rafmacalaba/datause-encoder-data huggingface dataset:
discussing in more detail the quality of the treatment and control groups in terms of their comparability. In the case of matching, the underlying assumption states that there are no unobservables that could create nonrandom selection into the program after matching treated and control children on the estimated probability of assignment to treatment. Although there is no definitive test to formally rule out selection on unobservables, a series of checks on observable variables do not provide a serious indication of this type of bias in our matching analysis. We performed balancing checks to assess the comparability of treatment and control children after conditioning for a large set of observable pre-program characteristics at the individual, household, and community levels that explain participation into the program. Regression analysis shows that, after controlling for the probability of participation, no additional conditioning variables help predict the receipt of treatment. Furthermore, there are no major statistically significant differences in the conditioning variables between the treatment and control within the same strata of similar probabilities of program participation. These balancing tests are particularly robust for the first specification of the model of participation (Model 1). In addition to the common support restriction, we also dropped observations with probability of participation below 0.1 or above 0.9.

As for the RDD approach, the analogous condition for identification is that the groups of people right below and above the threshold of eligibility need to be statistically equivalent and that the only difference between them is the treatment itself. To account for possible differences in addition to participation in the program, we run RDD models based on specifications including municipality fixed effects. Overall, the main findings of the paper regarding the impacts on high school completion appear to be stable across these different model specifications, signaling robustness to a variety of covariates. Unfortunately, it was not possible to control directly for school fixed effects in the econometric analysis.<sup>33</sup> This, however, seems to be less problematic in the RDD since treatment and control groups are in some cases comprised of children that attend the same

> 33 It was not possible to control for school fixed effects in the econometric analysis for at least three reasons. First, high school completion is observed only for individuals in the relevant ages that registered for the ICFES test and therefore it is impossible to know the school of children that were not matched to the ICFES database. Second, information with the name and code of schools exists only for a – probably not random- subsample of the students who took the ICFES test. Third, in many cases – particularly in small municipalities where the school supply is fixed – the inclusion of municipality fixed effects is expected to remove any existing school fixed effects.

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from outages, and the use and cost of generators). Second, it covers a wide range of issues in a comparable manner: from access to financial and infrastructure services, crime, corruption and government regulations, allowing a ranking of these issues. Third, the data can also go beyond benchmarking to test directly the impact of these objective conditions on the actual performance of the enterprise, how the actual investment climate conditions affect the productivity and employment growth of respondents. Fourth, large, randomly selected samples of enterprises allow for results to be compared across types of enterprises, with particular attention paid to enterprise size. For many of the countries in the region, this is the only source of detailed information on enterprise performance and disaggregated objective indictors of a wide variety of investment climate indicators.

Questionnaires are administered using a common methodology.<sup>9</sup> The questionnaires use a common core survey, a set of identical questions that enable cross-country analyses. Countries have the option to add a limited number of additional questions to gather additional information on selected areas of particular interest to policy makers. The data include countries in six different regions, covering the years 2000-2008.<sup>10</sup> The median sample size is 350 enterprises, with several large countries having substantially larger samples (China, India, Turkey and Vietnam have samples over 1500) (see Table A1 in the annex). The sample of enterprises in each country is stratified by size, sector and location.<sup>11</sup> Because of this stratification, large enterprises are, in general, over-sampled in the ESs compared to their

> 9 From http://rru.worldbank.org/InvestmentClimate/Methodology.aspx 10 The exact set of questions asked does have some variation across countries, particularly among the earlier surveys, so regressions with multiple investment climate variables are based on a somewhat smaller set of countries.

