rafmacalaba/datause-encoder-data huggingface dataset:
to increases in trade in developing regions, ranging from an increase of about 12 percent in imports and exports for MENA, to an increase of 18 percent for exports and more than 25 percent for imports in South Asia. Similarly, developing regions enjoy increases in real incomes in the _GVC Friendly Liberalization and Trade Facilitation_ scenario. This illustrates dramatically that despite choices made by the high-income economies and China, developing countries can be in control of their own trade outcomes rather than passive victims of the decisions of others.

Table 1 **.** Real income, real exports and real imports compared with COVID L-shape recovery – 2030 (%)

||Rhi|Real Income|<br>GVC fidl|Rhi|Exports|GVC fidl|Rhi|Imports|GVC fidl|
|---|---|---|---|---|---|---|---|---|---|
|%|esorng<br>leading<br>economies|Reshoring<br>all|reny<br>liberalization<br>+TF imports|esorng<br>leading<br>economies|Reshoring<br>all|reny<br>liberalization<br>+TF imports|esorng<br>leading<br>economies|Reshoring<br>all|reny<br>liberalization<br>+TF imports|
|Middle East &<br>North Africa|-5.1|-8.3|1.1|-1.3|-12.1|11.6|-9.9|-22.2|12.3|
|Rest of East<br>Asia & Pacific|-3.8|-4.8|4.7|-4.7|-22.6|14.2|-9.4|-24.4|20.4|
|Europe &<br>Central Asia|-3.5|-5.5|2.1|-0.8|-16.1|12.0|-8.4|-22.2|14.2|
|Sub-Saharan<br>Africa|-2.7|-5.5|2.8|-3.0|-15.2|11.3|-9.0|-21.6|15.8|
|China|-2.6|-4.2|-1.9|-20.8|-22.1|-20.4|-22.8|-27.7|-20.5|
|Latin America<br>and<br>Caribbean|-2.1|-1.8|2.2|-2.9|-21.7|12.6|-8.2|-21.4|17.1|
|South Asia|-0.8|-3.0|4.3|-4.3|-26.0|18.3|-6.7|-21.3|25.5|
|Western<br>Europe|-0.7|-0.6|-0.4|-22.2|-21.1|-22.8|-19.7|-19.0|-19.7|
|United States|-0.4|-0.5|-0.2|-24.9|-24.7|-25.2|-20.1|-20.5|-19.4|
|Rest of High<br>Income<br>Countries|0.1|0.5|0.3|-24.2|-23.0|-25.0|-20.9|-19.5|-21.0|
|World|-1.5|-2.2|0.4|-17.0|-21.4|-12.6|-16.9|-21.5|-9.2|

Source: ENVISAGE simulations.

Reshoring by leading economies hits developing countries hard, but selected sectors could see their exports expand as trade is diverted from leading economies to the rest of the world (Table 2). When leading economies reshore their production, some sectors see expansion of their exports, even in regions that were badly hit, such as MENA, which sees its computer and electronics sector expanding by 43 percent compared to the COVID L-shape recovery (highest increase within the region). Some of the biggest increases are recorded in the livestock sector in South Asia (103 percent) and in China (51 percent), and in the computer and electronics sector in Sub-Saharan Africa (51 percent).

Sectoral exports are hit hard when developing countries reshore. When developing economies respond in kind to reshoring by leading economies, this aggravates the impact on exports. Developing countries, which before enjoyed expansion in certain sectors, now suffer contraction. In East Asia and Pacific, where 14
-6

FIGURE 1: 1 KUWAIT SHARE INDEX <!-- Start of picture text --> 550 -<br>500<br>450 - Market Support<br>400<br>350<br>300<br>250<br>200<br>I 979 9s0o 1981 1982 103 1s *; 133 19G 1987 1 S<br>Source: Al-Amwal Co. WLL, Kuwalt.<br><!-- End of picture text -->

# FIGURE 2: KUWAIT SHARE INDEX VOLUME (In millions of shares)

<!-- Start of picture text -->
1.'.<br>1  .3<br>1.2-<br>I.'<br>0.9<br>0.6<br>0.7<br>0.6<br>0.5<br>0.3<br>0.4  I<br>1979  1980  1981  1982  193  1984  1985  1966  137  19l<br>Source:  Al-Amwal Co. WLL, Kuwait.<br><!-- End of picture text -->
construction and data used in our formal econometric analysis. Section 4 contains our estimation results, and section 5 concludes with a discussion of the broader implications of our results for the evolution of trade policy in developing countries.

