rafmacalaba/datause-encoder-data huggingface dataset:
difficult to achieve when coverage is already high<sup>5</sup> . In such a case, we would observe a slope coefficient below one together with a positive and potentially large intercept term, which is indeed what is often obtained in Figure 5, at least in urban areas where coverage is higher.

The capital and major cities have recorded impressive gains compared to small towns and rural areas in Africa. In Benin, Cameroon, Ethiopia, Gabon, Ghana, Guinea, Madagascar, Senegal, South Africa, and Zimbabwe, about three-fourths of the residents in capital and major cities have electricity. More than half of the population in cities of Ethiopia, Kenya and South Africa have landline telephones. In Benin, Senegal, Zimbabwe, and South Africa, more than 90 percent of city households are covered by piped water supply. More than three-quarters of the city dwellers in Senegal, South Africa and Zimbabwe depend on sewerage systems to meet their sanitation needs. Capital and major cities in Namibia are exceptional in the coverage of modern infrastructure services – 96 percent had piped water, 97 percent had flush toilets, and 88 percent of urban dwellers had electricity as of 1992 (Table 5).

There has been a revolution in the availability of communications services in Africa in the past decade. Governments have issued licenses for mobile operators and competition has spurred private investment and competition. Telecommunications, particularly mobile phones are now part of every-day life in most African countries. Although this is not discussed here, it can be shown using other data sources (household expenditure surveys rather than DHS) that mobile phone coverage is higher than landline use in all the countries for which information on both services is available. For instance, in the Democratic Republic of Congo, 11 percent of the population has cell phones compared to less than one percent of households using a landline. Given that households are using cell phones as substitutes to land lines and not as complements, households use either one of the two services and very few households use both.

In table 7, data are provided at the national level regarding within country coverage rates according to the quintile of wealth of the household. This is done for each country using the latest year of data available. Clearly, and as was to be expected, coverage is virtually inexistent among the very poor in most countries, and in quite a few countries, coverage is also low even in the top quintile. As richer households who do not yet have modern infrastructure services are likely to benefit the first from potential increases in access, this suggest that coverage among the poor will remain very low in most countries for quite some time.

> 5 We are grateful to Ken Simler for drawing our attention to this aspect of coverage expansion.

9
between tariffs and average wages, and a strong negative association between tariffs and the skill premium. Following a tariff reduction of 10 percent, for instance, the wage of an average unskilled worker would decline by 3.9 percent while the wage of an average skilled worker would decline by only 0.5 percent. This suggests that while average wages would decline, the skill premium would instead increase by 3.4 percent.

Our results suggest that tariff liberalization can help explain the decline in wages and the increase in wage inequality observed in Argentina both during the 1970s and the 1990s. There are important differences, however. The large tariff cuts of the 1970s can account for over one fourth of the 100 percent increase in wage inequality (i.e., concretely, for 27 percentage points). During the 1990s, instead, tariff reforms can only account for 10 percent of the observed increase in wage inequality. This implies that other concurrent factors, such as other policy reforms inherent to the 1990s (technological change, deregulation, privatization), also played a key role in the determination of the wages of Argentine workers during the last decade.

The remainder of the paper is organized as follows. In Section 2, we describe the data used in this paper and we motivate our work by describing the trends in trade liberalization and the trends in wage inequality. Section 3 presents the empirical analysis and Section 4 concludes.

# 2 The Trends in Tariff Reforms and Wage Inequality

A major input into our analysis is the historical data on Argentine trade policy and wages, spanning the 1974-2001 period. These data come from two different sources: customs data on imports and tariffs, and household survey data on wages and workers.

