rafmacalaba/datause-encoder-data huggingface dataset:
As a comparison point, Figures A7, A8 and A9, and Table A5 in the Appendix provide the outcome for the US. While there are also differences in average by group large outcomes across groups in the US, these differences are smaller than in Argentina. For example, if we focus on the average standardized outcome, the difference between the maximum and minimum across groups in the US is 38 percent of a standard deviation while it is 72 percent in Argentina.

The large differences in outcomes across native groups in Argentina stress the importance of not considering the native population as homogeneous. There may be many reasons for the observed differences across groups. In the following section we explore a particular one.

# **5 Historical Determinants of Socioeconomic Outcomes**

Can the differences in outcomes across Native groups be explained by the type of economy they had before the arrival of European colonizers? In particular, given the existing work on the importance of pre-colonial agriculture in current economic development (see Michalopoulos et al., 2018), we focus on whether these groups relied primarily on hunting-gathering, or had an incipient or advanced agriculture.

Table 1 column (3) shows our measure of the type of economy of each group before colonization. This measure is based on the previous literature on the Native groups of Argentina (Colombres, 2008; Ib ́a ̃nez, 2008; Lobos, 2011; Mandrini, 2008; Molocznik, 2011; Murdock, 1967; Nesis, 2005; Nordenski ̈old, 2002; Outes and Bruch, 1910; Sacco, 2011; Mart ́ınez Sarasola, 2011, 2014; Serrano, 2012).<sup>12</sup> Given that groups with agriculture tended to be colonized earlier than hunter-gatherer groups and there is evidence that years since colonization affects development (Feyrer and Sacerdote, 2009), we control for the years since colonization in some of the analysis. Our measure of the year of colonization is presented in the last column of Table 1.<sup>13</sup>

> 12 We were not able to find a clear assignment for the Mapuche, and as such they are dropped from the analysis in this section. While the Mapuche relied heavily on agriculture to the west of the Andes (see for example Murdock, 1967), in their expansion to the east they incorporated hunter-gatherer groups and relied less on agriculture.

> 13 We use several data sources to construct the variable shown in Table 1 measuring the year of colonization of each group. Firstly, we define the ancestral area for each group based on the literature (Colombres, 2008; Ib ́a ̃nez, 2008; Lobos, 2011; Mandrini, 2008; Molocznik, 2011; Nesis, 2005; Nordenski ̈old, 2002; Outes and Bruch, 1910; Sacco, 2011; Mart ́ınez Sarasola, 2011, 2014; Serrano, 2012). Secondly, we use the 2010 Census to calculate the population density of Natives of each group across counties. Thirdly, for each group, we take into account the three counties with the largest concentration of people from that group among counties in the ancestral land of the group. Fourthly, we find the year of colonization of those counties based on historical records of arrival of the Spanish or foundation of the county or main city in

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To assess the extent to which B&R economies are exposed to import competition from China, it is important to examine not only how important China is as a source of imports, but also the degree to which China’s specialization pattern is similar to that of the country in question. If a B&R economy sources a significant share of imports from China and has a similar production structure, competition shocks would be expected to be stronger. In contrast, if China is either not an important source of imports, or the two countries produce and export markedly different sets of products, competitive pressures would be expected to be weaker. As in the previous section, the degree of similarity in specialization patterns relative to China is measured by the export similarity index proposed by Finger and Kreinin (1979), using detailed product-level data at the 6-digit level. This index takes values between zero and one, and the higher its value the closest is the product distribution of exports in the two countries.

The evidence in Figure 7 reveals that several B&R economies for which China is an important source of imports have a specialization structure that differs considerably from that of China. These include Tajikistan, Myanmar, Kyrgyztan, Bangladesh, Mongolia and the Islamic Republic of Iran. To the extent that differences in export structure reflect underlying differences in production structures across countries, these countries are only weakly exposed to Chinese import competition in their own markets, even though they source a large share of imports from China. Mutual gains from further integration with these countries are likely to derive mainly from further exploitation of the corresponding comparative advantages. (Figure A2 in the Appendix displays more clearly the names of the B&R economies that are located close to the origin.) By contrast there are several other B&R economies that source a relatively large share of imports from China and have an export structure that is more similar to that of China. These include, most notably, Vietnam, Thailand, Malaysia, Philippines, India, Singapore and Indonesia. These countries are therefore likely to be relatively more exposed to import competition from China in their own markets in several industries. Further integration with these countries would likely involve stronger competitive pressures in final goods markets, which may have important implications for the adjustment of factor markets (notably labor markets). However, it is important to emphasize that there are various important sources of mutual gains from further integration: 27
**Figure 12: Gender gaps in property ownership are larger in rural than in urban areas**

