rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

Economic transformation from a predominantly agrarian and rural economy to an industrialized and urban one is considered a cornerstone of economic development (Matsuyama (2008), Syrquin (1988), Chenery (1960), Kuznets (1966), Clark (1957)). The classic literature on structural change focused on the long-run evolution of sectoral shares of agriculture, manufacturing and services in employment and output.<sup>1</sup> Recent studies on structural change in developing countries provide robust evidence that labor productivity in non-agriculture is significantly higher than in agriculture. Macmillan and Rodrik (2011) report that the productivity ratio between manufacturing and agriculture is on average 2.3 in Africa, 2.8 in Latin America and 3.9 in Asia. Such large productivity gaps imply that a structural change in favor of non-farm activities (manufacturing and services) may be desirable for both efficiency and poverty alleviation (Macmillan and Rodrik, 2011; Gollin, Lagakos and Waugh, 2014; Vollrath, 2009).<sup>2</sup> It is widely argued that misallocation of labor – reflected in persistent differences in labor productivity across areas and sectors – results from dualism in the labor market. The source of this dualism can be traced to formal and informal barriers/restrictions in the labor and land markets (for an excellent survey see Restuccia (2013)).<sup>3</sup> In many developing countries, especially in the 1950s and 1960s, dualistic policies were designed to ensure supply of labor at low wages to the urban manufacturing sector, and to relax the “savings constraint” and “wage goods constraint”; they include the so-called price-scissors (see Sah and Stiglitz (1987)), and geographic mobility restrictions such as Hukou in China and Ho Khau in Vietnam. Restrictions on sales of land were a salient feature of the policy regimes in many countries in Asia and Africa. This paper provides an analysis of the extent to which formal sales and rental restrictions on agricultural land can constrain employment transition from farm to non-farm activities in rural areas, and create policy-induced dualism within the rural economy. We provide evidence on the effects of land restrictions on disaggregated employment choices across five occupational categories. We also analyze whether land market restrictions affect per capita consumption in a village adversely.

> 1For recent surveys, please see Matsuyama (2008), Herrendorf et al. (2013).

> 2Gollin et al. (2014) show that the productivity gap between agriculture and nonagriculture cannot be explained by data quality and measurement problems.

> 3A partial list of contributions in this literature includes Adamopoulos and Restuccia (forthcoming), Hayami and Prescott (2008), Restuccia et al. (2008), Lewis (1954); Harris and Todaro (1970); Fei and Ranis (1964). In an interesting recent paper Gottlieb and Grobovsek (2015) find that policy distortions in land markets in Ethiopia may not have large effects on productivity because of strong endogenous price responses.

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of Type 2 errors, with almost three-quarters of those who are eligible not being covered by the program (0.71=5.48/7.71, i.e., _CR_ =0.29).<sup>17</sup> Nonetheless, targeting performance appears to be excellent by international standards, with _S_ =64%, _NS_ =8.3 and _CI_ =0.78.<sup>18</sup> Coady et al. (2004a,b) provide estimates of _NS_ for 85 programs. Argentina’s _Trabajar_ program has a _NS_ = 4.0, making it the best performer by this measure amongst all programs surveyed by Coady et al.<sup>19</sup> The median _NS_ is 1.25. By this measure, _Di Bao_ is a clear outlier in targeting performance internationally.

Turning to the fourth measure of targeting performance I find that while 29% of the poor receive DB, this is only true of about 2% of the non-poor. Thus I find that _TD_ =0.27. The mean DB payment across all those with _Y_ < _Z_ is 87.61 Yuan per person per year, while the corresponding mean for those with _Y_ ≥ _Z_ is 4.15. The overall mean DB payment across all recipients is 270.33, so _TD_<sup>_*_</sup> =0.31.

These calculations indicate that, while the program is good at concentrating benefits on the poor, it still falls well short of perfect targeting ( _TD_ =1) in which all of the poor and only the poor are covered, as would be implied by the program’s design (as was plain from Figure 2). Another way to see this is to calculate the total receipts for those with net income below the DB line. One then finds that only 12.1% of the aggregate DB gap is filled by the program. DB is a long way off reaching its own aim of bringing everyone up to the DB line.

