paths of female primary and secondary education in each country, given our exogenous projections of income and population. Our projections for life expectancy and the total fertility rate are taken directly from the UN’s Medium Variant population forecast. Given the paths of the three variables (life expectancy, total fertility rate, income per capita), we use the fixed-effects results reported in Table 5 to plot the paths of future net female primary and secondary enrollment rates. Here it is worth repeating that we estimate both equations in Table 5 using flog transformations on net enrollment rates, which insure that projections are bounded in the range 0-100.

To illustrate the implications of our approach, Table 8 presents projected future schooling rates by region. We compute these rates in several steps. First, we estimate the panel regressions, incorporating subregional dummies as well as country effects.<sup>8</sup> Then we combine the results with country projections of GDP per capita, life expectancy and the total fertility rate to predict future net female primary and secondary enrollment rates. We combine these rates with appropriate UN Medium Variant female population cohort data to calculate the actual number of females enrolled in primary and secondary school. Finally, we total enrolled females and relevant cohort females by region for primary and secondary schooling, and form the ratios to project regional primary and secondary enrollment ratios.

Although our economic and demographic projections are “moderate” by current standards, they nevertheless entail continued rapid progress in female education. By 2050, Sub-Saharan Africa increases its net female primary enrollment rate from 54.9 to 93.5, and its net female secondary enrollment rate from 19.7 to 78.0. South Asia also makes rapid progress, moving its female primary and secondary enrollment rates from 69.5 to 92.4 and 42.0 to 90.8, respectively. East Asia/Pacific, Latin American/Caribbean and Middle East/North Africa also move upward, but proportionately less because they start from higher bases. While educational progress is quite noteworthy in this scenario,

> 8  We include fixed effects for 25 subregions.

13
27

# **Table 11. MDGs: Policy Changes and Links with the Budget**

|**MDG**|**Policy Objectives**|**What to do?**|**Link with the Budget**|
|---|---|---|---|
|**_Goal 1. Eradicate extreme_**<br>**Target 1. **Halve, between 1<br>|**_poverty and hunger_**<br> 990 and2015, the proportionofpeople <br>|whoseincomeisless than$1a day<br>||
|_Indicator 1._Proportion of<br>population below the national<br>poverty line – poverty incidence<br>(%)|•<br>Reduce the incidence of poverty via<br>faster and more inclusive non-oil<br>growth|•<br>Target the poorest segments of the<br>population, boost rural incomes, and<br>stimulate private sector investment|•<br>Increase poverty spending and upgrade<br>economic infrastructure, e.g. Irrigation<br>pumps, clean water wells, construct<br>and rehabilitate rural roads<br>•<br>Target spending in agriculture to<br>develop agro-processing (e.g. canning,<br>food-drying, and juice production)<br>•<br>Target spending in livestock to<br>improve cattle quality through<br>scientific breeding, develop<br>transportation and refrigeration, and<br>increase market access for hides, milk<br>and meat sub-sectors<br>•<br>Establish economic zones (e.g. port of<br>Nouakchott)|
|**_Goal 2. Provide primary e_**|**_ducation for all_**|||
|**Target 3. **Ensure that, by2|015, childreneverywhere, boys and girls|alike, willbe able to complete afullco|urse ofprimary schooling|
|_Indicator 7._Proportion of pupils<br>starting grade 1 who reach grade<br>5|•<br>Give students the opportunity to access<br>the primary cycle and remain enrolled<br>throughout|•<br>Match the supply of education services<br>to the existing demand of parents|•<br>Construct, rehabilitate and equip 3200<br>classrooms<br>•<br>Recruit 3800 (mainly female) teachers,<br>ensure their geographical distribution<br>and set up incentives and training<br>schemes<br>•<br>Ensure that 80 percent of schools offer<br>a full primary cycle by upgrading them<br>•<br>Increase the percentage of school<br>canteens that offer free meals by 32<br>percent, in the most disadvantaged<br>zones<br>•<br>Fund teachers’ incentives and training<br>schemes and guarantee a school<br>manual for every4pupils by2010|
The positive and statistically significant coefficient on lagged ∆ _PREF_ indicates that MFN tariffs fall following a reduction in preferential tariffs in FTA members. The negative and significant coefficient on ∆ _PREFCU_ indicates that this is not true when the bloc takes the form of a customs union. The F-test shows that MFN tariffs in customs unions increase following a decline in preferential tariffs in the base specification. However, that effect is quite small.