<!-- Start of picture text --> sole ownership of property sole and joint ownership of property<br>BDI BFA<br>BFA NER<br>TCD NGA<br>NER TGO<br>UGA TCD<br>TZA BEN<br>KEN UGA<br>NGA GMB<br>BEN GHA<br>TGO KEN<br>MLI MLI<br>MOZ CIV<br>CIV TZA<br>GIN COD<br>GHA ZWE<br>COD GIN<br>RWA RWA<br>SLE SEN<br>ETH BDI<br>GMB SLE<br>ZMB MOZ<br>ZWE NAM<br>SEN ZMB<br>MWI ETH<br>LBR MWI<br>LSO LBR<br>NAM LSO<br>0 10 20 30 40 50 60 -10 0 10 20 30 40<br>Male ownership (%) - female ownership (%) Male ownership (%) - female ownership (%)<br>urban national rural urban national rural<br><!-- End of picture text --> _Note:_ Data for 27 countries from 2010-16 (excluding Comoros). Population aged 20-49 years. _Source:_ DHS.

To further investigate distributional patterns, we estimate gender gaps by quintile, using the DHS wealth index.<sup>27</sup> As shown in Figure 13, gender gaps in property ownership are in most countries larger for the poorest than for the richest quintile. This income gradient holds for both ownership concepts, but it is more pronounced for sole ownership.

**Figure 13: At national level, gender gaps in property ownership are** **_larger_ for the poorest quintile**

<!-- Start of picture text --> sole ownership of property sole and joint ownership of property<br>BDI BFA<br>BFA NER<br>TCD NGA<br>NER TGO<br>UGA TCD<br>TZA BEN<br>KEN UGA<br>NGA GMB<br>BEN GHA<br>TGO KEN<br>MLI MLI<br>MOZ CIV<br>CIV TZA<br>GIN COD<br>GHA ZWE<br>COD GIN<br>RWA RWA<br>SLE SEN<br>ETH BDI<br>GMB SLE<br>ZMB MOZ<br>ZWE NAM<br>SEN ZMB<br>MWI ETH<br>LBR MWI<br>LSO LBR<br>NAM LSO<br>-10 0 10 20 30 40 50 60 -10 0 10 20 30 40 50 60<br>Male ownership (%) - female ownership (%) Male ownership (%) - female ownership (%)<br>richest quintile all quintiles poorest quintile richest quintile all quintiles poorest quintile<br><!-- End of picture text --> _Note:_ Data for 27 countries from 2010-16 (excluding Comoros). Population aged 20-49 years. _Source:_ DHS.

Differences in gender gaps along the distribution reflect to a considerable degree rural-urban differences. The poorest quintile is more likely to live in rural areas, where, as shown in Figure 12, gender gaps in property ownership are larger than in urban areas. If we compare quintiles within rural and urban areas, the pattern highlighted in Figure 13 reverses, as shown in Figure 14. If anything, gender gaps in property ownership tend to be _smaller_ for the poorest rural quintile than for the richest rural quintile, and _smaller_ for the poorest urban quintile than for the richest urban quintile. However, the income gradient (i.e. the horizontal distance between green and blue diamonds) is smaller within urban and rural areas.

> 27 Unlike the analysis in this paper, this is a household-level wealth index, which does not account for intra-household differences. It should also be noted that the DHS wealth index is a relative measure of wealth (at the country-level) – wealth quintiles cannot be directly compared across countries.

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# Appendices

## **Appendix A: The Ad Valorem Equivalence of Trade in Services Barriers in Different Modes of Transport**

In order to be able to convert the estimated TRI indexes estimated in Section 2.3 into tax equivalent, this paper follows modified approach of the study of Kimura (2004a) based on the study by McGuire (2000)<sup>25</sup> as well as the estimations of Kang (2000)<sup>26</sup> . This is done using the study of Kang (2000) and his model of measurement of shipping restrictions on transport margins, also employed by McGuire (2000) and Kimura (2004a).

