rafmacalaba/data-use-mentions-extended huggingface dataset:
that countries use to implement a lower tariff toward a particular trading partner - arrangements which comply with basic GATT/WTO exceptions to the MFN principle of nondiscrimination - include free trade agreements, customs unions, unilateral preference schemes, and the lesser-known partial scope agreements.<sup>33</sup> Section 2.3.2 provides more detail on the potential economic significance of tariff preferences arising under these PTAs. We begin by characterizing the scope of products for which countries can and do offer tariff preferences (given their MFN tariffs), the size of the discriminatory preference margins that are granted, and the trading partners to which they are being offered. Our productlevel bilateral tariff preference data is sufficiently rich that we can examine the bilateral tariff rates offered by 27 of our economies to the 30 trading partners in our sample.<sup>34</sup> While we document considerable variation across countries, a number of interesting patterns emerge. First, high-income countries, which generally have very low MFN tariffs, have relatively fewer products over which they can offer any preferential tariffs. These countries tend to offer

> noteworthy because this policy was designed by the WTO Agreements to be less discriminatory than other policy tools, like antidumping. In practice, the use of safeguards is a politically useful way for governments to discriminate between foreign suppliers, such as against varieties from a low-priced trading partner while minimizing the impact on a high-priced trading partner. See, for example, Turkey’s safeguards on imports of footwear described in Bown, Karacaovali and Tovar (2015).

> 33These exceptions include the original GATT 1947’s Article XXIV and the 1979 ‘Enabling Clause’ that permits non-reciprocal and non-comprehensive preferential tariff coverage for developing countries involved in PTAs.

> 34Bilateral tariff data is not available for four of the countries in our analysis. Furthermore, for tractability, we restrict the analysis to variation across 30 trading partners. This obviously misses additional variation associated with other trading partners.

19
roughly to levels observed in 2000 (Patrinos 2022). Educational outcomes are low even in some ECA EU countries, such as Bulgaria, where almost half of teenagers lack basic reading, mathematics, and science skills (against one in five in the EU). In contrast, Poland’s educational outcomes have been high and years of quality-adjusted education have been increasing, especially in the younger cohorts, which has likely contributed to faster catch-up with the EU than among ECA peers (World Bank 2022g).

While several factors seem likely to have contributed to the apparent fall in educational attainment in ECA, insufficient investment, especially in pre-primary and primary education, has likely played a significant role. In ECA as a whole, government spending on education fell from 4.2 to 3.9 percent of GDP between 2009 and 2019. Widening income inequality among the families of students in the region may have also had an effect. Learning outcomes in many ECA countries are considerably higher for socioeconomically advantaged students than for disadvantaged students, who are often effectively segregated from high achievers (OECD 2021a).

But educational challenges not only weigh on an inclusive recovery, but also hinder the private sector and dampen long-term growth prospects.<sup>11</sup> Mismatches between labor market needs and skills form a significant constraint on potential output growth in ECA. ECA countries rank above the EU average in skill mismatches, the gaps being particularly large for Albania and Bulgaria (IMF 2021a). Across ECA, skills of graduates from vocational and higher education are often poorly aligned with needs. One result is the high proportion of young people neither employed nor in education or training (NEETs). NEET rates in 2021 were above the EU average in most ECA countries, and more than 10 percentage points higher for women than men in Bulgaria, Poland, and Romania. High NEET rates may reflect weak labor market policies and lower spending in ECA countries compared to the EU. Participation in training (based on survey data from recent years)
<!-- Start of picture text --> 27<br>Greater . 22<br> Cairo 27<br>30<br>15<br>Alexandria and Suez Canal 9 41<br>11<br>37<br>Lower 42<br>Egypt 38<br>38<br>20<br>Upper 27<br>Egypt 24<br>21<br>0 10 20 30 40 50<br>Percent of firms<br>ME ELS 2012 inest. MM ELMPS 2018 in est.<br>YY) Economic Census Establishment Census<br><!-- End of picture text -->
ekly aaa|||
||Date|ZAR|USD|ZAR|USD|||
|**Poverty line**||||||||
|Adult|Early 2016|1386|232|320|54|||
|Household (4 people)|Early 2016|5544|927|1279|214|||
|**Minimum wage**||||||||
|Domestic work|2015-2016|2550|427|588|98|||
|Hospitality|2015-2016|2750|460|634|106|||
|Wholesale and retail|2015-2016|3250|544|750|125|||
|Private security/contract cleaning|2015-2016|3500|585|808|135|||
|_Panel B: Benchmarking sample earni_|_ngs and certif_|_cation t_|_reatmen_|_t efects _|_on earnings_|||
|||Wee|kly|As % of|poverty line|As % of min|. wage|
||Date|ZAR|USD|Adult|Household|Hospitality|Retail|
|Baseline mean earnings if employed|Late 2016|562|94.1|1.76|0.44|0.89|0.75|
|Endline mean earnings|Early 2017|159|26.6|0.50|0.12|0.25|0.21|
|Endline mean earnings if employed|Early 2017|518|86.7|1.62|0.41|0.82|0.69|
|Treatment efect|Early 2017|54.1|9.05|0.17|0.04|0.09|0.07|