# **2 The WTO Antidumping Agreement and the Theory of Endogenous Trade Policy**

The proliferation of WTO-authorized antidumping laws and the global increase in use of this form of administered import protection has been widely recognized (Miranda, Torres and Ruiz 1998; Prusa, 2001; Zanardi, 2004). While antidumping was once a policy instrument used primarily by the US, Canada, EU and Australia, it is now used actively by over 40 WTO member countries. As table 1 indicates, some of the most frequent new users are developing countries.

To develop a theoretical motivation for our empirical analysis of the determinants of antidumping use by industries in developing countries, we proceed in two steps. In the next section we describe the WTO Antidumping Agreement, which sets out the general rules for national administration of antidumping law as well as the technical evidence necessary for a government to justify imposition of any new antidumping measure. Given the political-economic environment created by the WTO Antidumping Agreement, in section 2.2 we use the theory of endogenous trade policy to generate additional testable predictions for the econometric analysis.

# **2.1 The WTO’s evidentiary requirements for national use of antidumping**

Since the 1947 GATT, the rules of the international trading system have authorized countries to establish national antidumping statutes and to implement antidumping trade restrictions.<sup>6</sup> During the Kennedy and Tokyo Rounds in the 1960s and 1970s, negotiators attempted to put more structure on the GATT antidumping rules, but countries adopted the resulting Antidumping Codes only on a plurilateral basis. The 1995 inception of the WTO and its Antidumping Agreement (WTO, 1995) provided more detailed guidance for countries to implement and administer antidumping laws. First, because the Antidumping Agreement was part of the Single Undertaking, it established a common set of basic rules that would apply to all WTO members and be subject to the enforcement provisions of the WTO Dispute Settlement Understanding (DSU).<sup>7</sup> Second, relative to the GATT, the WTO Antidumping Agreement did impose

> 6 See Article VI of the 1947 GATT. National antidumping laws predate the GATT, and Article VI was largely written to accommodate these existing pieces of national legislation. Canada is credited with the first antidumping law with an implementation in 1904.

> 7 Nevertheless, Article 17.6 of the Antidumping Agreement does still imply that countries are allowed substantial discretion to implement their own version of antidumping – the WTO mainly requires that the country administer its use of antidumping in a way consistent with its own AD law.

4
Klapper, L., Lusardi, A., & Panos, G. A. (2013). Financial literacy and its consequences: Evidence from Russia during the financial crisis. _Journal of Banking & Finance_ , 37(10), 3904–3923.

Laeven, L., & Valencia, F. (2013). Systemic banking crises database. _IMF Economic Review_ , 61(2), 225– 270. International Monetary Fund, Washington, DC.

Lowe, P. (2002). Credit risk measurement and procyclicality. _BIS Working Papers_ , 116. Bank for International Settlements, Basel, Switzerland.

Mare, D. S., Moreira, F., & Rossi, R. (2015). Nonstationary Z-score measures. Working Paper.

Mehrotra A. N. & Yetman J. (2014). Financial inclusion and optimal monetary policy. _BIS Working Papers_ , 476. Bank for International Settlements, Basel, Switzerland.

Mehrotra A. N. & Yetman J. (2015). Financial Inclusion: Issues for Central Banks. _BIS Quarterly Review_ , March 2015, pp 83–96. Bank for International Settlements, Basel, Switzerland.

Melecky, M. & Popdiera, A. (2016). Financial Sector Strategies and Financial Sector Outcomes: Do the Strategies Perform?. Working Paper Morgan, P.J., & Pontines, V. (2014). Financial stability and financial inclusion. _ADBI Working Paper_ , 488. Asian Development Bank Institute, Tokyo, Japan.

Pearce, D., Ortega, C.R. (2012). Financial inclusion strategies: reference framework. Working Paper No. 78761, The World bank, Washington DC, USA.

Popov, A., & Udell, G. F. (2012). Cross-border banking, credit access, and the financial crisis. Journal of International Economics, 87(1), 147-161.

Prasad, E.S. (2010). Financial sector regulation and reforms in emerging markets: an overview. _IZA Discussion Paper_ , 5233. Institute for the Study of Labor, Bonn, Germany.