To control for potential endogeneity, we use the lagged values of the preferential tariffs of RTA partners as instrumental variables to estimate the impact of preferential liberalization on MFN tariffs. Specifically, Δ _PREFipj,t-1_ , _j_ = 1, 2, 3 (where _pj_ is an RTA partner of country _j_ ), are used as instruments for Δ _PREFij,t-1_ , with and without the interaction with the CU dummy. The results are reported in column (2) and first-stage regressions are reported in Table A.1.<sup>23</sup> The Cragg-Donaldson statistic indicates that our instruments are strong, while the p-value of the Hansen overidentification test indicates that our instruments are valid. The IV results show strong support for tariff complementarity in free trade areas. They also show that this effect is absent in customs unions: we cannot reject the hypothesis that preferential liberalization has no effect on MFN tariffs in customs unions.

In column (3), we use two- and three-times lagged levels of preferential tariffs to instrument for lagged changes. We also include the lagged level of MFN to control for the fact that high tariffs may be reduced by more, also instrumented by lagged levels in two and three periods. Again, we find strong evidence of tariff complementarity in FTAs but not in customs unions. The first stage regression from column (3) is reported in Table A.1. The instruments are not weak, as shown by the Cragg-Donaldson statistic, and the Hansen J-statistic suggests that they are valid.

> 23 To estimate IV with numerous fixed effects, we demean the data by country-industry and country-year and then estimate IV on the demeaned data, hence the R<sup>2</sup> for IV regression does not include fixed effects.

26
positions where neither the source nor destination report data. If both sides report, we use the two observations for validation purposes, as explained in the appendix.

The final data sets for the four investment types cover up to 239 countries and jurisdictions for the period 2001–18. Appendix table 1 lists every country by region and indicates for each one whether it appears as source and/or destination for the different investment types.<sup>3</sup> The data on loans and deposits cover 210 source countries and 210 destination countries. The data on portfolio equity and debt cover 92 source countries and 238 destination countries. The data on FDI cover 123 source countries and 239 destination countries. The country-to-region data on international reserves cover 182 source countries.

To show that our bilateral data provide good coverage of cross-border investment overall, we compare Lane and Milesi-Ferretti’s (2001, 2007) global IIP aggregates (summing across all countries) with our aggregated bilateral data (summing across all country pairs) on a yearly basis (figure 1). For portfolio investment, FDI, and international reserves, our data closely match Lane and MilesiFerretti’s aggregates. For loans and deposits, our data also match Lane and Milesi-Ferretti’s aggregates until 2007, and display the same trend thereafter, although our values are smaller. Part of the gap could be due to the fact that “other investment” in the IIP, which we use as a proxy for bank loans and deposits, covers more than just the loans and deposits included in our bilateral data.<sup>4</sup> Moreover, a greater number of South countries started to report aggregate IIP data than bilateral loans and deposits to the BIS over time. Thus, the larger aggregates in Lane and Milesi-Ferretti’s data might point to even greater financial integration of the South.

> 3 In appendix table 1, we consider a country to be a source (destination) of international investment if there are nonmissing data for at least one year in our sample period for that country as a source (destination) of that investment type. 4 For instance, the series “other investment” in the IIP data also includes some insurance and pension-related assets, such as technical reserves and entitlements (IMF 2009, table 6.1).