Calculations assume 1 rand _≈_ 0.167 USD in purchasing power parity terms; 4.33 weeks per month. Household poverty lines assume households of four people with only one earner. Control group respondents work 29 hours
14 -

-

investment goods to be used in equation 8, an attempt was made to allow for the wide array of investment incentives. In particular it was possible for both Turkey and Morocco to quantify the impact of accelerated depreciation, regional incentives, and interest rate rebates.

# The results

The output equation (equation 8) was estimated first with the addition of another term to allow for the cost of imported inputs and of a time trend to capture the effect of technological progress and changes in normal hours. The results are presented in table 1. Several problems arose in measuring output and factor prices. For example, it was not possible for either country to rely on a true price index for domestic output sold on domestic markets. Instead, only the GDP deflator for manufacturing was available. This index also includes the price of output sold on foreign markets, and its use is likely to exacerbate the multicollinearity with the export price. The problem was to some extent addressed by testing and imposing that the coefficients on pX and p are equal for Turkey and by dropping px for Morocco on the ground of statistical insignificance.

For factor prices the minimum wage and the average wage rates were used for Morocco and Turkey, respectively as a proxy of hourly labor costs at a normal operating rate (see footnote 5). The choice was dictated by data availability. Both the minimum and the average wage rates are likely to present significant shortcomings. The minimum wage measure does not allow for the possibility that workers are paid more, even under normal operating hour3, and the average rate measure blurs the effects of normal
Where _c_ is the cluster/EA, _t_ is the year and _O_ is the refugee’s country of origin. For this instrument to be valid, it has to influence our outcome variables only through the presence of refugees. In other words, this instrument has to be a good predictor of the number of refugees within a given cluster while remaining uncorrelated with the error terms. Our assumption to satisfy this exclusion restriction is that the distance between any given cluster and the border point through which refugees come into Uganda is completely independent from the outcome variables. We further relax that assumption in Section 5.2. By linking that distance with the number of refugees from country _O_ within the cluster _c_ , and summing up over all possible _O_ , we obtain a good predictor of the total number of refugees within cluster _c_ at time _t_ . However, we will discuss further the plausibility of our identifying assumption in Section 5 (Sub-section 5.2).

According to Figure 1, there are 17 source countries registered in the UNHCR refugees data set. Only 7 countries account for 99.7% of the total number of refugees in Uganda (excluding refugees in Kampala whose source country is not known) in the study period. We focus on these 7 countries to construct the IV.<sup>12</sup>

> 12 More specifically, these 7 countries include Sudan/South Sudan, DRC, Rwanda, Burundi, Somalia, Eritrea and Ethiopia. Despite its border with Uganda and having more than 1,000 refugees over the study period, Kenya is not included in the IV analysis as it contributes only about 0.002% of the total. In addition, the refugee border crossing points are lined only along the northern, western and southern borders of Uganda. It is thus difficult to assume that Kenyan refugees had to first cross one other international border before crossing into Uganda. Yet, this assumption can be made for refugees from Somalia, Ethiopia, and Eritrea (assuming they would enter Uganda
- 16 -

