rafmacalaba/datause-encoder-data huggingface dataset:
A critical strength of the data set is that the numbers are adjusted to show the roles of both explicit and implicit subsidies—and, to the extent possible, to bring them into conformity with international accounting standards. (There are no international standards now, and Grameen Bank, for example, has claimed profitability even in years when its earnings from business have not fully covered its costs. For an anatomy of Grameen’s accounting from the 1990’s, see Morduch 1999.) The adjustments in our data include an inflation adjustment, a reclassification of some long-term liabilities as equity, an adjustment for the cost of subsidized funding, an adjustment for current-year cash donations to cover operating expenses, an in-kind subsidy adjustment for donated goods and services, loan loss reserve and provisioning adjustments, some adjustments for write-offs, and the reversal of any interest income accrued on non-performing loans.

One strength of the sample is that the microfinance institutions have been selected based in large part on their ability to deliver quality data. A disadvantage is that participation in the database is voluntary. (Grameen Bank, for example, chose not to participate during our sample period.) The data set is thus not representative of all microfinance institutions, and the sample is skewed toward institutions that have stressed financial objectives and profitability. However, the institutions collectively serve a large fraction of microfinance customers worldwide, and the set favors the institutions best-positioned to meet the promise of microfinance – that is, to both reduce poverty and create sustainable financial institutions.

While the data set lacks direct measures of outreach to the poor, it includes proxies that include average loan size, the fraction of borrowers that are women, and the fraction living in rural areas. These indicators are correlated with each other, and also with self-reported measures of household poverty. Thus, at a broad level, these measures of outreach help to distinguish between institutions serving the poorest customers versus those that focus on individuals with low-incomes (but who are substantially better off than the poorest).

The limits of the data set are addressed in part through comparisons with the parallel work of Gonzalez and Rosenberg (2006). They also analyze the Mix Market data, but they merge it with two larger data sets – the Microcredit Summit Database and a broader, unadjusted 8
Finally, it is possible to attempt to measure the technological spillovers channel directly: where _ait_ is a measure of technological spillovers by country _i_ at time _t_ , and _At_ =<sup>�</sup><sup>_N_</sup> _j_<sup>_ajt_is</sup> technological spillovers for the world as a whole. By and large, _ait_ is not directly observable. Nonetheless, it can be proxied by indicators such as total citations for a country’s patents by foreigners, or more crudely by the total number of approved patents held by a given country (Hall, Jaffe & Trajtenberg 2001), or the total number of scientific articles published by residents of a given country.

# **3.2 Data sources and adjustments**

We take the measures (1)–(5) to the data, drawing on long historical GDP data from Maddison (2003), and modern data from a combination of several alternative databases. The former dataset spans 1–2001, but since only output data are available, with substantial gaps, it is only used for the computation of (1) (and for illustrative purposes rather than formal analysis). The latter measures draw on data from the World Bank’s World Development Indicators (output data) and Global Migration Database (migration data), the IMF’s International Financial Statistics (financial data) and Direction of Trade Statistics (trade data), and an esoteric mix of science and technology databases, such as the WIPO Patentscope database and the NSF science and engineering indicators (technology data). These were then merged with a series of control variables obtained, among other sources, the WDI and IFS. These are described in detail in the technical appendix.

There are two possible candidate measures for the growth rate _gy_ . The most straightforward measure, which we apply as our benchmark, is the real growth rate, measured in constant 2000 U.S. dollars. However, over longer periods of time, such growth rates will be distorted by the Penn effect. Consequently, we also utilize the growth rate obtained from an output series that has been adjusted for purchasing power parity (PPP).

Since the focus of the polarity measures are on longer-term growth spillovers rather than cyclical factors, all candidate series were subsequently filtered with a Hodrik-Prescott filter to extract only the trend component (using _λ_ = 6 _._ 25), before annual growth rates were calculated from this extracted trend.<sup>6</sup> Where necessary, the output series were interpolated in order to obtain unbroken series that would be amenable to the HP Several other variables were also cleaned to reduce the effects of idiosyncratic variation that would have exaggerated the weighting. For example, the trade data were corrected for reexports for the major entrepˆot economies of Singapore, Hong Kong SAR, and the United Arab Emirates. The financial flows measure excludes derivative transactions, since these are

> 6Due to the substantial volatility present in the other data series, they were not similarly filtered, as doing so would have called for a very large smoothing parameter.