8
Table 1: Descriptive Statistics of Male and Female Plot Managers and their Main Plots

|Variables|N|Pooled|Male|Female|Dierence|
|---|---|---|---|---|---|
|Plot Manager Characteristics||||||
|Age|6076|37.681|40.308|35.873|4.435 ***|
|Marital status: single, divorced, separated, or widowed|6076|0.147|0.094|0.184|-0.090 ***|
|Household head is female|6076|0.209|0.053|0.316|-0.263 ***|
|Literate|6076|0.267|0.518|0.095|0.422 ***|
|Have less than primary school education|6076|0.882|0.764|0.962|-0.198 ***|
|Completed at least primary school education|6076|0.118|0.236|0.038|0.198 ***|
|Plot Manager - First Exposure to SLM Technique||||||
|Learned mulching in the past 5 years|6076|0.230|0.232|0.228|0.004|
|Learned mulching in 6-10 years ago|6076|0.067|0.064|0.068|-0.004|
|Learned mulching in 10-20 years ago|6076|0.083|0.094|0.076|0.018|
|Learned mulching more than 20 years ago|6076|0.047|0.057|0.040|0.016|
|Learned strip tillage in the past 5 years|6076|0.062|0.056|0.066|-0.010|
|Learned strip tillage in 6-10 years ago|6076|0.039|0.041|0.039|0.002|
|Learned strip tillage in 10-20 years ago|6076|0.062|0.067|0.059|0.008|
|Learned strip tillage more than 20 years ago|6076|0.054|0.058|0.051|0.006|
|Learned intercropping in the past 5 years|6076|0.195|0.192|0.197|-0.005|
|Learned intercropping in 6-10 years ago|6076|0.141|0.132|0.148|-0.016|
|Learned intercropping in 10-20 years ago|6076|0.307|0.319|0.298|0.021|
|Learned intercropping more than 20 years ago<br>Plot Characteristics|6076|0.274|0.306|0.253|0.053 **|
|Self-reported plot area (hectares)|5386|0.956|1.065|0.875|0.190 ***|
|GPS measured plot area (hectares)|5318|0.796|0.906|0.716|0.190 ***|
|Household owns the main plot|5386|0.984|0.989|0.981|0.008 *|
|Main plot soil is fertile|5386|0.993|0.992|0.994|-0.003|
|Main plot is at|5386|0.643|0.631|0.652|-0.021|
|Main plot locates in high zone|5386|0.444|0.453|0.437|0.015|
|Main plot had erosion problem|5012|0.081|0.096|0.069|0.027 *|
|Used pesticides on the main plot|5012|0.061|0.122|0.012|0.110 ***|
|Used chemical fertilizer on the main plot|5012|0.009|0.014|0.005|0.009 **|
|Number of crops grown on the main plot|4642|1.912|1.897|1.924|-0.027 *|
|Grew maize on the main plot|5012|0.635|0.662|0.614|0.048|
|Grew sorghum on the main plot|5012|0.243|0.131|0.335|-0.204 ***|
|Grew cotton on the main plot|5012|0.096|0.187|0.020|0.167 ***|
|Grew sesame on the main plot|5012|0.161|0.228|0.105|0.123 ***|
|Grew cassava on the main plot|5012|0.168|0.196|0.146|0.050 *|
|Grew cowpea on the main plot|5012|0.349|0.303|0.387|-0.084 **|
|Grew pigeon pea on the main plot<br>Other Household Characteristics|5012|0.189|0.225|0.158|0.067 **|
|Number of household members|6076|5.221|5.513|5.020|0.493 ***|
|Number of children (age < 15 years)|6076|2.813|2.928|2.733|0.194 **|
|Note: Community clustered standard errors used for the|t statis|tics. * p|<0.10 **|p<0.05|*** p<0.01.|

20
contained in the TRI and the LRD. This was combined with the HHED to develop additional risk-weighted indices. The TRI was chosen for analysis before the AIRS and NPDES databases, both because of its ready availability and because of the importance of toxic release information for the analysis of risk. The analysis draws only on the first year of TRI data (1987), chosen largely because it was a census year with consequently detailed LRD data.

In the next stage of the project the AIRS and NPDES databases, and the information on media-specific releases in the TRI, were used to construct a wide range of pollution output intensities by medium (air/land/water). In addition to disaggregating the toxic pollution intensities by medium, indices were obtained for the US EPA's six criteria air pollutants (SO,, NO,, CO, TP, PM10, VOC,) and two water pollutant indicators, (BOD and TSS).

.

# 3.2. Operational Complexities

Although pollution intensity estimation is conceptually straight forward, several practical problems had to be confronted in actual calculation of the indices. An understanding of their resolution is important if the indices are to be correctly interpreted and applied.

12
Policy Research Working Paper 8537

# **Abstract**

The debate on the environment-poverty nexus is inconclusive, with past research unable to identify the causal dynamics. This paper uses a unique global panel data set that links (survey and census derived) poverty data to measures of environmental quality at the subnational level. The analysis uses vegetation vigor as a proxy for above-ground environmental quality and soil fertility as proxy for belowground environmental quality. Rainfall is used to account for endogeneity issues in an instrumental variable approach. This is the first global study using quasi-experimental methods to uncover to what degree environmental quality matters for poverty reduction. The paper draws three main conclusions. (1) The environment matters for poverty reduction. The panel regression suggests that a 10 percent increase in vegetation vigor is associated with a poverty headcount ratio reduction of nearly 0.7 percentage point in rural areas, and 1 percentage point in Sub-Saharan Africa. A 10 percent increase in soil quality leads to a roughly 2 percentage point decrease in poverty rates in rural areas and in Sub-Saharan Africa. (2) The effects of environmental quality on poverty are stronger than its effects on average income, suggesting that the poor benefit disproportionately from environmental quality. (3) In situ environmental quality improvements are pro-poor, in contrast to urbanization. Although urbanization has highly significant and sizable correlations with GDP per capita, it is not significantly correlated with poverty reduction.

This paper is a product of the Environment and Natural Resources Global Practice . 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://www.worldbank.org/research. The authors may be contacted at mheger1@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