rafmacalaba/datause-encoder-data huggingface dataset:
9 pure mean scaling effect, the poverty lines were adjusted as follows: z* = _z[,u(n)]3-,_ where ,u(n) is the mean household size in the distribution.

||1976<br>1981|1985|1996|
|---|---|---|---|
|**Median (1996**R$)*|127.98<br>124.04|120.83|132.94|
|**Inequality**||||
|Gini - 0 = 1.0|0.595<br>0.561|0.576|0.591|
|Gini - 0 =_0.5_|_0.566_<br>0.529|0.548|0.567|
|E(0) - 0 = 1.0|0.648<br>0.542|0.588|0.586|
|E(0) - 0=0.5|0.569<br>0.472|0.524|0.534|
|E(1) - 0 = 1.0|0.760<br>0.610|0.657|0.694|
|E(1)- 0 =0.5|0.687<br>0.527|0.580|0.622|
|E(2)- 0 1.0|2.657<br>1.191|1.435|1.523|
|E(2)- 0=0.5|2.254<br>0.918|1.134|1.242|
|**Poverty- R$30/ m**|**onth**|___||
|P(0) - **0**<br>**1.0**|0.0681<br>0.0727|0.0758|0.0922|
|P(0) - 0_=0.5_|0.0713<br>0.0707|0.0721|0.0847|
|P(1) - 0=1.0|0.0211<br>0.0337|0.0326|0.0520|
|P(1) - 0<br>_0.5_|0.0235<br>0.0315|0.0303|0.0442|
|P(2) - 0 =1.0|0.0105<br>0.0246|0.0224|0.0434|
|P(2)- 0=0.5|0.0132<br>0.0226|0.0204|0.0357|
|**Poverty- R$601 mo**|**nth**|||
|**P(0)**- 0=1.0|0.2209<br>0.2149|0.2274|0.2176|
|P()_- fl=0.5|0.2407<br>0.2229|0.2382|0.2179|
|P(1) - 0=_1.0|0.0830<br>0.0879|0.0920|0.1029|
|P(l) - 0_0.5_|0.0901<br>0.0875|0.0927|0.0960|
|P(2)_ - 0<br>1.0|0.0428<br>0.0525|0.0534|0.0703|
|P(2)_ - 00.5_|0.0471<br>0.0508|0.0521|0.0625|
|Note: *: For urban ar|eas only, and spatially deflated. See|Appendix 1.||

The median incomes in Table 2 behave roughly in tandem with the means reported in Table 1, and in accordance with the macroeconomic cycle: 1981 was a recession year, followed by stagnation in 1982 and a severe recession in 1983, from which the median - unlike the mean - had not yet recovered by 1985. Both subsequently rose to 1996. The table also confirms that the evolution of inequality over the period is marked by a decline from 1976 to 1981, and a subsequent deterioration over the remaining two sub-periods. Furthermore, this trend is robust to
Krafft, C., Assaad, R., Marouani, M. A., Cheung, R., & LaPlante, A. (Forthcoming).

Are labour markets in the Middle East and North Africa recovering from the COVID-19 pandemic? _IZA Journal of Development and Migration._ Krafft, C., Razzaz, S., Keo, C., & Assaad, R. (2019). The Number and Characteristics of Syrians in Jordan: A Multi-source Analysis. In C. Krafft & R. Assaad (Eds.), _The Jordanian Labor Market between Fragility and Resilience_ (pp. 173–202). Oxford University Press. OAMDI. (2021). _COVID-19 MENA Monitor Household Survey (CMMHH)_ (Version 5.0 JordanCMMHH) [dataset]. http://www.erfdataportal.com/index.php/catalog.

OAMDI. (2022). _Survey of Young People in Jordan—UNICEF_ (Version 1.0) [dataset]. Egypt: Economic Research Forum (ERF).

Obi, C. T. (2021). _The Impact of Living Arrangements (In-Camp versus Out-of-Camp) on the Quality of Life: A Case Study of Syrian Refugees in Jordan_ . https://doi.org/10.1596/18139450-9533 Razzaz, S. (2017). _A Challenging Market Becomes More Challenging: Jordanian Workers, Migrant Workers and Refugees in the Jordanian Labor Market_ .

