rafmacalaba/datause-encoder-data huggingface dataset:
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earn recognition on the US labor market, occupational status improves. There is an inverse relationship between the initial decline in skills and the subsequent recovery, proportional to the investment in human capital undertaken by the migrant in the destination country. Immigrants from countries very similar to the destination may experience little or no downward mobility, and hence little subsequent increase. In addition, immigrants without skills or with poor ones are also not likely to make large investments into skills upon arrival; for them too, the model predicts little or no subsequent increase.

My study focuses on occupational placement as an indicator of performance. It evaluates assimilation patterns using the “synthetic cohort” approach introduced by Borjas (1985) for earnings and implemented by Green (1999) for occupational choice. By using country of origin attributes to explain performance, the study draws on the conclusions of the occupational placement analysis by Mattoo et al. (2008), but also relates to Borjas (1987, 2000) and Jasso and Rosenzweig (1986) who focus on earnings. I evaluate the convergence in immigrants’ occupational placement by country, employing a model which has also been used by Borjas (2000) for earnings. Finally, my results of negative correlation between entry levels of occupational placement and improvements in occupational placement over time and by country when controlling for human capital characteristics relate to work by Borjas (2000), Duleep and Regets (1997) and Hu (2000).

# **3. DYNAMIC PATTERNS IN OCCUPATIONAL PLACEMENT OF US SKILLED MIGRANTS BY COUNTRY OF ORIGIN**

# **_3.1. EMPIRICAL FRAMEWORK_**

The study uses the “synthetic cohort” approach introduced by Borjas (1985) to evaluate assimilation in the sense of progress made by US immigrants over time relative to their initial position on the US labor market. Performance is measured by occupational placement (OP). I employ OLS with country fixed effects to explain the _OP_<sup>19</sup> as of Census year _t_ for migrant _i_ who came to the US in decade _d_ (equation 1). Decades are assimilated to cohorts in the sense used by Borjas (1985).<sup>20</sup> The implicit assumption is that migrants arriving within a decade are homogeneous with respect to ability _. OP_ is measured using three alternative variables, one

> 19 in log specification

> 20 Therefore the two terms are used interchangeably throughout the paper.

12
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**Table 1: Descriptive Statistics Means and Control vs. Treatment Differences**

|**A. Child Outcomes (n = 1152)**|**All**|**Control**|**Treatment**|**C vs. T (p-val)**|
|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|
|Age|8.378|8.359|8.386|0.920|
||(0.121)|(0.231)|(0.142)||
|Female|0.492|0.452|0.507|0.078|
||(0.014)|(0.026)|(0.017)||
|Child Growth|||||
|Height-for-age|-1.220|-1.263|-1.203|0.532|
|(z-score)|(0.043)|(0.082)|(0.050)||
|Symptom Reports|||||
|Any reports|0.463|0.516|0.443|0.021|
||(0.014)|(0.027)|(0.017)||
|Any cough reports|0.242|0.271|0.231|0.137|
||(0.012)|(0.024)|(0.014)||
|N. symptoms reported|2.041|2.262|1.954|0.142|
|(max: 21)|(0.094)|(0.183)|(0.110)||
|**B. Adult Cook Outcomes (n = 475)**|**All**|**Control**|**Treatment**|**C vs. T (p-val)**|
||**(1)**|**(2)**|**(3)**|**(4)**|
|Age|37.609|37.557|37.571|0.963|
||(0.975)|(0.601)|(0.511)||
|Female|0.922|0.915|0.917|0.763|
||(0021)|(0013)|(0011)||
|Activities of Daily Living (ADL) Reports|.|.|.||
|<br>N. ADLs with any difficulty|3.896|3.947|3.933|0.867|
|<br>(max: 12)|(0.262)|(0.161)|(0.137)||
|Symptom Reports|||||
|Any symptom reports|0.787|0.773|0.777|0.720|
||(0.032)|(0.020)|(0.017)||
|Any cough reports|0.329|0.377|0.364|0.278|
||(0.037)|(0.024)|(0.020)||
|N. symptoms reported|4.726|4.677|4.690|0.911|
|(max: 21)|(0.375)|(0.227)|(0.194)||
|**C. Household Level (n=475)**|**All**|**Control**|**Treatment**|**C vs. T (p-val)**|
||**(1)**|**(2)**|**(3)**|**(4)**|
|Household size|5.975|5.870|6.018|0.462|
||(0.091)|(0.157)|(0.111)||
|N. Children|2.521|2.399|2.570|0.284|
|(ages 0-14)|(0.072)|(0.132)|(0.087)||
|N. Prime age|3.181|3.159|3.190|0.833|
|<br>(ages 15-59)|(0.066)|(0.124)|(0.078)||
|<br>Age of household head|<br>46.419|<br>46.993|<br>46.187|0.524|
||(0.571)|(1.108)|(0.666)||
|Female headed household|<br>0.083|<br>0.051|<br>0.096|0.101|
||(0.013)|(0.019)|(0.016)||
|Stove use (share of months since|<br>0.632|<br>0.000|<br>0.888|0.000|
|<br>baseline using the stove)|(0.020)|(0.000)|(0.011)||
|<br>Indoor Air Quality|||||
|<br>log 24-hr Mean PM2.5|6.341|6.414|6.263|0.428|
||(0.095)|(0.134)|(0.135)||
|log 24-hr Maximum PM2.5|10.463|10.469|10.455|0.909|
||(0.060)|(0.083)|(0.089)||

