rafmacalaba/datause-encoder-data huggingface dataset:
WPS4240 <mark>I</mark> MPACT <mark>E</mark> VALUATION <mark>S</mark> ERIES <mark>N</mark> O <mark>. 16</mark>

# **Giving Children a Better Start: Preschool Attendance and School-Age Profiles**

Samuel Berlinski<sup>*</sup> Sebastian Galiani Marco Manacorda University College London Washington University Queen Mary University of London, and Institute for Fiscal Studies in St. Louis CEP (LSE) and CEPR

**Abstract:** The authors study the effect of pre-primary education on children's subsequent school outcomes by exploiting a unique feature of the Uruguayan household survey (ECH) that collects retrospective information on preschool attendance in the context of a rapid expansion in the supply of pre-primary places. Using a within household estimator, we find small gains from preschool attendance at early ages that magnify as children grow up. By age 15, treated children have accumulated 0.8 extra years of education and are 27 percentage points more likely to be in school compared to their untreated siblings. Instrumental variables estimates that control for non random selection of siblings into pre-school lead to similar results. We speculate that early grade repetition harms subsequent school progression and that pre-primary education appears as a successful policy option to prevent early grade failure and its long lasting consequences.

## JEL: I2, J1

Keywords: Preschool, Pre-primary education, Primary school performance

## World Bank Policy Research Working Paper 4240, June 2007

_The Impact Evaluation Series has been established in recognition of the importance of impact evaluation studies for World Bank operations and for development in general. The series serves as a vehicle for the dissemination of findings of those studies. Papers in this series are part of the Bank’s Policy Research Working Paper Series. 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._

> * Samuel Berlinski, Department of Economics, University College London, Gower Street, London WC1E 6BT, UK, <u>s.berlinski@ucl.ac.uk. Sebastian Galiani, Department of Economics, Washington University in St Louis, Campus Box</u> 1208, St Louis, MO 63130-4899, US, <u>galiani@economics.wustl.edu. Marco Manacorda, CEP, London School of</u> Economics, Houghton Street WC2A 2AE, London, UK, <u>m.manacorda@lse.ac.uk. We thank the World Bank for</u> financial support and Andres Peri, Emiliana Vegas, and Andrea Vigorito for helpful comments.
_2_ _WATER WHEN IT COUNTS_ To understand the origins of irrigation management problems in central Mozambique, we designed an intensive user-based water monitoring protocol. Plot-level water use data was collected three times per day for 146 consecutive weeks covering 159 plots in three irrigation schemes. We paired this high-frequency time series with household surveys on agricultural practices and production to understand sources of inefficiency in water allocation in a region suffering from low agricultural productivity and periodic droughts. This data system adds to previous demonstrations of the feasibility of user-based systems for monitoring water management (Fraternali et al., 2012; Lowry and Fienen, 2013; Buytaert and et al., 2014; Little, Hayashi and Liang, 2016).

Two stylized facts emerge from the monitoring phase of our study. First, water allocations are inefficient in a context lacking penalties for overuse. The recommended water requirements of crops on 20-40% of the plots were not met in most weeks over the course of one year. This occurs despite all three schemes having sufficient water to meet each crop’s weekly water requirement in the same weeks. Scarcity in this context is, therefore, purely an allocation problem. If users could be induced to more efficiently allocate water, scarcity could be eliminated under a water neutral manner. Second, we observe that water scarcity arises from a particular allocation pattern. Although water requirements vary widely across the crop cycle, water applications are inflexible. As farmers have staggered planting times within a given scheme, this inflexible water application fails to take advantage of efficiency gains available through a simple reallocation of water across plots and planting stages.

These stylized facts are consistent with a model of behavior where farmers rely on inefficient rules of thumb. They choose a fixed level of water to deliver to their plots upon planting and stick to this rule until harvesting. In such a model, scarcity can be
Van de Walle, D. (1998). "Infrastructure and Poverty in Vietnam." in Dollar, D., P. Glewwe, and J. Litvak, eds. _Household Welfare and Vietnam's Transition_ . Washington, D.C.: World Bank.

Wiens, T. B. (1998). "Agriculture and Rural Poverty in Vietnam." in Dollar, D., P. Glewwe, and J. Litvak, eds. _Household Welfare and Vietnam's Transition_ . Washington, D.C.: World Bank.

