rafmacalaba/datause-encoder-data huggingface dataset:
# **Figures 1(a)-(h): Argentine macroeconomic performance in comparative perspective**

<!-- Start of picture text --> (a) Growth in GDP per capita, 1990 to (b) Consumer Price Inflation, 2001-2013<br>2013<br>Argentina Average EM-G20<br>Argentina Average EM-G20<br>30.0<br>15.0<br>25.0<br>10.0<br>20.0<br>5.0 15.0 Disputed<br>Data<br>0.0 10.0<br>-5.0 5.0<br>0.0<br>-10.0<br>-5.0<br>-15.0<br>(c) General Government Balance, % of (d) General Government Gross Debt, % of<br>GDP, 1995-2013 GDP, 1993-2013<br>Argentina Average EM-G20 Argentina Average EM-G20<br>4.0 150.0 Devaluation (even<br>much of domestic<br>2.0<br>100.0 debt in dollars)<br>0.0<br>1995199719992001200320052007200920112013 50.0<br>-2.0<br>0.0<br>-4.0<br>-6.0<br>(e) External Debt, % of GDP, 1990 to (f) Total Reserves, % of GDP, 1990 to<br>2013 2013<br>Argentina Average EM-G20 Argentina Average EM-G20<br>150.0 35.0<br>Note: relatively<br>closed economy => 30.0<br>100.0 relatively high as % 25.0<br>of exports 20.0<br>50.0 15.0<br>10.0<br>5.0<br>0.0<br>0.0<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012<br><!-- End of picture text --> 5
readiness. Data were collected from children, their caregivers and a variety of other respondents in 310 villages in 9 districts across Indonesia. Respondents were 11,263 caregivers who resided in these districts. Among those, 7,982 were mothers who fully reported on outcomes and attributes we employ in our models below, including location of her own birth. They represent 171 distinct districts of birth. Our analysis further restricts this sample to women in birth cohorts surrounding the time of the INPRES program implementation (those born between 1966 and 1975), leaving us with 2,118 mothers from 50 different districts of birth. Figure 1 presents the geographic distribution of contemporaneous district of residence (Panel A), as well as the spatial location of districts of birth for both the full sample (Panel B) and the working sample (Panel C).

Previous analysis has compared contemporaneous household level characteristics in the Indonesia ECED data to households observed in the rural subsample of Indonesia’s nationally representative National Socioeconomic Household Survey (SUSENAS). Hasan, Hyson, and Chang (2013) report that the education levels of the heads of households, the rates of asset ownership among households, and the quality of construction materials used in the home among the households in the ECED data are comparable to those observed in the SUSENAS. Similar analysis using a village level census reveals that the villages in the ECED data share many characteristics in common with the rest of rural Indonesia. As such, the underlying data from which our analytical sample is drawn share several characteristics in common with households and villages typically found in rural Indonesia.

Our identification strategy described below relies on both the year and location of birth, and this is because the INPRES program followed specific rules for allocation of schooling expansion funds across districts. Therefore, our second source of data is information on construction planned under INPRES reported in Duflo (2001), which identifies the number of schools to be built in each district after 1974. We merge this district-level data to our working sample of 2,118 mothers in the ECED data. Appendix Table A1 reports descriptive statistics for the districts on Duflo (2001) data as well as for districts of birth captured in the working sample.

It is clear from these figures that our working sample represents a set of districts that had a 6
nomic activity (see Appendix I for a comparison of the municipalities in the manufacturing sample and those not included). They also were strong recipients of IDP during the period of study (see Figure I), had lower levels of conflict-related violence, although higher levels of urban crime (measured through homicide rates) relative to the rest of the country (see panel a of Figure II).

Figure III shows the annual evolution of the mean of all the firm outcomes employed in this study.