Notes: Table reports means and standard errors of key variables for the entire sample as well as separately for control and treatment groups. Column 4 reports p-values of a means test across the control and treatment groups. Panels A and B reports measures at the individual child and cook level. Panel C reports measures at the household level.

12
# Policy Research Working Paper **Series**

|**Title**|Author|Date|Contact<br>for paper|
|---|---|---|---|
|WPS992 Regional Integration<br>in Sub-Sahara<br>Africa: Experience<br>and Prospects|n Faezeh Foroutan|October 1992|**S.**Fallon<br>37947|
|WPS993 An Economic<br>Analysis of Capital<br>Flight from Nigeria|S. Ibi AJayi|October 1992|0. Miranda<br>34877|
|WPS994 Textiles and Apparel in NAFTA:<br>A Case of Constrained<br>Liberalizatio|Geoffrey Bannister<br>n Patrick Low|October 1992|A. Daruwala<br>33713|
|WPS995 Recent Experience<br>with Commercia<br>Bank Debt Reduction|l Stijn Claessens<br>Ishac Diwan<br>Eduardo Fernandez-Ari|October 1992<br>as|Rose Vo<br>33722|
|WPS996 Strategic Management<br>of Populatio<br>Programs|n Michael H. Bernhart|October 1992|**0.**Nadora<br>31091|
|WPS997**How**Financial Uberalizatio**n**<br>i<br>Indonesia Affected Firms' Capital<br>Structure and Investment<br>Decisions|John R. Harris<br>Fabio Schiantarelil<br>Miranda G. Siregar|October 1992|W. Pitayatonakarn<br>37664|
|WPS998 What Determines<br>Demand for Freigh<br>Transport?|t Esra Bennathan<br>Julie Fraser<br>LouisS. Thompson|October 1992|B. Gregory<br>33744|
|WPS999 Stopping Three Big Inflations<br>|Miguel A. Kiguel|October 1992|R. Luz|
|(Argentina,<br>Brazil, and Peru)|VissanLiviatan||34303|
sessions versus just business bank accounts. The decision to evaluate the combined effects of these interventions was based on the idea that firms may face information barriers in transforming business registration into increased access to formal credit markets. NBS Bank was interested in increasing its reach and saw this combined intervention as a potentially inexpensive mechanism for targeting potential clients (as they would have the assistance in registering the business as well).

