rafmacalaba/datause-encoder-data huggingface dataset:
Portuguese, read and write, and report pro-active parenting practices such as playing games with the child.<sup>14</sup>

# 3. **Experimental Design and Sample**

As discussed above, funding constraints limited the project to a total of 30 intervention communities, which were assigned at random within the pool of eligible communities in the intervention districts. Following operational requirements set by the program, the random assignment protocol was established in the following steps. First, the program compiled a list of all eligible communities in the three districts<sup>15</sup> and identified “operational areas” based on the geographic proximity and access between communities. To maximize the number of eligible communities in the sample, the five operational areas with the largest number of communities were selected, and each area was assigned six treatment communities. Within each operational area, communities were stratified by population size, forming blocks of two or three communities. For each block, one treatment community was assigned at random, resulting in 30 treatment communities and 46 control communities. Six of the original 46 control communities turned out to be neighborhoods in treatment communities where a preschool was built, and were re-classified to their treatment counterparts in our analysis.<sup>16</sup> We test for and confirm that our main results are robust to the ex-post re-classification of these six control communities.<sup>17</sup> The household survey collected data on a random sample of 2,000 households with preschool age children in the 76 evaluation communities. In the absence of a household level sample frame, we conducted a door to door census to identify households with at least one child in the preschooleligible age range of 36 to 59 months. We then drew a random sample of 23 households with eligible children per community. In addition, in each of the 4 largest treatment communities where oversubscription to the program was likely,<sup>18</sup> an additional 63 households were selected, yielding

> 14 See Supplementary Materials Table 7.

> 15 The list of communities was based on the best available information at the time of the baseline survey. With no official roster of communities in the three districts, a consultant was hired by the program to update existing lists based on interviews with officials at the administrative post level. 16 The sample selection, randomization procedure and the ex-post corrections of neighborhoods is discussed in detail in Supplementary Materials Section 3. Re-classification was possible thanks to precise geo-location data collected on the original sample of 76 treatment and control communities in 2014.

> 17 Results are presented in Supplementary Materials Section 7.

> 18 Individual level randomization was initially proposed for communities with oversubscription, though ultimately this was not systematically implemented and was abandoned as an evaluation strategy. Nevertheless, oversubscription did occur in several larger communities.

9
_55_ Table 10. Summary of Causal inferences for 19 SSA countries, 1971-1991. Real devaluations= %A Real exchange rate Greater than 15%

|Null Hypothesis|(1)|(2)<br>Causal inference|
|---|---|---|
|1. Real Devaluation does not cause Real export Growth|F=0.7540<br>(0.3861)|No Causality|
|2. Real Export Growth does not cause Real evaluations|F=0.3795<br>(0.5384)|No Causality|
|3. Inflation does not cause Real Export Growth|F=0.5373<br>(0.4643)|No Causality|
|4. Real Export Growth does not cause Inflation|F=0.0217<br>(0.8831)|No Causality|
|5. Real Devaluation does not cause Inflation|F=2.3995<br>(0. 1227)|No Causality|
|6. Inflation does not cause Real Devaluations|F= 1.0202<br>(0.3135)|No Causality|

P-values in parentheses. Number of observations is 286.

# E. Condusions

Pooled time-series and cross-section data for 22 African countries covering the period 1971-1991 are used to test a number of hypotheses.

First, we find that nominal devaluations have a long-mn impact on real devaluations; that nominal devaluations have significant contemporaneous relationship with real exchange rate devaluations; and that there is a significant long-run relationship between the two forms of devaluations. A 10 percent nominal devaluation at time t gives a 7.7 percent real depreciation. in the long run. We also show that expansionary monetary policy leads to appreciation of the real exchange rate for this group of SSA countries. The data confirm that devaluations can indeed be an effective policy tool to correct an overvalued currency if accompanied by restrictive monetary and fiscal policies.
# **TABLE 3.2: DEFINITION OF INDEPENDENT VARIABLES**

