rafmacalaba/datause-encoder-data huggingface dataset:
# evaluation of DICIPE.

We collected two rounds of data to measure the impacts of the DICIPE program, before the start of the intervention (baseline) and three years later (medium-term follow-up). Baseline data collection took place between September and December 2016 with 4,687 caregivers and children, while endline data collection was carried out from December 2019 to April 2020 with 3,765 caregivers and their children. We complement these survey data with classroom observation and qualitative interviews with caregivers, facilitators, and school administrators from a subsample of communities. This additional data collection was carried out between June and July 2019. We define the youngest child in the household aged 36 to 59 months at baseline as the “target child” that the intervention targeted. Baseline characteristics depict acute deprivation among the target children and families of our sample. The average target child is stunted and the vast majority of caregivers are illiterate (81%).

The impacts of the DICIPE preschool construction program on primary school-age outcomes are positive and large. First, take-up of preschool education increased massively after construction. Compared to a small base of 2% of children in control communities enrolled in preschool, the intervention increased preschool enrollment rates in treated communities by 73 percentage points. The program also had significant positive effects on enrollment in and progression through primary school: children in treated communities were 6 percentage points more likely to be enrolled in primary school (an effect of about 10%) and 3 percentage points less likely to repeat a grade (an effect of about 20%) compared to children in control communities. Overall, we document intent-to-treat (ITT) impacts of 0.16 of a standard deviation (SD) on a primary school enrollment index.

Second, we assess children’s cognitive and social-emotional skills and document ITT impacts of 0.16 SD on a skills index. The magnitudes of these estimates are consistent with other early childhood interventions in the literature (see, for example, Bos et al. 2024; Berlinski et al. 2009). Our qualitative surveys suggest that the close proximity of the preschool centers to the families’ residence and to the primary school may be a key mechanism behind increased enrollment in preschool and primary education. Moreover, 3
# **4.3.1  Education**

In the Northeast region and its states, the most important factor contributing to the likelihood of a household being poor is completed level of education by the household head. That is, the more education a household head has completed the lower the probability of earning an income, that takes the household below the absolute poverty line. Education variables are strongly, statistically significant and negatively correlated with the probability of being poor at all levels of education starting with the first part of primary education. Furthermore, this is the case for all eight years for both Rio Grande do Norte and the whole Northeast region. Controlling for other variables, the impact of educational attainment is quantitatively the largest of all included explanatory variables. Moreover, the negative effect of education on poverty is increasing with the level of completed education of the household head. Put differently, the more completed education the less likely it is that the household experience poverty.<sup>24</sup> Figures 10a and 10b plot the education parameter estimates for the 1981-99 period for Rio Grande do Norte and the Northeast.<sup>25</sup> Findings reveal that the impact of having completed primary education on the likelihood of being poor has been rather constant over the 1980s and 1990s. For completed high school education, the magnitude of estimated impacts is larger than of primary education. Furthermore, for Rio Grande do Norte and the other individual states, the impact is closely linked to developments in the Brazilian economy. The effect on poverty of completed education was larger during economic upturns such as after the implementation of the Real Plan and low during recessions such as during the Collor Plan years and the Asian crisis. Furthermore, Figure 10a shows that the impact on poverty of tertiary education is significantly and numerically larger, hence more poverty reducing than that of secondary education. Furthermore, the pattern of development is rather different for higher education, as it is less procyclical than is primary and secondary education. The largest effect of tertiary education on the probability of experiencing poverty was during the stabilization and post devaluation in 1985. The

> 24 Ferreira, Lanjouw and Neri (1999) using PVV data in their analysis of Brazil as a whole also find that education is the central personal attribute determining the likelihood that a household experience poverty using PVV data in their analysis of Brazil as a whole.

> 25 The probit parameter estimates and standard deviations for each of the nine years are available from the authors upon request.

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<!-- Start of picture text --> Probability of losses not exceeding a certain level<br>1.00<br>0.98<br>0.96<br>0.94<br>0.92<br>0.90<br>0 250 500 750 1000 1250 1500<br>Government liabilities (million LCU)<br><!-- End of picture text --> Fig. 6: Risk of losses as measured by a cumulative loss-frequency distribution An important summary measure of this distribution is the annual expected losses, or the losses to be expected on average every year. The annual expected loss is the sum of all losses weighted by the probability of occurrence. Graphically, the expected losses are represented by the area above the cumulative distribution curve. However, it has to be kept in mind that disasters are not average events, rather they are extreme events occurring very rarely. Over a specified time period, like 100 years, catastrophes may occur, and the losses suffered over this period will be close to the sum of annual expected losses over these years.Based on available information, potential losses due to earthquake events in terms of percent of capital stock lost can be established for a country, state or region.

