rafmacalaba/datause-encoder-data huggingface dataset:
# **2.2 External Comparison Group**

For comparison purposes, the study also enrolled a sample of non-stunted children from the same neighborhoods, where non-stunted was defined as having a height for age _z_ -score greater than -1 standard deviations. At baseline, every fourth stunted child in the study was matched with one non-stunted child who lived nearby and was the same age (plus or minus 3 months) and sex. At age 7, this sample of 32 was supplemented with another 52 children who had been identified in the initial survey as being non-stunted and fulfilled all other inclusion criteria.

While the non-stunted group was better off than the stunted group in terms of their personal development and their socioeconomic status, the non-stunted children were still living in the same economically and socially disadvantaged Kingston neighborhoods. Members of the non-stunted comparison group did not receive any of the interventions, but did receive the same free health care as those in the stunted experimental group. From age 7 onwards, this group was surveyed at the same time as the participants in the experiment.

This sample is used to investigate the extent to which the early childhood stimulation intervention helped to compensate for initial disadvantage by comparing the stunted treatment group with the non-stunted external comparison group. We define complete catch-up as no difference between the treated stunted group and the non-stunted comparison group.

In order to better understand the external validity of the catch-up analysis we compare the non-stunted group to the general population using data from two surveys that are representative of urban Jamaica: (1) the 1992 Jamaican Survey of Living Conditions (JSLC) that was collected when the children were 7 years old and when most of the non-stunted sample was first surveyed, and (2) the 2008 Jamaica Labor Force (JLF) survey that was collected in the same year as the last follow-up. Unfortunately the labor supply and earnings questions in the JLF and in our survey were asked in different ways, and there was a 50% non-response rate in the JLF to the earnings questions among those who were employed. Only the education variables are directly comparable.

5
**<u>Table 5: Results from frontier production function</u>**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|**_Production function_**<br>Log of total cultivated area in hectares|0.291***|0.310***|0.289***|0.312***|
||(29.446)|(31.479)|(29.270)|(31.445)|
|Log of total labor use in days|0.340***|0.348***|0.341***|0.355***|
||(24.701)|(25.339)|(24.709)|(25.327)|
|Lo of chemical fertilizer use in k|0096***|0101***|0091***|0096***|
|g g|.<br>(5.382)|.<br>(5.526)|.<br>(5.104)|.<br>(5.320)|
|Log of pesticide use in kg|<br>0.079***|<br>0.076***|<br>0.085***|<br>0.082***|
||(3.731)|(3.487)|(4.011)|(3.825)|
|Log of manure use in kg|<br>0.119***|<br>0.117***|<br>0.117***|<br>0.118***|
||(12.888)|(12.525)|(12.684)|(12.707)|
|Dummy zero chemical fertilizer use|0.075|0.082|0.064|0.061|
||(1.436)|(1.536)|(1.219)|(1.156)|
|Dummy zero pesticide use|-0.135***|-0.151***|-0.160***|-0.173***|
||(3.259)|(3.557)|(3.793)|(4.070)|
|Dummy zero manure use|<br>0.526***|<br>0.509***|<br>0.515***|<br>0.516***|
||(8.861)|(8.583)|(8.674)|(8.668)|
|Share of rented in land|<br>-0.002|<br>-0.015|<br>0.019|<br>-0.003|
||(0.050)|(0.391)|(0.509)|(0.073)|
|Number of years parcel possessed|<br>0.004***|<br>0.003***|<br>0.003***|<br>0.004***|
||(3.534)|(3.058)|(3.401)|(3.416)|
|Parcel located in wet land|0.105**|0.095*|0.116**|0.116**|
||(2.110)|(1.917)|(2.327)|(2.324)|
|Irrigated land|0.029<br>|0.037<br>|0.036<br>|0.037<br>|
||(0.564)|(0.715)|(0.691)|(0.712)|
|Good soil quality|0.123***|0.133***|0.120***|0.129***|
||(4.648)|(5.018)|(4.518)|(4.835)|
|Medium soil quality|<br>0.082**|<br>0.085**|<br>0.083**|<br>0.092***|
||(2.350)|(2.421)|(2.363)|(2.620)|
|Used improved seed|0.029|-1.231**|0.034|0.091|
||(0.313)|(2.451)|(0.360)|(1.044)|
|Use knowledge from extension services|-0.006|0.065|-0.004|-0.010|
||(0.061)|(0.498)|(0.040)|(0.106)|
|Experienced crop shock|-0.133*|-0.197|-0.125|-0.198***|
||(1.722)|(1.375)|(1.559)|(2.779)|
|hld h l id h ll|<br>22|<br>15|<br>21|<br>1|
|Houseo as parce outse te ce|0.0|-0.0|0.0|-0.09|
||(1.055)|(0.740)|(1.002)|(0.929)|
|Time dummy|<br>-0.084***|<br>-0.077***|<br>-0.082***|<br>-0.077***|
||(4.344)|(3.948)|(4.238)|(4.002)|
|Share of land with grains|||0.498***|0.471***|
||||(3.147)|(2.932)|
|Share of land with tubers|||<br>0.566***|<br>0.532***|
||||(3.551)|(3.302)|
|Share of land with tree crops|||0.670***|0.665***|
||||(4.177)|(4.098)|
|Constant|3.369***|4.028***|2.862***|2.783***|
||(16.741)|(7.951)|(11.704)|(11.391)|
|**_Inefficiency parameters_**|||||
|Number of parcels|-0.135***||-0.133***||
||<br>(4.780)||<br>(4.798)||
|Simpson land fragmentation index||-0.446***||-0.917***|
|||(8.070)||(3.450)|
|Shannon crop diversity measure|-0.350***|<br>-0.223***|-0.328***|<br>-0.429***|
||(5917)|(7885)|(5884)|(3945)|
|Total cost distance in hours|.<br>0.034|.<br>-0.045*|.<br>0.031|.<br>-0.120*|
||(0.701)|(1.724)|(0.664)|(1.785)|
|Total cost distance squared in hours|-0.001|0.006|-0.001|0.015|
||(0.065)|(1.231)|(0.073)|(1.264)|
|Used improved seed|0062|-1342**|0051|0189|
||.<br>(0.404)|.<br>(2.564)|.<br>(0.331)|.<br>(1.014)|