# **PANEL A: HOUSEHOLD CHARACTERISTICS**

|**Variable Name**|**Source**|**Definition**|
|---|---|---|
|EDUCATION|Household<br>Survey|Level of education of head of household: primary, secondary or<br>universityeducation(reference level:primary)|
|INCOME|Household<br>Survey|Monthly household income: low income (less 1000soles), middle<br>income (1001-2500 soles) and high income (more than 2501 soles).<br>(reference level: low income)|
|AGE|Household<br>Survey|Age of head of household (in years)|
|FEMALE|Household<br>Survey|Sex of head of household (=1 if female)|

# **PANEL B: GOVERNANCE MEASURES - HOUSEHOLDS**

|**Variable Name**|**Source**|**Definition**|
|---|---|---|
|DISHONESTY<br>(number)|Household<br>Survey|Number of institutions (from a list of 42) the household ranked as<br>dishonest|
|DISHONESTY<br>(average)|Household<br>Survey|Average evaluation of honesty/dishonesty of 42 institutions (high<br>values mean more honest)|
|TRUST IN STATE<br>INSTITUTIONS|Household<br>Survey|Average trust on judicial and government institutions (high values<br>mean more trust)|
|KNOWLEDGE<br>REPORT<br>CORRUPTION|Household<br>Survey|Dummy =1 if head of hosuehold lnows where to report a corrupt act|
|MEMBER OF<br>ASSOCIATION|Household<br>Survey|Numer of associations (from a list of 9) that head of household belongs<br>to|
|MEMBER OF AT<br>LEAST ONE<br>ASSOCIATION|Household<br>Survey|Dummy =1 if head of household is member of at list one association|

**PANEL C: CORRUPTION INDICES**

|**Variable Name**|**Source**|**Definition**|
|---|---|---|
|OVERALL<br>CORRUPTION|Public<br>Official<br>Survey|Corruption index representing the average of four standardized<br>corruption Indices: bribery over regulatory/legal decisions, bribery over<br>public contracts, corruption in personnel management and corruption<br>in budget management.|
|PERCEIVED<br>CORRUPTION|Public<br>Official<br>Survey|Corruption index representing the widespread of payment of tips or<br>payments “under the table” in the Public Administration.|
|CORRUPTION IN<br>PERSONNEL MGT|Public<br>Official<br>Survey|Corruption index representing the percentage of cases where decisions<br>on personnel management are based on unofficial payments (job<br>purchase).|
|CORRUPTION IN<br>BUDGET MGT|Public<br>Official<br>Survey|Corruption index representing the frequency within the institution of<br>irregularities/diversion of funds or any other type of budget abuse.|
|CORRUPTION IN<br>PUBLIC<br>CONTRACTS|Public<br>Official<br>Survey|Corruption index representing the widespread of bribes to buy public<br>contracts.|

25
than to children. Even among young adults, educational performance is poor. The educational deficit, including the question of quality, has a spatial dimension in Haiti. Most of the illiterate aged above 15 years live in rural areas (education is further addressed in Section 7).

|Table|5.2: Illiteracy rate in Haiti<br>1970– 2000 (percent)|during|
|---|---|---|
|Year|Total<br>Male|Female|
|1970|78.0<br>74.0|82.0|
|1982|63.0<br>61.7|64.4|
|1992|45.0<br>51.0|39.0|
|1995|42.0<br>45.6|37.9|
|2000|39.5<br>33.4|43.3|

_Source:_ EBCM 1999-2000.

The decline in Haiti’s infant mortality corroborates the improvement in measured adult illiteracy, although the level is still very high. The infant mortality rate dropped dramatically from 148 per 1,000 live births in 1970 to 79 per 1,000 in 2002 (Table 5.3).<sup>14</sup> In view of the lack of economic growth and the dearth of social programs, it is not clear what caused this decline. The large volume of remittances may have played a role, as may service provision by NGOs, though more research is needed on this matter. To reduce the infant mortality rate further and reach the Latin American and Caribbean average of 28 per 1,000 live births, a number of measures are required, especially in rural areas. These include general livelihood improvements such as access to clean water and sanitation, high quality education and healthcare, and a daily calorific intake sufficient to cover basic needs. Moreover, Filmer and Pritchett (1997) find that a 10 percent increase in income is associated with a 6 percent lower infant mortality rate.