In Kang (2000)<sup>27</sup> , shipping margins are used as the proxy for shipping price. The shipping margins from country _i_ (exporter) to country _j_ (importer), _M ij_ are defined as Where _IM ji_ represents the observed CIF value reported by importing country _j_ for all products imported from country _i_ , and _EX ij_ represents the observed FOB value of all exports reported by the _i_ ’th country as exported to the _j_ ’th country.

The shipping margin _M ij_ are function of bilateral restrictions _Rij_ , the distance between the trading partners _Dij_ and the scale of bilateral trade _SCij_ . Since it is assumed that this function is of a Cobb-Douglas form, the natural logarithmic form represents the following linear relationship:

> 25McGire, Greg, Michelle Schuele and Tine Smith, 2000, “Restrictiveness of international trade in maritime services”, in Findlay, Christopher and Tony Warren (eds), Impediments to Trade in Services: Measurement and Policy Implications, (London: Routledge)

> 26 Kang, Joog-Soon (2000), “Price Impact of Restrictiveness on Maritime Transportation Services,” in Findlay, Christopher and Tony Warren

> (eds), Impediments to Trade in Services: Measurement and Policy Implications, (London: Routledge).

> 27 Kang, Joog-Soon (2000), “Price Impact of Restrictiveness on Maritime Transportation Services,” in Findlay, Christopher and Tony Warren

> (eds), Impediments to Trade in Services: Measurement and Policy Implications, (London: Routledge).

56
**Figure 3:** Weak vs Strong State Capacity (LAPOP)

<!-- Start of picture text --> 2004 2006 2008 2010 2012 2014 2016 2018 2004 2006 2008 2010 2012 2014 2016 2018 2004 2006 2008 2010 2012 2014 2016 2018<br>year year year<br>Strong State Weak State Strong State Weak State Strong State Weak State<br>(a) Trust in President (b) Trust in Municipal Gov. (c) Trust in Nati. Legislature<br>2004 2006 2008 2010 2012 2014 2016 2018 2004 2006 2008 2010 2012 2014 2016 2018 2004 2006 2008 2010 2012 2014 2016 2018<br>year year year<br>Strong State Weak State Strong State Weak State Strong State Weak State<br>(d) Confidence in Jud. Syst. (e) Trust in Media (f) Trust in Church<br>1 1 1<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br>1 1 1<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br><!-- End of picture text --> and the two measures of attitudes towards democracy do not follow a weak-strong state capacity divide.<sup>4</sup>

# _2.2 Nighttime Light Density (Proxy for Income)_

The lack of subnational-level statistics for GDP or measures of household income is a common problem in developing countries and El Salvador is not the exception. Indeed, no municipalitylevel measures of income, let alone other measures of economic development, are consistently available over time for the universe of Salvadoran municipalities. While the Multipurpose Household Survey (EHPM) does provide income data for some municipalities, its coverage is limited to 50 municipalities. Nonetheless, even for these so-called self-represented municipalities some years are missing for the period under analysis. That is, for the period 2001-2018 the EHPM allows the construction of an unbalanced panel with 772 municipality-year observations, excluding 78 municipality-year observations due to the lack of data. In response to the lack of GDP figures on a consistent basis at the subnational level, the seminal work by Henderson et al. (2012) proposes the use of a proxy that cover periods or regions for which GDP data are not available at all or not available in a timely fashion: density of nighttime lights (NTL density from now on). Henderson et al. (2012) show that NTL density indeed works well as a proxy for economic development at the

> 4Figures A.1 and A.2 depict the evolution of all the attitudinal measures by each of the four Salvadoran macro regions.

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# **IV. Privatizations as a Source of Foreign Exchange**

# **IV.1 Data**

Foreign investors can participate in privatization transactions through foreign direct investment or portfolio equity investment. In the case of portfolio equity investment, the foreign buyer engages in a purely financial investment with the indivi eal share not exceeding 10 percent. For an investment to qualify as foreign direct investment, the investor has to acquire 10 percent or more and is usually interested in having, at least, substantial influence over the operations of the company.

