rafmacalaba/datause-encoder-data huggingface dataset:
Table 5: Balanced sample

||P|R|C|L|PO|L2|EFW|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|PRt�5|0.651***<br>(0.0600)|0.619***<br>(0.0650)||||||
|CLt�5|||0.619***<br>(0.0592)|0.565***<br>(0.0662)||||
|Polity2t�5|||||0.577***<br>(0.0634)|0.560***<br>(0.0645)||
|EFWt�5|||||||0.726***<br>(0.0609)|
|Human capitalt�5|0.708**|0.870**|0.631***|0.737***|0.542*|0.557*|0.0342|
||(0.317)|(0.348)|(0.240)|(0.260)|(0.318)|(0.331)|(0.0958)|
|Total emigration ratet�5|0.819***|0.848***|0.518**|0.589**|1.127***|1.203***|0.0616|
||(0.317)|(0.329)|(0.260)|(0.274)|(0.379)|(0.399)|(0.116)|
|Log populationt�5|0.0456*|0.0452*|0.00971|0.0129|0.0644**|0.0673**|-0.00437|
||(0.0246)|(0.0257)|(0.0153)|(0.0171)|(0.0281)|(0.0306)|(0.00657)|
|Time dummies|yes|yes|yes|yes|yes|yes|yes|
|AR(1) test|0.000|0.000|0.000|0.000|0.000|0.000|0.001|
|AR(2) test|0.551|0.562|0.579|0.621|0.856|0.852|0.210|
|Hansen J test|0.414|0.429|0.252|0.113|0.391|0.181|0.997|
|Observations|456|456|456|456|432|432|216|
|N. countries|76|76|76|76|72|72|36|
|N. instr.|74|62|74|62|74|62|62|

*** p<0.01, ** p<0.05, * p<0.1. Robust standard errors clustered by country in parentheses. One step system GMM estimator. The sample is a balanced sample comprising data at ...ve year interval between 1980 and 2005. AR(1) and AR(2) are the p-values of Arellano-Bond test for serial correlations. The values reported for the Hansen J test are the p-values for the null hypothesis of instrument validity. All the variables are treated as pre-determined. They are instrumented for using their own ...rst to third lags in columns 1, 3 , 5. They are instrumented for using their own ...rst to second lags in columns 2, 4, 6, 7. In addition to these instruments, the system GMM also uses as instruments for the level equations the explanatory variables in the ...rst di¤erences lagged one period.

21
# **4. Results**

Table 2 reports regression estimates of enrollment growth models using growth rate in total female enrollment (grades 6-10). For each dependent variable, three regression specifications are used. Specification (1) controls for district fixed-effects. Specification (2) controls for sub-district (upazila) level fixed-effects. Specification (3) replaces the variables - total number of schools in 1999 and total number of madrasas in 1999 – by gender-disaggregated versions (e.g. coeducation, boys-only, girls-only).

**Table 2:** Determinants of regional enrollment growth (1999-2003) [Dependent variable: Regional growth rate of total female enrollment, grades 6-10]

||(1)|(2)|(3)|
|---|---|---|---|
|total female enrollment in 1999 in the region, grade 6-10 (in logs)|-0.274|-0.292|-0.296|
||(39.57)**|(40.53)**|(40.70)**|
|female enrollment in 1999 in the region, grade 6 (in logs)||||
|female enrollment in 1999 in the region, grade 10 (in logs)||||
|# of madrasas in the region, 1999|0.018|0.025||
||(5.55)**|(7.19)**||
|# of schools in the region, 1999|0.054|0.061||
||(20.90)**|(22.36)**||
|Δin # of madrasa (1999-2003)|0.057|0.075|0.074|
||(2.76)**|(3.52)**|(3.49)**|
|Δin # of school (1999-2003)|0.085|0.093|0.096|
||(7.09)**|(7.49)**|(7.69)**|
|Mean age of schools|-0.001|-0.001|-0.001|
||(4.39)**|(4.12)**|(3.38)**|
|Mean age of madrasas|0.002|0.001|0.002|
||(2.80)**|(2.32)*|(2.95)**|
|# of co-educational madrasas in the region, 1999|||0.022|
||||(5.25)**|
|# of girls-only madrasas in the region, 1999|||0.045<br>|
||||(5.61)**|
|# of boys-only madrasas in the region, 1999|||-0.015|
||||(0.68)|
|# of co-educational schools in the region, 1999|||0.062|
||||(20.32)**|
|# of girls-only school in the region, 1999|||0.071|
||||(13.28)**|
|# of boys-only schools in the region, 1999|||0.019|
||||(1.69)+|
|Govt (=1 if a government secondary school present)|0.037|0.040|0.058|
||(2.08)*|(2.28)*|(3.15)**|
|Urban|-0.002|-0.016|-0.011|
||(0.13)|(1.05)|(0.71)|
|Constant|1.793|1.873|1.888|
||(46.31)**|(46.67)**|(46.45)**|
|N|4784|4784|4784|
|R-squared|0.29|0.32|0.32|
|Fixed-effects|District|Thana|Thana|
|Note: (1) Unit of analysis is region (union). (2)<br>10%; * significant at 5%; ** significant at 1%.|Absolute|value of|t statistics in parentheses. + significant at|