Ruiz, I., & Vargas-Silva, C. (2013). The Economics of Forced Migration. _Journal of_ _Development Studies_ , _49_ (6), 772–784. https://doi.org/10.1080/00220388.2013.777707 Ruiz, I., & Vargas-Silva, C. (2017). Are Refugees’ Labour Market Outcomes Different from Those of Other Migrants? Evidence from the United Kingdom in the 2005-2007 Period: Refugees and Other Migrants Labour Market Outcomes: Evidence from the UK. _Population, Space and Place_ , _23_ (6), e2049. https://doi.org/10.1002/psp.2049 UNHCR. (2021a). Global Trends: Forced Displacement in 2020. _UNHCR Global Trends_ , 72.

32
developing countries where the information base of the poor might be skewed<sup>19</sup> in a way that detracts from their ability to hold elected officials accountable for the quality of public services. In the absence of any information on public service standards, targets and performance, it is difficult for the citizens to hold the public service accountable for their lack of performance. This is especially true in the case of PSM reforms because intermediate improvements in the quality of public management systems are difficult to measure and ̳downstream‘ improvements in services and development outcomes take several years, and may not be easily attributed to the reform of ̳upstream‘ institutions (World Bank 2012a). Overall, this makes it difficult to overcome collective action problems and mobilize potential beneficiaries for PSM reforms. As Heredia and Schneider (2003) argue, beneficiaries of administrative reforms are usually dispersed while opponents are concentrated, to the point that there are a few grounds to hope that reform proposals will emerge from electoral campaigns or elected legislatures.

In the Sierra Leone context, efforts to overcome information asymmetries and collective action problems are further compromised by the resilience of ethnic cleavages and their ̳politicization‘ for electoral purposes. This situation undermines the potential effect that electoral competition usually plays in mobilizing pressure for reforms. More importantly, if popular support can be safely ensured by ethnic-based distribution of patronage and public resources, establishing a functioning bureaucracy becomes less relevant and money can be diverted elsewhere, for personal or political ends. In fact, a weak and politicized bureaucracy would make easier to achieve these goals. Keefer and Vlaicu (2008) provide a rigorous empirical test of this argument and demonstrate that sharply different policy choices across democracies can be explained as a consequence of differences in the ability of political competitors to make credible pre-electoral commitments to voters. According to the authors, in democracies where political competitors can make credible promises to only small segments of the electorate (such as, for instance, their co-ethnics), governments prefer to pursue clientelistic policies, high targeted spending, high rent seeking, and low public good provision. Existing analysis of national electoral politics in Sierra Leone (Kandeh 2003,

> 19 Today, about 20 newspapers are regularly printed in Freetown, though most are of poor quality and often carry sensational or unsubstantiated stories. Moreover, the total circulation remains very small, with estimated 20,000–40,000 copies distributed in Freetown on a daily basis. In addition, all newspapers are published in English, while only 30 percent of the population is fluent in the language, and close to 70 percent is illiterate.

12
# **1. Empirical Models**

Our methodology relies on the gravity model of trade that explains bilateral imports as a function of the GDP of the importer and the exporter, bilateral distance among trading partners, and fixed effects to control for unobservable variables such as the policyinduced and other trade frictions affecting each country’s trade potential with the rest of the world (Anderson and Van Wincoop 2003; Feenstra 2002). Because we are interested in the impact of the growth of China and India’s demand on LAC exports, as well as the impact of China and India’s trade flows with LAC and the rest of the world on LAC exports to third markets, we need to address these two questions with different samples and with different augmented specifications of the gravity model. In addition, the models discussed below were estimated with data covering all LAC-countries’ trade flows with the world, but we do not include data for trade among the rest of the world. Hence the models and the resulting econometric estimates need to be interpreted as applying to LAC countries only.<sup>3</sup>

## **1.1 The growth of Chinese and Indian bilateral trade with LAC**

The basic gravity framework in the existing literature is given by: where _M ijt_ are imports of country _i_ from country _j_ at time _t_ . The right-hand side of (1) includes the standard explanatory variables plus a minor extension. _Yit_ the GDP of the importer at time _t_ , _Y jt_ is the GDP of the exporter at time _t_ , _Dij_ is the bilateral distance, _Bij_ is a dummy that takes the value 1 if the exporter and the importer share a border, and l _ij_ is a dummy that takes the value 1 if the exporter and the importer share a common language. In a modest departure from the standard gravity model found in the literature, _Linderijt_ is the absolute value of the difference of GDP per capita between the importer

> 3 In econometric terms, these estimations with the LAC data can be interpreted as providing estimates of the relevant parameter for LAC in models with data from the whole world, but allowing for strict heterogeneity between the LAC coefficients and those from the rest of the world.

