rafmacalaba/datause-encoder-data huggingface dataset:
# **Measuring Skills in Developing Countries**

Rachid Laajaj* and Karen Macours** JEL codes: O12, O13, O15 Keywords: skills, measurement, agricultural productivity

- _Universidad de Los Andes_

_**Paris School of Economics and INRA_<sup>1</sup> 1 Jack Pfeiffer provided invaluable research assistance and programming skills, field support, tenacity and ideas throughout the project and we gratefully acknowledge all his contributions to this project. Mattea Stein, Irene Clavijo, Juan Diego Luksic, and Freddy Felipe Parra Escobar provided excellent research assistance. Data collection was organized through IPA Kenya. We are indebted the excellent field team for key contributions during piloting and translation and for all efforts during the main baseline collection. We are indebted to Chris Soto for many suggestions and insights from the psychometric literature. We are grateful for inputs received from colleagues at PSE and seminar participants at EUDN, IFPRI, Navarra, Nova, Oxford, Trinity College Dublin, World Bank, the ISI World Statistics Congress, to Gero Carletto and Renos Vakis for continuous encouragements and support, to the World Bank’s Living Standards Measurement Study (LSMS) team for funding of the survey experiment through a grant from DFID, and to the FAO and DFID-ESRC for funding of the follow-up data collection rounds. Contacts: <mark>r.laajaj@uniandes.edu.co</mark> ; karen.macours@psemail.eu
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I POLICY RESEARCH WORKING PAPER 2229

# **Summary findings**

Since its creation in 1991, the Peruvian Social Fund (FONCODES) has spent about US$570 million funding microprojects throughout Peru. Many of these projecrs have involved building and renovating school facilities.

Paxson and Schady analyze the targeting and impact of FONCODES investments in the education sector, using data from FONCODES, Peru's 1993 population census, Peru's 1994 and 1995 Living Standards Measurement Surveys, and a 1996 household survey conducted by the Peruvian Statistical Institute.

They present their results based on various descriptive and econometric techniques, including nonparametric regressions, differences-in-diff'erences, and instrumental variables estimators.

They show that FONCODEiS projects in the education sector have reached poor districts and, to the extent they live in those districts, poor households. FONCODES has had a positive effect on school attendance rates for young children, but not on the likelihood that childrer. will be at an appropriate school level for t1 eir age.

Among other recommendations, they su,ggest that FONCODES consider random assignment of some education projects for a subsample of the population, to test the robustness of the study's assumptions and results.

Lack of disaggregated data on such measlirn s as the time children spend in school, pupil-teacher ratios, anc scholastic achievement precluded analysis of the impac: of FONCODES education projects on scho )l quality. Collecting such data, and undlerstanding how improvements in school infrastructure inte:ract with other school-level changes to produce more learning, should be a research priority.

- - This paper a product of the Poverty Division, Poverty Reduction and Economic Managernent Network is part oi: a larger effort in the network to understand the functioning and impact of social funds. Copies of the paper art' available fr te from the World Bank, 1818 Hf Street, NW, Washington, DC 20433. Please contact Norbert Schady, rocm MCS-138, telephone 202-458-8247, fax 202-522-1557, email address nschady@worldbank.org. Policy Research Worl:in- Papers are also posted on the Web at http://www.worldbank.org/r-esearch/workingpapers. November 1999. (34 page!;)