9
# **1. Introduction**

After two decades of multiparty democracy Mali was viewed as a democratic success story. The fifth presidential elections were scheduled to take place in March 2012 and another peaceful and democratic transfer of power was widely anticipated. Reality was different, however. A secessionist movement sparked by a Kel Tamasheq<sup>1</sup> rebellion led to a political and constitutional crisis culminating in a coup d’état in March 2012 and an attempt to take over the country by force. The three northern regions of Gao, Timbuktu and Kidal became occupied by various rebel and Islamist factions until early 2013 a coalition composed of the Malian Army, French troops and the ECOWAS-led African-led International Support Missions to Mali (AFISMA) recaptured the occupied areas. Most rebel fighters melted into the civilian population or retreated into the mountains, caves and inhospitable desert terrain.<sup>2</sup> Fighting between the Malian Army and rebel factions broke out again in May 2014 and ever since northern Mali remains insecure and contested.

At the height of the crisis over 500,000 people were displaced, almost half of the population of the north estimated at 1.2 million in 2009. As of October 2014, the number of Internally Displaced Persons (IDPs) was estimated at 86,026<sup>3</sup> and the total number of Malian refugees at 143,471 with around 55,414 residing in Mauritania and 53,491 residing in Niger, 32,771 in Burkina Faso and 1,330 in Algeria.<sup>4</sup> Socio-economic data on refugees and IDPs are rare and longer-term consequences of displacement on household welfare are not well researched and understood.<sup>5</sup> Data are often limited to the economic impact of refugees on host states and the ways in which refugees and IDPs engage in income-generating activities.<sup>6</sup> Information on returnees, once back in their place of origin, is hardly available.<sup>7</sup> This paper addresses these lacunae by providing data on the welfare of IDPs, refugees and returnees that includes economic impact but also security, social cohesion, education,

> 1 In this paper the term Kel Tamasheq (those who speak Tamasheq) is used instead of Tuareq as this is how the Kel Tamasheq identify themselves.

> 2 Francis David (2013): The regional impact of the armed conflict and French intervention in Mali. NOREF, Norwegian Peacebuilding Resource Centre

> 3 UNOCHA (Nov 2014): Mali: Evolution de Movements de Population

> 4 See UNHCR: http://data.unhcr.org/SahelSituation/country.php?id=501

> 5 Vervimp and Maystadt (2014): Forced Displacement and Refugees in Sub-Saharan Africa, p. 2

> 6 Betts et al. (2014): Refugee Economics. Rethinking Popular Assumptions, p. 6

> 7 European Commission (2014): Development, Refugees and IDPs. Issue Paper.

2
# **3 Results**

## **Baseline characteristics**

Baseline HIV and STI prevalence rates are reported in table 1. At baseline, 16.7% of the study participants tested positive for HIV and 13.5% tested positive for any of the two STIs (syphilis and trichonomiasis).<sup>6</sup> Consistent with data from the most recent DHS survey in Lesotho, the HIV prevalence rate was significantly higher for females (20.4%) than males (8.7%) participants and significantly higher for the older compared to the younger age cohorts.

Baseline characteristics, by assignment group, are presented in table 2.<sup>7</sup> Prevalence rates for HIV, and the two curable STIs, were similar across groups (panel A). The three assignment groups also had similar demographic and socioeconomic characteristics (panel B). Self-reported sexual behavior outcomes (panel C) were also similar on all but two outcomes – self-reported use of a condom during last intercourse (significantly higher at the 10%-level in the pooled intervention vs. control group) and self-reported likelihood that the last partner was HIV infected (significantly higher at the 10%-level in the pooled intervention vs. control group).

The last row in panel C reports average standardized pre-treatment effects of the four sexual behavior measures; i.e., we estimate a seemingly unrelated regression system, where _Y_ is a vector of _K_ related sexual behavior outcomes, _IK_ is a _K_ by _K_ identity matrix, standardized _T_ is a vector ofpre-treatmentassignment effect,to intervention _β_<sup> ̃</sup> = _K_<sup><u>1</u>∑</sup> _k_<sup>_K_</sup> =group(s)1 _σβ_ ˆˆ _<u>kk</u>_<sup>,where</sup> indicators,<sup>_β_ˆ</sup> _k_<sup>isthe</sup> and<sup>point</sup> derive<sup>estimate</sup> an average<sup>onthe</sup> treatment indicator in the _k_<sup>_th_</sup> outcome regression and _σ_ ˆ _k_ is the standard deviation of the control group for outcome _k_ (see Kling et al., 2004; Duflo et al., 2008). We find no significant difference between assignment groups in this aggregate measure of self-reported sexual behavior ("practice safe sex"). As shown in panel D, among HIV negative individuals at baseline – the main sample for the HIV incidence analysis – all four self-reported

> 63.8% of the respondents were tested positive for syphilis while the prevalence of trichomoniasis was 10.4%. To increase precision, we consider the joint measure of syphilis and trichomoniasis prevalence as the main STI outcome.