# **III.2 Internal forced displacement**

Municipal data on forced displacement caused by violent conflict was obtained from the _Registro ́Unico de V ́ıctimas_ (RUV, Registration of Victims) from the _Unidad para la Atenci ́on y Reparaci ́on Integral de las V ́ıctimas_ of the Colombian government. This is the best source of information on individuals who were displaced by violence because it combines all official sources available where individuals registered upon arrival in the new locations (even if they registered with authorities a long time after their arrival, in which case they are included in the year in which they arrived in the data). Although not all individuals register, these data are an excellent approximation of the total number of individuals received in each municipality because being registered is a condition for accessing any type of governmental support from local authorities. To be registered, individuals have to declare their displacement under oath, attest to the exact dates of the event, their municipality of origin, and some of their socioeconomic conditions, as well as to describe the facts leading to their displacement.

The RUV data, consequently, offers information on the municipalities from which migrants were expelled and into which they were received. It is available annually by municipality between 1984 and 2016. In this paper, however, we focus on the period between 1995 and 2010 because it is the period for which firm data is also available. The time evolution of total displacement is presented in Figure I and shows strong variation between 1995 and 2010. The total number of receiving municipalities is presented in panel b of the same figure (Colombia has 1,122 municipalities).

11
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34
It is not an easy task to understand the driving forces of these transformations. Given that, by definition, the rank of types and income are correlated, it is extremely difficult to disentangle the changes that may have affected, in opposite directions, the distribution of outcome and the distribution of opportunities.<sup>31</sup> However, the trend of the North-South divide and labor market reforms may be considered among the determinants of redistribution since 2002. First, the different reforms realized in the recent past to reduce the gap in the opportunities accessible to different individuals have not been able to fulfill the desired goal. In particular, as shown by Pavolini (2011), among others, different public services, particularly different measures and interventions of the welfare state, are still suffering from territorial divergences with consequences in terms of an increase in IOp over time, as witnessed by the lower growth rates experienced by the Southern types.

Second, the labor market reforms introduced in 1998 and extended in 2000 and 2003, which mainly aimed to reduce the labor protection legislation (particularly for temporary workers), have increased wage flexibility and job turnover, increasing the “instability” in the opportunity faced by individuals (Jappelli and Pistaferri, 2009). This instability may explain why growth appears more opportunity regressive in the second period, a period of crisis. Boeri and Garibaldi (2007) suggest that although job flexibility generates instability, it may provide more job opportunities during periods of positive growth. This is not the case during recessions because these workers, in all categories of atypical job contracts, are more likely to be fired and are often excluded from social security benefits. We suggest that such an effect has been stronger in the southern regions, thereby explaining the territorial gradient in the diverging trends of different types.

# **_Opportunity and Growth in Brazil: The Data_**

Our theoretical framework may be of particular interest in the analysis of developing and emerging economies that experience lively growth processes with a dramatic impact on poverty and redistribution. For this reason, the second country considered in this paper is Brazil. To perform this analysis, the 2002, 2005, and 2008 waves of the Brazilian Pesquisa Nacional por Amostra de

> 31This may be a challenging question for future research.

27
# **Do Middle Classes Bring Institutional Reforms?**

Norman Loayza Jamele Rigolini Gonzalo Llorente<sup>1</sup> The World Bank The World Bank and IZA The World Bank JEL: D3, H5, O1, O4 Keywords: Poverty, Middle Class, Income, Institutions, Development 1 The authors gratefully acknowledge financial support from the Spanish Fund for Latin America and the Caribbean (SFLAC). We are also grateful to Nancy Birdsall, Francisco Ferreira, Luis Servén and participants to the Barcelona workshop on Socio-Economic Mobility and Middle Class in Latin America for insightful comments and suggestions. Tomoko Wada provided excellent research assistance. 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.
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Table 11. Robustness test: The e¤ect on utilization at the control facilities when controlling for proximity to project facility.

|Dependent variable|Out-Patient<br>Services|Delivery|Family<br>planning|Antenatal<br>care|
|---|---|---|---|---|
|Speci...cation|(1)|(2)|(3)|(4)|
|Distance to nearest project facility|-1.13<br>(2.11)|-0.10<br>(0.07)|0.07<br>(0.22)|-0.56<br>(0.52)|
|Constant|696***|12.4***|13*|96***|
||(66)|(2.75)|(7)|(18.1)|
|District ...xed e¤ects|No|No|No|No|
|Observations|25|25|25|25|
|R<sup>2</sup>|0.02|0.06|0.01|0.03|

a. *** [**] (*) denote signi...cance at the 1 [5] (10) percent level. b. Robust standard errors in parenthesis.