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polish_ **_vd._** _The papers_ **_carry_** _the names of the authors and should_ **_be_** _cited accordingly. The findings, interpretations, and conclusions expresse:. in this paper are entirely those of the authors. They do not necessarily represent the vieiv_ **_of_** _the World Bank, its Executive Director, o: the countries they represent._ Produced by the Policy Research Dissemination Center
percentage points for being ethnic minority, and an additional 3.8 percentage points more for being ethnic minority women. While this may seem to indicate that ethnic minority women face fewer disadvantages in these countries compared to the Latin American countries, it is worth noting that educational attainment rates are generally lower in African countries (on average, only one in five people over the age of 25 in either of the two countries has completed primary school) and therefore small differences across groups may reflect qualitatively sizable impacts. For instance, a penalty of 10 percentage points on ethnic minority women is a sizable impact where the literacy rate is just over 20 percent. On the other hand, a possible explanation for not observing large ethnicity-based divisions in Senegal and Sierra Leone can be because ethnicitybased differences are less relevant in driving educational gaps than other characteristics not captured by the linguistic proxy variable used in this paper, such as land ownership and lineage.

# **_4.3. Secondary School Completion_**

The multivariate findings on secondary school completion in the five countries included in the sample are partly affected by the fact that secondary school completion rates among individuals aged 25 and above were low in the sampled countries (especially in Africa) when these censuses were conducted. Of the countries in the sample, Peru has the highest level of secondary school completion; one in two Peruvians at or over the age of 25 have completed secondary school according to the 2007 census, followed by one in four Bolivians in the 2001 census. In contrast, the corresponding rates are 6 percent and 3 percent, respectively, for Senegal in 2002 and Sierra Leone in 2004. Thus, inter- and intra-group differences in secondary school completion in these two countries must be interpreted with caution.

The results are qualitatively similar to those for primary school completion, and in the three Latin American countries, the intersectionality of gender and ethnicity plays an important role in hindering secondary school completion rates for ethnic minority women (see Table C2 in Appendix C for the full probit results). In Bolivia and Peru, the gap in secondary school completion between ethnic minority women and Spanish-speaking men is more than twice as large as the gap between ethnic minority men and Spanish-speaking men. In Bolivia, women in the reference group are 4.2 percentage points less likely than Spanish-speaking men to fall in the “completed secondary school” category as opposed to any other category, whereas the penalty for ethnic minority men is 11.8 percentage points. Ethnic minority women face an additional 9.3 22
bilateral security measures across sectors. Something that has been so far overlooked in other analyses of the of transnational terrorism on bilateral trade We extract 1968-2000 bilateral imports of the United States at the product level (SITC4/5 digits) from the NBER World Trade Data complied by Feenstra and Lipsey. The data however, provides only values of flows that exceed 100,000$ per year. This constitutes a potential problem as most origin countries of terrorism are LDCs that export little of many products and too much of a very few set of others where they are really specialized. Thus, neglecting small amounts could result in an over-representation of products of specialization in the dataset, possibly less sensitive to terrorism attacks. This could end up underestimating the impact of terrorism activities on trade. To deal with this problem, we completed the NBER dataset with the FLUBIL trade dataset from the French National Institute (INSEE), reporting flows over 1,000$. FLUBIL is basically an updated version of the OECD dataset on bilateral trade flows where some aggregation check-ups and minor corrections have been undertaken. It also completes the NBER dataset as it runs until 2002.

The sources of the rest of the variables that are used (i.e. traditional gravity and control variables), are listed in the appendix of the paper.

# **5 Econometric Results**

We want to study a bilateral US imports relation based on the trade equation (1) or its developed version equation (2), where exogenous security measures directly affect transaction costs. Let transaction costs be expressed as: _Tj_ = _Distj. S_<sup>_βj_</sup> _.e_<sup>(�</sup> _v_<sup>_ηv.dvj_)</sup> . Thus, trade costs depend on geographical distance between _j_ exporter and the US border, a set of dummy variables ( _dv_ ) designating common language and contiguity with the US, and finally security measures at the US borders. Let further � _S_ = _S_ ( _Zk_ ), represent a variable of exogenous security depending on a set of _K_ alternative variables _Zk_ , each representing a measure of past incidents frequency.