Winters, A. (2000). "Trade and Poverty: Is There a Connection?" WTO Special Study #5.

# **Figure 1—Commune Rice Prices in 1993 and 1998**

<!-- Start of picture text --> 5.5<br>5<br>4.5<br>4<br>3.5<br>3<br>2.5<br>2<br>2 2.5 3 3.5 4 4.5 5 5.5<br>Real Rice Price 1993<br>Real Rice Price 1998<br><!-- End of picture text --> 42
5 Finally, ou June 25, 1991, Slovenia prorlaimed its independence with three documents--the Basic Constitutional Document on Sovereignty and Independence of the Republic of Slovenia, the Constitutional Law for its realization, and the Declaration on Independence." These documents were designed as the final step toward independence, transferring all remaining powers and duties from federal to Slovene insitutions and asserting full control over borders and diplomatic relations and over domestic economic policies. Numerous new 'or renamed) institutions opened on that day, including the Bank of Slovenia; the Customs, Air Traffic, and Telecommunications Administrations; the Office for Standardization and Measurement; and the Patent Office. A package of new "laws of independence" was also adopted, including laws on citizenship, foreign affairs, customs, foreign exchange, the central bank, banking, bank restructuring, and 1 prices. And amendment **10018** to the existing Slovene constitution established the coat of arms and the flag of the Republic of Slovenia.

The federal government reacted negatively and forcefully to these acts of independence, and a "7-day war" erupted that led to about 70 casualties. In early July the European Community brokered a 3-month moratorium on further acts of both dissolution and armed aggression. When the 3-month moratorium ended in early October, Slovenia introduced its own currency, the tolar. And on December 23th, it adopted a new constitution.

# The New Constitution'9

Slovenia's new Constitution consists of 174 articles organized in a preamble and 10 chapters:

- I. General Provisions (13 articles)

16 Temeljna ustavna listina o samostojnosti in neodvisnosti Republike Sloxenije (Basic Constitutional Document on Soveregnty and Independence of the Republic of Slovenia], Ustavni zakon (Constitutional Law], Deklaracija ob neodvisnosti (Declaration on Independence], Official Gazette PS, No 1 (1991), June 1991. (Upon declaring independence, Slovenia began renumbering the issues of its official gazette from the beginning.)

**'** Zakon o driavljanstvu republike Slovenije [Law on Citiz2nship of the Republic of Slovenia], Zakon o tujcih [Law on Foreigners], Zakon o potnih listinah drlavljanov Republike Slovenije (Law on Passports of the Citizens of the Republic of Slovenia], Zakon o nadzoru driavne meje [Law on Control of State Border), Zakon o zunanjih zadevah [Law on Foreign Affairs], Zakon o carinski slutbi (Law on Custom Services], Zakon o kreditnih poslih s tujino [Law on External Credit Relations], Zakon o deviznem poslovanju [Law on Foreign Exchange], Zakon o Banki Slovenije (Law on Bank of Slovenia], Zakon o bankah in hranilnicah (Law on Banks and Savings institutions], Zakon o conah [Law on Prices], all in Official Gazette RS, No 1 (1991) pages 6-71.

- 18 Official Gazette RS, No 1 (1991), page 1.

- **19** Supra note 3.
# **5.2 What determines return migration?**

The return migrants in our sample had been abroad for an average of 5.5 years for the Tongans, and for 3 years for the New Zealanders and Papua New Guineans.

Table 9 presents the marginal effects from probit estimation of the correlates of being a return migrant among the sample of individuals who have migrated. We pool current and return migrants across the three countries, with country fixed effects included, since the sample sizes are small for each country, and the results from countryby-country estimation are qualitatively similar across countries. Approximately 40 percent of the combined sample are return migrants. The one variable that differs most across countries is the role of short-term career considerations, which are a stronger predictor of return migration for New Zealanders than the other countries. We therefore interact this variable with a dummy variable for New Zealand top students.

Again substantial item non-response on the online surveys and the abridged nature of the telephone surveys means that not all variables are available for every returned and current migrant. For this reason we first investigate the role of various sets of variables, before combining together. We always include age and gender, with older migrants more likely to have returned than younger migrants, as was clear in Figures 1, 2 and 3. As with the initial decision to migrate, there is no significant gender difference in whether migrants return. In contrast to the small and insignificant role of being born outside the home country on the initial decision to emigrate, those who do migrate and were born overseas are much less likely to return. This is consistent with these individuals having weaker ties to the home country, and an easier time assimilating and obtaining residence abroad.