Firms were invited to NBS Bank’s information sessions in the businesses' area of operation. Each session included a maximum of 30 participants and was led by both NBS Bank representatives experienced in dealing with small business clients and a professional trainer in financial literacy. The information sessions comprised 20 hours of activities (two days of eight hours each and a follow-up session one week later, lasting four hours), with information provided on the following modules: (i) formal and informal financial institutions, and the role of banks; (ii) the benefits of bank accounts; (iii) identifying the specific problems that businesses face, namely the intertwining of business and household responsibilities; (iv) the benefits of separate business and household responsibilities; (v) how business bank accounts allow for the mental and physical separation of household and business funds and (vi) practical examples of using bank accounts for business purposes. At the end of the second day, NBS Bank offered a recently launched business bank account, which had a lower minimum balance (MWK 500) than previous products offered by the bank. This business bank account was available to all firms in Malawi with a BRC.

# **3.7 Sources of Data for Measuring Impacts**

We use two sources of data for measuring the impacts of these interventions. The first are data from our administrative records of program take-up. This includes information on which firms we assisted to get business registration certificates and a TPIN, as well as information on attendance at the bank information sessions and on which firms signed up for business bank accounts at the conclusion of these sessions.

The second source of data is the baseline and four rounds of follow-up surveys. The interventions took place from June to September 2012. Four rounds of follow-up surveys were conducted after the interventions. The first follow-up survey took place between November 2012 and March 2013, on average 4 months after the interventions. The second follow-up survey took place between November 2013 and March 2014, on average 16 months after the interventions. The third follow14
<!-- Start of picture text --> South<br>Tanzania Ghana Uganda Ethiopia Zambia Africa Tunisia<br>(2011) = (2013) (2013) (2011) = (2015) (2070) = (2010)<br>0.0 | 90<br>-1.0 BO<br>_ 720 70<br>3 -3.0 60<br>2 .<br>: -4.0 50<br>E ° ®<br>g 5.0 ° . 40<br>i<br>£ -60 ° 30<br>=<br>= 70 20<br>gé “30° m* ContributoryGini market: income pensions7 plus as deferredpensions incomeF 1<br>-9.0 0<br>Source: Zambia: authors’ calculations based on LMCS 2015; Uganda: Haas and others (2016); all<br>others: Higgins and Lustig (2016).<br><!-- End of picture text -->

<!-- Start of picture text --> 20 70<br>15 . 60<br>10<br>50<br>|<br>40<br>. 30<br>“! 5<br>-10 20<br>: ® . : 10<br>“ Tanzania Ghana Uganda Ethiopia Zambia © South rey °<br>anzania a ganda iopia mbia ul unisia<br>2011) 2013) 013),—«OTA). O15) (2010)Afica (2010)<br>Source: Zambia: authors’ calculations based on LMCS 2075; Uganda: Haas and others (2016); all<br><!-- End of picture text -->
the regional and national levels. As such, this research should be understood as a contribution to the broader effort to strengthen the GDPFS for the benefit of all, particularly LICs.

Global experts and representatives from GPCs, RSMCs, NMHSs and donors observed and concluded during a 2013 WMO-World Bank Group-GFDRR workshop that RSMCs, particularly those hosted by MICs and LICs, generally need major investment to ensure they are able to provide the required forecaster guidance and develop specialized numerical products. The incremental cost of providing global products is far lower than the cost of producing regional products. Further, and building on past and current assessments of NMHS capacities, without significant investment in NMHSs, the cascading forecast system will not succeed in delivering national benefits and/or be sustainable (WMO et al 2013).

A practical example is the situation with Myanmar’s Department of Meteorology and Hydrology (DMH). The European Centre for Medium-Range Weather Forecasting (ECMWF) has provided DMH access to its entire suite of graphical products and provided the tools to extract the most useful information for Myanmar. However DMH has difficulty accessing these products due to limited Internet bandwidth (WMO 2015b). The cost to ECMWF to make this service available is financially small, however the policy and resource issues that need to be overcome domestically in Myanmar potentially come with significant costs. National investment in information technology, communications and NWP expertise is needed, even if an NMHS is not running NWP, to be able to access and utilize these products.

This represents a major caveat for the current research in that only one component of the cascading forecast value chain (see Figure 2.1) is explored, with the assumption that the other components are able to deliver sufficiently to produce the benefits offered by improved access to NWP in LICs through a cascading forecast system. While such assumptions are needed when resource-limited assessments are performed on individual components of a broader effort, it must be stressed that sufficient capacity is needed, and currently often lacking, at all levels and across actors to realize the potential benefits.