Table 5.3: Infant Mortality in Haiti, 1970-2002

|Year|1970|1980|1990|1995|2000|2002|
|---|---|---|---|---|---|---|
|Mortalityrate(per 1,000 live births)|148|132|102|91|81|79|

_Source:_ WDI, World Bank 2004.

Life expectancy has increased over the last three decades in Haiti, but it is still very low. As in many parts of Latin America and the Caribbean, men in Haiti have a significantly lower life expectancy than women (Figure 5.2). In 2002, men and women could expect to live an average of 50 and 54 years, respectively. AIDS is a significant problem in Haiti and, according to the Global Health Council, the large number of AIDS cases has cut average life expectancy by eight years. Were it not for AIDS, therefore, life expectancy would have been 60. Other areas of concern include alcohol and substance use, male violence against women,<sup>15</sup> and general violence.

> 14 Infant mortality rate is the number of infants dying before reaching one year of age, per 1,000 live births in a given year. Data source: WDI, World Bank 2004.

> 15 In fact, results from the household survey surprisingly showed practically no use of or contact with drugs, and levels of alcohol and tobacco use that were not alarmingly high. These results, however, may be explained to some extent by underreporting because of the link to gangs and illegal trade.

23
attainment, while socioemotional-skills measures seem independent from education. In addition, while the link between both types of skills with labor earning is consistent across subgroups, the link between socioemotional skills and labor-force participation is particularly strong for women, the youth, and the less educated.

The remainder of this paper is organized as follows: section 2 describes the data set and the measures of cognitive and socioemotional skills, section 3 introduces the empirical strategy with its limitations, and section 4 presents the results. The final section offers our conclusions.

# **Data**

## **2.a. Data set**

We use a cross-sectional survey of adults called Skills Toward Employment and Productivity (STEP), which was collected in Colombia in 2012. The STEP is a cross-sectional household survey implemented in around twenty low- and middle-income countries by the World Bank since 2012 (Pierre et al. 2014). The survey covers a wide range of background information (demographics, education, employment, etc.) and randomly selects one individual in each household between the ages of 15 and 64 to be further surveyed and tested on information related to cognitive skills, socioemotional skills, health, and other characteristics.

Colombia STEP Household Survey is representative of most of the country’s adults. It is formally representative of the country’s thirteen main cities and their metropolitan areas, which are used by the national statistical agency for its labor market and household surveys.<sup>5</sup> The thirteen cities represent the large majority of Colombia’s urban population, which represents 80 percent of the country’s population. The sample size is 2,617. The age, gender, and education attainment distribution are similar to that for national household surveys for the same urban areas.

## **2.b. Measures of cognitive and socioemotional skills**

We have two types of measures of cognitive and socioemotional skills, which correspond to our two methods, OLS/Logit and structural estimations of latent skills. The first type is raw measures of cognitive skills from the survey, one for cognitive, eight for socioemotional skills. The second type are estimated factors of latent skills, one for cognitive skills and one for socioemotional skills.

> 5 The 13 main metropolitan areas are: Bogotá (the capital), Medellín, Cali, Barranquilla, Bucaramanga, Cúcuta, Cartagena, Pasto, Ibagué, Pereira, Manizales, Montería, and Villavicencio.

5
# VII – References

Bell, Martin, and Salut Muhidin. "Cross-national comparison of internal migration." (2009).

Bell, Martin, and Elin Charles-Edwards. "Cross-national comparisons of internal migration: an update of global patterns and trends." (2013).

Bertaud, Alain 2010. _Land Markets, Government Intervention and Housing Affordability_ . Working Paper 18. Brookings Institute, Washington, DC.