5
areas, we set tariff collections on imports from SADC, Kenya, Uganda and Rwanda at zero. We then increased the tariff rates for the other regions in our model so that the overall weighted average collected tariff rate is unchanged. We used the trade flow data, disaggregated by regions and sectors of our model to weight the tariff rates. This adjustment has the impact of raising the collected tariff rates for the regions in our model where positive tariff rates apply. The resulting adjusted tariff rates are reported in Table 4b.

**4.5.3. Regions other than Kenya or Tanzania.** Tariff rates for the eight regions other than Kenya and Tanzania in our model are taken from the GTAP 8.1 database. GTAP 8.1 uses the third version of the MAcMap-HS6 database. This database is jointly developed by the Paris based think tank CEPII and the International Trade Center (ITC) of Geneva and is documented in Guimbard, Jean and Mimouni (2011). It is based on the ITC’s raw data. The authors calculated applied protection, including incorporating preferential tariff protection in 2007 and the ad valorem equivalents of specific tariffs. They estimate the world average applied protection level in 2007 to be 4.4 percent. Compared to 2004, this is a decline by nearly 0.7 percentage points, mainly due to unilateral liberalizations and to new preferential trade agreements. The increasing share of developing countries in world trade, where protection is higher, tended to raise the world average, but this was counterbalanced by the decline in the ad valorem equivalent of specific tariffs, linked to the surge in world prices of agricultural products. The results for our sectors and African regions other than Kenya and Tanzania are in tables 4c, 4d, 4e and 4f.

# **4.6 Social Accounting Matrices**

The core data of the model comes from the GTAP 8.1 data set, described on the GTAP website: <u>https://www.gtap.agecon.purdue.edu/databases/default.asp. The GTAP 8.1 data set</u> contains 57 sectors and 129 regions. We explain our aggregation of the data set in appendix A.

# **4.7 Trade Data by Regional Partner and Sector**

To obtain data on imports and exports from the different regions of our model, we used trade data from the GTAP 8.1 data set. Although trade data are available from WITS access to the COMTRADE database, the data must constitute a balanced data set that satisfies all accounting identities. For example, exports of any product from a region of our model must equal the imports of the rest of the world for this product from that region. And for each region 16
increase of household consumption in the second to fourth quarters of 2017. Consumer lending in 2017 was supported by simplified procedures of applications for consumer loans, as well as falling interest rates.

_Figure 5: Difference in dynamics of consumption and income: role of savings and loans_ <!-- Start of picture text --> 30<br>50 20<br>25<br>20 40<br>15<br>15<br>30<br>10<br>10<br>5 20<br>0 5<br>10<br>-5<br>-10 0 0<br>-15<br>real households consumption deposits of households, % of cash income<br>real cash income credit to households, % of cash income (right axis)<br>(a) consumption and income, growth rates, % yoy (b) households savings and credit<br>Source: Belstat, NBB, own estimates.<br>2014m1 2014m4 2014m7 2014m10 2015m01 2015m04 2015m07 2015m10 2016m01 2016m04 2016m07 2016m10 2017m01 2017m04 2017m07 2017m10<br>2013Q1 2013Q3 2014Q1 2014Q3 2015Q1 2015Q3 2016Q1 2016Q3 2017Q1 2017Q3<br><!-- End of picture text --> The reduction of interest rates in 2017 was a result of the balanced macroeconomic policy that was run by economic authorities during the period of stagnation. It resulted in inflation rates that were unprecedentedly low for Belarus (Figure 6). Moreover, overall growth of consumer prices in Belarus in 2015–2017 was to a large extent determined by the increase in the state regulated utility tariffs – part of the policy aimed at increasing cost coverage by tariffs, which was implemented very gradually due to potential negative effects on vulnerable groups of the population (Bornukova, Chubrik, Shymanovich, 2017).

_Figure 6: Inflation rates, % yoy_ <!-- Start of picture text --> 80<br>60<br>40<br>20<br>0<br>CPI food non-food<br>utilities other services<br>2014m1 2014m4 2014m7 2014m10 2015m01 2015m04 2015m07 2015m10 2016m01 2016m04 2016m07 2016m10 2017m1 2017m4 2017m7 2017m10<br><!-- End of picture text --> Source: Own estimates based on Belstat data.