Privatization transactions involving portfolio equity investments are rare, but typically quite large in volume. Henec, this information is generally well documented and easily available. In most cases, hc -ever, foreign investors tend to be more interested in direct investments, allowing them to shape the future of the enterprise bought, as independent as possible from interests of local groups, including the state. Information about the participation of foreign buyers in privatization programs including their share in the purchase is typically not readily available, especially not at a disaggregated level. Hence, in cases where no additional information could be obtained, the amount of foreign direct investment resulting from privatizations had to be estimated based on the nationality of the buyer. If no information was at all available regarding the buyer's identity, it was assumed that the investor was local, involving no foreign investment. In this respect, the foreign exchange values obtained are a lower bound of actual inflows. On the other hand, inflows are possibly overestimated in cases where committed investments were included in the purchasing price. Especially in Eastern European transactions, contracts are often written such that future investments are included. While adjustments could be made based on additional information in the cases of Hungary and Poland, the numbers for Czechoslovakia have to be taken with a grain of salt due to data limitations.

# IV.2 Overall Trends

The involvement of foreign investors in privatization programs is sought after by most governments despite - often strong - political opposition, fighting against the "selling out" of domestic interests. However, the participation of foreign investors is often essential, particularly for large projects, because the local capital market is too thin and needed technological and managerial expertise is not available. Furthermore, the sale of such assets promises the receipt of sizable amounts of foreign exchange, needed in a situation of limited access to foreign capital. But in order to attract foreigners, the privatizing authorities first have to prove their political willingness and commitmen **t** o an extensive privatization effort, characterized by a reliable and transparent system of rules and regulations. Hence, during the take-off period, most privatization programs do not show any extensive foreign participation, but over time the number of foreign buyers rises quickly, should the program prove reliable.

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9 Table 2 Current Accuunt and Public Sector Budget Balances as a Percent of GDP

|CoMntry|Current|Account/|GDP|Public Se|ctor Bud|get/GDP|
|---|---|---|---|---|---|---|
||1980-86|1987|<br>1988|1980-86|1987|1988|
|Egypt|-11.6|-11.8|-7.9|-11.7|8.4|-16.3|
|<br>Malaysia|-6.3|7.6|<br>4.7|-10.1|-7.6|-4.3|
|<br>Nigeria|-1.5|-0.3|<br>-3.5|-6.2|-9.8|-8.0|
|<br>South Korea|-2.1|7.5|<br>8.2|-1.5|0.4|1.6|
|Thailand|-4.9|-1.0|<br>-3.2|-4.7|-2.4|-2.0|
|Tunisia|-7.4|-1.0|<br>0.9|-5.2|-3.0|-3.9|

Source: World Bank .mprovements in the current account balance associated with<sup>trade were important to</sup> reducing deficit spending and improving a country's capacity to manage<sup>extemal debt. The injection</sup> of spending provided by export growth during the decade in Malaysia, South<sup>Korea, Thailand, and</sup> Tunisia reduced the need for expansionary fiszal policy and deficit<sup>spending. Exports enabled these</sup> countries to import without incurring additional extemal debt. Events in<sup>Nigeria, however, were</sup> dfflerent as the current account balance further deteriorated. Weak export<sup>growth In Egypt and the</sup> financing of a continuing stream of imports with external debt made it one<sup>of the most highly indebted</sup> countries In th6 world in the 1980s.Y

# DeveloDment Strategles

Economic growth is closely associated with the choice<sup>of trade regimes In the six countries.</sup> While it Is difficult to measure precisely the development<sup>strategy employed in each country, the</sup> evidence suggests that South Korea during the 1980s was strongly outward-focused.<sup>The trade</sup> SY Egypt's budget deficit of -16.3 percent relative<sup>to GDP was the largest such deficit in 1988 among</sup> all countries. Higher deficits, however, had been<sup>recorded in the early 1980s. In 1982, for example,</sup> Malaysia initiated structural reform in response to a public<sup>sector budget deficit of 19% and a current</sup> account deficit of 14% of GDP.
no TFP studies using data from China which employ robust methodologies and perhaps because of this, the available papers find diverse results, with the effect of non-state ownership being mostly positive but sometimes statistically insignificant and sometimes negative.

Concentration of ownership is important, with majority private ownership having mostly positive effects on the level of TFP. The overall positive effect is again driven primarily by foreign owned firms. The effect of majority domestic private ownership tends to be positive but smaller in magnitude. Studies that distinguish between privatized SOEs and newly created private firms suggest that _de novo_ firms are more productive than or at least as productive as SOEs privatized to domestic owners. The effect of employee (insider) ownership on the level of TFP is found to be mostly statistically insignificant or in one case actually positive. Estimates of the effects of privatization on TFP growth suggest that in CEE privatization had a positive effect on the rate of change of TFP in the early transition period and that the effect disappears in the later stage.