<!-- Start of picture text --> Figure 6. Monthly precipitation by district<br>June 2002 June 2003 June 2004<br>Monthly Monthly Monthly<br>precipitation precipitation precipitation<br>(mm) (mm) (mm)<br>4.5 - 10 3.6 - 10 - 10<br>10 - 25 10 - 25 10 - 25<br>25 - 40 25 - 40 25 - 40<br>40 - 55 40 - 55 40 - 55<br>55 - 121 55 - 95 55 - 161<br>No data<br>for Mat District<br>. . .<br>Source: Author’s drawing based on weather data from IEWE, Albania.<br><!-- End of picture text --> From the longer time perspective, the climate will be changing. The following analysis of this paper will remain static to the extent that the data are collected during the fairly short period of time and no dynamic consumer response, such as home appliance purchasing, is assumed. However, it is still indicative to see the impacts of climatic conditions by evaluating the estimated demand function with respect to expected climate change. In Albania, average temperature increased around 1 °C in the past 30 years, while precipitation declined especially during winter and spring (Figure 7). In the future, temperature is projected to increase 1.2–1.8 °C by 2050 and 2.1–3.6 °C by 2100, and more droughts and less precipitation are expected (ESMAP, 2009). Therefore, on one hand the electricity demand may continue increasing unless significant improvements in energy efficiency of the economy take place. On the other hand, the country’s water resources may become scarcer, intensifying the country’s climate vulnerability. If more droughts happen, as in 2002 and 2007, the country will be faced with significant supply
**What Did the Maoists Ever Do for Us?**

# **Education and Marriage of Women Exposed to Civil Conflict in Nepal**

Christine Valente<sup>a</sup> University of Sheffield, U.K.

_JEL_ Codes: I20, J12, O12 Keywords: Civil conflict, Education, Marriage, Gender, Nepal.

> aEmail: <u>christine.valente@sheffield.ac.uk. I thank INSEC for sharing their conflict data. I also thank Martha</u> Ainsworth, Quy-Toan Do, Helge Holterman, Steve McIntosh, Gudrun Østby, Kati Schindler, Olga Shemyakina, Hassan Zaman, the World Bank’s Nepal Country Director's office, and participants at two Gender and Conflict Research Workshops at the World Bank (Washington) and PRIO (Oslo) for their useful comments. Funding by the World Bank-Norway Trust Fund is gratefully acknowledged. The views expressed in this paper are mine alone and do not necessarily reflect those of the funding agencies. All errors are my own.
economic inclusion. At

> 2These program components are similar in spirit to the ILO’s Start and Improve Your Business program as in deMel, McKenzie, and Woodruff (2014) and the personal initiative training in Campos et al. (2017).

> 3We study the differential impacts of a variation of this program that delivers coaching at women’s homes or businesses in Lang and Seither (2022). The full design is pre-registered as AEARCTR-0003214Z (Lang, Magruder, and Seither, 2022).

4These differences in timing for the endline survey were caused by COVID-19 restrictions that prevented travel within Uganda.

4
- National Development and Reform Committee (2009). (www.ndrc.gov /jjyx/gjyx/) Beijing: National Development and Reform Committee.

- NBS (National Bureau of Statistics). 2009. ―There Are 225.42 Million Migrant Workers at the End of 2008.‖ March 25, http://www.stats.gov.cn/tjfx/fxbg/t20090325_402547406.htm).

- ———. 2010a. ―Monitoring Reports for Migrant Workers in 2009.‖Forthcoming. In _Population and Labor Report in 2010_ , ed. Cai Fang, pp 1-22. Beijing:  Social Sciences Academic Press.

______. 2010b. China Statistical Yearbook, 2010. Beijing: China Statistics Press.

- Park, Albert, John Giles, and Yang Du. 2011. ―Labor Regulation and Enterprise Employment in China.‖ Hong Kong University of Science and Technology, mimeo.

- People’s Bank of China. 2010. ―Employment of Migrant Workers: Analysis of Fifth Rural Household Survey.‖ In _Population and Labor Report in 2010_ ,  ed. Cai Fang, pp 35-46. Beijing: Social Sciences Academic Press.

- Sheng, Laiyun. 2009. ―Migrants Are Faced with New Challenges on Their Employment during the Financial Crisis.‖ Chinese Academy of Social Sciences, unpublished mimeo.

- Wang, Dewen, Zhanxin Zhang, Jie Cheng, and Huili Hou. 2009. ―The Impact of the International Financial Crisis on the Transfer of the Rural Labor Force in China’s Impoverished Areas.‖ _Chinese Rural Economy_ (9): 211–27.