Royston, P. (1991). Comment on sg3.4 and an improved D’Agostino test. _Stata Technical Bulletin_ 3: 23– 24. Reprinted in _Stata Technical Bulletin Reprints_ , vol. 1, pp. 110–112. College Station, TX: Stata Press Sahay, R., Čihák, M., N’Diaye, P., Barajas, A., Mitra, S., Kyobe, A., Mooi, Y. & Yousefi, S. R. (2015). Financial inclusion: can it meet multiple macroeconomic goals? _IMF Staff Discussion Note_ , 15/17. International Monetary Fund, Washington, DC.

Swiss Federal Council (2009). Strategic directions for Switzerland’s financial market policy. Report in response to the Graber postulate. Bern, Switzerland.

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21
interact it with the treatment to see if the treatment is more powerful on individuals ―surrounded‖ (in some economic sense) by other treated individuals. These methods are inspired by measures of contagion used in epidemiologic studies.

In contrast with medical sciences, however, in social sciences the mechanisms by which contagion takes place are largely unknown. Baseline surveys as well as qualitative information gathered from focus group discussions may help in understanding and identifying channels through which future program benefits might spread from one firm to another — for example, professional association memberships, personal contacts and so on.

# **6.3 Heterogeneity**

Differences among beneficiaries, especially if they are unobserved, can pose particular challenges for evaluation. Policy interventions can have diverse impacts across economic agents. By focusing on average treatment effects, an evaluation ignores valuable information on the heterogeneity of the effects.<sup>43</sup> As Ravallion (2009, p. 37) puts it, practitioners should ―never be happy with an evaluation that assumes common (homogeneous) impact‖. He also argues that knowing more about the heterogeneity of the effects and the role of contextual factors is key to better understand the impact of the intervention and make evaluation more relevant for good policy-making. The challenges linked to the heterogeneity of the effects are relevant across types of interventions, whether they are of a targeted or a non-targeted nature.

First, the treatment effects of a program can be related to the observable characteristics of the beneficiaries. For example an export promotion program can have differential effects for participant firms depending on their prior export experience or on their workforce skill levels. If the export promotion program consists of a matching grant scheme which co-finances firms’ export business plan, the opportunity costs for participant firms may differ in terms of the alternative uses they could give to their funds.<sup>44</sup> A simple approach to address the heterogeneity of the effects when differences are observable is to add interaction effects with the treatment dummy variable in a regression framework that estimates the average treatment on the treated effect. Treatment effects can also vary with the distribution of outcomes themselves. Volpe Martincus and Carballo (2010) examine the impacts of export promotion activities across quantiles of the distribution of Chilean firms’ growth rates of exports using quantile treatment effects estimation. They find stronger effects at the lower end of the distribution, which are combined with data on firms’ export histories to show that smaller and relatively inexperienced firms as measured by their total exports benefit more from export promotion.

> 43 See Abbring and Heckman (2007) and Ravallion (2011) on the heterogeneity of impacts in evaluation studies.

> 44 While these opportunity costs may not be observable, they are likely to vary with observable characteristics such as the conditions in the local market or in the sector of activity.

34
Uganda, is also the region with the highest concentration of the country’s poor, with a poverty rate of 26 percent (UBOS, 2021). In the analysis sample, as discussed in the subsequent section, nearly 52 percent of all parcels of land owned or cultivated by the household were inherited or allocated by family or local leaders, suggesting that there is limited mobility of land across households.

# **3. Data**

The majority of the analysis that follows relies on household survey data collected through the Methodological Experiment on Measuring Maize Productivity, Soil Fertility, and Variety (MAPS), and the related plot-level soil sample testing results. These plot-level soil analyses are complemented by, and compared to, geospatial soil data extrapolated from the Africa Soil Information Service (AFSIS).

# **3.1.MAPS**

The Methodological Experiment on Measuring Maize Productivity, Soil Fertility, and Variety (MAPS) is a two-round household panel survey aimed at testing alternative methods of measuring maize production and key agricultural inputs, including soil fertility, maize variety, and plot area.<sup>7</sup> The resulting MAPS dataset includes a unique collection of objectively measured variables paired with data on household socioeconomics, demographics, and agricultural practices. MAPS Round I was fielded in 2015, and Round II was implemented in 2016. As the second round of the study did not include soil analysis, in this paper we utilize only MAPS Round I, which collected detailed data on the first (and the main) agricultural season of the calendar year.