Bryan, Gharad, and Melanie Morten. "The aggregate productivity effects of internal migration: Evidence from Indonesia." (2017).

Ceriani, Lidia; Olivieri, Sergio Daniel; Ranzani, Marco. 2019. _Evaluating the Accuracy of Homeowners' Self-Assessed Rent in Metropolitan Lima (English)_ . Policy Research working paper; no. WPS 8983. Washington, D.C. : World Bank Group. http://documents.worldbank.org/curated/en/287901565890099664/Evaluating-the-Accuracyof-Homeowners-Self-Assessed-Rent-in-Metropolitan-Lima Chow, Gregory C., and Linlin Niu. "Housing prices in urban China as determined by demand and supply." _Pacific Economic Review_ 20.1 (2015): 1-16.

Christiaensen, Luc, Joachim De Weerdt, and Ravi Kanbur. "Urbanization and poverty reduction: the role of secondary towns in Tanzania." (2016).

Christiaensen, Luc, and Ravi Kanbur. "Secondary towns and poverty reduction: refocusing the urbanization agenda." _Annual Review of Resource Economics_ 9 (2017): 405-419.

Coxhead, Ian, Nguyen Viet Cuong, and Linh Hoang Vu. "Migration in Vietnam: new evidence from recent surveys." _World Bank: Vietnam Development Economics Discussion Papers_ 2 (2015).

Demombynes, Gabriel; Vu, Linh Hoang. 2016. _Vietnam’s household registration system (English)_ . Washington, D.C.: World Bank Group. http://documents.worldbank.org/curated/en/158711468188364218/Vietnam-shousehold-registration-system De Soto, Hernando. 2000. The Mystery of Capital: Why Capitalism Triumphs in the West and Fails Everywhere Else. New York: Basic Books.

Ferré, Céline, Francisco HG Ferreira, and Peter Lanjouw. "Is There a Metropolitan Bias? The relationship between poverty and city size in a selection of developing countries." _The World Bank Economic Review_ 26.3 (2012): 351-382 45
- 2 -

20 percent. The share in CNP of investment and current public expenditure has grown; the average private consumption per person has also grown annually at an average rate of 2 percent. If that had been so for all the households, it would have resulted in a significant reduction in poverty. However, if the distribution of per capita consumption had worsened, the impact of growth on poverty would be much less. The important questions, therefore aret How has the distribution of per capita consumption changed over time across the states, and to what extent has it nullified the beneficial impact of growth on poverty?

How have the poor and ultra poor fared in the wake of changes in consumption and its distribution?

What have been the regional patterns and to what degree can these patterns be explained considering our knowledge of the structural characteristics of these states and the state-wise performance indicators of anti-poverty programs?

Using the National Sample Survey data for sixteen major states of India the paper addresses the above questions, in Part I.

This is followed by an analysis of India's poverty alleviation strategies (Part II). The potential indirect role of agriculture and manufacturing, as well as the contribution of direct poverty alleviation interventions are analyzed, againbt the backdrop of the results pertaining to poverty and inequality in Part I.

In particular, we examine the extent to which the regional allocation of funds for direct poverty alleviation programs is sensitive to the regional distribution of the poor and the ultra poor, and whether the recent evidence
**Table 6: Impacts on Business Practices and Innovation**