# 2 Background

## 2.1 Global hydrometeorological system

This section draws extensively from Rogers and Tsirkunov (2013). The global hydrometeorological enterprise is composed of several institutions that contribute to the production, delivery, and use of weather, climate and water information products and services. Since atmospheric and hydrologic processes know no boundaries, the provision of hydrometeorological products and services by public institutions relies on information exchanged from the global to the local level; a process that is coordinated by the WMO. Data gathered across scales and boundaries provide complementary information, which are assimilated to deliver as accurate a picture of weather and climate as possible. The main institutions involved in this global enterprise and relevant to the current research are reviewed here from national to global: 6
# **Figure 3: Real wages of unskilled natives might initially be lower if South-North migration flows increase, but they eventually recover**

Impact on real wages of unskilled native workers, percent difference relative to baseline <!-- Start of picture text --> Australia, New Zealand and Other Oceania<br>Canada<br>European Economic Area<br>High-Income Asia 2020<br>High-Income Middle East 2025<br>Russian Federation 2030<br>USA<br>Bangladesh<br>Brazil<br>Central Asia<br>China<br>India<br>Indonesia<br>Low-Income Sub-Saharan Africa<br>Mexico<br>Middle-Income Middle East and North Africa<br>Other Former Soviet Union and Other Eastern Europe<br>Other Latin America and Caribbean<br>Other Low-Income South and East Asia<br>Other Middle-Income Asia<br>Other Middle-Income Sub-Saharan Africa<br>Pakistan<br>South Africa<br>Turkey<br>-2 0 2 4 6 8<br><!-- End of picture text --> Source: Authors’ simulations 13
# **Table 5: Regressions Including the Interaction of Competition and Concentration with Credit Information**

The regressions below are estimated using country fixed effects and robust standard errors clustered at the country-year level. The dependent variable _Access to finance_ is a dummy variable that indicates whether the firm has access to a loan, overdraft, or a line of credit. The _Lerner index_ is a measure of competition (higher values imply lower levels of competition). The _Herfindahl index (HI_ ) and _Concentration 3_ are measures of concentration _. Log firm size_ is the logarithm of the number of permanent employees. _Log firm age_ is the logarithm of the firm’s age in years. _Government-owned_ and _Foreign-owned_ are dummy variables that equal one if the firm has government or foreign ownership, respectively. _Exporter_ is a dummy variable that indicates if the firm is an exporting firm. _Manufacturing_ is a dummy variable that takes value 1 if the firm is in the manufacturing industry. _Financial development is_ measured as domestic credit to the private sector as a fraction of GDP. The _inflation rate_ is measured as the growth rate of the GDP deflator (annual). _Credit information_ is the country average credit information index that measures the coverage, scope and accessibility of credit information available through either a public credit registry or a private credit bureau. Results in column (b) are weighted by the inverse of the standard deviation of the Lerner Index. *** p<0.01, ** p<0.05, * p<0.1

|||Ac|cess to finance||
|---|---|---|---|---|
|Variables|||||
||(a)|(b)|(c)|(d)|
|Lerner index|-2.758***<br>[0.568]|-3.219***<br>[0.476]|||
|Concentration 3|||-0.333<br>||
||||[0.341]||
|Herfindahl index||||-0.729*|
|||||[0.424]|
|LernerCredit information|0.557***|0.642***|||
||[0.132]|[0.104]|||
|Concentration 3Credit information|||0.059<br>[0.076]||
|H IndexCredit information||||0.184*<br>[0.101]|
|Log firm size|0.087***|0.088***|0.089***|0.089***|
||[0.004]|[0.004]|[0.004]|[0.004]|
|Manufacturing|0.019**|0.015*|0.024**|0.025**|
||[0.009]|[0.009]|[0.009]|[0.010]|
|Exporter|0.033***|0.031***|0.031***|0.031***|
||[0.008]|[0.010]|[0.008]|[0.008]|
|Foreign owned|-0.080***|-0.092***|-0.084***|-0.082***|
||[0.011]|[0.012]|[0.011]|[0.011]|
|Government owned|-0.175***|-0.190***|-0.177***|-0.176***|
||[0.027]|[0.033]|[0.028]|[0.028]|
|Log firm age|0.011***|0.015***|0.011***|0.010**|
||[0.004]|[0.004]|[0.004]|[0.004]|
|Financial development|0.244***|0.271***|0.211**|0.283***|
||[0.080]|[0.056]|[0.085]|[0.069]|
|Inflation rate|-0.346**|-0.432***|-0.368***|-0.409***|
||[0.144]|[0.164]|[0.140]|[0.151]|
|Constant|1.119***|1.225***|0.711**|0.553***|
||[0.226]|[0.203]|[0.346]|[0.195]|
|Observations|65,428|65,428|64,345|65,428|
|R-squared|0.224|0.199|0.219|0.217|