By approaching labor by the GDP of the importer, the productivity term _a_ by GDP per capita and the number of varieties by GDP of the exporter in equation (2), taking logs and indexing by time (t), the relation to estimate for each good (g) that enter the US market becomes: where _α_<sup>_g_</sup> and _αt_ are good and time fixed effects, _u_<sup>_g_</sup> _jt_<sup>istheresidual.The</sup><sup>_β_</sup> _k_<sup>_′_areexpectedtobe</sup> negative: an increase in past incident shares, increases current security measures (to prevent from potential future incidents), which leads to a decrease in US imports. The US GDP has been removed from the equation as its variation is fully captured by the time fixed effect. Also, as we do not observe the price index _P_ , it is not a strong assumption to assume that it is captured by the time and product 16
9 (2007) suggests that public investment spending may be particularly instrumental for rentextraction and therefore higher in countries with weak governance.

Some states in India have a long-standing reputation of weaker institutions to limit rentseeking compared to other states. Specifically, the socially and economically lagging states in the north of India –Bihar, Madhya Pradesh, Orissa, Rajasthan and Uttar Pradesh—are described as having weaker governance than others among the 14 major states (Ahluwalia, 2000; Acharya, 2004; Subramanian, 2007).<sup>7</sup> Four of them—Bihar, Madhya Pradesh, Rajasthan, and Uttar Pradesh—were lumped together famously by an acronym of their first letters, BIMARU, which means “prone to illness” for having the poorest demographic outcomes among all states (Ahluwalia, 2000). In an examination of growth performance across the major states before and after the economic reforms of the late 1980s and early 1990s, Ahluwalia (2000) finds that Bihar, Uttar Pradesh and Orissa exhibit striking growth decelerations after reforms. This suggests that there may be some basis to the reputation of weak governance that these states suffer from.<sup>8</sup> This study will examine over longer periods, 1960-2005, whether these states on average have systematically higher shares of capital spending, and whether voter turnout within these states has differential effects on budget composition compared to turnout in other states.<sup>9</sup> Table 2 reports the state fixed effects,  _i_ , in the following regression of the variation in budget composition with variation in voter turnout.

> 7 The 14 major states are Andhra Pradesh, Bihar, Gujarat, Haryana, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Orissa, Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh, and West Bengal.

> 8 In contrast, and contrary to their lumping with lagging states, Madhya Pradesh and Rajasthan have been performing well in recent years after economic reforms (Ahluwalia, 2000).

> 9 The data on budget composition is compiled from annual issues of the Reserve Bank of Bulletin devoted to finances of state governments. The data on voter turnout in state assembly elections is compiled from Butler, Lahiri, and Roy (1995) and from the website of the Election Commission of India.
out of government funding allocations. Although the supply of and demand for data may fluctuate in the short run, gaps between demand and supply erode the long-run sustainability of an NSS.

Canada’s chief statistician, Ivan Fellegi, argues that “the greater the authority of the chief statistician, the more important it is to have a variety of mechanisms through which the different needs of different client groups can be determined” (Fellegi 1995). Assessments of NSSs in the Organisation for Economic Co-operation and Development (OECD) identify deficiencies in understanding evolving user needs (PACAC 2019; APAC 2020). Similar deficiencies are found in low- and middle-income NSSs. An AIDDATA survey of policy makers, government technocrats, and NSS staff found that the most important and frequent users of statistics were international organizations and development partners, not domestic users (Sethi and Prakash 2018).

The four supply side pillars of the SPI are a good proxy for the NSS’s capacity to produce and disseminate a range of data products. The empirical model presented here aims to proxy the demand for official statistics. It draws on the same criteria underlying the SPI.<sup>8</sup> Our empirical approach is derived from the literature on inferring the “social efficiency” of economic indicators by the measured deviation of these indicators from the efficiency frontier (e.g., Sen 1981; Moore et al. 2000; Wang et al. 1999). The efficiency frontier is identified from the residual of a regression of an indicator of interest (in our case, the SPI) on a set of control variables, a methodology sometimes referred to as benchmarking (Ravallion 2005).