||**New**|**Firms**<br>Uncondition|**Existin**<br>al Impacts|**g Firms**|Impact<br>**New**|s Conditional<br>**Firms**|on Operat<br>**Existin**|ing Firm<br>**g Firms**|
|---|---|---|---|---|---|---|---|---|
||Business|Innovation|Business|Innovation|Business|Innovation|Business|Innovation|
||Practices|Index|Practices|Index|Practices|Index|Practices|Index|
|Experimental impacts:|||||||||
|First‐Follow‐up|0.152***|0.099***|0.081***|0.105***|‐0.013|0.019|0.015|0.072**|
||(0.025)|(0.019)|(0.027)|(0.029)|(0.017)|(0.023)|(0.018)|(0.029)|
|Second Follow‐up|0.339***|0.270***|0.133***|0.126***|0.050***|0.134***|0.017|0.058**|
||(0.021)|(0.018)|(0.026)|(0.028)|(0.012)|(0.019)|(0.014)|(0.027)|
|Third Follow‐up|0.358***|0.219***|0.183***|0.141***|0.060***|0.072***|0.003|0.029|
||(0.022)|(0.019)|(0.032)|(0.029)|(0.014)|(0.022)|(0.019)|(0.029)|
|PSM impacts for National/Zonal w|inners||||||||
|First‐Follow‐up|0.128***|0.145***|0.070**|0.111***|‐0.073***|0.052|‐0.008|0.068***|
||(0.038)|(0.032)|(0.029)|(0.029)|(0.027)|(0.042)|(0.017)|(0.021)|
|Second Follow‐up|0.388***|0.262***|0.150***|0.166***|0.045***|0.083***|0.029**|0.093***|
||(0.030)|(0.029)|(0.028)|(0.025)|(0.016)|(0.030)|(0.012)|(0.027)|
|Third Follow‐up|0.391***|0.293***|0.189***|0.138***|0.089***|0.143***|0.006|0.027|
||(0.032)|(0.036)|(0.025)|(0.028)|(0.020)|(0.041)|(0.020)|(0.034)|
|Control Mean: First follow‐up|0.406|0.225|0.687|0.390|0.750|0.414|0.791|0.449|
|Control Mean: Second follow‐up|0.409|0.214|0.676|0.407|0.770|0.404|0.816|0.492|
|Control Mean: Third follow‐up|0.341|0.181|0.559|0.341|0.721|0.382|0.778|0.474|
|Obs: First follow‐up|995|995|423|423|616|616|387|387|
|Obs: Second follow‐up|1071|1071|458|458|708|708|413|413|
|Obs: Third follow‐up|927|927|409|409|574|574|344|344|

Notes: Robust standard errors in parentheses, *, **, *** indicate significance at the 10, 5, and 1 percent levels. Experimental estimates are ITT estimates and control for randomization strata. Propensity score matching (PSM) estimates compare non‐experimental winners to experimental control group. Business Practices is the proportion of 22 business practices employed; innovation index is an index of standardized z‐scores for 12 different measures of innovative activities.

Obs is the number of observations in the experimental sample.

43
- 17 -

59. Since the effect of the planned regional tariff regime in lowering effective protection was small, the Costa Rican Government agreed to go beyond the regional agreement to further reduce effective protection by undertaking the following modifications to the regionally agreed rates:

- (a) intermediate and raw material goods produced outside the CACM (non-competing goods): a minimum 10 percent; and

- (b) capital goods: a minimum 20 percent.

To achieve these rates, the Costa Rican Government agreed to add the indicated rates through import _urcharges:

- (a) raw materials and intermediate goods (non-competing goods): a 5 percent rate; and

- (b) capital goods: between a 10-15 percent rate.

60. Implementation of tariff reform. Based on regional negotiations, the following rate structure emerged:

# TABLE III: COMPARISON OF OLD TARIFF REGIME, NEW TARIFF REGIME AND PLANNED REGIME

(z)

||Old<br>Regime|Ne<br>Regi|w 1/<br>me|Planned<br>Regime|
|---|---|---|---|---|
||Max. Avg.|Max.|AvR.|<br>Avg.|
|Final Consumer Goods|||||
|Regionally Produced|530.2 92.5|<br>143.0|65.I|<br>32.6|
|Non-Regionally<br>Produced|||||
|Essential|130.3 40.6|<br>65.5|15.2|<br>5.4|
|Non-Essential|314.2 135.0|<br>253.0|18.4|<br>34.0|
|Intermediate<br>Goods and Raw Materials|||||
|Regionally Produced|1393.4 54.3|<br>203.0|34.1|<br>20.9|
|Non-Regionally<br>Produced|332.8 32.6|<br>108.0|11.2|<br>5.0|
|Capital Goods|||||
|Regionally Produced|248.5 73.9|153.0|35.6|<br>24.7|
|Non-Regionally<br>Produced|73.0 23.7|<br>28.0|<br>15.1|<br>5.5|

1I The two rows indicate the planned rates as of January 1, 1986. Some of the parametes might likely have changed since then, as Costa Rica modified some surcharge rates.