The weak coverage of the program — in terms of both coverage of those living below the DB line and coverage of the DB gap — is naturally limiting its impact on poverty, despite excellent targeting in the sense of avoiding leakage to the non-poor. Table 3 gives various poverty measures before and after DB transfers. To test robustness to the location of the DB lines, Figure 3 gives the empirical cumulative distribution functions of income — with

> 17 Ravallion (2007) proposes a method of testing robustness of such calculations to income measurement errors and implements the method for the same program.  The results indicate that, if anything, the survey-based incomes probably understate the coverage of the eligible population.

> 18 To calculate _CI_ from the micro data I used the regression-based method of Jenkins (1988).

> 19 _Trabajar_ is a combination of a workfare program and social fund, whereby participants are offered low-wage work to do things of value to poor communities; see Jalan and Ravallion (2003). Coady et al., calculate the normalized share from _Trabajar_ from Jalan and Ravallion (2003) who estimate that 80% of _Trabajar_ participants come from the poorest 20% of the Argentine population ranked by income net of _Trabajar_ receipts (which are roughly constant across recipients).  The corresponding normalized share for the poorest decile is much higher, at about 6.0, though still less that for DB.

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Government strategies for much of the reform period can be summed up in the words of a high official, Du Runsheng (1989: 192): “Prosperity to few, then to many, then to all.” Wherever there has been a tradeoff, efficiency considerations have taken precedence over equity considerations. In creating a “developmental state,” the government overwhelming prioritized economic growth. However, it appears that the leadership became more sensitive to rising income inequality in the mid2000s, when policies to promote a more “harmonious society” were introduced. We examine the policies that have been introduced and those that seem promising for the future.

# <<A>>Policy Implications

Policy can be addressed at two levels: the redistribution of primary income through income transfers and alterations in the primary income generation processes. We focus on both of these in turn. “Harmonious society” policies have concentrated on the former and on people at the bottom of the national income distribution. In 2007, no less than 97 percent of poor households (defined as those with real income per capita of less than 1.25 dollars a day [purchasing power parity]) were rural dwellers (Li et al. 2013). There was a series of pro-rural policies. One of these policies concerned agricultural taxes and fees, which had been oppressive and regressive, averaging 5.3 percent of rural household income overall and 13.9 percent for the lowest income decile in the 1995 CHIP survey. Agricultural taxes and fees were abolished in 2006, so the average tax rate on rural household incomes was only 0.3 19
**Figure 6: Population differences between 2014 Census and 2014 HBS**

Share of total population by region <!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Nord Centru Sud Chisinau<br>Population Census Population HBS<br><!-- End of picture text --> Source: Authors’ estimates based on HBS and Population Census data.

The HBS questionnaire includes multiple questions about incomes, expenditures of households, as well as some social, demographic and labor market characteristics. The CEQ analysis relies as much as possible on information about social transfers and taxes reported in the survey. If the survey does not include questions on certain items, the values were either simulated or imputed. The numbers on total fiscal revenues and expenditures were taken from the administrative data provided by Ministry of Finance, Ministry of Health, Labour and Social Protection and National Bureau of Statistics of the Republic of Moldova.

In the analysis we include all existing social contributions and almost all direct taxes applied to households (this amounts to about 93 percent of total revenue collection for these types of taxes). For indirect taxes the coverage is also very high – 97 percent of total indirect taxes are reflected in the analysis. For social policy this ratio is 91 percent and for in-kind health and education services – 86 percent.