# 3. Cash Income and Poverty Dynamics

## 3.1. Cash income

As we noted in the previous section, the 2015‐2016 crisis was different from the previous 2009 and 2011 episodes. Inter alia, one of the key differences manifested itself in the fact that the latest recession affected the poorest deciles the most, while higher income households suffered welfare losses to a smaller degree, and later into the crisis. Figure 7 illustrates this point in more detail. It shows the evolution of real cash income (which includes all types of cash incomes like earnings, dividends, government transfers and other sources) 6
find a positive association<sup>betwean openness and productivity</sup> growth, but the increase in productivity<sup>associated with more</sup> open economies is not very significant.

A more ambitious attempt to<sup>use adjusted trade shares as a</sup> proxy for trade policy openness<sup>was attempted by Leamer (1988).</sup> Leamer uses a theoretical<sup>model to predict the pattern and volume</sup> of trade in the absense of protection.<sup>He then measures</sup> "openness" as a function of<sup>the extent to which actual trade</sup> deviates from the pattern of trade<sup>rcedicted by the model.</sup> Edwards (1989) has used Leamer's measure to show a positive<sup>and</sup> statistically significant impact<sup>of openness on growth. Although</sup> this approach is quite promising--particularly<sup>since it relies on</sup> easily available data--the methodology<sup>in its current form has a</sup> number of shortcomings. In particular,<sup>Pritchett (1991) showed</sup> that Leamer's measure is negatively<sup>correlated with several other</sup> measures of openness, including<sup>import penetration, tariff</sup> levels, and the extent of non-tariff<sup>barriers. Leamer (1988)</sup> concludes that "in the absence<sup>of direct measures of barriers, it</sup> will be impossible to determine<sup>the degree of openness for most</sup> countries with much confidence." Direct measures of trade barriers<sup>could include</sup> administrative data, such as<sup>average tariff rates or coverage</sup> ratios for nontariff barriers.<sup>Problems typically arise,</sup> however, in attempting to aggregate<sup>these data into an overall</sup> index. Coverage ratios for nontariff<sup>barriers cause the greatest</sup> difficulty. Since the coverage<sup>ratio indicates the percentage of</sup> 5
Hill, 1993). Wealth gaps in education have also been observed across developing countries (Deolalikar, 1997; Filmer, 1999). The higher the economic position of the household, the higher the school attainment is. Moreover, the household's economic position could also affect the role played by other household and community characteristics on school enrollment (Deolalikar, 1997). Richer households are in a better position to finance children's education both directly and in terms of the opportunity cost of children's time derived from labor force participation. This is more of an issue when we take into account the family size including the number of children in the household often resulting in differential education outcomes for male and female children. It has been shown that various measures of education are usually negatively associated with number of siblings (Lloyd, 1994 for example).

Similarly, social norms are just as important. In some societies, social norms may limit female education such that parents may choose in favor of son's schooling as compared to educating their daughters. However, parents' education (especially of mothers) encourages children's enrollment and education in most developing countries (Filmer, 1999). This effect of female education on primary enrollment is strongest among households falling in the poorest quintiles in developing countries (Deolalikar, 1997).

# _4. Data and Variable Definition_

This analysis uses data from the new Living Standard Measurement Survey (LSMS) for Kosovo collected by the World Bank (World Bank, 2001b)<sup>5</sup> . The Kosovo LSMS is a household survey, which was administered to 2,880 households (2,400 Albanian and 480 Serbs) randomly 5 For more information on the Kosovo LSMS, see http:H/www.worldbank.org/Isms/guide/select.html 10
# **Table 2: HIV/AIDS and Social Capital Sensitivity Analysis**