A number of results are obtained from Table 2. First, there is clear evidence of convergence: regions with higher initial enrollment (in 1999) experienced slower growth. Second, between 1999 and 2003, regions with more schools had a higher growth rate in enrollment. But more importantly, regions with more madrasas experienced higher 10
**Figure 13. Perception versus reality: reading**

<!-- Start of picture text --> 100%<br>16%<br>90%<br>36%<br>80% 10%<br>70% 10% 61% Perceived<br>Read Level = 4<br>60% 78% Perceived<br>Read Level = 3<br>19% 91%<br>Perceived<br>50% 30% Read Level = 2<br>Perceived<br>40% Read Level = 1<br>17% Perceived<br>Read Level = 0<br>30%<br>23%<br>20%<br>34% 22%<br>17%<br>10% 11%<br>8%<br>0% 6% 5% 1% 4% 1%1% 1%0%<br>0 1 2 3 4 0%<br>Actual Read Level<br>Parents' Perception of their Children's Read Level<br><!-- End of picture text -->

**Figure 14. Perception versus reality: Math**

<!-- Start of picture text --> 100%<br>90% 20%<br>80%<br>51%<br>70% 19%<br>69% Perceived<br>Math Level = 3<br>60%<br>87% Perceived<br>Math Level = 2<br>50%<br>Perceived<br>34% Math Level = 1<br>40%<br>Perceived<br>31% Math Level = 0<br>30%<br>20% 25%<br>27%<br>10% 15%<br>11%<br>6%<br>0% 4% 1% 2%<br>0%<br>0 1 2 3<br>Actual Math Level<br>Parents' Perception of their Children's Math Level<br><!-- End of picture text -->

* Data based on surveys of 2,803 households, and the testing of 5,377 children in 4 random blocks in the District of Jaunpur, UP. Child tests are weighted by number of children in village divided by number of children tested in surveyed households.

15
4 measured by changes J total factar productivity. Due to the lack of aDpropriate data, the same analysis at plant level as<sup>well as the</sup> analysis of the patterns of entry and exit into and out of each industry is postponed until such data become available. However, unlike other countries which are part of this research project, Turkish data differentiate public from private enterprises. Public enterprises in Turkey account on average for a quarter of industrial output although their share varies greatly from one industry to another (see Tables 2-5). The availability of separate data for priv.ate and public sectors highlights important differences that have historically existed between public and private firms and permits a test of whether the reforms of the 1980s have narrowed these differences over time.

II. BACKGROUND ON TURKEY<sup>9</sup> Beginning in 1980, after two years of economic recession that followed the foreign debt crisis of 1977, Tur-;y adopted a stabilization program that represented a radical break with her traditional inwardloo:cing development strategy. Liberalization of foreign trade and payments was a key feature of the stabilization program.