In order to ensure high quality data collection and supervision, the MAPS sampling design was limited in its geographic scope. The sampling for MAPS Round I was completed in a multi-stage process. First, three strata were identified in the primary maize-growing regions of Eastern Uganda, namely Serere district, Sironko district, and a 400km<sup>2</sup> area spanning Iganga and Mayuge districts. From each stratum, enumeration areas (EAs) were randomly selected with probability proportional to size (15 from Serere and Sironko each, and 45 from the Iganga/Mayuge stratum). In each selected enumeration area, a full household listing was conducted as part of the study, identifying households who cultivated at least one maize plot and whether they had pure stand and/or intercropped plots. Finally, 12 households were selected from each enumeration area and with an effort to have an even split of purestand versus intercropping maize households. Due to the low incidence of pure stand maize plots, and cases in which plots identified as pure stand in

> 7 MAPS was implemented through a collaboration between the World Bank’s Living Standards Measurement Study (LSMS), the Uganda Bureau of Statistics, the World Agroforestry Centre, the CGIAR Standing Panel on Impact Assessment (SPIA), and Stanford University, with generous support from the UK government. It is part of a larger methodological research agenda undertaken by the World Bank’s LSMS, aimed at identifying improved methods of agricultural and household data collection using more objective, yet scalable, methods.

5
<!-- Start of picture text --> Pooled schemes Scheme 1<br>2222<br>©©<br>3©lg>e5 ©4=e5<br>Se22€ Sy =<br>oe£<5=g@8 <5=$<br>~.Nzy.NF]<br>°°<br>Jul 15 Jan 16 Jul 16 Jan 17, Jul 15 Jan 16 Jul 16 Jan 17,<br>1 Share of plots in deficit — voareeemeGaily 1 Share of plots in deficit — yoereaem®Gaily<br>Scheme 2 Scheme 3<br>2222<br>©©<br>©>©><br>ee<br>8 8<br>3 —e & =<br>& “3 8 +5<br>6 3G 3<br>“0- N4= “0.N8=<br>° °° °<br>Jul 15 Jan 16 Jul 16 Jan 17, Jul 15 Jan 16 Jul 16 Jan 17,<br><!-- End of picture text -->
|[Cash|linkedtospecifi|cserviceuse|
|---|---|---|
|1)JS¥, India|Deliveries may be done by unskilled support staffvs. skilled<br>nurses or doctors. Best practices, such as partogram, neonatal<br>resuscitation, and kangaroo care, are notfollowed. The system<br>of referral to a higher level for emergencies is inadequate.<br>Most mothers and babies are discharged within hours after<br>delivery because the hospitals lack amenities, and families<br>wantto return home having got the cash incentive. As a result,<br>there is inadequate time for mewborn-care counselling,<br>stabilisation of the post-partum mother, and detection of<br>danger signs in the mother and the infant.|Unable to detect a signifcant impact ofIS¥ on<br>[maternal mortality.<br>Difference-in-difference<br>|analysis does not show significant effect on<br>perinatal and neéo-natal mortality. However,<br>JSY payment was associated with a reduction<br>of aboutfour perinatal deaths per 1,000<br>|pregnancies in the matching and with-versus-<br>without analyses, and a reduction of about<br>two neonatal deaths per 1,000 livebirths.|
|2/SDIP, Nepal<br>|No assessment made of impact on quality of care, but the<br>limits to service provision (after as before) are highlighted,<br>particularly at lower level facilities. (Shortages of staff and<br>equpment limitthe services, especiallyfor emergency<br>obstetric care.)<br>|Not assessed, but limited impact expected<br>given limited increase in emergency obstetric<br>care and quality of care constraints<br>|
|vouchers|f|f|
|3|Chiranjeevi Yojana,<br>Gujurat, India, 2005<br>onwards|Not assessed, but authors note that PNC is notincluded inthe<br>package||Not assessed|
|4)Safe Motherhood<br>Vouchers, Kenya,|Not assessed|Not assessed|
|5/RH Vouchers, Uganda||The proportion of diagnoses correctly treated ranged from 79% <br>for balanitis to98% forgonorrea.||Knowledge ofSTI symptoms increased 18<br>percent between the first and second years<br>(adjusted odds ratio, aOR=1.43; 95 percent<br>Cl=1.22-1.68). The prevalence of syphilis, as<br>measured bythe VDRLtest, decreased 42<br>percent between the two surveys<br>(aOR=0.63795 percent Cl=0.48-0.79). There was<br>a greater reduction in the prevalence of<br>syphilis among respondents who lived within<br>10 kilometers of a contracted facility (57%<br>decreaseversus20%forthosefurtheraway)|
similar to that of Kling, Leibman and Katz (2007). The summary index is defined to be the equally weighted average of z-scores of the individual measures.<sup>18</sup>