Source: SIECA, Central Bank of Costa Rica, Bank Staff Estimates.
NONREPORTING NONMEMBER ECONOMIES

# 1. Estimates and Projections

|**_To_**|**PepAsatin**|**Sep.**<br>**rotis**|**Peiod**|**_GcO_t **<br>**rato**|Ca|**cm**|TFt|**NoLm**|b<br>Fi_lo|**1in**|**xlO00**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1995|<br>3S4023|54.3|1985-89|0.7.|18.2|10.4|2.35|65.8|74.2|25|31|
|1990|<br>366790|54.0|1990-94|0.67|16.2|9.2|2.18|67.3|76.4|22|27|
|1995|<br>379235|54.4|1995-99|0.65|15.2|8.6|2.11|68.9|78.2|19|23|
|**2000**<br>2005|<br>391724<br> <br>405W?T|S2.8<br>53.3|2000-04<br>200S-09|0.67<br>0.62|15.0<br>15.0|8.3<br>8.7|2.10<br>2.10|70.5<br>71.5|79.9<br>80.7|16<br>14|19<br>17|
|2010|<br>417q37|52.0|2010-14|0.56|14.6|9.0|2.09|72.5|81.5|11|14|
|2015|<br> <br>429)29|52.3|2014-19|0.50|14.0|9.0|2.08|73.6|82.4|9|12|
|2020|<br> <br>440565|54.8|2020-24|0.44|13.4|9.1|2.07|74.7|83.2|7|9|
|2025|.50264|57.8|2025-49|0.29|12.8|9.9|2.07|77.2|85.2|4|5|
|2050|<br>484722|66.8|2050-74|0.11|12.2|11.0|2.06|79.7|87.2|3|4|
|2075|496515|64.9|2075-99|0.07|11.9|11.3|2.06|81.0|88.3|2|4|
|2100|50696|72.1|2100-24|0.04|11.8|11.4|2.06|82.0|89.1|2|3|
|2125<br>2150|512240<br>514817|73.6<br>74.4|2125-49|0.02|11.7|11.5|2.06|82.4|89.S|2|3|

# 2. Age structure (percent): 1990 and 2025

10 S 6 4 2 0 2 4 6 8 10 <!-- Start of picture text --> 75 +<br>70-74<br>65.69<br>80-64<br>55-59<br>50-54<br>45-49<br>40-44<br>35-39<br>30-34<br>25-29<br>20-24<br>15-19<br>10-14<br>5-9<br>0-4<br>10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text -->

# 3. Distribution of population (percent):

# 1990 and 2025

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Table 3: The results for each series using the ETS smoothed data and the elastic net estimator. Relative MSE (1) is the MSE for the expanding window ARMA model results versus the benchmark model given in columns 6. Relative MSE (2) is the MSE for the elastic net model versus the benchmark in column 6. A relative MSE less than 1 indicates that the proposed model beat the benchmark.

|Country|Series|Relative MSE (1)|Relative MSE (2)|N|Benchmark|
|---|---|---|---|---|---|
|sv|inf07|1.29662|0.986124|34|ar|
|sv|inf08|0.948536|0.948536|34|median|
|sv|inf09|1.0029|1.00293|34|mean|
|sv|inf10|1.00788|1.13624|34|mean|
|sv|inf11|1.18043|1.03241|34|mean|
|sv|inf12|1.03771|1.01186|34|median|
|sv|inf13|1.04429|1.29648|34|mean|

28