# **_3.2 Step 2: Estimation of the public sector’s financial resilience_**

Given limited resources to reduce human and economic losses, governments need to be financially resilient, or be able to provide sufficient funds to finance reconstruction of public capital, provide relief to households and support business in their recovery efforts. Sources of funding for reconstruction include aid, budget diversion as well as multilateral and international lending. However, these are not infinite and come at a cost.

Based on the information on direct risks to the government portfolio, financial resilience can be evaluated by assessing the government’s ability to finance its obligations for the specified disaster scenarios. Financial resilience is directly affected by the general conditions prevailing in an economy, i.e., changes in tax revenue have important implications on a country’s financial capacity to deal with disaster losses. The specific question underlying the CATSIM tool is whether a government is financially prepared to repair damaged infrastructure and provide adequate relief and support to the private sector for the estimated damages of 10- 50- 100- and 200-year events? For this assessment, it is necessary to examine the government’s sources, including sources that will be relied on (probably in an ad hoc manner) after the disaster and sources put into place before the disaster (ex ante financing). These sources are described below (based on Mechler, 2004 and Hochrainer, 2006).

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Table 5B: Effects of embodied _foreign_ services on manufacturing export RCA, for LESS services intensive manufacturing sectors

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Df|0.359<br>||||||
||(4.761)||||||
|forshf|-0.233<br>|||-1.231<br>|||
||(0.560)|||(1.549)|||
|**Df*forshf**|**-2.248**|||**15.603**|||
||**(9.165)**|||**(25.722)**|||
|Db||9.509**|||||
|||(4.404)|||||
|forshb||-0.525|||-0.390||
|||(0.589)|||(0.984)||
|**Db*forshb**||**-13.422***|||**-17.385**||
|||**(7.452)**|||**(14.001)**||
|Dfb|||4.016||||
||||(3.735)||||
|forshfb|||<br>-0.120|||0.334|
||||(0.786)|||(1.844)|
|**Dfb*forshfb**|||<br>**-6.610**|||<br>**-10.217**|
||||**(5.555)**|||**(15.852)**|
|log(GDP/capita)|0.371|0.284|<br>0.325||||
||(0.273)|(0.250)|(0.260)||||
|TFP|<br>0.058**|<br>0.056**|<br>0.057**|0.067*|0.057|0.064*|
||(0.028)|(0.028)|(0.028)|(0.038)|(0.035)|(0.036)|
|log(emp)|<br>0.959***|<br>0.931***|<br>0.940***|<br>0.966***|<br>0.944***|<br>0.963***|
||(0.155)|(0.162)|(0.158)|(0.169)|(0.183)|(0.177)|
|SKratio|0.196|0.226|0.187|0.147|0.152|0.153|
||(0.653)|(0.666)|(0.667)|(0.831)|(0.871)|(0.862)|
|K/L|<br>0.068***|<br>0.073***|<br>0.071***|<br>0.070***|<br>0.076***|<br>0.073***|
||(0.023)|(0.025)|(0.024)|(0.022)|(0.025)|(0.024)|
|rwage|<br>0.018**|<br>0.019**|<br>0.018**|<br>0.022*|<br>0.022*|<br>0.021*|
||(0.009)|(0.009)|(0.009)|(0.012)|(0.012)|(0.012)|
|GVC Participation|3.972***|4.098***|4.056***|4.254***|4.373***|4.306***|
||(0.706)|(0.696)|(0.708)|(0.847)|(0.838)|(0.844)|
|Country FEs|Yes|Yes|Yes||||
|<br>Sector FEs|Yes|Yes|Yes||||
|Year FEs|Yes|Yes|Yes||||
|Country*Year FEs||||Yes|Yes|Yes|
|<br>Sector*Year FEs||||Yes|Yes|Yes|
|Observations|2,931|2,931|2,931|2,931|2,931|2,931|
|R-squared|0.637|0.644|0.640|0.665|0.669|0.666|

Notes: Dependent variable is manufacturing export RCA. We keep only the first seven sectors with high financial and business service input intensity, as listed in Appendix 4. Robust standard errors in parentheses, clustered by country*sector. *** p<0.01, ** p<0.05, * p<0.1.