We begin by describing the customs data. We measure trade policies with sectoral tariffs. Data on ad-valorem import tariffs come from official Tariff Schedules, which specify the tariff rate levied on each item of the Harmonized System (HS). In order to make our trade data comparable with the wage data, we need to build tariff measures at the 3-digit level of the ISIC classification. To do this, we first match each heading in the Harmonized System 4
# PRE Working Pager Series

||Ida|**ALAhor**|_DAR_|Contact<br>**for**agLp r|
|---|---|---|---|---|
|WPS581|How Successful is World Bank<br>Lending for Structural Adjustment?|Patrick Conway|January 1991|S.**Fallon**<br>37947|
|WPS582|Adjustment Programs and Bank<br>Support: Rationale and Main<br>Results|Vittorio Corbo<br>Stanley Fischer|January 1991|L. Oroposa<br>39075|
|WPS583|World Bank Lending for Education<br>Research, 1982-89|Marlaine E. Lockheed<br>Alastair G. Rodd|January 1991|C. Cristobai<br>33640|
|WPS584|Whither Hungary and the European<br>Communities?|Alfred Tovias<br>Sam Laird|January 1991|G. Ilogon<br>33732|
|WPS585|Financial Innovation and Money<br>Demand: Theory and Empirical<br>Implementation|Patricio Arrau<br>Jose de Gregorio|January 1991|S. King-Watson<br>31047|
|WPS586|The Challenging Arithmetic of Poverty<br>in Bangladesh|Martin Ravallion|February 1991|WDR Office<br>31393|
|WPS587|Quantifying the Magnitude ard<br>Severity of Absolute Poverty in the<br>Developing World in the Mid-19830s|Martin Ravallion<br>Gaurav Datt<br>Dominique van de Walle<br>Elaine Chan|February 1991|WDR Office<br>31393|
|WPS588|Obstacles to Developing' Small and<br>Medium-Sized Enterprk ;: An<br>Empirical Assessment|Brian Levy|February 1991|E. Madrona<br>37496|
|WPS589|To Prescribe or Not to Prescribe:<br>On the Regulation of Pharmaceutical<br>in Developing Countries|Jeffrey S. Hammer<br>s|February 1991|0. Nadora<br>S6-065|
|WPS590|The Domestic Financial Market and<br>the Trade Liberalization Outcome:<br>The Evidence from Sri Lanka|Premachandra Athukorala<br>Sarath Rajapatirana|February 1991|M. Kienzle<br>30733|
|WPS591|Global Indicators of Nutritional Risk|Rae Galloway|February 1991|0. Nadora<br>31091|
|WPS592|Official Credits to Developing<br>Countries: Implicit Transfers to the<br>Banks|Asli DemirgUg-Kunt<br>Harry Huizinga|February 1991|S. King-Watson<br>33730|
for government social programs, which we compare to auxiliary survey data on their expenditures. The Indonesian Household Socioeconomic Survey (known as SUSETI) can be linked to the UDB and is therefore used in this paper rather than the nationally representative _Susenas_ . The remainder of this section first presents the SUSETI data and discusses the procedure for linking households with the UDB, before describing the methods for evaluating targeting accuracy under the pre- and post-UDB regime.

# **Indonesian Household Socioeconomic Survey (SUSETI)**

The SUSETI sample comprises 5,682 households located in 600 villages spread across six districts where the country's conditional cash transfer program (known by its Indonesian acronym PKH) was to expand in 2011.<sup>11</sup> The survey was originally designed for the purposes of a high-stakes experiment exploring different targeting methods (see Alatas et al., 2013, 2016 for further details). We exploit the fortuitous timing of SUSETI to provide insight into the effectiveness of the UDB. The baseline data were collected in March 2011, and a later endline survey was conducted for the targeting experiment in February 2012. We rely primarily on the baseline round of data because it is more comprehensive in terms of household characteristics, predetermined with respect to PKH benefits rolled out in late 2011, and closer in time to the UDB registration survey conducted in mid-2011.

SUSETI households were randomly selected among those who met the PKH demographic eligibility criteria of having an expectant mother or at least one child under the age of sixteen. This population is important for at least two reasons. First, according to nationally representative household survey data from 2011 ( _Susenas_ ), around two-thirds of Indonesian households have at least one child aged below sixteen. Moreover, these two-thirds of households are more than twice as likely to fall below the poverty line as households with older or no children. These general patterns hold for SUSETI and

> 11 The survey was conducted in three provinces meant to represent a wide range of Indonesia’s diverse cultural and economic geography: Lampung (Central Lampung and Bandar Lampung districts), South Sumatra (Ogan Komering Ilir and Palembang districts), and Central Java (Wonogiri and Pemalang districts). The survey initially included 5,998 households, but there is an attrition of about 5% of the original households between the baseline and endline waves. The analysis relies on the 5,682 households surveyed in both waves. Attritors do not systematically differ from non-attritors along baseline characteristics used in SUSETI and in the UDB to construct the PMT scores (results available upon request). The PKH program is not evaluated as it had not yet been fully rolled out by the time of our analysis, making it difficult to assess baseline targeting.