# **_Empirical Model_**

Our empirical model relates the values of the SPI index and its pillars with the variables that may determine a country’s demand for and supply of statistical data. This reduced-form model is represented in the following form: S 1L 2 3 4S 5 6 7 + ii,kk ii ii ii ii ii ii , ii (1) II = ββ LL(G SS) + ββ EECCII + ββ SSLLpp + ββ RR + ββ EEGGSS + ββ CCAA +ββ FFRRGG where _SPIi,0_ is the SPI for country _i_ ; _SPIi,1...4_ are the four supply side pillars of the SPI; and ii,jj ii _Log_ +ππRRππLL + εε (kk= 0, ... ,4; jj= 1, ... ,7) _(GDP)i_ represents the log of GDP of country _i._ We expect richer countries to spend more on

> 8 The criteria are as follows: _Simple_ : It must be understandable and easy to describe. _Coherent_ : It must conform to a common-sense notion of what is being measured; _Motivated_ : It must fit the purpose for which it is being developed. _Rigorous_ : It must be technically solid. _Implementable_ : It must be operationally viable. _Replicable_ : It must be easily replicable. _Incentive consistent_ : It must respect country incentives.

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# **GROWTH BEFORE AND AFTER TRADE LIBERALIZATION**

# **ABSTRACT**

The empirical study of the impact of trade liberalization has not convinced the skeptics about the economic gains after trade reforms. Some have even argued that trade reforms have led to economic collapse and to deindustrialization. Using a sample that excludes countries that were subject to major exogenous disruptions, we note that post-reform economic growth was 1.2 percentage points higher than before the reforms. This is remarkable considering that pre-reform periods were characterized by highly expansionary state policies and large external borrowing, and that we eliminate the crisis years that preceded trade liberalization in the comparisons. Through multivariate fixed effects estimations we calculate that annual per capita GDP growth rates increased by up to 2.6 percentage points after the trade reforms, compared to a counterfactual that takes into consideration the evolution of several growth determinants. Moreover, trade liberalization has been followed by an acceleration in investment, exports of goods and services, and manufacturing exports, and as opposed to common belief, outward orientation did not lead to significant deindustrialization and actually seems to have increased export diversification. Acceleration occurred irrespective of income per capita level and was indeed quite significant in Sub-Saharan Africa. As expected, small countries benefited most from the reforms.

Ataman Aksoy World Bank maksoy@worldbank.org Gonzalo Salinas Department of Economics Oxford University gonzalo.salinas@economics.ox.ac.uk
growing season rainfall spanning November of the previous year to March of the following year. We take advantage of the long time series to compute 30-year, long-term average rainfall, and long-term measures of rainfall variability such as the standard precipitation index (SPI), rainfall stress, and coefficient of variation.

The RALS data are large data sets collected from 8,839 households in 2012, 7,934 in 2015, and 7,241 in 2019. All three RALS waves were designed to be statistically representative of the rural population at the provincial and national levels, and 6,531 panel households were successfully interviewed over all three waves. This paper only uses data for the households that formed a balanced panel. Sampling and survey details can be found in CSO/MAL/IAPRI (2012); and CSO/MAL/IAPRI(2015).

# 4.2 Variables used in the main regressions

# 4.2.1 Dependent variables

This paper aims to explain the effects of climate shocks on vulnerability and resilience. We computed poverty incidence using real household income deflated using consumer price indices (CPI) for the relevant survey years. We then estimated a level of real income that would equate the rural poverty rate in the 2015 RALS to the official national rural poverty rate reported in the 2015 LCMS. This gave us a national poverty line of ZMW 2,697 per adult, per year in 2015 kwacha. That is, a household is deemed “poor” if real per adult income is less than the threshold of ZMW 2,697 per year.