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# **Table 9**

# **Cooperativeness and the long-route of accountability (official records)**

||District-le<br>2009 p<br>|**Depende**<br>vel percentage<br>arliamentary e<br>|**nt Variable**<br>of citizens wh<br>lections (officia<br>|o voted in the<br>l records)<br>|
|---|---|---|---|---|
||OLS<br>(1)|OLS<br>(2)|OLS<br>(3)|OLS<br>(4)|
|**% parents investing in the group account in the district**|0.11<br>(0.133)|0.16**<br>(0.033)|0.16**<br>(0.033)|0.17**<br>(0.016)|
|ln of household income (LSMS)||-0.03**|-0.04***|-0.02|
|||(0.033)|(0.007)|(0.533)|
|Average years of schooling of the adult population (LSMS)||-0.00|0.00|0.00|
|||(0.882)|(0.619)|(0.663)|
|% people who belong to a social organization (LSMS)|||0.08**<br>(0.046)|0.06*<br>(0.082)|
|% people who participate in community activities (LSMS)|||0.04|-0.00|
||||(0.324)|(0.933)|
|Average perception of village members‟ willingness to work<br>together (LSMS)|||-0.02*|-0.03**|
||||(0.090)|(0.024)|
|% of people “trusting others” (LSMS)|||0.01|0.02|
||||(0.766)|(0.517)|
|Standard Deviation of household income (LSMS)||||-0.00<br>(0.857)|
|Prevalently Muslim||||-0.02<br>(0.177)|
|Religious heterogeneity (LSMS)||||-0.04**<br>(0.014)|
|Northern region||||0.06**<br>(0.027)|
|Constant|0.47***|0.83***|0.90***|0.76**|
||(0.000)|(0.000)|(0.000)|(0.046)|
|County fixed effects|YES|YES|YES|YES|
|<br>_Joint significance of county f.e. (p-value)_|_0.000_|_0.001_|_0.002_|_0.002_|
|Geographical areas dummies|YES|YES|YES|YES|
|_Joint significance of geographical areas (p-value)_|_0.030_|_0.012_|_0.005_|_0.001_|
|Observations|36|36|36|36|

P values in parentheses: *** p<0.01, ** p<0.05, * p<0.1. In all OLS regressions, we control for three geographical areas dummy variables: coastal, center and mountain areas; Tirana is the excluded geographical area.

The estimates show a highly significant correlation between district-level cooperativeness in the public goods game, and voter turnout in the parliamentary elections. The correlation is robust to the inclusion of the district-level controls generated by the 2008 LSMS survey. The estimated coefficient indicates that a one standard deviation increase in the proportion of cooperating parents in a district is associated, _ceteris paribus_ , with a 2 percentage point increase in participation in the elections.

Table 9 also indicates that turnout is higher in districts where more citizens belong to social organizations, and in the northern region, which as previously discussed is traditionally allied to the Democratic party that is in power since 2005. Consistent with the literature, the estimates also suggest that participation in parliamentary elections is lower in districts characterized by religious heterogeneity.

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now incorporate declining costs into this argument and show that gains from an FTA are likely to be larger in this case. Assume perfect initial symmetry between A and B. Specifically, they face the same unit cost curve and demand for the commodity under consideration. In Figure 3, taken from Corden (1972), AB is the unit cost curve, **DADA** is the demand in A and DA+BDA+D1B is the horizontal sum of demands in A and B. As drawn, DADA lies half way between the vertical axis and **DA+BDA+B.** The price at which A and B can import the good from C, PM, is higher than the price at which they can export it to the latter, P1. The difference between PM and P. is accounted for by transportation costs and tariffs levied by C. In the initial equilibrium, both A and B employ made-to-measure tariffs which raises the domestic price just enough to make domestic production competitive with imports from C. In Figure 3, the tariff inclusive price in the initial equilibrium is given by PM+t. Both countries produce the good in quantities (OC) just enough to satisfy domestic demand.

