rafmacalaba/datause-encoder-data huggingface dataset:
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- Harrison, Ann and Hanson, Gordon H. (1999). “Who Gains from Trade Reform? Some Remaining Puzzles” _Journal of Development Economics_ 59, 125-54.

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- Helpman, Elhanan and Itskhoki, Oleg (2010). “Labor Market Rigidities, Trade and Unemployment” _Review of Economic Studies_ 77, 1100-37.

- Heston, Alan; Summers, Robert and Aten, Bettina (2011). Penn World Table Version 7.0, Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania, May 2011.

- International Labor Organization (2012). Key Indicators of the Labor Market, Geneva.

- International Monetary Fund (2012). International Financial Statistics, Washington, D.C.

- Krishna, Pravin and Mitra, Devashish (2005). “Reciprocated Unilateralism in Trade Policy” _Journal of International Economics_ 65(2), 461-87.

- Krishna, Pravin and Mitra, Devashish (2008). “Reciprocated Unilateralism in Trade Reforms with Majority Voting” _Journal of Development Economics_ 85(1), 81-93 _._

- Krugman, Paul R. (1993). “What do Undergrads Need to Know about Trade?” _American Economic Review_ 83(2), 23-6.

- Milner, Helen V. and Kubota, Keiko (2005). “Why the Move to Free Trade? Democracy and Trade Policy in Developing Countries” _International Organization_ 59(1), 107-43.

- Mitra, Devashish; Thomakos, Dimitrios D. and Ulubasoglu, Mehmet A. (2002). “‘Protection for Sale’ in a Developing Country: Democracy vs. Dictatorship” _Review of Economics and Statistics_ 84(3), 497-508.

- Moore, Marc and Ranjan, Priya (2005). “Globalization vs. Skill-Biased Technological Change: Implications for Unemployment and Wage Inequality” _Economic Journal_ 115(503), 391-422.

- Munch, Jakob Roland and Skaksen, Jan Rose (2009). “Specialization, Outsourcing and Wages” _Review of World Economics_ 145, 57-73.

- Nickell, Stephen; Nunziata, Luca and Ochel, Wolfgang (2005). “Unemployment in the OECD since the 1960s: What do We Know?” _Economic Journal_ 115(500), 1-27.

- Organization for Economic Co-operation and Development (2012). Labor Force Statistics, Paris.

- Pavcnik, Nina; Blom, Andres; Goldberg, Pinelopi and Schady, Norbert (2004). “Trade Liberalization and Industry Wage Structure: Evidence from Brazil” _The World Bank Economic Review_ 18(3), 319-44.

Pissarides, Christopher A. (2000). Equilibrium Unemployment Theory Cambridge, MA: MIT Press.

- Sachs, Jeffrey D. and Warner, Andrew (1995). “Economic Reform and the Process of Global Integration” _Brookings Papers on Economic Activity_ 1, 1–118.

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countries (Table 3). Although women's labor market experience is converging to that of men, the process is gradual and traditional roles continue to dominate in many developing countries.<sup>3</sup> _Wages._ Women typically earn less per week or year of work than men, even after controlling for age and education. This may be due in part to their lower labor force attachment (past experience and expected future tenure), in part to occupational segregation, and in part to social norms that condone lower pay to women. In our 3 sample countries, at age 20 women earn almost as much as men, but the disparity increases with age and by age 50 they earn only 60-70% as much per month of work (Table 4). The combination of fewer years of work and lower wage rates means that any pension system that links benefits to earnings or contributions is likely to cover a smaller percentage of women and to produce lower benefits for women.<sup>4</sup> _Different retirement ages for men and women._ Rules of the system often allow women to retire earlier than men. For example, women are permitted to retire 5 years earlier than men in Chile and Argentina. This enables them to retire at the same time as their husbands, who tend to be several years older. Early retirement may seem to be a privilege-that women who derive disutility from work appreciate--but it is a privilege that costs in terms of lower pension rights eamed. This cost may not be fully realized until the woman is too old to reverse her decision to retire early. It may also discourage employers from hiring or promoting older women, for fear they will retire soon. These differential rules started in traditional DB systems and they frequently continue in reformed systems-but the penalty for early retirement is greater in a DC system that is actuarially fair.