Columns 1 and 2 then examine the role of wealth in the return migration decision. Recall that the liquidity constraints theory would predict that poorer individuals should be more likely to return, since they would be more likely to have migrated to alleviate liquidity constraints at home. In contrast, we find wealth to be positively correlated with return migration. This is consistent with the channel discussed in Section 4.3, whereby family wealth may also increase the income opportunities available domestically, either directly through employment in a family enterprise, or through family social networks helping to arrange better paying jobs.

- 33 -
all other households during a household visit. The midline survey asked about expenditure on key categories of goods, savings, recent health experiences and food security.

Finally, after the end of the harvest a further survey was conducted on all who had previously been surveyed. This survey collected data on wellbeing, saving, some measures of consumption as well as yields and value of production.

Table 3 displays summary statistics of the main variables of interest as well as the p-value of the test that the means are equal for all four treatment groups. There are no significant differences across treatment groups.

Households of participants are large (with 9 and 14 members on average in Burkina Faso and Senegal respectively). Farming is the main source of income, although income from non-farm self-employment activities is quite high in Burkina Faso. The average land holding is 5 acres in Burkina Faso and 7 acres in Senegal. In each country about half of the participants were literate with levels of education slightly higher in Senegal.

Prior to our intervention agricultural insurance was not present in these villages and health insurance was also almost nonexistent. However, drought risk and ill-health are widespread. Almost a quarter of participants reported experiencing food shortages as a result of dry weather in the last year, 35% of participants had been sick themselves for more than 7 days or their spouses had been sick, and 25% of participants had children that had been seriously ill in the past three months.

Furthermore we see gender differences in exposure to risk. Men offered the insurance product are 12 percentage points more likely to report an agricultural shock occurring within the previous year than women in that group. However, women are concerned more often with the food security of their household than are men (Table 4). Together, this may suggest that women are more concerned with nonagricultural shocks to welfare.

11
<!-- Start of picture text --> as : \ ve.steF ore heves P¢y<br>%e e yd hig °: zs By .<br>~e rat “antr &e Se ¢v f<br>See<br>;; *. oot.yao®tattySiebad* S@\NWekwheB ST! isedguscaeGe +3ee°f<br>&. a, ba N « ° = P2eh ‘ s a ;<br>a?<br>ioe n ae Ta NS fe oe “ou<br>Pon | * 4 % oo Mey 5 Ri . 32 :<br>SD= 4a ya 2 Ba. » oe f<br>ON en gigSonnOPA “aeReeees ksSee.et a<br>5<br>YONI ais “Pees gee hit<br>ee Za ats oat . i yas<br>has eer she” ag at.<br>+ ‘ AE.a eee a6 2 “2 eee<br>e p e Bis ge<br>e<br>we? °<br>ee *% ; * ooo Meategete a a8, * “ : be. -<br>e °‘ an AESoe aoeeo Heed4 aene =<br>. 2 . 99 RR a Seo" ‘<br>YA,s ¢ enHseRIES.$75teedSEbeeons,:*.3<br>Fe “6? “<br>a «6 :<br>Le<br><!-- End of picture text -->
**Table 5. Tax compliance across the sectors of the economy** **<u>(measured at median income)</u>**

|**Economic Sector**|**NACE code**|**Share of employees**<br>**in the EU-SILC data**|<br>**Tax compliance**<br>**(median imputed tax**<br>**income/ median**<br>**survey income)**|
|---|---|---|---|
|**Agriculture**|a|3.55%|92.33%|
|**Mining, manufacturing,**<br>**electricity, and water supply**|b – e|28.66%|85.13%|
|**Constructon**|f|9.13%|74.06%|
|**Trade**|g|16.92%|96.67%|
|**Transport**|h|7.30%|68.37%|
|**Accommodaton and food service**|I|2.67%|81.79%|
|**Informaton and communicaton**|j|3.69%|65.72%|
|**Financial services**|k|1.63%|76.93%|
|**Real**<br>**estate,**<br>**professional, rental**|l–- n|6.37%|84.18%|
|**Public administraton**|o|5.90%|81.81%|
|**Educaton**|p|4.72%|94.05%|
|**Human health**|q|6.34%|87.81%|

_Source: own based on imputation outcomes._ 26
OECD) (OECD 2003: Table 2.1a; OECD 2007: Table 6.1a). That poses interesting questions about what caused the “shift” of the student score distribution. While extension of compulsory comprehensive education can explain higher performance for low-achievers who were mostly in vocational tracks, it is more complicated to explain higher performance of top-achievers. The question is whether the introduction of lower secondary schools could have an impact on students in former general secondary schools and what was in the reform that increased scores so significantly.