19
# information type.

Table A.5: Results hold controlling for type of news received in feedback treatment

||**Depen**<br>|**dent variable**<br>|
|---|---|---|
||Binary = 1 if subject<br>chooses to invest|Portion of earnings<br>invested(conditional)|
|Pre-investment earnings rank|0.0871|0.0953|
||[0.350]|[0.879]|
|IOp treatment|0.666|10.64**|
||[0.223]|[0.020]|
|IOp X Pre-investment rank|-0.111|-1.722**|
||[0.214]|[0.022]|
|Bad news vs Good news (0/1)|0.278|-2.196|
||[0.461]|[0.490]|
|Constant|0.515|23.42***|
||[0.436]|[6.76e-07]|
|Observations|335|255|
|Regressions include sample tha|t received rank feedback||
|(Pseudo) R-squared|0.006|0.045|

Note: Results are from logit and OLS regressions, respectively, with robust standard errors. Columns 2 and 3 are partitioned by whether subjects receive feedback on their actual investment rank (prior to investment). IOp treatment is a dummy equal to 1 if Round 3 investment funds are determined by childhood zip code, and 0 for the control group. Pre-investment rank is subjects’ actual earnings rank based on the total value of the Round 3 investment funds. It is on a scale of 1-10, where higher values indicate higher earnings. Bad News is a binary variable equal to 1 if the subject learned they ranked lower than they thought. It is 0 if they learned they ranked at least as good as they thought. Robust p-values are in brackets. Significance also indicated using stars, for reader convenience: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1

# _A.3. Earnings versus rank_

In this sub-section we consider whether the difference between high and low value zip code groups is essentially an income effect. Similar to skill, high earners may have attributes that influence their investment choices. These people will also often occupy higher income rank positions. To address this, we look at the control group. Specifically, we look at behavior of those in the control group that actually earn more but do not learn their rank. Comparing investment between high and low earners in this group will show whether there could be unobservable characteristics that link the ability to earn with investment behaviors. We look at earnings granted for use in the Round 3 investment task, and define a ”high income” binary variable equal to 1 if the subject’s earnings were above the median amount. We perform a rank sum test, by the high income dummy, on both unconditional and conditional investment amounts. Both tests return null results. For unconditional investment, we fail to reject the hypothesis that higher and 30
- _4.3 The economic crisis and child nutritional status_

# _4.3.1 Cross-sectional cohort analysis_

Before turning to the DD specifications, we visually examine HAZ for children under three years old in each survey round, by migrant household status. Whereas children in migrant households were substantially better off in 2008 (Figure 2, left panel), this was no longer as apparent in 2012 (right panel). The pattern foreshadows the principal results, indicating that the effect of the economic shock on child HAZ in migrant households was substantially negative.