# **VI.1. Hypothesis 1**

To test Hypothesis 1, we examine the effects of NSP on objective and subjective measures of economic well-being. Objective measures are captured by annual household income and consumption; whether the household head is unemployed or involved in subsistence agriculture; and net migration rates. Subjective measures are captured by the proportion of respondents who report that the economic situation of the household has improved in the past year and by the proportion who report that the economic situation of the village will improve in the forthcoming year.

Results in Panel A in **<u>Table 2</u>** show that the general average treatment effect on household income, consumption, and unemployment is not statistically significant, whereas involvement in subsistence agriculture is lower in treatment villages by 3 percent. The effect in the two eastern districts is significantly higher for income, which increases by 9 percentage points, and for unemployment, which decreases by 2 percentage points. The effect of NSP in the two eastern districts on consumption and on the share of villagers involved in subsistence agriculture and husbandry is not statistically different from the average treatment effect. Results in Panel C of **<u>Table 2</u>** indicate that the average effect on net migration is not statistically significant in non-eastern districts, but is significant in the two eastern districts, with average net migration higher by almost 19 families per year as compared to control villages.

As results in Panel B in **<u>Table 2</u>** show, NSP has a strong positive effect on subjective economic outcomes. Both male and female respondents in villages receiving NSP are more likely to report that their economic situation has improved from last year and are more likely to indicate that they expect the economic situation in the village to improve in the next year. For all measures, the proportion of respondents that perceive their economic situation positively is approximately 5 percentage points higher in villages receiving NSP, which corresponds to an increase of 11 to 18 percent depending on the measure. In the two eastern districts, the results are generally the same.

> 18 The z-scores are calculated by subtracting the control group mean from the treatment groups mean, and dividing by the control group standard deviation. Thus, each component of the index has a mean equal to 0 and a standard deviation equal to 1 for the control group.

15
data the few cases where women control own farm revenues, we exclude farm selfemployment from our measure of labor force participation. We focus instead on wage work (agricultural and non-agricultural) and off-farm self-employment.

The concern we have with own-farm work may also arise with respect to off-farm family businesses in which women labor as unpaid family workers. To avoid this as much as possible we define women who work in a household enterprise as participating in offfarm self-employment if the surveys identify them as specifically involved in the operation of the enterprise. This includes ownership or time spent in managing the day-to-day activities of the business, including finances or management of inputs. It should be noted that the surveys do not cover women who are not currently living in the household and may have migrated for work reasons, such as to pursue manufacturing jobs.

We compile our measure of women’s participation in market based work based on detailed data on the main and secondary occupations in the past year of female household members aged 15 and older. We focus on a binary labor participation outcome as opposed to hours/days worked, since individuals often work multiple jobs in a given period, and accurate measurement of time worked is not always straightforward.<sup>10</sup> Table 3 presents summary statistics for participation in off-farm work by women aged 15 and older and for the sake of comparison, for men’s participation as well. Offfarm work includes off-farm self-employment activities as described above and any wage work. The cross-country differences for women are large, ranging from a low of 3 percent in rural Yemen to a high of 40 percent in Madagascar. The variance across countries is much lower for men ─ ranging from 33 percent in Rwanda to around 52 percent in India, Morocco and Pakistan. As expected, women participate far less in off-farm work activities than men. Furthermore, fewer of those not working are instead enrolled in school relative to men in the same age group. This difference is particularly pronounced in Yemen, Morocco, and Pakistan.

The type of work underlying these figures also differs widely across countries. The few women in off-farm activities in Yemen are primarily involved in off-farm selfemployment activities. In Malawi and Madagascar, roughly equal shares of women were

> 10 We obtain similar results when looking at days worked in off-farm activities over the last year; these results are available upon request.

17