Figure 4: Change in reading literacy distribution between PISA 2000 and 2006 <!-- Start of picture text --> Reading in PISA 2000 and PISA 2006<br>200 400 600 800<br>reading literacy<br>2000 factual 2006 factual 2006 reweighted<br>.004<br>.003<br>.002<br>.001<br>0<br><!-- End of picture text --> Figure 5: Change in reading literacy distribution between PISA 2003 and 2006 <!-- Start of picture text --> Reading in PISA 2003 and PISA 2006<br>200 400 600 800<br>reading literacy<br>2003 factual 2006 factual 2006 reweighted<br>.004<br>.003<br>.002<br>.001<br>0<br><!-- End of picture text -->

# <u>Estimates of score change for students in different tracks</u>

Results for differences-in-differences propensity score matching estimates of the effect of abolishing tracking system for 15-year-olds are presented in Tables 3, 4 and 5. Table 3 contains estimates of factual and counterfactual mean scores for all students in PISA 2000, 2003 and 2006. In addition, results for students in vocational and non-vocational tracks are presented. Factual scores were weighted by survey weights provided in the official PISA datasets. Counterfactual scores were constructed using matching methods with survey weights taken into account in the manner described above.

18
- Wilson, N. (2012): “Economic Booms and Risky Sexual Behavior: Evidence from Zambian Copper Mining Cities,” _Journal of Health Economics_ , 31(6), 797–812.

- World Bank (2011): _Global Economic Prospects June 2011: Regional Annex_ . World Bank Group.

- WORLD BANK (2012): _World Development Report 2012._ World Bank Group.

- Wright, G. and J. Czelusta (2003): “Mineral resources and economic development.,” in _Paper prepared for the Conference on Sector Reform in Latin America (Nov 13-15), Stanford Center for International Development_ .

# **A Appendix**

The Appendix presents results that are not central to the understanding of the paper, but that can be used for extra information. It also shows a wide battery of robustness checks. The Appendix presents extra summary statistics in Section A.1 and further robustness checks, treatment heterogeneity, and effects on other outcome variables in Sections A.2-A.5. Finally, we present analyses based on other mining data sets in Section A.6.

## **A.1 Descriptive statistics**

Table A.1: Distribution of the sample by country.

|Country|Number of women|Country|Number of women|
|---|---|---|---|
|Benin|11,633|Mali|36,453|
|Burkina Faso|22,489|Mozambique|4,912|
|Burundi|9,329|Namibia|15,783|
|Cameroon|29,785|Niger|14,024|
|Central African Republic|5,877|Nigeria|49,125|
|Congo DR|9,717|Rwanda|24,756|
|Cote d’Ivoire|11,103|Senegal|29,677|
|Ethiopia|29,216|Sierra Leone|7,186|
|Ghana|14,918|Swaziland|4,879|
|Guinea|14,389|Tanzania|10,792|
|Kenya|16,493|Togo|8,500|
|Lesotho|3,311|Uganda|34,899|
|Liberia|3,981|Zambia|7,107|
|Madagascar|24,047|Zimbabwe|23,493|
|Malawi|47,306|Total|525,180|