# **Demographic and biological differences**

_Longevity._ Besides these labor market and policy differences are important demographic-biological differences between men and women. Women at age 60 have a life expectancy that is 3-5 years greater than that of men, in most countries. In Chile a 60year-old women is likely to live another 23 years, while a 60-year-old man lives another 19 and a 65-year-old man lives another 15.5. A woman who retires at age 60 has a future lifespan in retirement that is 7.5 years more than that of her husband, when he retires at age 65. Thus annuitization, that provides longevity insurance, is especially important to 6
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<!-- Start of picture text --> Figure S1.2b: Residuals with Time Trend, NO2 at ITO<br>Blue Line (introduction)<br>Source: Based on data described in the text<br>Figure S1.2c: Residuals with Time Trend, NO2 at ITO<br>Blue Line (second extension)<br>Source: Based on data described in the text<br>1<br>.5<br>0<br>-.5<br>-1<br>22nov2005 26nov2005 30nov2005 04dec2005 08dec2005 12dec2005 16dec2005 20dec2005 24dec2005 28dec2005 01jan2006 05jan2006 09jan2006 13jan2006 17jan2006 21jan2006 25jan2006 29jan2006<br>1<br>.5<br>0<br>-.5<br>-1<br>08oct2006 12oct2006 16oct2006 20oct2006 24oct2006 28oct2006 01nov2006 05nov2006 09nov2006 13nov2006 17nov2006 21nov2006 25nov2006 29nov2006 03dec2006 07dec2006 11dec2006 15dec2006<br><!-- End of picture text --> 52
**Notes:** Robust standard errors in parentheses (clustered on country pairs). *** p<0.01, ** p<0.05, * p<0.1.  The data cover 1995-2018. Estimation in all columns include country-pair fixed effects and importer and exporter time period interaction terms estimated with Stata’s _ppmlhdfe_ command (Correia et al. 2019a, 2019b).  Estimation in all columns also include controls for the regional trade agreement, tariff preference margins, and the seven “deep” clauses shown in Table 4.  Estimation in column 3 also includes the two subcategories for category 1 shown in Table 1.

pg. 36
**Table 4:  Predicting export market participation**

||De<br>|pendent varia<br>|ble is dummy <br>|for export ma<br>|rketparticipat<br>|ion<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Mining|0.007***|0.005**|0.005**|0.002|0.052*|0.007***|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.03)|(0.00)|
|Manufacturing|<br>0.007***<br>|<br>0.005**<br>|<br>0.002<br>|<br>-0.003<br>|<br>-0.026<br>|<br>0.014***<br>|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.02)|(0.00)|
|Age (in dataset)|0.012***<br>(0.00)|0.005***<br>(0.00)|0.007***<br>(0.00)|0.005***<br>(0.00)|0.005***<br>(0.00)|0.007***<br>(0.00)|
|Kigali|-0.068***<br>(0.00)|-0.004<br>(0.00)|-0.034***<br>(0.00)|-0.025***<br>(0.00)|-0.024***<br>(0.00)|-0.032***<br>(0.00)|
|Other Tax Centre|<br>-0.060***<br>(0.00)|<br>0.014***<br>(0.00)|<br>-0.029***<br>(0.00)|<br>-0.013***<br>(0.00)|<br>-0.010***<br>(0.00)|<br>-0.025***<br>(0.00)|
|Sales missing (or zero)|-0.060***<br>(0.00)|0.139***<br>(0.00)|-0.013***<br>(0.00)|0.003<br>(0.00)|0.009***<br>(0.00)|-0.005**<br>(0.00)|
|Sales (million USD)|0.046***<br>(0.00)||||||
|(Sales)<sup>2</sup>|<br>-0.000***<br>||||||
||(0.00)||||||
|Log (Sales + 0.001)||0.072***<br>(0.00)|||||
|Sales<sup>0.5</sup>|||0.207***<br>(0.00)|0.168***<br>(0.00)|||
|Manuf *√Sales|||||0.149***<br>(0.00)|0.181***<br>(0.00)|
|Mining *√Sales|||||0.200***<br>(0.01)|0.242***<br>(0.00)|
|Agric *√Sales|||||0.244***|0.285***|
||||||(0.01)|(0.01)|
|Missing Labor Productivty||||-0.201***<br>(0.01)|||
|Manuf * (Missing log(LP))|||||-0.134***<br>(0.01)||
|Mining * (Missing log(LP))|||||<br>-0.213***<br>(0.02)||
|Agric * (Missing log(LP))|||||-0.166***<br>(0.02)||
|Log(LP) (zero for missing)||||0.018***<br>(0.00)|||
|Manuf * log(LP)|||||-0.002<br>(0.00)||
|Mining * log(LP)|||||0.027***<br>(0.00)||
|Agric * log(LP)|||||0.012***<br>(000)||
|Time|-0.009***<br>(0.00)|-0.003***<br>(0.00)|-0.004***<br>(0.00)|-0.002***<br>(0.00)|.<br>-0.002***<br>(0.00)|-0.004***<br>(0.00)|
|R-squared|0.244|0.307|0.330|0.351|0.361|0.340|
|N|26,340|26,340|26,340|26,340|26,340|26,340|