11
# **Table 4. Flows Into and Out of Poverty**

# **A. Movements in "Traditionally Measured" Poverty**

|Poverty status|**Russia (95-96)**<br>12||<br>Poverty status|**Poland (94-95)**||
|---|---|---|---|---|---|
|months ago|This month's|poverty status|12 months ago|This month's|poverty status|
||Poor|Non-poor||Poor|Non-poor|
|Poor|44.7%|55.3%|Poor|55.3%|44.7%|
|Non-poor|13.8%|86.2%|Non-poor|11.2%|88.8%|

# **B. Movements in "Underlying" Poverty**

||**Russia (95-96)**|||**Poland (94-95)**||
|---|---|---|---|---|---|
|Poverty status|12||Poverty status|||
|months ago|This month's|poverty status|12 months ago|This month's|poverty status|
||Poor|Non-poor||Poor|Non-poor|
|Poor|79.4%|20.6%|Poor|80.2%|19.8%|
|Non-poor|5.2%|94.8%|Non-poor|4.9%|95.1%|

Note: Underlying poverty measures are based on simulations assumning log normality of expenditure distributions. Poverty is measured by equivalent expenditure where the equivalence scale is household size raised to the power of 0.75. The poverty line is such that the poverty rate is 20% in all years. Measured poor are those whose equivalent expenditure in the current year falls below the poverty line. Underlying poor are those whose equivalent expenditure purged of transitory shocks falls below the poverty line. Standard errors are never larger than one fifth of the transition probabilities.

Panel A of Table 4 shows movements in and out of poverty as measured by expenditures in the current month while panel B shows movements based on individuals' underlying poverty status. Both panels show that mobility in and out of poverty is markedly higher in Russia than in Poland. The contrast between traditionally measured poverty flows and underlying poverty flows is even stronger. According to the traditional measure, 55.3% of the Russian poor can expect to escape poverty by the next year, while only 20.6% of those in underlying poverty can expect to escape poverty. In Poland, these figures are 44.7% and 19.8% respectively. Hence, a large fraction of the movements in and out of poverty are not related to persistent changes in economic fortunes, but merely reflect one-time transitional shocks affecting either expenditure this month or expenditure 12 months ago. When poverty is defined by the lowest quintile in the per capita income distribution, Galasi (1998) finds that in Hungary 40.5% of the poor in 1994 (according to the traditional measure) can expect to escape poverty by the next year, which is similar to the figure found for Poland.

In Poland, for example, 44% of those who escape poverty according to the traditional measure, are individuals whose persistent expenditure remained above the 26
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similarity in performance on the socially mediated task across genders is thus parallel to that of the private learning task when added incentives are provided. These results indicate that providing a small incentive to communicators is extremely cost-effective at both improving farm profitability and eliminating discrimination against women in productive social tasks.

We conduct this experiment in an important setting for policy. Agricultural yields have remained low and flat in Sub-Saharan Africa over the last 40 years, and low adoption of productive technologies is thought to be a major cause (World Bank 2008, Conley and Udry 2010, Duflo et al 2011). Lack of persuasive sources of information may make farmers reticent about new technologies, which makes the communication task we assign to men and women in our field experiment extremely important. Yield and other outcome data collected from over 4,000 farmers living in our study villages suggest that success in the assigned tasks has large welfare implications. Use of the new technology increases yields by over 30% in arid areas (BenYishay and Mobarak 2014; Beaman et al 2015).