- Xiao, J. 2008. ―The Biggest Printing Enterprise Is on the Verge of Bankruptcy in Shaoxing.‖ _City Express_ ( _Dushi Kuaibao_ ), October 9, 11–14.

- Yang, N. 2008. ―Ministry of Agriculture Alert: Attach Great Importance to Land Transfer Conflict When Migrants Return.‖ _First Financial Daily_ , December 9, A3.

- Zeng, Xiaoquan, Yuxue Cui, and Dajian
This paper builds on the existing literature in two important aspects. First, to our knowledge, this study is the first to identify causal impacts of migration and remittances on child outcomes and labor supply in sending households in Indonesia. In doing so, it explicitly deals with the endogeneity of migration. Second, it identifies the gender dimensions of these impacts.

# **3. Data**

The data used in this paper comes from the Indonesia Family Life Survey (IFLS). This survey started in 1993 and is nationally representative. It covers 13 out of 27 (now 33) provinces in Indonesia, but represents 83% of the national population in 1993. The first wave of IFLS covered 7216 households, and later waves of the survey attempted to capture as many of these original 7216 households as possible. Unique household-level and individual-level panel data sets were constructed. The final data set used for analysis in this paper covers 6128 households tracked in all four waves, excluding those few households with zero expenditure or zero household size reported. Data was also collected about the communities in which these households lived.<sup>5</sup> In addition to basic household characteristics, the IFLS includes a consumption module and also collects a few questions related to international migration. Some information on human development outcomes (education, health), labor supply (hours worked), and household assets is available. While information collected on income is weak, there is detailed expenditure data on food and non-food items, such as health, education and durables. We will use per capita expenditure, constructed in the IFLS, as a proxy measure of welfare, and housing and land ownership as measures of asset ownership.

The regression analysis in this paper focuses on the 2007 and 2000 IFLS rounds for the following reasons. The survey was not designed to focus on international migrants and remittances, and only limited information were collected about migrant characteristics and remittances sent by these migrants. The earlier years of the IFLS have a very small sample size
|(0.3741)|(0.2726)|(0.3957)|(0.3622)|(0.3822)|(0.2919)|
|Observations|26,803|26,803|26,803|26,803|26,803|26,803|26,803|
|R-squared|0.336|0.150|0.092|0.148|0.206|0.140|0.093|

Note: This table reports district fixed-effect RDD-DD regressions of income and poverty of households using household-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

80
property. The second important feature of the ejidal sector in the context of this paper relates to governance. All ejidos have the same minimal administrative structure: a president, secretary, oversight committee, and treasurer are elected by vote for three year terms, and are not allowed to serve consecutive terms in the same position. Many villages also maintain committee structures beyond this basic arrangement. Communities hold regular assemblies to make decisions on a variety of issues, such as enrollment in government programs that affect the entire ejido, including the PSA. In all of the communities that we interviewed, decisions are made based on majority voting. Evidence of a vote to participate in the program is required along with the other application materials. In order that there be a formal vote for participation in a PSA (or other government) project, a clear sequence must occur. First, a general announcement of a meeting must be made. When the meeting is held, there must be 50% plus 1 in attendance. In the case that attendance is insufficient, the meeting ends and there is another meeting announcement. In the second meeting the assembly is held, with a formal vote, even if attendance does not reach 50%. The vote is validated by the National Agrarian Registry (RAN).

In the surveys used for this study, we asked a series of questions about these meetings. Participation in community meetings is mandatory, although not all communities sanction lack of attendance – 59% do have some kind of punishment for non-attendance, and within the past year, 70% have applied these sanctions. Attendance rates tend to be fairly high; we asked leaders the number of members attending the most recent assembly, and find the median attendance to be 62%<sup>6</sup> .

> 6For the random sample of households to whom we administered the household survey, we also asked how many assemblies a household member was sent to within the last year. Across our more than 8,000 households, less than
. As shown in Figure 2, however, countries typically set their tari¤s on agricultural goods well below the bound tari¤s imposed by such multilateral tari¤ agreements; see Gibson et. al (2001, pp. 20-21) for further data and discussion. This implies that policy makers have ample policy space to adjust tari¤s strategically in response to rainfall shortages. We will nevertheless address this concern directly in the econometric analysis below.