We are also interested in understanding transitions in and out of poverty between 2012 and 2019. A household is chronically poor if it is recorded as being poor in all the three survey waves. A household that has escaped poverty is one that was poor either in 2012 and/or 2015 but not in 2019, while household entering poverty is one that was not poor in 2012 and/or 2015 but fell into poverty in 2019. It is worth noting that the preceding poverty transitions span a long period between 2012 and 2019 and that we are only able to observe poverty outcomes in the survey years. Thus, even if a household is observed as poor throughout the three waves, it is possible that it moved in and out poverty in the intervening years. Unfortunately, our data does not capture those transitions between survey years, so our observations reflect the minimum true extent of poverty mobility.

As stated before, our focus is not to explain drivers of poverty dynamics in Zambia, which in our view are well captured Ngoma et al., (2019) for the period 2012 – 2015 and Diwakar et al., (2020) for the period 2012 – 2019. Both analyses are based on the RALS data. We are interested here in examining the correlates among climate shocks, CSA, vulnerability, and resilience. We define vulnerability and resilience in the spirit of Barret and Constas (2014), where we explicitly consider welfare movements in and out of poverty over the three survey waves. Vulnerable households are those that were poor in all three survey waves or those 8
# Figure A5. Impulse Responses to a Positive Demand Shock

<!-- Start of picture text --> CPI Inflation Output<br>0.6 0.8<br>0.6<br>0.4<br>0.4<br>0.2<br>0.2<br>0 0<br>5 10 15 20 5 10 15 20<br>Interest Rate Oil Prices<br>1.5 0.6<br>1 0.4<br>0.5 0.2<br>0 0<br>5 10 15 20 5 10 15 20<br><!-- End of picture text --> Notes: The impulse responses are based on the economic model introduced in the Appendix A. The impulse responses follow a one-standard deviation of a positive demand shock.

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expected increase is lower in the deserts while it is higher in West Africa, Central Africa, North of the Sahara desert, and East Africa. Under the PCM scenario (Right), on the other hand, farmers reduce irrigation overall except for the desert areas. The reduction is largest in the lowland and wet zones.

Crop choice would also vary widely across the AEZs. Table 10a describes which crops would likely be chosen in each AEZ. Although the combination of fruits/ vegetables and maize is expected to increase in Africa under CCC 2020, many AEZs would see the decline of this combination, especially in the high elevation semi-arid AEZ. Similarly, maize is likely to decline Africa wide under CCC 2020, but it will increase in some AEZs such as high elevation dry savannah.

By 2100, as shown in Table 10b, these differential responses continue to magnify. Fruits and vegetables increase Africa wide under the CCC scenario, but they are expected to decline substantially in the lowland semi-arid AEZ. Under the PCM scenario, millet increases overall, but it declines substantially in the lowland dry savannah and lowland semi-arid AEZs. Not all the crops exhibit differential responses across the AEZs. For example, maize is chosen less often across all AEZs under both climate scenarios.

In Figures 3 and 4, we extrapolate the results for two crops, maize and fruits/vegetables, to all of Africa using the AEZ information. Maize will be reduced across all of Africa due to higher temperature, but farms in the lowland wet zones are hit the hardest under CCC. Under PCM, however, farms in the high elevation are the most affected. In the case of fruits and vegetables, the variation across the AEZs is much larger. Under CCC, it will increase substantially in high elevations, lowland savannahs, and in Southern Africa. Farms in the deserts or lowland wet zones will lose this type of crop. The probability distribution under PCM will be similar to that under CCC with some areas seeing larger increases while other areas seeing larger losses.

# **6. Conclusion and Policy Implications**

This paper quantifies differential farm adaptations taken by cropland farmers in Africa in 16 Agro-Ecological Zones. We rely on the economic data from the recently completed GEF/World Bank project and the FAO classification of the AEZs of Africa. We focus on two important farm adaptation decisions in cropland: irrigation and crop switching. Simple logit and multinomial logit models are used to examine the sensitivities of these decisions to climate.

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