More broadly, gender disparities in the United States and other developed countries have been studied extensively, and we add to that literature by investigating the sources of disparities in developing countries where gender inequality is more pronounced (Jayachandran 2015). Gender discrimination also has more devastating consequences in developing countries, where 6 million women are reportedly “missing” at birth, and many do not survive into adulthood (World Bank 2011; Duflo 2012).

The rest of the paper is organized as follows: In section 2, we describe the context and our experimental design. Section 3 discusses the data, while results on communicators’ unmediated and mediated tasks are presented in sections 4 and 5. Mechanisms are discussed in section 6. We offer a brief conclusion along with implications for policy in section 7.

# **2.  Context and Experiment**

# **2.1 Extension Network**

Malawi is predominantly agricultural, and 85% of the population lives in rural areas (Malawi Integrated Household Survey 3, 2011). 56.6% of the rural population was classified as poor in 2011 (MIHS3). Maize is the primary staple food in Malawi. With one rainy season, there is 5
current poverty and thus we see a population-weighted lower-middle-income country estimate of _CPY_ that is almost 50% higher than the unweighted estimate.

# _Relative loss within countries_

We next contrast the relative sizes of the three types of well-being losses at the country level. The results are again presented by country-income groups in Table 2, which examines the proportion of countries where (a) the _FPY_ estimate exceeds _CPY_ , (b) the _YLL_ estimate exceeds the _CPY_ estimate, and (c) the _YLL_ exceeds the sum of _CPY_ and _FPY_ . Regardless of national income level, the long-term effect of school closures may have larger poverty consequences than the immediate poverty impacts due to social distancing (first column). For 77% of all countries and no less than 68% in any income category, the _FPY_ estimate is greater than _CPY_ . This underscores the importance of mitigating learning losses associated with school closures in nearly every country.

Second, the relative importance of excess mortality tends to increase with national income (second and third columns). Only 13 percent of low-income countries have a larger _YLL_ than _CPY_ , while 61 percent of high-income countries have a larger _YLL_ than _CPY_ . A similar gradient exists when comparing mortality with the combined poverty shock of contemporaneous changes and learning losses. Only 4 percent of lowincome countries have a larger _YLL_ than the sum of _CPY_ and _FPY_ . This rises to 29 percent of upper-middleincome countries and 24 percent of high-income countries. High-income countries were more successful at mitigating the possible poverty impact of the pandemic rather than its mortality impact. This is not the case for lower-income countries with fewer available resources.

**Table 2: Within-country comparison of** **_YLL_ ,** **_CPY_ and** **_FPY_ : proportion of countries, by country income group, where one dimension exceeds others**

||**Future vs Current poverty**|**Life Years lost vs ad**|**ditional poverty years**|
|---|---|---|---|
||**_FPY_>****_CPY_**|**_YLL_>****_CPY_**|**_YLL_>****_CPY_+****_FPY_**|
|LICs|0.83|0.13|0.04|
|LMICs|0.73|0.18|0.03|
|UMICs|0.68|0.46|0.29|
|HICs|0.84|0.61|0.24|
|World|0.77|0.37|0.16|

_Note: The table reports unweighted proportions of countries where the inequality holds._ While Table 2 explores “how frequently” _YLL_ estimates are larger than _CPY_ (or _CPY+FPY_ ), Figure 2 quantifies the degree to which poverty impacts exceed or fall short of mortality costs. The figure plots for each country the ratio _CPY_ / _YLL_ (blue circles) as well as the ratio ( _CPY_ + _FPY_ )/ _YLL_ (red crosses). These two ratios thus provide the number of additional poverty years estimated in the country for each year of life lost. These two ratios can be interpreted as “break-even” values, which is the value of the normative parameter for which one would conclude that the well-being loss coming from the country’s years of αα life lost is exactly equal to the well-being loss coming from its additional poverty years. These break-even αα values can help the reader assess which channel leads to the larger wellbeing loss. If the reader’s preferred 9
# _1.4_ Omnibus balance tests of localities’ sample