> 11 From http://www.enterprisesurveys.org/Methodology/default.aspx#weights: ES have been conducted following simple random sampling or random stratified sampling. In a simple random sample, all members of the population have the same probability of being selected and no weighting of the observations is necessary. In a stratified random sample, all population units are grouped within homogeneous groups and simple random samples are selected within each group

12
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59
adjustment to the long-term equilibrium in the following period:<sup>10</sup> where _p_<sup>_d_</sup> _t_<sup>isthedomesticpriceinperiod</sup><sup>_t_and</sup><sup>_pw_</sup> _t_<sup>isthecorrespondingworld</sup> price; _δ_ captures the instantaneous response of domestic prices to changes in world prices, and _θ_ is the error-correction parameter, which captures the speed of adjustment of _p_<sup>_d_</sup> _t_<sup>toitslong-runequilibrium</sup><sup>_γpw_</sup> _t_<sup>.Weusetheresid-</sup> uals from the Engle-Granger cointegration test ( _μ_ � _t_ ) for each state as the error-correction term. Again, SUR is used to estimate 11 equations of type (2): one for each of the states in our sample. Prior to estimation we ensure that the first differences of the price time series used in the error-correction model (1) are stationary using an ADF test.

# **3.2 The impact of local sugar prices on wages and employment**

To estimate the impact of local prices on wages and employment in Brazil we use a Heckman sample selection model where we only observe the wages of the respondents that have been employed during the survey period. The wage equation estimates the impact of sugar prices (as well as individual and regional characteristics) on a person’s wage. We allow the impact of sugar prices to vary according to individual characteristics including sector of employment and geographical location of the household. Note that even workers in non-sugar sectors in non-sugar producing regions can see their

> 10See Baffes and Gardner (2003) for a detailed derivation.

11
# **Measuring Progress Towards Universal Health Coverage: With An Application to 24 Developing Countries**

By Adam Wagstaff*<sup>a</sup> , Daniel Cotlear<sup>b</sup> , Patrick Hoang-Vu Eozenou<sup>b</sup> , Leander R. Buisman<sup>c</sup> a Development Research Group, World Bank, Washington DC, USA b Health, Nutrition and Population Global Practice, World Bank, Washington DC, USA c Institute of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, The Netherlands

**Key words:** universal health coverage; financial protection; service coverage; equity

**JEL classification:** I10, I14, I15

## **Acknowledgments**

We thank Andrew Farlow and two anonymous referees for very helpful suggestions on a previous draft. The findings, interpretations and conclusions expressed in this paper are entirely those of the authors, and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments of the countries they represent.

> *Corresponding author: Adam Wagstaff, Development Research Group, The World Bank, 1818 H Street, NW, Washington DC 20433, USA. Tel: +1 202 473 0566, awagstaff@worldbank.org.
_Source:_ Bonch-Osmolovskiy and Topińska (2012).

# **8. Conclusions**

This study sheds light on the impact of improving employment and education conditions on poverty and social exclusion indicators in ten NMSs. The paper presents a simple partial equilibrium model that is flexible enough to be implemented in a number of different settings using widely available data. The simulation model analyzes poverty and social exclusion outcomes when employment and education indicators are altered. The model incorporates the key structural relationships between education, employment probability, labor earnings conditional on employment, individual and household income, and measures of risk of poverty and social exclusion. The model structure is straightforward, and is designed to take advantage of the widely available EU-SILC data, ensuring that it can be useful to a large number of analysts in any country with EU-SILC data.

The microsimulation model has some limitations, namely the assumption that structural parameters are stable over time and its partial equilibrium approach. The former limitation may be particularly important because pre-financial crisis (2008–2010) data are used to simulate post-crisis structural relationships. A validation exercise shows that the microsimulation model performed well at predicting at-risk-of-poverty, anchored poverty, and low work intensity outcomes over the 2005–2008 period. However, performance at predicting severe material deprivation was poor, echoing results of other researchers.

The paper takes as a starting point the assumption that Europe 2020 national targets for increased employment and education will be met, demonstrating how the different pillars of the Europe 2020 strategy may reinforce one another. We find that at-risk-of-poverty incidence could decrease by more than 3.7 million people and low work intensity by about 3.0 million people by 2020 if NMSs achieved their national targets for employment and education.<sup>19</sup> More significantly, anchored poverty could fall by about 9.6 million people in the NMSs when countries achieve their 2020 employment and education targets. Although anchored poverty is not an explicit Europe 2020 target, it is an important indicator of progress in improving poor people’s quality of life.