Most of the programs included in the analysis match the administrative numbers fairly well (Table 7). Direct taxes and social contribution cover 82-87 percent of the aggregate administrative numbers for those items. Some mismatch could be explained by the natural bias in the survey sample and some income misreporting at the top of the distribution. Contributory pensions are overestimated quite significantly, while other direct transfers show lower numbers than in aggregate statistics. This could be explained by some flaws in the sample frame, that does not fully reflect the current demographic structure of the population. Indirect taxes included in the analysis represent only half of the total collection from these sources. However, this is not a signal of underestimating those aggregates, because not all VAT and especially import duties fall onto consumers and that is why these numbers are not expected to match completely. The in-kind health and education benefits were scaled down intentionally using the ratio between total expenditures in HBS and National Accounts as it is advised by the CEQ methodology (Lustig, 2018).

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|Montenegro|-|-|0.000|61|
|---|---|---|---|---|
|Syrian Arab Republic|0.000|48|0.000|62|
|Timor-Leste|-|-|0.000|63|
|State of Palestine|-|-|0.000|64|
|Bhutan|0.000|60|0.000|65|
|Maldives|0.000|57|0.000|66|

# **4. Evidence on the impacts of China’s trade shocks on exports of B&R economies**

This section examines econometrically the extent to which the multilateral exports of B&R economies were impacted by China’s trade shocks during the period 1995-2015. In doing so, the analysis seeks to quantify the importance of supply and demand shocks, and to assess which type of shock was more important for each country and sector.

# **4.1 Methodology**

The econometric analysis in this section builds on Autor et al. (2013), who focus on the impact of increased Chinese import competition on local labor markets in the US. Autor et al. (2013) emphasize that local labor markets in the US were differentially exposed to Chinese import competition because of initial heterogeneity in production structure, and argue the transition of China to a market economy (and consequent rise of its productivity and trade flows) can be regarded as an exogenous trade shock to local labor markets in the US. Although the rise of China also represented a global demand shock (manifested in the rise of China’s imports), Autor et al. (2013) note that such demand shock was relatively unimportant for the US. This is because the increase in US imports from China was much stronger than the rise in US exports to China.

Figure 5 shows that the overall trade deficit of B&R economies with China increased over this period: Panel A reveals that the B&R trade deficit (computed as if B&R economies other than China were a single economy) rose from about 1.3% in 1995 to 4% in 2015; while Panel B indicates that the trade surplus of China with B&R economies decreased in the last years of this period.

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# Gender, Social Support, and Political Speech: Evidence from Twitter

Rachel Heath<sup>∗</sup> Roy Van Der Weide<sup>†</sup>

- ∗Department of Economics, University of Washington; rmheath@uw.edu

> †World Bank; rvanderweide@worldbank.org

> ‡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. The authors gratefully acknowledge the financial support from the Office of the Chief Economist for the Middle East and North Africa (MNACE) under the regional Labor and Gender Research Programs. The authors would also like to thank Rwan Adil Osman Mahmoud for excellent research assistance and Jed Friedman and Hans Hoogeveen for their generous comments.
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Table 2. Scenarios to 1992

|...__<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_..._<br>"Reference"<br>scenarioa|_<br>____<br>gcena<br>No.1|___________<br>rios<br>No.2c|
|---|---|---|
|Average annual<br>percentage<br>changes|||
|<br> <br><br>CNP- OECD area<br>2.5|2.1|2.1|
|- United States<br>2.4|1.6|-2.1|
|-Japan<br>2.8|2.5|2.4|
|<br> <br>- Germany<br>2.4|<br>2.2|2.0|
|<br>Employment- OECD area<br>0.8|0.6|0.7|
|<br>Consumption deflator- OECD area<br>2.9|2.7|2.9|
|Levels in 1992|||
|<br>Current account($bn.)- United States -168|-118|-108|
|<br>to<br>of<br>to<br>-<br>Japan<br>102|88|92|
|<br> <br> <br> <br> <br>of<br>to<br>to<br>- Germany<br>24|<br>16|14|
|<br>General<br>govt. net lending (ZGNP)- U.S.<br>-0.6|1.1|1.1|
|<br>Interest rates (short-term)<br>- U.S.<br>3.2|3.2|4.1|

# a. Eztrapolation of present policy "intents"; ezchange rates stable at November 1986 levels.

- b. Reductions in US government expenditure and increases in US income **tax** of $ 70 bn. and $ 50 bn. respectively by 1992.