<!-- Start of picture text --> Figure 2. Children under three years old (Cross-sectional cohort)<br><!-- End of picture text --> <!-- Start of picture text --> Figure 2. Children under three years old (Cross-sectional cohort)<br>2008 2012<br>5 5<br>0 0<br>Height-for-age z-score Height-for-age z-score<br>-5 -5<br><!-- End of picture text --> In Table 2, we present the full set of child anthropometric outcomes estimating the DD models for the cross-sectional cohort comparison for this age cohort based on equation (1).

Examining the simple DD without controls, the first finding is that while on average children in
THE DEMOCRATIC REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE billion a year to preventive maintenance of its network infrastructures in order to ensure their long-term sustainability.

**Table 10. Illustrative investment targets for infrastructure in the DRC**

||**Economic target**|**Social target**|
|---|---|---|
|ICT|Fiber-optic links to neighboring capitals and submarine<br>cable|Universal access to GSM signal and public broadband<br>facilities|
|Power|Develop 8,400 MW of new generation capacity (800 MW<br>domestic, 7,600 MW for export), 6,000 MW of<br>interconnectors|Raise electrification to 19%<br>(38% urban and 8% rural)|
|Transport|Achieve regional (national) connectivity with good quality<br>2-lane (1-lane) paved road|Provide rural road access to 80% highest-value<br>agricultural land, and urban road access within 500 m|
|WSS||Achieve MDGs|

Sources: Mayer and others 2008; Rosnes and Vennemo 2009; Carruthers and others 2009; You and others 2009.

**Table 11. Indicative infrastructure spending needs in the DRC, 2006–15**

||$ million per yea|r||
|---|---|---|---|
|Sector|CAPEX|O&M|Total needs|
|ICT|246|242|487|
|Power (trade)|1,424|49|1,473|
|Transport (basic)|1,082|391|1,474|
|WSS|1,278|431|1,709|
|Total|4,045|1,112|5,157|

Sources: Mayer and others 2008; Rosnes and Vennemo 2009; Carruthers and others 2009; You and others 2009.

_Note:_ Figures refer to investments (except those in the public sector) that also include recurrent spending. Public sector covers general government and nonfinancial enterprises.

O&M = operations and maintenance; CAPEX = capital expenditure This total spending requirement is high in absolute terms and even more so relative to GDP (figure 10). At close to $5.2 billion, in absolute terms, the spending need for infrastructure is among the highest in Africa. Relative to the size of the DRC’s economy, the spending amounts to a staggering 75 percent of 2006 GDP. This is by far the highest burden of infrastructure spending for any African country, and is substantially higher than the average of low-income, fragile states. Investment alone would absorb around 57 percent of GDP. To put this in perspective, one of the highest levels of infrastructure investment observed in recent economic history has been in China which dedicated 15 percent of GDP to infrastructure investment during the mid-2000s.

24
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Figure 11: Cumulative Regional log Job Creation and Job Destruction - 1992-2010 <!-- Start of picture text --> 5<br>4<br>3<br>Job Creation<br>2<br>1<br>0<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>-1<br>-2<br>Job Destruction<br>-3<br>-4<br>-5<br><!-- End of picture text --> Each point reflects an individual regression coefficient where the dependent variable is the log job creation or log job destruction from 1991 to the year listed on the x-axis, calculated using RAIS. For all years, the independent variable is the regional tariff change reflecting tariff changes from 1990-1995 (described in the text), with state fixed effects and pre-trend control. Positive job creation estimates imply larger declines in cumulative job creation in regions facing larger tariff declines. Negative job destruction estimates imply larger increases in cumulative job destruction in regions facing larger tariff declines. Vertical bar indicates that liberalization was complete in 1995. Dashed lines show 95 percent confidence intervals. Standard errors adjusted for 112 mesoregion clusters. Job creation and destruction measured as in Davis and Haltiwanger (1990).