Notes: Linear probability model estimated with OLS. Significance at the 10%, 5%, and 1% levels are indicated by *, **, and ***.

27
enjoy a substantial medium term rise in asset wealth but incur negative health consequences due to exposure to various pollutants.

We ignore informal gold mines that are responsible for much of the pollution discussed in the literature, and look at the short-term effects of a recent revival in mining. Gold has long been mined in Ghana, so much so that the colonial name for the country was the Gold Coast. Gold exports dropped significantly after independence as a result of an over-valued exchange rate, under-investment in primary production, and stagnating gold prices after the end of the Bretton Woods agreements. But in the last two decades, gold mining has experienced a revival in Ghana. The sector has been liberalized and new entry has taken place. As a result, in the recent past several new formal mines have opened while others have closed down following a depletion of their economically viable ore deposits. Using recent highly disaggregated location-specific data from a combination of sources, we investigate whether variation in gold mining between 2000 and 2010 is associated with tell-tale signs of proto-urbanization, such as an increase in consumption and a shift in employment composition towards non-farm production and wage employment. Ghana is a good country to study urbanization, given the rapid growth of both the population and the economy over the last two decades. Rapid urbanization is less likely to be observed in a country with a stagnant population or economy.

Using cross-sectional data, we find that locations in the immediate vicinity (up to 10 km) of gold mines have more night light and proportionally higher employment in industry and services and in the wage sector. Non-farm employment decreases at distances of 20-30 km from mining locations. These findings are consistent with agglomeration effects predicted by central place theory that induce non-farm activities to coalesce in one particular location. Exploiting variations in mining intensity over time and space, we find that an increase in gold production is associated with more wage employment and apprenticeship, and fewer people employed in private informal enterprises. We also find that the changes arising from increasing gold production are not reversed when large gold mines shrink. However this pattern cannot unambiguously be ascribed to agglomeration effects given that we also observe an increase in informal mining after formal mines decrease output. Although we do not have detailed information on their activities, we suspect that the informal miners flock to the sites of abandoned mines to seek gold residues in the mines themselves and in the tailings. Our results tend to suggest that this activity is 4
prior training experiences in shaping women’s decisions to seek training in EICT. Finally, women with greater agency are more likely to opt for EICT.

Overall, our analysis reveals a complex association between socio-demographic characteristics, education and training, employment, networks, role models and support, gender attitudes, and agency, and training choices, with some notable gender differences as well as similarities. For women, education, prior training in EICT, role models, and agency are predominant in shaping their training decisions. For men, education, age, and network characteristics (size and composition) significantly influence their training choices. Our LASSO analysis (Table B.3) used as a robustness check, further highlights these gender differences while also providing a more parsimonious set of predictors.<sup>11</sup> For men, the LASSO selects only education as a significant predictor, suggesting it may be the most robust factor. For women, the LASSO identifies a broader range of factors, including age, household wealth, education, network size, male role models, and access to professional advice outside the family.