Now consider a CU between A and B which eliminates tariffs on intra-trade and replaces the old made-to-measure tariff, **t,** by a union-wide made-to-measure tariff, tu. Only one of the two countries, say A, will produce the good in the post-union equilibrium. Both production costs and consumer price will decline to PM+tU. Each country will expand consumption from OC to OC' and make a welfare gain equal to the shaded area enclosed within the pre- and post-union prices and the demand curve. Unlike the usual triangular gains under increasing costs, the FTA yields gains comprising a rectangle and a triangle. These gains can be further multiplied if there are many goods subject to scale economies. For example, if there are two goods subject to increasing returns, each partner can specialize in one of them and obtain the other from the partner at a lower cost.

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# Conceptual Aspects of Global Value Chains<sup>∗</sup>

Pol Antràs Harvard University

## 1 Introduction

In the last few decades, a series of technological, institutional, and political developments have fueled a significant globalization of production processes across countries.<sup>1</sup> More and more firms now organize production on a global scale and choose to offshore parts, components or services to producers in foreign and often distant countries. The typical “Made in” labels in manufactured goods have become archaic symbols of an old era. These days, most goods are “Made in the World.” Some aspects of this new wave of globalization are not particularly novel. Significant and sustained increases in the trade-to-GDP ratio had been experienced in the past. The period 1870-1914, for instance, witnessed a major increase in international trade flows, largely fueled by the invention of the steamship, and that period is often referred to as the “First Globalization”. Similarly, international trade in raw materials and intermediate inputs has been a prominent feature of world trade flows since time immemorial. For example, Assyrian merchants who settled Kanesh (in modern-day

> ∗This paper was written as a background paper for the 2020 World Development Report entitled “Trading for Development in the Age of Global Value Chains.” I am grateful to my fellow co-directors, Caroline Freund and Aaditya Mattoo, and to Penny Goldberg, Daria Taglioni and the rest of the core WDR team for helpful comments. Any errors are my own.

> 1 Three types of developments were particularly key: (i) the information and communication technology (ICT) revolution, (ii) an acceleration in the rate of reduction in man-made trade barriers, and (iii) political developments that brought about a remarkable increase in the share of world population participating in the capitalist system (see Antràs, 2015, for more details).
# **Table 4: Estimated short-term relations between climate and income/life expectancy in Chile**

|**Explanatory variables**|**(1)**<br>(log percapitaincome)|**(2)**<br>(life expectancy)|
|---|---|---|
||9.4701|58.0242|
|Constant|(248.77)|(18.04)|
||-0.0592|1.0571|
|Temperature|(-7.37)|(2.27)|
||-|-0.0560|
|Temperature<sup>2</sup>||(-3.12)|
||-0.3153|-4.9969|
|Precipitation|(-4.30)|(-5.57)|
||0.1195|1.5325|
|Precipitation<sup>2</sup>|(3.68)|(3.87)|
||0.3504|0.9710|
|Education level|(30.11)|(7.44)|
||-|-2.5520|
|Urbanization rate||(-2.81)|
||-0.6291|-|
|Urbanization rate<sup>2</sup>|(-10.52)||
|Number of obs.|324|333|
|R<sup>2</sup>|0.823|0.364|

_Source:_ Authors’ estimation based on assumptions explained in the text. Note: Numbers in parenthesis are t-values. When t-values are numerically larger than 2, we will consider the coefficient to be statistically significant, corresponding to a confidence level of 95%.

The findings at the bottom of the table show that just these four explanatory variables (temperature, precipitation, education, and urbanization rates) explain more than 82 percent of the variation in incomes between the municipalities in Chile. This is a very good fit, which suggests that we have included the most important explanatory variables, and that including addition variables would make little difference. The same four variables only explain about 36 percent of the variation in life expectancy, which is less impressive but still good for a cross-section model.

Education is by far the most important variable, explaining about 73 percent of the variation in incomes and about 26 percent of the variation in life expectancy. The remaining variables are also all statistically significant, but sometimes linearly and other times non-linearly. As it is difficult to judge the relationships directly by looking at the estimated coefficients, we have plotted the estimated relationships in Figure 1. The axes are scaled to represent the actual range of temperatures, precipitation, incomes, and life expectancies experienced in different Chilean municipalities, so that the magnitude of climate impacts can be seen in the appropriate perspective.

Panel (a) shows a negative relationship between average annual temperature and per capita income, with inhabitants of the warmest regions earning less than half the income of inhabitants from the coolest regions.