||T0 vs T1|T0 vs T2|T1 vs T2|
|---|---|---|---|
|Yucatán (dummy)|0.196|0.541|-0.060|
||(0.767)|(0.730)|(0.715)|
||[0.799]|[0.459]|[0.933]|
|Marginalized Locality Index 2010|-1.261|-0.886|0.793|
||(0.975)|(0.980)|(1.000)|
||[0.196]|[0.366]|[0.428]|
|Proportion of women 3 years or older that<br>|0.408|0.459|-0.082|
|speak indigenous language||||
||(0.491)|(0.479)|(0.413)|
||[0.406]|[0.338]|[0.843]|
|Grants awarded from CONAFOR between<br>|0.077|0.067|-0.015|
|2007-2017||||
||(0.105)|(0.090)|(0.070)|
||[0.461]|[0.456]|[0.829]|
|Proportion of adult women in adult<br>lti|-1.953|-6.956|-4.068|
|popuaon||||
||(6.780)|(5.770)|(5.381)|
||[0.773]|[0.228]|[0.450]|
|Adult Population|0.004|0.001|-0.002|
||(0.003)|(0.003)|(0.003)|
||[0.275]|[0.639]|[0.603]|
|Constant|1.185|3.731|1.897|
||(3.531)|(3.033)|(2.821)|
||[0.737]|[0.219]|[0.501]|
|Chi2|2.997|3.142|1.649|
|p-value|0.8092|0.7908|0.9490|
|Pseudo R2|0.0577|0.0448|0.0235|
|_N_|73|76|77|
|Specification|Logit|Logit|Logit|
|Base|T0|T0|T1|

* _p_ <0.1; ** _p_ <0.05; *** _p_ <0.01. _P-values in squared brackets. Robust standard errors clustered by agrarian unit in parenthesis. Coefficients are log odds._

10
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# **5 Reference**

# **References**

- **Ashraf, Nava, Oriana Bandiera, and B Kelsey Jack** , “No margin, no mission? A field experiment on incentives for public service delivery,” _Journal of public economics_ , 2014, _120_ , 1–17.

- **Bank, World** , “The Human Capital Project,” _World Bank_ , 2018.

- **Barrera-Osorio, Felipe and Dhushyanth Raju** , “Teacher performance pay: Experimental evidence from Pakistan,” _Journal of Public Economics_ , 2017, _148_ , 75–91.

- **Behrman, Jere R, Susan W Parker, Petra E Todd, and Kenneth I Wolpin** , “Aligning learning incentives of students and teachers: Results from a social experiment in Mexican high schools,” _Journal of Political Economy_ , 2015, _123_ (2), 325–364.

- **B ́enabou, Roland and Jean Tirole** , “Incentives and prosocial behavior,” _American economic review_ , 2006, _96_ (5), 1652–1678.

- **Bruns, Barbara, Deon Filmer, and Harry Anthony Patrinos** , _Making schools work: New evidence on accountability reforms_ , The World Bank, 2011.

- **Cilliers, Jacobus, Ibrahim Kasirye, Clare Leaver, Pieter Serneels, and Andrew Zeitlin** , “Pay for locally monitored performance? A welfare analysis for teacher attendance in Ugandan primary schools,” _Journal of Public Economics_ , 2018, _167_ , 69–90.

- **Filmer, Deon, James Habyarimana, and Shwetlena Sabarwal** , “Teacher Performance-Based Incentives and Learning Inequality,” 2020.

- **Fryer, Roland G** , “Teacher incentives and student achievement: Evidence from New York City public schools,” _Journal of Labor Economics_ , 2013, _31_ (2), 373–407.

- **Gilligan, Daniel O, Naureen Karachiwalla, Ibrahim Kasirye, Adrienne M Lucas, and Derek Neal** , “Educator incentives and educational triage in rural primary schools,” Technical Report, National Bureau of Economic Research 2018.

- **Glewwe, Paul, Nauman Ilias, and Michael Kremer** , “Teacher incentives,” _American Economic Journal: Applied Economics_ , 2010, _2_ (3), 205–27.

- **Jr, Roland G Fryer** , “Financial incentives and student achievement: Evidence from randomized trials,” _The Quarterly Journal of Economics_ , 2011, _126_ (4), 1755–1798.

15