56
<!-- Start of picture text --> 3N8SoN<br>a<br>= Q33<br>£,38 558<br>s gm<br>im =<br>38<br>oo<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016<br>year year<br>———— China ———— Developed Countries -———— Developing Countries| ———_ Non-BRI ——— BRi(exclude China) ——— BRI<br>Source: UNCTAD aggregated FDI Source: UNCTAD aggregated FDI<br><!-- End of picture text -->

<!-- Start of picture text --> = oe < =<br>-> i—<br>Pe nt s<br>2-<br>|<br>2<br>£<br>ao<br>fa}<br>irs =<br>a Leese<br>eat rd<br>s- I<br>=} 17.<br>E t<br>On Ria<br>¢<br>°<br>0 5000 10000 15000 20000<br>Distance (kms)<br>=S==2 FDI 2001 ———_ FDI 2012<br>Source: UNCTAD FDI outflow<br><!-- End of picture text -->
industries (Cette, Nevous, and Py 2022; Gal et al. 2019). Conversely, concerns have been raised that friendshoring or nearshoring of global value chains may be associated with productivity losses (Moran and Oldenski 2016; Quian, Liu, and Steenbergen 2022).

Better measures of output gaps. Output gap estimates are important inputs into macroeconomic policy decisions, especially monetary ones. Hence, multivariate filter-based potential growth estimates could be tailored to capture more closely the relationship between domestic inflation and domestic monetary policy by controlling for additional external factors. These include global output gaps, global commodity price cycles, and global financial cycles. Especially for EMDEs, estimates could also be extended backwards in time and systematically tested, and adjusted, for major structural breaks.

19
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics

|IV<br>1980 share<br>(8)<br><br>-0.339***<br>(0.073)<br>0.422***<br>(0.123)<br>✓<br>50.04<br>0.085<br>(0.085)<br>0.107<br>(0.149)<br>✓<br>82.88<br>rmal share or<br>12 mesoregion<br>uted for 1980-<br>corresponding<br> percent, ** 5|
|---|
|(7)<br>-0.485***<br>(0.066)<br>-0.079<br>(0.060)<br>✓<br>0.574<br>0.026<br>(0.057)<br>-0.066<br>(0.056)<br>✓<br>0.582<br>-2010<br>ases) in info<br>justed for 1<br>trends comp<br>d using the <br>cant at the 1|
|(6)<br>-0.461***<br>(0.063)<br>✓<br>0.569<br>0.049<br>(0.053)<br>✓<br>0.579<br>OLS<br>1991<br>eases (decre<br>entheses) ad<br>t share. Pre-<br> instrumente<br>. *** Signif|
|(5)<br>-0.458***<br>(0.085)<br>0.293<br>0.126*<br>(0.071)<br>0.026<br>ply larger incr<br> errors (in par<br>nt employmen<br>pre-trends are <br> specifcations|
|IV<br>1980 share<br>(4)<br>-0.083*<br>(0.046)<br>0.305***<br>(0.071)<br>✓<br>50.20<br>-0.301***<br>(0.046)<br>0.036<br>(0.070)<br>✓<br>80.47<br>if change im<br>ons. Standard <br>e in the releva<br>able in 1991, <br>ntal variables|
|(3)<br>-0.171***<br>(0.051)<br>0.009<br>(0.041)<br>✓<br>0.334<br>-0.303***<br>(0.043)<br>0.030<br>(0.045)<br>✓<br>0.475<br>2000<br> regional tar<br>on observati<br>mated chang<br>pendent vari<br> for instrume|
|(2)<br>-0.174***<br>(0.045)<br>✓<br>0.334<br>-0.314***<br>(0.038)<br>✓<br>0.474<br>OLS<br>1991-<br>ates for the <br>11 microregi<br>r of the esti<br>lap with de<br>ics reported|
|(1)<br>-0.182***<br>(0.051)<br>0.114<br>-0.312***<br>(0.034)<br>0.270<br> coefcient estim<br>tarif declines. 4<br>red standard erro<br>trend due to over<br> partial F-statist|
|Change in share:<br>Panel A: Informally employed<br>Regional tariff change<br>Dependent variable pre-trend (80-91)<br>State fixed effects (26)<br>R-squared<br>First stage partial F-Statistic<br>Panel B: Not employed<br>Regional tariff change<br>Dependent variable pre-trend (80-91)<br>State fixed effects (26)<br>R-squared<br>First stage partial F-Statistic<br>Decennial Census data. Negative (positive) <br>non-employed share in regions facing larger <br>clusters. Weighted by the inverse of the squa<br>1991 period. To address endogeneity of pre-<br>1980 employment category share. First-stage<br>percent, * 10 percent level.|