The trade liberalization measures introduced in the 1980s aimed at shifting the economy towards an export-led growth by dismantling the set of complex and highly restrictive rules and regulations that had in

- 9.This part relies heavily on the following sources: Baysan and Blitzer (1985), Foroutan (1987), Milanovic (1986), and the World Bank Report No. 5365-TU (1984)
- 6 -

other countries like Belgium, Portugal or the United Kingdom. 5/ For the basic data employed in this study, annual value and quantity information were drawn for every five-digit Standard International Trade Classification (SITC) iron and steel product exported by France over 1962-1987 and unit values were computed for these shipments. In addition, similar statistics were drawn for several higher level products (four-digit SITC) where more detailed disaggregate data were not available. An effort was made to hold the four-digit items to a minimum, however, since their unit values can be affected by product-mix differences. In cases, several products had to be excluded from further analysis when tests showed they were only exported to a limited number of countries, or when full 1962-87 value and quantity data were not avpilable. Altogether, this left 11 distinct four and five-digit SITC steel products for further ialysis that composed 40 to 60 per cent of all French iron and steel exports over the 25 year period (see Table 1).

While detailed unit value information on each of these products is presented in the appendix, several summary statistics were used in connection with this data. First, an attempt was made to estimate the size of any overall price margins French associated countries may have paid over or under other exporters (Mf g) from the following 5/ By comparing the various Europearn countries' share in the trade of former colonial associates with similar data for a control group of developing countries, Kleiman (1976) develops an index which shows the extent to which trade is greater (more intense) than swould be expected due to relative shares in world trade. The results suggest th.at former colonial associates' exports to, and imports from, the United Kingdom were three times the normal level for developing countries, while similar ratios for the French associates were about eight times higher. Results for the Italian, Belgium and Portugese colonies suggest they were even more trade dependent then the French associated countries. The Kleiman approach may be useful for analyzing relations between other groups of countries (say between the U.S. and Latin America) to determine if similar high trade intensities exist.
CHINA (excluding Taiwan)

# **-** 2. Projections NRR-1 by 2000

|Tow.|PuIAtlon|Sap.<br>ratio|Period|c<br>rate|0|Cm|TF4|Nl_n|Fol|**Io**<br>to|sGith<br>lO00|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1W0|1121960|47.7|1990-94|1.37|20.4|6.7|2.28|69.6|71.7|27|32|
|1995|<br>1201556|48.5|199S-9|1.19|18.6|6.6|2.16|70.9|73.0|22|26|
|2000|1275464|50.1|2000-04|1.00|16.6|6.5|2.11|72.2|74.4|18|22|
|2005|1341017|48.0|2005-09|0.86|15.3|6.7|2.10|73.1|75.5|15|19|
|2010|1399864|45.1|2010-14|0.79|14.8|6.9|2.10|74.1|76.7|13|16|
|2015|14S6442|*4.4|2014-19|0.75|14.7|7.1|2.09|75.1|77.9|10|13|
|2020|1S12424|47.4|2020-24|0.70|14.3|7.3|2.08|76.1|79.3|8|10|
|2025|1566159|50.2|2025-49|0.38|13.0|9.2|2.07|78.2|82.3|4|6|
|2050|1720696|64.0|2050-74|0.12|12.3|11.1|2.06|80.3|85.4|3|4|
|2075|M7847|69.3|2075-W|0.07|12.0|11.3|2.06|81.3|87.2|2|4|
|2100|1804227|72.2|2100-24|0.04|11.8|11.3|2.06|82.2|88.6|2|3|
|2125<br>2150|1824474<br>**1834915**|73.4<br>74.2|2125-49|0.02|11.7|11.5|2.06|82.5|89.1|2|3|

# 3. **Age** structure (percent): 1990 and 2025

<!-- Start of picture text --> 75+<br>70-74<br>65-69<br>i lB eo~~~8-64 12gE<br>55-59<br>50-54<br>45-49<br>40-44<br>36-39<br>30-34<br>25-29<br>20-24<br>15-19<br>10-14<br>5.9<br>0-4<br>6 8 10 10 8 6 4 2 0 2 4 6 8 1<br><!-- End of picture text --> <!-- Start of picture text --> 10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text -->

# 4. Comparison of Country with Region and Income Group, 1985-90

TFR **eo** Growth rate Country Region Income group Country RegicAl icmem group Country Region Income group 45
# **2 Context**