> 7In expectations, 30% of the sample should have been assigned to the high and low lottery arm, respectively. While the share assigned to the high [low] group is higher [lower] than 0.3, we cannot reject the null hypotheses that the sample comes from a distribution with means 0.3 for each group (results available upon request).

9
**Table 2: Study characteristics**

||Mean|Standard<br>deviation|
|---|---|---|
||(1)|(2)|
|Focused on high-income country|0.67|0.48|
|Publication timing|||
|_2007-2012_|0.33|0.48|
|_2013-2018_|0.41|0.50|
|_2019-2021_|0.26|0.44|
|Published in peer-reviewed journal|0.85|0.36|
|Discipline|||
|_Economics_|0.50|0.50|
|_Education_|0.44|0.50|
|_Psychology_|0.06|0.23|
|Randomized control trial|0.70|0.46|
|Evaluation of expansion in coverage|0.46|0.50|

_Notes_ : Observations are studies, N=54.
<!-- Start of picture text --> Trinidad and Tobago: GDP and Oil Prices Algeria: GDP and Oil Prices<br>a rr) wo<br>©3<br>a<br>a.ao?8gs9 o S56gg a®a)8gf© o fe)gg<br>g Go Bo a<br>Nn<br>- x -<br>1960 1980 2000 2020 1960 1980 2000 2020<br>Year Year<br><!-- End of picture text -->
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75
<!-- Start of picture text --> TH index - Drought Number of Droughts<br>nehpi f A eyif al4<br>Ce ee 2<br>rae<br>y Oe ee | ranele : J<br>=<br>if<br>; : mi2.0.5. a+ a<br>m2.1.3.0a jet a Ter,B0.0,01 i<br>oot. ae ONe data i<br>%4 of vulnerable hh with main income from agriculture % of vulnerable hh without access water supply<br>by state system by state<br>HHwith predictedDraughtprabability> 0.50 HHwith predicted probability> 0.50<br>‘att Droughts7<br>Fs :<br>La<br>ls<br>t ,<br>Mii? 28 : a<br>oloia: fF See cd<br><!-- End of picture text -->

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<mark>WPS3571</mark>

**Does tariff liberalization increase wage inequality? Some empirical evidence**

Branko Milanovic and Lyn Squire<sup>1</sup>

# ABSTRACT

The objective of the paper is to answer an often-asked question : if tariff rates are reduced, what will happen to wage inequality ? We consider two types of wage inequality : between occupations (skills premium) and between industries. We use two large databases of wage inequality that have become recently available and a large data set of average tariff rates all covering the period between 1980 and 2000. We find that tariff reduction is associated with higher inter-occupational and inter-industry inequality in poorer countries (those below the world median income) and the reverse in richer countries. However, the results for inter-occupational inequality though must be treated with caution.

_JEL classification_ : F1, F13, D31, J31 _Keywords_ : tariffs, trade, liberalization, wages, inequality Corresponding author: <u>bmilanovic@ceip.org</u> World Bank Policy Research Working Paper 3571, April 2005 _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 polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 World Bank Research Department and Carnegie Endowment for International Peace, and Global Development Network respectively. The work was in part financed out of World Bank Research Grant PO85725 (project on Globalization and Middle Classes). Milanovic would also like to acknowledge the support of McArthur Foundation. The paper is forthcoming in the book “Globalization and Poverty” edited by Ann Harrison (University of Chicago Press). The authors are grateful to Gouthami Padam for assistance with data collection, and to Ann Harrison, Doug Irwin, and participants of two National Bureau of Economic Research conferences for their comments and suggestions.

1
Table 2: Descriptive statistics of households (survey round 1) by IHS3 status

||Non IHS3 EAs|IHS3 EAs|p-value:<br>non IHS3 = IHS3|
|---|---|---|---|
|Female headed household|0.304<br>(0.461)|0.260<br>(0.439)|0.800|
|Highest level of education, HoH|6.337<br>(3.831)|6.191<br>(3.334)|0.066|
|Head attended secondary school|0.230<br>(0.422)|0.186<br>(0.389)|0.003|
|Household size|4.380<br>(1.998)|4.969<br>(2.291)|0.000|
|Number of children under 14|2.149<br>(1.568)|2.388<br>(1.718)|0.005|
|Number of EA|[23]|[159]||

Standard deviations in <u>parentheses.</u> 35
**Figure 2:** Opportunity cost of carbon emissions and marginal abatement costs for revenueneutral policies with equal emissions price _τ_