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To date, payouts have generally been made available to farmers by ICICI Lombard only some months after the end of the monsoon season. This in part reflects delays in certifying weather records by the IMD. However, we believe that this process could be sped up significantly, given that rainfall can in principle be measured almost in real time. Reducing the delay between the realization of rainfall shocks and the settlement of claims should significantly improve the attractiveness of the product, particularly for households facing liquidity constraints. For example, payouts relating to phase 1 (sowing) of the monsoon could be made during the monsoon season itself, providing funds to help with the replanting of crops in the wake of crop failure.

# 5. Market Structure and Growth

Even before weather insurance became available in 2003, Indian farmers seeking to protect their crops could in principle attempt to do so through government crop insurance. India began to pilot crop insurance in a limited way between 1972 and 1978. These early pilots were succeeded by the Pilot Crop Insurance Scheme (PCIS) of the 1979–1984 period and, afterward, the Comprehensive Crop Insurance Scheme (CCIS) of 1985–1999. Both PCIS and CCIS were targeted to farmers with loans from financial institutions. While the PCIS was voluntary, purchase of a CCIS policy was compulsory if a loan was taken from a formal financial institution. In 1999 the National Agricultural Insurance Scheme (NAIS) was introduced, replacing the CCIS. NAIS is available to borrowers and nonborrowers, although the vast majority of clients remain those forced to buy insurance as a condition of receiving a loan.