This paper focuses on East Africa, which hosts more than a fifth of worlds’ refugees (UNHCR 2020). Despite its rich endowment in human, social, and natural capital, the region – – including some neighbouring states in Horn of Africa is plagued by a complex history of political instability, ethnic tension, and weak governance, making displacement a longstanding regional challenge (UNHCR and the World Bank 2015). War and insecurity have triggered major displacements of people in the region, and most of the displacement cases have become protracted (UNHCR and the World Bank 2015; Vemuru et al. 2020). Exploring the determinants of social cohesion is especially important in this region because in many instances, social cohesion between refugees and host communities has broken down, sometimes resulting in violence or xenophobia. It is a region in which contact theory might offer – particularly useful insights for the design of humanitarian and development programmes – from livelihoods to health and education that facilitate greater refugee-host community interaction.

Our data is derived from research in Uganda, Kenya, and Ethiopia. These countries have a long history of accommodating refugees from neighbouring countries, including Somalia, South Sudan, the Democratic Republic of Congo, Eritrea, and Burundi. Yet each one has different legal and policy frameworks relating to refugees: Uganda allows refugees the right to work and freedom of movement, Kenya does not, and Ethiopia has gradually started to expand socio-economic rights for refugees. They also have different economic profiles with, for example, different degrees of opportunity for agriculture or commerce in refugee-hosting areas. For site selection within researched countries, we have always included the capital city and one refugee camp to compare the research results between camp and urban contexts.

## **2.1 Uganda**

In this paper, we primarily focus on our data collection in Uganda. Of the three researched countries, Uganda offers the most generous regulatory environment for refugees in the country. Uganda’s Refugee Act of 2006 provides refugees with the right to work and a significant degree of freedom of movement. Given fewer restrictions, refugees in Uganda generally enjoy a better degree of socio-economic rights and freedom compared to Ethiopia and Kenya (UNHCR and the World Bank 2015). Uganda has been recognised as having one of the most progressive refugee policies in the world because of its longstanding ‘self-reliance’ model, which has been implemented practically since independence, and repackaged at different historical junctures (UNHCR and the World Bank 2015). In addition to allowing refugees to work, its settlements aim to provide refugees with access to plots of land to cultivate and to pursue self-reliant status.

Uganda has 1.4 million refugees, making it the largest refugee-hosting country in Africa. We conducted our fieldwork in Kampala and Nakivale refugee settlement, which was se4
-10-

<!-- Start of picture text --> 60%<br>50% azwe<br>SOt,( *e<br>a lso<br>E40% - ken<br>09 ~ tgo .z<br>- 30% bwa c nga zmh b . sdn<br>mclv<br>E . ~~~sen sl<br>benn<br>aen mwi<br>t 20% - mf a * cmr<br>. cog . SOm<br>rwa a bdi . gha<br>10% - zar uga.<br>0%<br>0% 10% 20% 30% 40% 50% 60% 70% 80%<br>average inflation 80-86<br><!-- End of picture text --> :IS Source: World Bank FIGURE la
16. **The Retail business line is also more profitable when judged by profitability ratios such as the pre-tax return on assets (ROA) or risk-adjusted ones such as the pre-tax return on capital (ROC)** . While the former indicator is not weighted for risk, the latter attempts to capture the relative riskiness of different business lines by estimating their capital requirement for credit risk and inserting it in the denominator of the equation<sup>14</sup> . As shown in Figure 2.9 below, Retail’s estimated ROC is 39 percent, compared to only 16 percent for Corporate. While Corporate is a nominally profitable activity, it is worth noting that its ROC is close to the base rate (SELIC) during 2007, which is often used as a standard hurdle rate. The Government business line generates a lower ROC partly due to its proportionally higher NPLs and related credit expenses, while Treasury exhibits a ROC closer to the banking system average of 29 percent, but – as previously mentioned – these two business lines are not so relevant for the purposes of this paper. Using the ROA indicator also yields similar relative performance results for the Retail and Corporate business lines (3.0 percent and 1.3 percent respectively).