- c. Same US fiscal policy as in Scenario 1 and further dollar depreciation by 15 per cent in 1988.

Source: Model-based simulations done on the responsibility of the World Bank.
all contribute to low levels of investment in skill formation at early ages, or to environments that do not promote the acquisition of important cognitive or noncognitive abilities by children.

Finally, there may also be equity considerations for public investments in early childhood. Efforts to equalize initial endowments avoid many of the moral hazard problems inherent in programs that seek to equalize outcomes in adulthood. Young children do not reduce their effort in response to an early stimulation program, but adults may work less if they know they are eligible for unemployment benefits. In the language of the World Bank’s 2006 World Development Report, Equity and Development, it may therefore be more effective to promote equality of opportunities (in early childhood) than equality of outcomes (in adulthood) (World Bank 2005a; see also Currie 2001).

# **2. Evidence on returns to investments in early childhood in the United States**

If the theoretical case for investments in early childhood is sound, one would expect to see high returns to programs that effectively build skills early in the life cycle. This section briefly discusses the empirical evidence from the United States, focusing on findings from two randomized evaluations of pilot interventions, as well as on evaluations of the impact of the Head Start program.

The Perry Preschool Program is probably the most-studied preschool intervention in the United States. Between 1962 and 1967, a sample of 128 low-income African-American children ages 3 or 4 who were assessed to be at high risk of school failure were randomly assigned into treatment and control groups. The treatment group received a half-day preschool every weekday plus a weekly home visit—both for eight months of the year, for two years. Project staff collected data on both study groups from ages 3 to 11, and again at ages 14, 15, 19, 27 and 40. Analysis of these data showed that the treatment group outperformed the control group on a variety of measures of educational attainment, including lower grade repetition, higher rates of high school graduation, and higher performance on various intellectual and language tests up to age 7, school achievement tests at ages 9, 10, and 14, and literacy tests at ages 19 and 27. At age 40, those who received the preschool intervention had median earnings that were more than one-third higher than those who did not, were significantly more likely to be employed, had lower fractions of lifetime arrests, and were sentenced to significantly fewer months in prison (Schweinhart 2005; see also Currie 2001; Carneiro and Heckman 2003).

The Carolina Abecederian Project provided a particularly intensive intervention: At birth, children were randomized into a treatment group that received “enriched center-based child care services emphasizing language development for eight hours per day, five days a week, 50 weeks 5
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analyze trends in each dimension separately, especially when many indicators of wellbeing are considered.

The criticisms of multidimensional poverty measures may be justified in most cases. However, these measures will continue to develop since it is now universally agreed that welfare and poverty are multidimensional. It is clear that income or consumption is not sufficient to capture all aspects of poverty. Following the official definition of the World Bank and United Nations, poverty goes beyond a lack of income, since it means a lack of basic capacities to participate effectively in society. For instance, if there is a lack of health centers, it is difficult to treat illnesses even if we have a high income. Another advantage to our approach is that analysis could be carried out using a single dimension or, alternatively, by combining information on different aspects of poverty. To improve the conceptual framework, the choice of dimensions to be included in wellbeing needs to be better justified. In the absence of a clear consensus on these dimensions, a conceivable way is to connect them to international treaties and agreements such as the MDGs. For instance, the Bristol approach was often used by UNICEF to assess child deprivation in developing countries.

This paper applies the same approach for analyzing gender inequalities in multidimensional deprivation in two countries in West Africa: Burkina Faso and Togo. These two countries have a common border and belong to WAEMU (West African Economic and Monetary Union). Although both are poor economies, one country (Togo) is coastal, while the other (Burkina Faso) is landlocked. Our purpose is to analyze whether the nature of gender inequalities differs from one country to another. The multidimensional poverty estimation is based on recent household surveys. The CWIQ (Core Welfare Indicators Questionnaire) 2011 is used for Togo, while the data for Burkina Faso come from EICVM (Enquête Intégrale sur les Conditions de Vie des Ménages) 2009/2010. Both surveys are nationally representative and include information on several dimensions of wellbeing as well as the usual socioeconomic and demographic characteristics of households. Some dimensions are common to household members and include _housing_ (ownership of dwelling, overcrowding/occupancy, roof quality, wall and 4
TABLE 4 Variance decompositions: Various inflation measures (percent)