<!-- Start of picture text --> $/tCO2<br>τ μ−μ ∗ IBER μ ∗ IBER<br>IBOR μ<br>τ ( μ − μ ∗ IBOR )<br>μ 0<br>τ μ 0 −μ ∗ ABR ( q ABR ∗ /q 0)<br>ABR<br>LSR,OBR<br>τ<br>IBER −c ′ ( μ )<br>μ<br>μ IBOR μ IBER μ LSR, μ OBR<br>μ ABR<br><!-- End of picture text --> _Note: The downward-sloping black line labeled −c_<sup>_′_</sup> ( _μ_ ) _reflects the firm’s marginal abatement costs. Marginal abatement costs increase with increasing abatement (lower μ). Colored lines represent the opportunity cost of carbon emissions under each policy variant. The firm optimally chooses emission intensity where the opportunity cost of emissions is equal to the marginal abatement cost._ Figure 3 illustrates the output quantity outcomes under the different revenue-neutral policies, with the y-axis originating at no-policy unit costs. With downward-sloping demand, emissions reduction policies that raise unit costs less will lead to less output contraction. The intensity- and output-based rebating policies are ordered according to the revenue-neutral conditions, although the general rebating formulas are given.

When all emissions revenues are rebated, neither OBR, IBER, nor IBOR pass on any embodied emissions costs to consumers. Their relative prices are then determined by the relative amount of intensity abatement that drives their unit costs, as indicated in Figure 2. For the same emissions price, both forms of intensity-based rebating encourage more abatement than OBR (as shown in Propositions 3 and 5), but the emissions-based rebating 17
# **I. Introduction**

This paper exploits a unique longitudinal data set from Tanzania to examine the impact of child labor on education, employment choices, and marital status over a 10-year horizon. The question is important for many reasons. The assumption that work is harmful to children’s development underpins both the theoretical literature and the policy debate on child labor. For example, from the policy perspective, there is a general perception that the worldwide returns to eliminating child labor are substantial (see International Labour Organization [ILO], 2003). However, the evidence that rigorously quantifies the consequences of child labor is limited.

Theoretically, it is unclear whether and to what extent child labor is harmful. In rural settings in developing countries (which is where more than 70 percent of the world’s child labor occurs; ILO, 2002), child labor tends to be a moderate-intensity activity, while at the same time schooling is part time and intermittent. Child labor may also provide the child with work experience that subsequently could be rewarded in the labor market. The overall effect is ambiguous.

Empirically, although there is a large and growing body of evidence that child labor is harmful (reviewed in Section 2), the existing literature has many limitations, some of which we seek to overcome in this paper. First, most papers focus exclusively on schooling as an outcome. Although schooling is central, it is also important to consider other outcomes, in particular outcomes that allow us to measure possible effects of child labor on economic activity -- in this paper we look at occupation, crop choices, and labor productivity. Second, most papers in this literature examine correlations, rather than causal relationships. There are many reasons in the context of child labor why correlation need not imply causation. Both between and within households there is selection along observable (education, wealth, occupation, children’s age, 1
<!-- Start of picture text --> Access to Impoved Water Sources<br>(% of population)<br>Access to Electricity Network 110<br>(% of population)<br>100<br>87 85 100 99<br>95<br>80 93<br>90 89<br>60<br>60<br>80<br>80<br>40<br>70<br>64<br>20<br>15 60<br>10<br>0 50<br>Botswana Upper Middle Income SSA Middle Income World Botswana Upper Middle SSA Middle Income High Income OECD World<br>Income<br>Road Density in Terms of Population<br>(road-km/1000 people)<br>Teledensity 20<br>(total telephone subscribers/1000 people)<br>1600<br>17.3<br>1393<br>15<br>1200<br>800 10 9.2<br>635<br>7 6.7<br>468 501 6.2<br>400 372 5<br>3.3<br>99<br>0<br>Botswana Upper Middle SSA Middle Income High Income OECD World 0<br>Income Botswana Upper Middle SSA Middle Income High Income OECD World<br>Income<br><!-- End of picture text --> 35
- 14 -

# As one would expect, the highest volume of Investment per household took place

In Gongzhullng and Xiajlang where farms are larger.

# Table 4: Changes In Ownership of Consumer Durables in Study Areas

|County,<br>Year|Gongzh<br>(N-20|ullng<br>0)|Ju<br>(N-|rong<br>199)|T<br>(N-2|al<br>00)|Xla<br>(N-|jiang<br>200)|
|---|---|---|---|---|---|---|---|---|
|:em|1983|1987|1983|1988|1983|1988|198|3<br>1988|
|Own 1.V.|6|42|11|38|2|26|5|48|
|Own radio/tape|3|14|2|17|2|12|34|44|
|g. no. of<br>bicycle|.62|.98|1.14|1.33|1.10|1.43|1.02|1.26|
|, own sewing<br>machine|46|57|22|37|11|33|10|19|
|=wnwatch|64|83|68|94|53|91|59|82|