50
- 25 -

4.12 Controlling for female education, the elasticity of CP with respect to GDP per capita is smaller. It could be argued that progress in contraceptive prevalence may occur quickly (without timelags) as GDP per capita grows, whereas the female education route would require "waiting". But this argument may be more hypothetical than real. Consider a typical, poor country with per capita GDP under $ 300 and a female secondary enrollment rate of 10 percent. If this country wants to promote contraceptive prevalence by I percent in 1985, it would involve an increment of female secondary school enrollment of 2.4 percent in 1975 and a wait of 10 years. That country might in theory raise GDP per capita and achieve the same objective more quickly, say in one year, without any time lag. However, to do this, the country would need annual GDP per capita growth of 14 percent, which is not usually feasible. A per capita GDP growth of this magnitude was achieved by few countries during the entir decade of the 1980s, especially in Sub Saharan Africa."<sup>6</sup> Thus, while "waiting time" may seem a disadvantage in the female education route to fertility reduction, the income route may also not be quick. Building a sound institutional and administrative framework for effective family planning programs also needs time.

4.13 Does provision of family planning services appear more attractive in financial terms, relative to female education? Unfortunately due to the absence of precise estimates of costs of<sup>secondary</sup> education across the world, we are unable to compare the cost of education alternatives.<sup>The differences</sup> in unit costs of education as well as family planning program services can be expected<sup>to vary</sup> substantially across continents. Family planning program services probably generally cost considerably less per capita than secondary female education does. However, benefitlcost comparisons, once<sup>cost data</sup> become available, must be done with great care to ensure that the multiple benefits from both<sup>female</sup> _16_ **Sc.** World Bank (1989), Sustainable Growth **with** Equity: Sub-Saharan **Africa.**
Information and Spillovers from Targeting Policy in Peru’s Anchoveta Fishery Gabriel Englander<sup>∗</sup>

**JEL codes:** Q28, O13, Q56, D83, Q22 **Keywords:** information spillovers, targeted policies, place-based policies, fisheries, Peru

> ∗Development Research Group, The World Bank; aenglander@worldbank.org. I am indebted to Maximilian Auffhammer, Solomon Hsiang, James Sallee, Dale Squires, and Reed Walker for invaluable guidance. I am deeply grateful to Peru’s Ministry of Production, Sociedad Nacional de Pesquer ́ıa, Mariano Guti ́errez and Jorge Risi for providing data. I thank the many stakeholders in Peru who have taken the time to teach me about the anchoveta industry, especially Eloy Aroni, Juan Carlos Suiero, Mariano Guti ́errez, Leonor Lamas, Rosa Vinatea, and Jos ́e Luis Rojas Toledo. I thank coeditor Matthew Notowidigdo, three anonymous referees, Michael Anderson, Manuel Barron, Bjorn Bergman, Abdoulaye Cisse, Eyal Frank, Meredith Fowlie, Nick Hagerty, Alain de Janvry, Larry Karp, David Kroodsma, Megan Lang, Peiley Lau, Gianmarco Le ́on-Ciliotta, Anouch Missirian, Frederik Noack, Paula Ochiel, Kate Pennington, Wenfeng Qiu, Elisabeth Sadoulet, Jeffrey Shrader, and seminar audiences at Columbia University, Universidad del Pac ́ıfico, University of British Columbia, NOAA Southwest Fisheries Science Center, and UC Berkeley for helpful comments. Javier Duarte, William Fulton, and Gonzalo Moromizato provided excellent research assistance. I acknowledge generous financial support from NOAA grant No. NA18OAR4170326, California Sea Grant College Program Project No. E/MRE-9.
strong but far from perfect). Firms report facing less severe financing obstacles in countries that score higher on the added measures of banking outreach, even when the level of private credit is controlled for in regressions (Beck, Demirguc-Kunt, and Martinez Peria, 2007). We show that the added banking outreach measures are significantly associated with the profitability and outreach of microfinance institutions whereas other measures of banking sector development and efficiency are not.

Our primary goal is to offer evidence on the effects of competition on the profitability and outreach of microfinance institutions. By combining a new dataset on the performance of microfinance providers with another on the outreach of banks, we also hope to make a contribution to the broader issue of how competition can affect access to financial services, especially among the poor.

The rest of the paper is organized as follows. Section 2 describes recent research on competition between financial institutions. In section 3 we describe our data, lay out the basic regression equation, and discuss our hypotheses. In section 4 we present the base results comparing the relationship between bank penetration measures and microfinance outcomes with that between other standard measures of financial development and microfinance performance. We then compare the characteristics of microfinance institutions in high- and low-bank penetrations areas, and discuss the exogeneity of the bank penetration measures. Section 5 builds on the base results in an attempt to identify the types of microfinance institutions that account for the basic relationships we find between bank penetration and microfinance outreach and profitability.