The effect of ownership on profitability has been estimated mostly in CEE and shows a small positive or insignificant effect of privatization to domestic or foreign owners on profitability levels in the early as well late transformation periods, together with an insignificant effect of privatization to domestic and foreign owners on the rate of growth of profitability. The effect varies across types of ownership, and concentrated domestic private ownership, managerial ownership, and to a lesser extent foreign ownership tend to have a positive effect on profitability, while state keeping a golden share or concentration of worker ownership appear to be unrelated to profitability. The studies of private ownership on profit of firms in China vary considerably in terms of methodology sample size and findings, with most indicating a positive and usually significant effect.

The effect of privatization on the level of firm revenues, capturing the effect of privatization on the scale of operation of the firm, is mostly strong and positive. In terms of revenue growth, we observe in CEE a high positive effect of privatization to foreign owners in the early period and a small effect in the later period, as well as an insignificant effect of privatization to domestic owners. Overall, the studies of CEE and CIS countries indicate that privatization tends to have a positive effect on the scale of operation, while 4
Table 2. Descriptive Statistics – Cox’s Bazar Panel Survey

||Average<br>Std. Deviation|Observations|
|---|---|---|
|_A. Measures of the Distancefrom an Individual’s Location to the 33_|_Refugee Camps_||
|Weighted distance to refugee camps|0.325<br>0.205|17,407|
|Inverse of weighted distance to refugee camps|5.042<br>3.874|17,407|
|_B. Control Variables_|||
|Age|26.217<br>18.154|17,032|
|Male [=1]|0.486<br>0.500|17,030|
|Household Head [=1]|0.235<br>0.424|17,037|
|Married [=1]|0.588<br>0.492|13,760|
|Household size|5.861<br>2.336|17,407|
|Enough household income in 2017 [=1, measuredpre-infux]|0.479<br>0.500|17,350|
|_C. Adult and Household Welfare_|||
|Wealth Index|-0.006<br>2.135|12,334|
|Proportion of service access - subcomponent|0.679<br>0.329|17,282|
|Proportion of adequate shelter materials - subcomponent|0.652<br>0.256|17,282|
|Proportion of total assets - subcomponent|0.306<br>0.149|17,282|
|Household income (Takas)|19,357<br>36,964|12,553|
|Household expenditures (Takas)|6,178<br>5,850|12,553|
|Non-food household expenditures (Takas)|1,830<br>2,190|12,553|
|Food household expenditures (Takas)|4,349<br>4,800|12,553|
|Food Variety Consumption (Share of Products)|0.414<br>0.128|12,553|
|Self-Produced Food (Share of products)|0.770<br>0.421|12,553|
|Humanitarian Food Aid (Share of Products)|0.091<br>0.287|12,553|
|Gift or Barter Food (Share of Products)|0.207<br>0.181|12,553|
|Received vouchers, cash, in-kind assistance [Yes=1]|0.427<br>0.495|1,322|
|Received food in-kind [Yes=1]|0.649<br>0.477|4,020|
|Assistance for basic services [Yes=1]|0.656<br>0.475|7,546|
|Assistance for household items [Yes=1]|0.150<br>0.357|4,117|
|_D. Labor Market Outcomes_|||
|Labor force|0.138<br>0.345|11,037|
|Employed [Yes=1]|0.195<br>0.397|6,953|
|Monthly wage (Takas)|1,088<br>8,403|8,735|
|Weekly hours worked|17.523<br>24.736|8,703|
|Written contract [Yes=1]|0.142<br>0.349|1,633|
|Work for a government organization [Yes=1]|0.075<br>0.263|1,938|
|<br>Work for a private company [Yes=1]|0.072<br>0.258|1,938|
|Work for an NGO [Yes=1]|0.105<br>0.306|1,938|
|Work for a household [Yes=1]|0.096<br>0.295|1,938|
|Work self-employed [Yes=1]|0.564<br>0.496|1,938|
|Work for a mill [Yes=1]|0.064<br>0.245|1,938|

_Notes:_ The table presents descriptive statistics for the sample of hosts living around the refugee camps in Cox’s Bazar. Detailed variable definitions can be found in Appendix B.

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