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similar to the approach taken in Hardouvelis, La Porta, and Wizman (1994), who examine the exitent of comovement in emerging country funds discounts.

For the capital account balance-GDP ratio, we construct the principal component indices for the entire 1970-93 sample. As far as the covariation of the balance in the capital account as a percent of GDP, the share of the total variance explained by the first principal component is about 40 percent for both sets of countries (Table A. 1). The main reason that the proportion of the total variation accounted for by the first common factor is lower than for other external variables, such as reserves and the real exchange rate is that the sample covers 24 years, in which important country-specific developments take place. 1/ The most visible example of idiosyncratic behavior during 1970-93 is El Salvador, where a civil war lasting 10 years affects the performance of the capital account. Not surprisingly, its weight in the constructed index is far lower than any of the other countries. Two less obvious examples where country-specific factors play a key role are Colombia and Costa Rica, where capital account developments are heavily influenced by sizable terms of trade shocks, such as the boom in coffee prices in the latter part of the 1970s.

To test for spillover effects we next examine the dynamic interaction between capital account developments between, the small and large countries. Here, we test the hypothesis that there are "contagion" or "spillover" effects from the large countries to the smaller ones. As noted earlier, mechanisms for spillovers may be in the form of foreign investors first selecting the larger countries as a place to invest in and,. as confidence increases, diversifying the portfolio among a broader range of countries or due to highly integrated goods and capital markets (see Hoffinaister and Vegh (1994), and Talvi (1994)). We start with the basic relationship that links the balance in the capital account (as a percent of GDP) for country i to the real rate of interest in the U.S. and introduce the "large country index" of the capital account, described in Table A. 1, and denoted by LCI), lagged one period, as an explanatory variable for the small countries' capital account balances. Hence, in 1/ See Calvo, Leiderman, and Reinhart (1993) and Calvo and Reinhart (1995).
With respect to agriculture, the results raise additional questions. First, it would be interesting to examine whether the ‘reverse Okun’s Law’ can be observed in a larger sample of country, going beyond the limited sample of three in this paper. Second, the notion of agriculture as a shock-absorber need not be limited to agriculture but can extend into informal work more generally. East Asia generally has low unemployment, even in times of crisis. This finding could be explained by the fact that in times of crisis laid-off workers do not register as unemployed but rather pursue work opportunities in the informal sector.

Finally, it would be interesting to break down employment further and take the analysis beyond the agriculture/non-agriculture dichotomy. While Okun’s Law captures the ‘net effect’ of growth on employment it ignores shifts within the labor market. For example, ‘creative destruction’ may create more high-skilled jobs in the ‘modern’ sector at the expense of jobs in more ‘obsolete’ industries (Schumpeter 1942). Workers made redundant in the process may either find jobs requiring similar qualifications elsewhere, enter the informal economy, or register as unemployed. By focusing on more specific employment categories, more light could be shed on the effect of growth and ‘creative destruction.’

# **References:**

Balakrishnan, Ravi, Mitali Das, and Prakash Kannan. 2009. "Unemployment Dynamics During Recessions And Recoveries: Okun's Law And Beyond." In IMF, (eds.), _IMF World Economic Outlook: Rebalancing Growth._ Washington, DC: IMF.

Bhalotra, Sonia R. (1998) ‘The Puzzle of Jobless Growth in Indian Manufacturing’, _Oxford Bulletin of Economics and Statistics_ 1: 1: 5-32.

Cazes, Sandrine, Sher Verick, and Fares Al Hussami. 2011. "Diverging Trends In Unemployment In The United States And Europe: Evidence From Okun's Law And The Global Financial Crisis." Geneva: Employment Sector: Employment Working Paper 106.

Fields, Gary (2004) ‘Dualism in the Labor Market: A Perspective on the Lewis Model after Half a Century’, _The Manchester School_ 72:6:724-735.

Hendry, D, and A. Pagan, and J. Sargan (1984) ‘Dynamic Specification ́in Z. Griliches and M. Intrilligator (eds.) _Handbook of Econometrics_ North Holland.

Hodrick, R. and E. Prescott (1980) _Post-War U.S. Business Cycles: An Empirical Investigation_ CarnegieMellon University mimeo.

Hodrick, R. and E. Prescott (1997) ‘Post-War Business Cycles: An Empirical Investigation’, _Credit and Banking_ 29: 1-16.

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