# [Figure 2 about here]

Table 1 reports summary statistics on the ...nal panel data set employed in the regression analysis. As can be seen, most observations refer to developing countries in Latin America and Caribbean, Asia-Paci...c and Africa.<sup>17</sup>

[Table 1 about here]

> 16 As shown in Table A.3, this latter de...nition includes as well SITC product categories "08", "09", "11", "12" and "29".

> 17 We adopt a broad de...nition of the term "Asia-Paci...c", comprising all nations in Asia and Oceania.

16
- Chelsky, J., Morel, C., and Kabir, M. (2013). Investment financing in the wake of the crisis: The role of multilateral development banks. _World Bank - Economic Premise_ , (121).

- Corsetti, G., Guimaraes, B., and Roubini, N. (2006). International lending of last resort and moral hazard: A model of IMF’s catalytic finance. _Journal of Monetary Economics_ , 53(3):441–471.

- Cortina, J. J., Didier, T., and Schmukler, S. (2018). Corporate Borrowing and Debt Maturity: The Effects of Market Access and Crises. Discussion Paper 13008, Center for Economic and Policy Research (CEPR).

- Dasgupta, D. and Ratha, D. (2000). What factors appear to drive private capital flows to developing countries? and how does official lending respond? Policy Research Working Paper Series 2392, The World Bank.

- Dobbs, R., Pohl, H., Lin, D.-Y., Mischke, J., Garemo, N., Hexter, J., Matzinger, S., Palter, R., and Nanavatty, R. (2013). Infrastructure productivity: how to save 1 trillion a year. _McKinsey Global Institute_ , 88.

- Dreher, A., Fuchs, A., Parks, B., Strange, A. M., and Tierney, M. J. (2017). Aid, China, and growth: Evidence from a new global development finance dataset. Working Paper 46, AidData, Williamsburg, VA.

- Dreher, A., Fuchs, A., Parks, B., Strange, A. M., and Tierney, M. J. (2018). Apples and dragon fruits: the determinants of aid and other forms of state financing from China to Africa. _International Studies Quarterly_ , 62(1)
theory understands for economic policy. The timing of policies is irrelevant, expectations play no role whatsoever and forward-looking behavior as well as the fact that agents make purposeful decisions is plainly ignored.<sup>1</sup> Furthermore, the implicitly assumed connection between program designed policies and intertemporal fiscal solvency is unwarranted.

An issue for which the existing policy evaluation process is particularly ill equipped is the question of fiscal space for investment in infrastructure since decision-makers cannot evaluate the intertemporal trade-offs they face and thus cannot assess how current actions affect future behavior and performance or how future actions restrict the set of current feasible actions available.<sup>2</sup> In the Financial Programming approach, policies are judged on the basis of their effect on conventional fiscal balance and gross public debt targets where the distinction between current and capital expenditures becomes blurred, intertemporal effects are overlooked and a bias against public investment may arise at times of fiscal retrenchment when it is clearer that investment is “the least rigid component of expenditure” (De Haan et al., 1996). In support of this last assertion, Echeverry et al. (2004) report recent data from the Colombian ministry of finance estimating in 97% the proportion of the central government budget that is “inflexible”,

> 1 For a more detailed critique of the Financial Programming Model see Edwards (1989).

> 2 Other tools used by the IMF as analytical basis for policy advice and program design in middle income countries are the _Balance Sheet Approach_ and the _Debt Sustainability Analysis._ These tools are not useful either to address the question at issue.

1
have two indicators with one<br>disaggregation each.. One must be a mortality rate.|
||||(6.4) Fertility rate;|||
||||(6.5) Contraceptive prevalence rate;|||
||||(6.6)Adolescent birth rate.|||
|7||Gender Statistics|(7.1) Proportion or number of<br>women who are victims of physical,<br>sexual, or psychological violence;<br>(7.2) Proportion of women in<br>government, management or senior<br>positions;|(7.1) Age; disability status;<br>relationship to perpetrator;<br>(7.2) None;<br>(7.3) None.|To receive full point: Must have all indicators with one<br>disaggregation.<br>To receive half point:Must have at least one indicator<br>with one disaggregation.|
||||(7.3)Data on child marriages.<br>||**Table A3 continued**|

36