24
# Capital Adjustment and the Optimal Fuel Choice<sup>_∗_</sup>

Marie Hyland<sup>_†_</sup> Jevgenijs Steinbuks<sup>_‡_</sup> Keywords: capital adjustment, fuel choice JEL: D22, D24, Q41

> _∗_ This work makes use of data from the Central Statistics Office (CSO). The possibility for controlled access to the confidential micro-dataset on the premises of the CSO is provided for in the Statistics Act 1993. The use of CSO data in this work does not imply the endorsement of the CSO in relation to the interpretation or analysis of the data. This work uses a research dataset which may not exactly reproduce statistical aggregates published by the CSO. We thank Gerard Doolan of the CSO for support with the data and clearance. A significant amount of data preparation work was previously carried out by Stefanie Haller and we are very grateful to have been able to use these previously prepared data. We also thank Nolan Ritter, Mike Toman, and the participants of EMEE 2016 workshop for helpful suggestions and comments. Hyland gratefully acknowledges financial support from the ESRI Energy Policy Research Centre. The views expressed in this paper are those of the authors and do not represent the views of the World Bank, its board of directors or the countries they represent.

> _†_ Economic and Social Research Institute and Trinity College Dublin, Ireland. Email: hylandm1@tcd.ie

> _‡_ Development Research Group, The World Bank, United States. Email: jsteinbuks@worldbank.org
– Table 7: Export entry Average and size heterogeneity in effects of SPS and TBT provisions.

Panel A: All and essential SPS and TBT <u>provisions</u>

|||Entry in agricu|ltural pro|ducts||Entry in a|ny product||
|---|---|---|---|---|---|---|---|---|
|Prov:|All SPS|Essential SPS|All SPS|Essential SPS|All TBT|Essential TBT|All TBT|Essential TBT|
|Prov|0.192|0.297<sup>_∗∗_</sup>|||-0.245|0.142|||
||(0.143)|(0.129)|||(0.151)|(0.152)|||
|1<sup>_st_ </sup>tercile _×_ Prov|||0.279<sup>_∗_</sup>|0.420<sup>_∗∗∗_</sup>|||-0.245|0.205|
||||(0.150)|(0.148)|||(0.157)|(0.176)|
|2<sup>_nd_ </sup>tercile _×_ Prov|||0.191|0.288<sup>_∗∗_</sup>|||-0.207|0.179|
||||(0.152)|(0.141)|||(0.146)|(0.161)|
|3<sup>_rd_ </sup>tercile _×_ Prov|||0.0825|0.131|||-0.276<sup>_∗_</sup>|0.0588|
||||(0.142)|(0.132)|||(0.156)|(0.156)|
|fxed-efects|fkj fkt|fkj fkt|fkj fkt|fkj fkt|fkj fkt|fkj fkt|fkj fkt|fkj fkt|
|cluster|ij|ij|ij|ij|ij|ij|ij|ij|
|N|648452|648452|648452|648452|2736535|2736535|2736535|2736535|