> 11While the LASSO provides a more conservative estimate of significant predictors, the broader set of factors identified in our combined analysis offers valuable insights into the complex interplay between socio-demographic characteristics, education and training, employment, networks, role models and support, gender attitudes, and agency, and training choices, particularly for women

25
The original food basket transferred consists of the following basic products: powdered fortified milk (8 packages of 240 gr. each), beans (2 kg), rice (2 kg), corn flour (3 kg), soup pasta (6 packages of 200 g), vegetable oil (1 lt.), cookies (1 kg), corn starch (100 g), chocolate drink in powder) (400 g), cereals (ready-to-eat) (200 g), and sardines (2 cans of 425 gr. each).<sup>10</sup> The basket offers approximately 400 calories per day per capita for an average household of 4.2 equivalent adults.

The sample size was calculated so that statistical tests had the power to detect statistically significant and biologically relevant differences in several nutritional and economic variables. Specifically, the calculations of the sample size prior to the baseline survey were based on 53 communities per treatment group, a power of 80 percent, and a minimum detectable difference in food per capita consumption between each treatment and control group between 17.8 percent (in T2 and T3) and 18.5 percent in T1.<sup>11</sup> The final sample consisted of 33 households per community and around 52 communities per treatment group.<sup>12</sup>

> 10 This is the food basket (basket A) provided between June and October 2004. There were small changes in the contents of the food basket provided between November 2004 and April 2005 (basket B): Cereals were replaced by dried meat (100gr), and corn starch by lentils (500 gr).

> 11 Also, ICC=0.220 and σ =69

> 12  For more details the reader is referred to Gonzalez de Cossio et al. (2006).

9
ditures.

The equilibrium of the agricultural goods market, on the other hand, is given by: In sum, aggregate real production is determined as a combination of both: Given the presence of intermediate inputs, the GDP is calculated as: The labor market clearing condition between household labor supply and demand in each sector enables writing the total number of hours worked as:

# **4.3 Calibration**

The model is log-linearized and calibrated in quarterly frequency to resemble the economy of Bangladesh (Table 4). Some parameter values are set such that the steady-state conditions are consistent with long-run historical averages and microeconomic evidence. Other parameters are calibrated in line with the values commonly used in the literature for developing and developed countries or according to Gallic and Vermandel’s (2020) estimations. More specifically, _β_ is set to 0.98, as Ahmad et al. (2012) find similar values for the long-run discount factor in a group of developed and developing economies. The depreciation rate of physical capital ( _δK_ ) is 0.025, with the capital share in technology ( _α_ ) of 0.31, both as in Amin et al. (2018, 2019).

Turning to the agriculture sector, the share of agricultural goods in the consumption basket of households ( _φ_ ) is set to 50 percent, given the higher share of food products in developing economies’ consumption. In addition, the land-to-employment ratio is 0.12, based on the hectares of arable land per person in Bangladesh (World Bank WDI). Parameter values imply the steady-state share of the agriculture sector close to the values observed in data (World Bank WDI). The elasticity of land productivity with respect to the weather ( _θ_ ) is set to 20.59 with alternative values considered in the following subsection. Finally, an extreme weather event is proxied by a weather shock with the persistence 0 and the standard deviation of 0.58, such that it results in the contemporaneous impact of 1 percent of GDP.

When it comes to fiscal parameters, the steady-state share of spending in GDP is 0.14 (IMF WEO), while the debt-to-GDP ratio equals 30 percent (IMF WEO). Lumpsum tax function parameters ( _ρB_ and _ρG_ ) are 0.33 and 0.1, as in Gali et al. (2007).

15
the capital market activity. Section 4 presents the results on which firms use these markets. Section 5 shows the results on firm performance and the evolution of the FSD. Section 6 explores additional heterogeneity in firm behavior according to the type of securities issued, market of issuance, and firms’ home market. Section 7 studies the role of firm age. Section 8 concludes.