The microsimulation model presented in this paper permits alternative assumptions, such as falling short of—or surpassing—the 2020 education and employment targets. It also lends itself to a range of policy simulations, such as the impact of changes in social transfer levels, targeting of increases in education or employment to particular population sub-groups, and other policies, some of which will be explored in future work. As a result, we believe that it provides an important and practical analytical tool for countries to help simulate quantitative outcomes to policy reforms.

19 Poverty and social exclusion rates are calculated in 2008 and populations are from 2010.
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feature of Baulch’s PBM is the incorporation of transaction costs. Transaction costs are not observed but are constructed by the extrapolation of a single observation of these costs.

In this paper, we propose a modification of the PBM. Instead of extrapolating fixed transaction costs, we estimate these costs. Furthermore, rather than estimating fixed regime probabilities for (a few) selected trade pairs using a switching regression technique, we propose a simple but economically justifiable technique of calculating regime probabilities that vary by location and time. This method allows us to track developments in market integration for any location in a network. Finally, and in contrast to previous work, we analyze and explain regime probabilities over the years. The empirical work is based on the Malawi maize market.

The paper is organized as follows. In section I, we discuss the empirical literature on market integration. In section II, we present a description of the Malawi economy, its maize market and maize trade. In section III, we propose a modification of the PBM. In section IV, we discuss estimation results of transaction cost equations, present calculated regime probabilities and explain variations in these regime probabilities. In section V, we present our conclusions.

# **<<A>>Market integration in the literature**

In this section, we briefly discuss and assess the two major lines of research on market integration, notably research using the co-movement in spatial prices and research using spatial price equilibrium as its starting point. The section aims to highlight where this paper fits in the literature.

# _<<B>>Using co-movement in prices to assess market integration_

A popular way to investigate market integration empirically is to use a time-series estimation of an equation that explains prices of one location using current and lagged prices of another j k j k location, employing the following formula: pt = β0 + ∑βτ 1τpt−τ + εt, where pt ( pt ) is the price in location _j (k)_ at time _t_ , and _ε_ is a random error. Market integration is determined by (the sum of) the coefficient(s) of prices, which should be equal to one for complete market integration 3
using any functional forms on the weather indicator (Dell et al., 2014). Some studies use measures of variability changes or anomalies in weather realizations from the geographyspecific long-run distributions, where the latter help detect irregular patterns in weather conditions (Hoffmann et al., 2021).<sup>16</sup> We create weather anomalies based on the 15-year national distributions of the growing season weather, characterized by their mean and variance, for our cross-sectional analysis. We difference the 2018 growing season realization of the weather indicator at the cluster level from the national 15-year growing season mean and scale it by the country’s 15-year growing season standard deviation: where weather value is observed for a cluster, _c_ in district _d_ of province _p_ in 2018 growing months ( _g_ ). The 15-year national mean and standard deviation is constructed for the growing months _g_ .<sup>17</sup> Note that such construction allows a measure of irregularities from the long-run weather conditions in the country: level changes in a cluster matter in proportion to the national variation in weather. In so far as 2018 was dubbed one of the worst in two decades, this measure should also capture the drought-like conditions in 2018. Moreover, by eliminating scale effects, this measure allows us to rank clusters on a common scale and retain the spatial variation in the intensity of deviations from the ‘usual’ weather, which is the source of variation we exploit in our analysis.

# **3.6 Weather Anomalies in 2018**

In Figures 2, 3 and 4, we map the rainfall, NDVI and temperature anomalies from 2018, respectively, at the district-level. The maps highlight the spatial differences across districts, with some districts drier, hotter, or with less vegetation than others.