29
<!-- Start of picture text --> 10000<br>Uruguay<br>Colombia<br>Paraguay<br>Hinduras Jamaica<br>Nicaragua<br>1000<br>GDPpercapita(in$PPP)<br>Latin America and the Caribbean 70 60 50 40 30 20 100<br>10000<br>Malaysia<br>Philipp.<br>PNG<br>Indon-urb Indon-rur 1000<br>Asia GDPpercapita(in$PPP)<br>Yemen<br>100<br>70 60 50 40 30 20<br>10000<br>S.Africa<br>Zimbabwe<br>Lesotho<br>from individual data from nationally-representative household surveys (for the list of surveys see Annex). GDP per<br>1000<br>GDPpercapita(in$PPP)<br>calculated<br>Nigeria<br>Sub-Saharan Africa<br>100<br>70 60 50 40 30 20<br>Note: All Ginis capita are in logs.<br>Gini<br>Gini<br>Gini<br><!-- End of picture text -->
distinguish between the aggregates at domestic and world prices identified in equations (8) and (9). We then calculated the tariff aggregators for expenditure and tariff revenues using the MacMapHS6 v2.1 database (Boumellassa, Laborde and Mitaritonna, 2009) that provides detailed information on bilateral tariffs and trade flows at the HS6 level. Finally, we performed a series of simulation experiments.

# **3.1 The aggregation procedure**

For concreteness, we first present a simple illustration based on a one level aggregation problem where HS6 products are aggregated to the sectoral aggregation of the model. Then, we introduce real-world problems such as those arising from differences in the rates of protection applied on goods from different sources; where and where the model for an importing region is an aggregate of different countries. We focus first on the formation of aggregates for a single good, using data at the six-digit product level<sup>5</sup> .

## **3.1.1 The expenditure aggregator**

We first illustrate our approach in the simple case where one country imports goods from only one partner, the rest of the world. The Constant Elasticity of Substitution (CES) form is an appealing choice for several reasons: (i) its simplicity and parsimony, (ii) the availability of some relevant parameter estimates; and (iii) the ability to handle situations where the number of firms or commodities is endogenously determined (Feenstra 1994; Zhai 2008). Using a CES aggregator for products _j_ being aggregated in group _i_ , the price index for composite imported good _i_ at domestic prices is (omitting the _i_ index) given by: where _tj_ is the _ad valorem_ tariff at the six-digit level of the Harmonized System.

Since our focus in this paper is primarily on aggregation of existing products, the α _j_ coefficients can be inferred using standard calibration procedures (Mansur and Whalley 1984). Given these values, the value of τ<sup>_e_</sup> can be identified using

> 5 Our usage differs slightly from Broda and Weinstein‘s (2003, p548) distinction between goods at the tariff-line level and varieties supplied by individual countries because we need to distinguish between composite goods, six-digit products, and six-digit varieties.

11
# **XI.  Econometric Analysis of Local MBA Programs**

## _A. Introduction_

The MBA survey data can be used to perform numerous econometric analyses in order to determine what factors affect firm-level opinions of local MBA quality. Following the discussion in the previous sections, this statistical analysis is based on country-specific and firm-specific variables.

At the firm level, four significant pieces of information are available: Size of the firm, average number of MBAs the firms hires on a yearly basis, sector of firm, and type of MBA graduates the firm requires (either graduates with general management skills or specific areas of expertise such as finance).

At the country level, various factors can be taken into consideration: GDP per capita in the country, the number of commercial and business administration students in the country (and other measures of availability of education), region, and size of country (in terms of population).

The question arises: How does each of these factors relate to the quality of MBA graduates in each country? At the firm level, it is hypothesized that the size of the firm and the number of MBA graduates it hires each have an effect on the firm’s impression of the quality of MBAs. Similarly, the sector of the firm and the type of MBA graduate it hires should also affect the firm’s impression of the quality of local MBAs. More specifically, larger firms can generally offer higher salaries and thus attract better MBA graduates. Also, firms that hire more MBAs have a superior pool of MBAs from which to hire than firms that hire fewer MBAs (though it should be noted that there is very little correlation in the data between the size of the firm and the number of MBAs they hire each year). Similarly, firms in different sectors may attract different qualities of MBAs, while firms with different needs also attract different qualities of MBAs.

At the country level, it is hypothesized that countries with higher per capita incomes, higher literacy rates, and greater numbers of business graduates should provide MBAs that are more satisfactory to hiring firms. Besides country-specific effects, it is useful to look at regional effects to see if they explain differences in perceived MBA quality.

Thus the equations estimated take the following form: Quality of MBA students = **F** (Size of firm, Number of MBA graduates they hire, Sector of firm, Type of skills required, Country specific factors, Regional Dummy Variables) Four different relationships will be investigated: (1) Determination of the technical skills of MBA students, (2) Determination of the communication skills of MBA students, (3) Determination of the language skills of MBA students, and (4) Determination of the overall quality of MBA students.

23