# **2. Data**

To assess which firms issue securities, the comparative performance of issuing and non-issuing firms, and the evolution of the size distribution of firms as they issue securities, we assemble a comprehensive data set covering corporate bond and equity issuances in capital markets around the world as well as balance sheet information on publicly listed firms. The data on firm capital raising activity cover the period 1991-2011 and come from the Thomson Reuters Security Data Corporation (SDC) Platinum database, which provides transaction-level information on new issuances of common and preferred equity and publicly and privately placed bonds with an original maturity of more than one year.<sup>5</sup> Given that the SDC Platinum database does not collect data on debt issuances with maturities shorter than one year, the data set does not cover commercial paper. For offerings in more than one market, we consider each market a separate issuance. The data set includes 532,423 security issuances: 138,968 equity issuances and 393,455 bond issuances by listed and unlisted firms. Security issuances are classified as domestic or international based on the location of the main exchange where the issuances take place and compared with the issuing firm’s nationality. The data set includes 411,180 issuances in domestic markets and 116,811 issuances in foreign markets (4,432 issuances are not possible to be classified and have been assigned missing values).

To examine the comparative characteristics and performance of issuing and non-issuing firms,

> 5 SDC Platinum collects data on security issuance mostly from filings with local regulatory agencies and stock exchanges. These data are augmented with data from other sources such as offering circulars, prospectuses, surveys of investment banks, brokers, and other financial advisors, news sources, trade publications, and wires.

7
# **5. Modeling Policy Scenarios**

In this section we present four policy scenarios, which have been constructed such that to include the AfT categories identified by the Task Force on Aid for Trade (WTO, 2006). The AfT policy scenarios are carried out as comparative static analysis in variables that are exogenous in the closure of the model. Table 2 reports the description of the policy scenarios.

**Table 2. Scenario Design and Instrument Description**

|_Scenario_|_AfT policy Intervention for_<br>_the recipient countries_|_AfT Policy Impacts_|
|---|---|---|
|_1. Trade adjustment_<br>_assistance(TAA)_|_Increase in the aid budget_|- Donor countries: negative Income<br>transfer<br>- Recipient countries: Unconstrained<br>positive income transfer|
|_2. Institutional reform (IR)_|<sup>_Reduce transaction costs &_</sup><br>_introduce quality assurance_|-Donor countries: Negative Income<br>transfer<br>- Recipient countries: Constrained positive<br>income transfer for reducingexport tax|
|_3.Technical assistance &_<br>_capacity building_(TA<br>&CB)|_Update the production_<br>_processes, access market_<br>_information and improve_<br>_efficiency_|- Donor countries: Negative Income<br>transfer<br>- Recipient countries: Constrained positive<br>income transfer for raising efficiency for<br>all factors|
|_4. Economic_<br>_Infrastructure_|_Improve infrastructure_|- Donor countries: Negative Income<br>transfer<br>- Recipient countries: Constrained positive<br>income transfer for raising investments|

Source: Authors' modelling Design The first scenario, called “trade adjustment assistance”, involves fiscal support and policy advice to help countries cope with any transitional adjustment costs from liberalization. This scenario implies an income transfer from the donors to the recipient countries. The exogenous shocked variable in the AIDCGEM is the AfT transfer (AIDr) in region _r_ . The AfT transfer is not constrained, that is, it is not related to any project or public expenditure in the recipient countries. The aid for trade data comes from OECD.STAT data base. The amount of aid for

**15**
Policy Research Working Paper 9912

# **Abstract**

After the 1919–1922 Greco-Turkish conflict, 1.2 million Greek Orthodox were forcibly displaced from Turkey to Greece, increasing the host population by 20 percent within a few months. Refugees were pro-vided with farmland, new houses and schools, and were granted the Greek citizenship. This paper analyses the long-term social integration of refugees and the effect of their resettlement on social cohesion. Combining historical and modern population censuses and surveys, this paper finds that, by the 2000s, refugees display a high rate of intermarriage with Greek natives, report levels of trust in others and in institutions similar to natives, and exhibit higher political and civic participation. At the community level, places with a higher share of refugees in 1928 are more likely to have at least one sport association 80 years later. There is no impact on political fragmentation nor on crime. The historical refugees’ integration starkly contrasts with the social marginalization of recent Albanian immigrants who, unlike the former, neither spoke Greek nor had the same religion as locals upon arrival. These results suggest that early investments in inclusion policies can be effective at fostering refugees’ assimilation, at least when newcomers and locals have similar cultural profiles.

This paper is a product of the Social Sustainability and Inclusion Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at elie.murard@ua.es.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. 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._ Produced by the Research Support Team