Figures 5, we plot the distributions of growing season weather anomalies in 2018 (a declared drought year) and 2019 (a typical weather year). In Figure 6, we also plot the

> 16In this approach, the weather shock is calculated as the level difference from the within-spatial area long-run mean and divided by the within-spatial area long-run standard deviation. This construction implies that the level changes matter not in absolute terms but in proportion to an area’s usual variation (Dell et al., 2014; Barrios et al., 2010).

> 17An alternative would be to use cluster-specific or district-specific long-run weather conditions to construct this measure of weather shock. The issue is that creating a standardized measure constructed from district distribution makes this measure a function of the district fixed effect and plausibly endogenous. In this specification, the district fixed effects account for spatial heterogeneity, while deviations from a national distribution provide an nation-wide ranking of cluster based on weather conditions within and across districts.

10
Table A11: Import and Assembly versus Pure Assembly

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Dep. Var.|DVA|R|Imp/Ma|terial|ln(Exp|Variety)|ln(Imp|variety)|
|Sample:|IA|PA|IA|PA|IA|PA|IA|PA|
|Year Dummies:|||||||||
|2001|0.0298***|0.0237|-0.0232***|0.00386|-0.123***|-0.0524|-0.0366*|-0.0264|
||(0.007)|(0.032)|(0.007)|(0.033)|(0.019)|(0.101)|(0.022)|(0.088)|
|2002|0.0494***|0.0422|-0.0295***|0.0359|-0.114***|-0.0604|0.0601**|0.0335|
||(0.008)|(0.034)|(0.007)|(0.035)|(0.020)|(0.106)|(0.024)|(0.093)|
|2003|0.0682***|0.0618*|-0.0700***|0.00539|-0.224***|-0.0959|0.101***|0.119|
||(0.007)|(0.034)|(0.007)|(0.038)|(0.021)|(0.107)|(0.023)|(0.093)|
|2004|0.0706***|0.0486|-0.0917***|0.0271|-0.286***|-0.133|0.118***|0.217**|
||(0.008)|(0.032)|(0.008)|(0.044)|(0.022)|(0.106)|(0.024)|(0.096)|
|2005|0.0980***|0.100***|-0.118***|-0.0290|-0.349***|-0.221**|0.203***|0.228**|
||(0.008)|(0.034)|(0.009)|(0.047)|(0.024)|(0.107)|(0.025)|(0.105)|
|2006|0.140***|0.132***|-0.161***|-0.0467|-0.202***|-0.136|0.283***|0.285***|
||(0.008)|(0.038)|(0.010)|(0.045)|(0.025)|(0.106)|(0.029)|(0.102)|
|�wL<br>PY<br>�<br>it|-0.0044|0.0009|0.0270|0.251*|-0.0343|-0.231|-0.0417|0.0059|
||(0.016)|(0.065)|(0.052)|(0.136)|(0.055)|(0.226)|(0.037)|(0.241)|
|�<br>P <sup>D</sup>M<sup>D</sup>+P <sup>I</sup>M<sup>I</sup><br>PY<br>�<br>it|-0.0247***|0.0073|||0.0143|-0.0867|0.0097|-0.123|
||(0.009)|(0.058)|||(0.025)|(0.164)|(0.026)|(0.224)|
|ln(K=L)it|||-0.0037|-0.0071|||||
||||(0.005)|(0.011)|||||
|N|13062|1744|13040|1733|13062|1744|13062|1744|
|R<sup>2</sup>|.0686|.0459|.0867|.0579|.0647|.0208|.0419|.0372|

Note: Firm and year ...xed e¤ects are always included. Data set: merged NBS-customs data. IA and PA stand for import and assembly and pure assembly, respectively. Columns (1) and (2) use ...rm DVAR as the dependent variable; columns (3) and (4) use ...rm imports-to-materials ratio as the dependent variable; columns (5) and (6) use log of the ...rm’s export varieity as the dependent variable; columns (7) and (8) use log of the ...rm’s export varieity as the dependent variable. Bootstrapped standard errors are in parentheses. * p<0.10; ** p<0.05; *** p<0.01.

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