Each of the schemes described above employs an “area approach.” An insurance payout is triggered if measured crop yields from the area fall below a certain threshold, based on cropcutting experiments conducted on a sample of monitored selected plots. Distinct from the earlier
|P076174|2005|2008|1/25/2008|3 Closed|Indonesia|
|---|---|---|---|---|---|
|P076174|2005|2008|6/26/2008|4 Closed|Indonesia|
|P076174|2005|2009|1/21/2009|5 Closed|Indonesia|
|P076174|2005|2010|3/15/2010|6 Closed|Indonesia|
|P076174|2005|2011|9/13/2010|7 Closed|Indonesia|
|P076174|2005|2011|4/28/2011|8 Closed|Indonesia|
|P076174|2005|2012|4/28/2011|8 Closed|Indonesia|
|P076183|2002|2002||0 Closed|Yemen,Republic of|
|P076183|2002|2003|1/31/2003|2 Closed|Yemen,Republic of|
|P076183|2002|2004|1/30/2004|4 Closed|Yemen,Republic of|
|P076183|2002|2005|4/29/2005|6 Closed|Yemen,Republic of|
|P076183|2002|2006|9/27/2005|7 Closed|Yemen,Republic of|
|P076183|2002|2007|11/19/2006|8 Closed|Yemen,Republic of|
|P076183|2002|2007|6/26/2007|9 Closed|Yemen,Republic of|
|P076183|2002|2008|12/28/2007|10 Closed|Yemen,Republic of|
|P076183|2002|2008|6/28/2008|11 Closed|Yemen,Republic of|
|P076185|2005|2005|4/29/2005|1 Closed|Yemen,Republic of|
|P076185|2005|2006|12/19/2005|2 Closed|Yemen,Republic of|
|P076185|2005|2007|11/19/2006|3 Closed|Yemen,Republic of|
|P076185|2005|2007|6/26/2007|4 Closed|Yemen,Republic of|
|P076185|2005|2008|9/11/2007|5 Closed|Yemen,Republic of|
|P076185|2005|2008|4/11/2008|6 Closed|Yemen,Republic of|
|P076185|2005|2009|7/9/2008|7 Closed|Yemen,Republic of|
|P076185|2005|2009|6/10/2009|8 Closed|Yemen,Republic of|
|P076185|2005|2010|12/30/2009|9 Closed|Yemen,Republic of|
|P076185|2005|2010|4/10/2010|10 Closed|Yemen,Republic of|
|P076185|2005|2011|1/9/2011|11 Closed|Yemen,Republic of|
|P076185|2005|2012|8/26/2011|12 Closed|Yemen,Republic of|
|P076185|2005|2012|4/9/2012|13 Closed|Yemen,Republic of|
|P076185|2005|2013|1/27/2013|14 Closed|Yemen,Republic of|
|P076192|2002|2002|6/20/2002|2 Closed|North Macedonia|
|P076234|2004|2004|6/16/2004|1 Closed|Azerbaijan|
|P076234|2004|2005|5/27/2005|3 Closed|Azerbaijan|
|P076234|2004|2006|10/31/2005|4 Closed|Azerbaijan|
|P076234|2004|2007|1/4/2007|5 Closed|Azerbaijan|
|P076234|2004|2007|1/15/2007|6 Closed|Azerbaijan|
|P076234|2004|2008|8/30/2007|7 Closed|Azerbaijan|
|P076234|2004|2008|11/20/2007|8 Closed|Azerbaijan|
|P076234|2004|2008|1/14/2008|9 Closed|Azerbaijan|
|P076234|2004|2009|8/27/2008|10 Closed|Azerbaijan|
|P076234|2004|2009|12/19/2008|11 Closed|Azerbaijan|
|P076234|2004|2010|7/16/2009|12 Closed|Azerbaijan|
|P076234|2004|2010|2/8/2010|13 Closed|Azerbaijan|
|P076234|2004|2010|4/21/2010|14 Closed|Azerbaijan|
|P076234|2004|2011|8/29/2010|15 Closed|Azerbaijan|
|P076234|2004|2011|10/17/2010|16 Closed|Azerbaijan|
|P076234|2004|2011|5/17/2011|17 Closed|Azerbaijan|
– i.e., the size of the market at stake and the likely importance of this market to foreign exporters in developing countries.

In table 3 we address this question by taking the information on the 6-digit HS product codes that the GTA provides and matching them to import data made available for free on the Internet by the United Nations through Comtrade.<sup>22</sup> First, are developing country exporters affected by this measure? The answer is clearly yes, as five out the top seven foreign sources of Brazilian imports of this product in recent years were developing countries (India, Indonesia, Malaysia, China and South Africa). Second, how large were these exports and was there a substantial reduction in exports associated with the new trade barrier? For these five developing countries, annual exports prior to the crisis were not trivial, as they ranged from US$ 2 million to US$ 16 million per country per year, which is well within the range of the size of exports associated with a number of disputes recently initiated by developing countries.<sup>23</sup> While Brazilian imports from each of these sources did fall substantially in 2009, as indicated earlier, it may be hard to attribute this (without more information) to the Brazilian tariff increase, given the 3823.70.10 and 3823.70.20.

22 In this particular case the measure was applied at the 8-digit level, and since the UN data is only provided at the 6-digit HS level there may be some mismeasurement. Furthermore, the 2009 import statistics may not be completely accurate if all of the 2009 import data has not yet been reported to Comtrade. Nevertheless, even reliance on the data for the earlier years in table 3 provides revealing information.

23 See, for example, Bown (2009a, chapter 6, table 6-6) which reports the size of exports of stake in a number of recent WTO disputes filed by developing countries regarding foreign imposition of antidumping measures against their exporters. This list included seven examples of disputes over products with (pretrade barrier levels of) bilateral exports of less than US$ 12 million per year.

27