||12|13|14|15|16|17|18|19|20|21|
|---|---|---|---|---|---|---|---|---|---|---|
||OLS|OLS|OLS|OLS(LDC)|2SLS|2SLS|2SLS<sup>a</sup>|3SLS|2SLS(LDC)|2SLS<sup>a</sup>(LDC)|
|HIV|-0.513**|-0.604**|-0.503|-0.548**|-0.834**|-0.861*|-0.690*|-1.066*|-1.024*|-0.815*|
||(0.258)|(0.284)|(0.325|(0.271|(0.500)|(0.492)|(0.416)|(0.576)|(0.572)|(0.466)|
|||||||4.291**|3.724**|1.991|||
|||||||(1.486)|(1.456)|(2.288)|||
|Rule of law||||-2.236|4.438**||||-2.476|-2.212|
|||||(2.563)|(1.518)||||(2.495)|(2.415)|
|Govt Effectiv|8.522***<br>(1.906)||||||||||
|Voice & Account||4.251*<br>(2.528)|||||||||
|Law & Order|||3.395*<br>(1.794)||||||||
|GINI|-22.565**|-15.845*|-13.766|-6.283|-7.483|-7.816|-8.451|-39.432***|-4.9673|-4.871|
||(7.138)|(8.332)|(9.928)|(6.059)|(7.120)|(7.206)|(6.343)|(9.230)|(7.074)|(6.303)|
|Population dens|-3.722***|-0.903|-1.624**||||||||
||(0.701)|(0.560)|(0.729)||||||||
|Age|-0.846**|-0.302|-0.258||||||||
||(0.375)|(0.332)|(0.447)||||||||
|Scandinavian|21.092***<br>(3.835)|27.713***<br>(4.744)|26.339***<br>(4.908)||||||||
|Nordic|||||27.359***<br>(3.735)|26.698***<br>(4.011)|28.212***<br>(3.876)||||
|Constant|128.079***|90.394***|70.987*|44.857**|51.912**|53.496***|55.248***|172.138***|40.889*|40.193*|
||(31.603)|(35.514)|(39.460)|(21.127)|(24.681)|(25.006)|(22.092|(34.161)|(24.302)|(21.782)|
|Observations|72|73|59|50|70|70|75|26|47|50|
|R-squared|0.509|0.373|0.420|0.091|0.489|0.484|0.471|0.454|0.072|0.083|
|RESET|0.037|0.268|0.314|0.527|0.136|0.195|0.280||0.0510|0.157|
|Shea partial R<sup>2</sup>|||||0.595|0.600|0.566||0.609|0.569|
|Cragg-Donaldson|||||0.004|0.004|0.002||0.004|0.003|
|Hansen J|||||0.167|0.221|||0.195||

The dependent variables is trust. White -corrrected standard errors in parentheses. *** , ** and * denote statistical significance at the 1%, 5% and 10% level. N is the number of observations. RESET refers to p-values for the Ramsey RESET test for OLS or the Ramsey/Pesaran-Taylor RESET test for 2SLS. HIV instrumented by circumcision rate and Sub-Saharan African dummy except in<sup>a</sup> where only the latter is used as instrument. In 3SLS, HIV instrumented by Sub-Saharan Africa and rule of law instrumented by settler mortality. Cragg-Donaldson refers to p- values for validity of excluded instruments in the first stage regression. Hansen J refers to p-values for testing overidentifying restrictions 21
# **1. Introduction**

The new WTO negotiations initiated in Doha focus on the reduction of trade barriers between developed and developing countries. One of their main objectives is a drastic reduction of trade barriers in the agricultural sector that will profoundly influence the economies of very poor countries, such as Vietnam, which had a GNP per capita of $310 in 1997. Most households in poor countries rely on agriculture as their primary source of income, and export commodities such as rice comprise a large proportion of household expenditure. As a result, trade policyinduced changes in prices of export staples will affect the economic activities of households in these countries. However, as Winters (2000) and Dollar and Kraay (2001) point out, little research focuses on the impact of trade policy on well-being in these economies. One of the most important questions is how labor markets adjust to product market integration.<sup>1</sup> This paper uses variation in the domestic price of a country’s primary staple and export product to consider the relationship between product market integration and the labor supply of household members in Vietnam.<sup>2</sup> From 1993 to 1998, the Vietnamese rice market experienced international and national integration. The government of Vietnam had instituted a rice export quota to suppress the domestic price of rice and ensure food security in 1989. The government has since gradually liberalized its export regime by increasing the quota from less than 1 million metric tons in 1992 to 4.5 million in 1998 (see Benjamin and Brandt 2004). In addition, the government also integrated the national rice market in 1997 by lifting internal barriers to rice trade across regions.

> 1 Most research on the labor market responses to trade in developing countries focuses on manufacturing sector and relatively wealthy countries such as Mexico where manufacturing is a large employer.  Hanson and Harrison (1999) provide a nice survey of these studies.  In most of the very poor world, agriculture is the main employer and manufacturing employment is rare.

> 2 In addition to participation responses, one could in principle also analyze the implications of product price changes for wages.  However, only a small share of people actually work for wages outside the households in Vietnam.

1