Panel B: Different types of integration of SPS and TBT <u>provisions</u>

|||Entry in agricultural|products|Entry in an|y product|
|---|---|---|---|---|---|
||MR SPS|Harmonization SPS|Transparency SPS|Harmonization TBT|Transparency TBT|
|1<sup>_st_ </sup>tercile _×_ Prov|0.228<sup>_∗∗∗_</sup><br>(0.0864)|0.234<sup>_∗∗_</sup><br>(0.0953)|0.0550<sup>_∗_</sup><br>(0.0302)|-0.116<br>(0.157)|-0.0993<sup>_∗∗∗_</sup><br>(0.0338)|
|2<sup>_nd_ </sup>tercile _×_ Prov|0.170<sup>_∗∗_</sup>|0.0870|0.0191|-0.149<sup>_∗∗_</sup>|-0.0838<sup>_∗∗∗_</sup>|
||(0.0830)|(0.0964)|(0.0309)|(0.0681)|(0.0325)|
|3<sup>_rd_ </sup>tercile _×_ Prov|0.0676|0.0234|-0.00587|-0.336<sup>_∗∗_</sup>|-0.103<sup>_∗∗∗_</sup>|
||(0.0742)|(0.0790)|(0.0243)|(0.167)|(0.0321)|
|fxed-efects|fkj fkt|fkj fkt|fkj fkt|fkj fkt|fkj fkt|
|cluster|ij|ij|ij|ij|ij|
|N|648452|648452|648452|2736535|2736535|

_Notes_ : PPML estimation is used. Standard errors clustered by origin-destination presented in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. Provision measures are normalized between 0 and 1. The specifications control for tariffs, GDP of the destination, and PTA depth.

37
7

# **IV.   DATA DESCRIPTION: INEQUALITY DYNAMICS AND FISCAL POLICY AT THE STATE LEVEL IN BRAZIL**

# **The Data**

Annex A presents a description of data definitions and sources. The measures of inequality in income per capita (after public and private transfers, but before taxes) and the employment rate variable were constructed using data from an annual household survey ( _Pesquisa Nacional por Amostra de Domicilios_ , PNAD) undertaken by Brazil’s Institute of Geography and Statistics (IBGE) and compiled by the Socio-Economic Database for Latin America and the Caribbean (SEDLAC), a joint effort of the Centro de Estudios Distributivos Laborales y Sociales of the Universidad Nacional de La Plata and the World Bank’s Poverty, Gender and Equity group for Latin America. PNAD is a representative survey both at the national and state level, but the surveys are not undertaken during Census years (2000 and 2010 in our sample).<sup>3</sup> State-level fiscal data were constructed based on a dataset compiled by the National Treasury Department at the Ministry of Finance. The dataset provides comprehensive information on revenue, expenditures, assets, and liabilities. Some financing items such as disbursements and other financing flows, as well as financing outflows such as amortization payments, are classified “above the line” in the dataset. Hence, we adjust the revenue and expenditure concepts to include only non-financing items. As a result, the difference between our adjusted revenue and expenditure measures equals the nominal fiscal balance and, if interest rate payments are also subtracted, the difference equals the primary fiscal balance.<sup>4</sup> We also used the Treasury database to obtain information on “social” public expenditures both at the state and municipal level, which comprises expenditure on education and culture, health and sanitation as well as social security and social assistance (similar to what was used in Ferreira, Leite and Ravaillon, 2010).

Moreover, we used Regional National Accounts Statistics from IBGE to obtain a series for state-level GDP and the respective deflators. Finally, we also use information on federal social transfers at the state level from a dataset constructed by the Institute for Applied Economic Research (IPEA). This comprises information on three main federal social programs: _Bolsa Familia_ , _Beneficio de Prestacao Continuada_ , and _Renda Mensal Vitalicia_ . These federal social transfers are direct cash transfers to households.

> 3 We linearly interpolate values for these years based on information from adjacent years. We also exclude households that reported zero income so that different measures of inequality could be computed in a comparable manner. Sensitivity analysis shows that our results remain broadly the same when households with zero income are included in the calculation of inequality indicators.

> 4 - The National Treasury dataset used is publicly available at https://www.tesouro.fazenda.gov.br/pt/prefeituras <u>governos-estaduais/sobre.</u>