||Import prices|PPI|Headline CPI|GDP deflator|Core CPI|
|---|---|---|---|---|---|
|Factor|||All countries|||
|Global|54.4<br>(51.8, 45.6-66.1)|42.1<br>(41.1, 15.3-61.7)|15.7<br>(22.9, 6.2-36.5)|12.6<br>(18.7, 3.2-30.5)|4.7<br>(12.5, 1.7-15.4)|
|Group|2.1<br>(8.5, 1.1-15.1)|3.2<br>(6.6, 0.9-8.8)|9.1<br>(12.8, 3.4-21.4)|12.1<br>(16.0, 4.6-25.5)|14.1<br>(18.8, 2.9-26.6)|
|Global + Group|63.2<br>(60.4,51.5-70.4)|49.9<br>(45.2,19.7-67.3)|34.1<br>(35.7,25.4-44.6)|35.3<br>(33.8,19.1-45.0)|26.3<br>(27.2,10.9-40.4)|
|Factor|||Advanced economies|||
|Global|54.4<br>(52.7, 45.6-66.1)|56.1<br>(48.4, 33.9-63.7)|21.4<br>(23.7, 11.0-36.6)|13.4<br>(19.1, 6.5-31.5)|7.9<br>(15.5, 3.1-21.4)|
|Group|2.5<br>(7.5, 1.4-15.1)|4.5<br>(7.7, 1.1-9.6)|11.5<br>(14.0, 4.1- 24.1)|12.6<br>(15.9, 3.5- 25.0)|17.7<br>(21.9, 8.7-34.8)|
|Global + Group|63.2<br>(60.2,49.5-72.5)|60.6<br>(51.6,31.2-68.1)|35.9<br>(37.7,31.1-44.8)|37.3<br>(33.6,18.1- 45.8)|34.3<br>(33.5,18.9-46.4)|
|Factor|||EMDEs|||
|Global|56.9<br>(48.2, 40.2-64.9)|16.2<br>(28.3, 11.1-40.4)|11.6<br>(21.4, 3.4-36.3)|9.3<br>(18.0, 2.2-26.1)|1.7<br>(8.0, 1.3-9.1)|
|Group|<br>1.0<br>(12.8, 0.7-13.1)|<br>1.4<br>(4.7, 0.8-4.0)|<br>7.7<br>(10.4, 0.9- 11.8)|<br>9.5<br>(16.2, 6.2- 25.5)|<br>2.9<br>(11.5, 1.5-13.5)|
|Glbl G|<br>61.4|<br>22.6|<br>25.6|<br>33.7|<br>11.8|
|oa + roup|(61.1,57.0-65.5)|(33.0,13.5-41.0)|(31.9,16.7-43.9)|(34.2,22.2-37.4)|(15.0,3.0-16.5)|

Notes: The contributions of global and group inflation factors to inflation variance are estimated with a two-factor dynamic factor model for each of the five different inflation measures: Import prices, PPI, headline CPI, GDP deflator, and core CPI (Annex 2.1). The sample includes 38 countries (25 advanced economies and 13 EMDEs), except for import prices, which are only available for 21 countries (17 advanced economies and 4 EMDEs). In each pair of rows, the first number in the first row indicates medians across countries. The first number in the second row (in parentheses) is the unweighted mean variance share across countries. The second and third numbers in the second row (in parentheses) indicate the interquartile range (25th and 75th percentiles). The results for headline inflation may differ from Tables 2.1 and 2.2 because of a smaller sample size, to match the sample with available data for other measures of inflation. CPI = consumer price index; EMDEs = emerging market and developing economies; GDP = gross domestic product; PPI = producer price index.

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