Relative to NGOs, commerically-oriented microbanks tend to make larger loans and serve fewer women as a share of customers. But we find that with greater bank penetration, commercially-oriented microbanks push toward deeper outreach to the poor (as proxied by smaller average loans sizes) and greater outreach to women.

Section 6 explores competing explanations and the robustness of causal claims. The first centers on selection issues by focusing on the subset of older microfinance institutions (those in existence prior to 1996). Since a substantial part of the entry of 4
7 issues the vouchers to the approved farmers and also monitors the use of inputs by voucher recipients.

The purported objective of the program is dual: 1) increase overall maize and rice output, and 2) increase access to modern inputs among poor and vulnerable smallholders. Yet, as indicated above, conflicting targeting rules may derive from these dual objectives. To reduce poverty, subsidized inputs should be targeted to the poorer smallholders. If increasing aggregate output is the objective, subsidies should be targeted to those with the highest marginal input productivity. These often include non-users (Rickert-Gilbert, Jayne, and Black, 2009), and may or may not coincide with the poorer farmers.

During the pilot, which forms the basis for our empirical analysis here, the villages were advised to target literate farming households willing to use the input vouchers for the purported crops and able to meet the co-financing requirement. Application of these criteria in most villages would lead to an exclusion of the poorest (those considered unable to cofinance input purchases). However, the rules were not formally presented and enforced, and actual implementation was left to the discretion of the VVC and the village assembly.

They were however further refined and more formally introduced during the expansion of the program in 2009,<sup>9</sup> when it was also formally established that village leaders had to be excluded from the VVCs and that the VVCs needed to have equal representation of men and women. The fertilizer voucher was further reduced to one bag of urea to reduce the required top up and facilitate participation by poorer farmers. Use of the pilot data thus presents an opportunity to analyze how communities identify beneficiaries in less prescribed environments, the extent to which the advice was adhered to, and whether the adjustments in the targeting rules were well advised.

> 9 One important adjustment was the requirement that the beneficiaries should not be cultivating more than one hectare of maize and rice.

villages within its area and other members elected by the village assembly. The term of office for all councillors is five years.
# **3 Data Description and Descriptive Statistics**

## **3.1 The data set**

Our data are from a longitudinal matched employer-employee data set, “Quadros de Pessoal” [QP], collected by the Portuguese Ministry of Employment, which covers virtually all workers and firms in the Portuguese private sector, around 200,000 firms and more than 2 million workers each year over the 1986-2000 period. It provides comprehensive information on workers’ demographic characteristics (age, gender, schooling), job characteristics (occupational group, professional category, wage, hours worked and plant tenure), along with employing firm identifier codes. Firm-level characteristics include sales, number of employees, equity, percentage of foreign capital, geographical location and date of creation, along with industry code. This code allows us to merge this data set with trade data, disaggregated at the industry level with imports by country of origin, collected by the Portuguese National Statistics Office (INE). Trade data are only available for manufacturing for the period 1988-2000. As the Portuguese classification of industries has been revised in 1994 to match the NACE-Rev 2, a concordance was needed and the analysis considers 74 manufacturing industries.<sup>12</sup> There are two important characteristics that guarantee this data set’s coverage and reliability. First, data are collected from a compulsory administrative census that the Ministry of Employment runs annually to check the firm’s compliance with labor law. Second, and unique to this data base, firms are required by law to make the information provided to the Ministry available to every worker in a public place of the establishment. Another important advantage is its longitudinal nature that results from the fact that a unique identification number is attributed to each worker the first time he enters the data set. This is based on the worker’s social security number, which does not change over time.

To control for any coding errors, we have performed extensive checks to guarantee the accuracy of the worker data using information on gender, date of birth and maximum schooling level achieved, as described in Appendix B. After these checks, we kept for analysis full-time wage earners, aged between 16 and 65, earning at least the national minimum wage, working in firms operating in mainland Portugal. The final panel used in the analysis contains information on 2,998,727 workers, 467,269 firms, imports and exports from 74 manufacturing industries over the period 1986-2000, yielding a total of 15,613,149 worker-year observations.

> 12The concordance used can be found at www.ine.pt/Prodserv/nomenclaturas/CAE.hml. Note that 56 industries have direct equivalents in the old and new classifications and are, therefore, defined at the 2-digit level of the NACE-Rev. 2. The remaining 46 categories of NACE-Rev. 2 do not have direct equivalents in the old classification and are aggregated into 18 sectors.

10