Figure 2.9: Estimated Return on Capital for Different Business Lines <!-- Start of picture text --> Retail 39%<br>Corporate 16%<br>Government 8%<br>Treasury 30%<br>Total 29%<br>SELIC = 12%<br><!-- End of picture text --> <!-- Start of picture text --> SELIC = 12%<br><!-- End of picture text --> _Source: Study calculations based on Bacen data._ 14 This is also the approach undertaken by the Cruickshank Report (2000). Even though economic capital (i.e., capital needed to support the economic risks of the business) would be a conceptually more appropriate variable than regulatory capital, the latter is easier to estimate and is often in practice the binding constraint, particularly for larger, well-diversified banks. The estimated regulatory capital is allocated to the various business lines based on their respective credit exposures measured using Basel I rules (i.e., specific credit risk weights by asset type), with the residual book equity kept in Treasury. This is also consistent with the fact that such business lines mostly incur credit risk, while Treasury typically ‘collects’ and is responsible for managing market and interest rate risk. It should be noted that the capital charge for credit risk used in the study is based on Bacen’s more conservative ratio of 11 percent rather than Basel I’s recommended minimum level of 8 percent.

15
Table 3: Average wages across cohorts, 2005-2009

|all frms|exp|orters|
|---|---|---|
|(1)|(2)|(3)|
|log age<br>0.153***<br>[0.002]|||
|log export-age|0.085***<br>[0.002]||
|log mean export-destination-age||0.080***<br>[0.003]|
|N (obs.)<br>178,674|29,798|29,798|
|R-squared<br>0.173|0.284|0.271|
|industry-year efects<br>Y|Y|Y|
|region efects<br>Y|Y|Y|
|Notes:<br>Mean export-destination-age is defned as|the averag|e of export-|
|destination-age across destinations within frm-year cell|s. Robust st|andard errors|
|in brackets, clustered by frm-year. ***1% level, **5%|level, *10%|level.|

28
- 8 -

consideration. Many of the early studies, such as those of FTry and Tun Wai and Wong, simply produced a list of variables that are correlated with investment in particular countries. Despite the problems associated with fixed factor coefficients in the accelerator model and the limitations of the partial adjustment model described above, Blejer and Khan's model represented an early attempt to develop the theoretical underpinnings of an investment model tailored to a developing economy. The model developed below is in the same spirit, but attempts to use realistic microfoundations as the starting p,oint for a macroeconomic model of investmen **t** hat incorporates the effects of government policy. In addition, the econometric testing that follows will in.orporate the recent literature on stationarity testing and cointegration to avoid the spurious correlations absociated with trended time series and to allow for an analysis of the long run equilibrium relationship between investment and its determinants.

# 2. Microfoundations for a Developing Economy

The microfoundations described below have emerged from a survey of fifty private sector firms in Egypt. The purpose of the survey was to identify those factors that influenced private sector investment decisions in order to develop a realistic analytical model and to contribute to the interpretation of the econometric results. The methodology, questionaire and detailed survey results are available elsewhere.'<sup>9</sup> The discussion here will be limited to deriving a theoretical model of the firm's investment decision-making process that reflects the conditions that may prevail in many developing, as well as in some developed, . onomies.

"9 Shafik, 1989.
outcomes.

## **2.2 Main Data Source: RAIS**

Our analysis uses the _Rela ̧c ̃ao Anual de Informa ̧c ̃oes Sociais_ (RAIS) that provides matched employer-employee data on the near universe of formal jobs in Brazil between 2006 and 2021. Employers must report annual information on all formal jobs in the last year to the Brazilian Labor Ministry ( _Ministerio do Trabalho_ , MTE) which uses the data for administering various social security programs. Firms’ compliance with reporting requirements is high as they have to fear large penalties when failing to submit complete records. Each record in the data entails information on a contract between an establishment and a worker in a given year, including average monthly earnings, contracted hours, type of contract, occupation, the establishment’s location and industry, and hiring and separation dates. We also observe the type of separation: layoff, quit, termination of temporary contract, death, retirement, or transfer. Individual and establishment identifiers allow us to track separations of workers from an establishment.<sup>7</sup> Moreover, the data includes worker characteristics, i.e. their age, gender, education, and race, as reported by the employer. Cornwell _et al._ (2017) document that a substantial share of workers have different races reported by different employers and that changes in race are not entirely random as they are correlated with wage changes upon job transition. To address these inconsistencies, we follow Gerard _et al._ (2021) and assign each worker the modal race as reported in all their contracts within the RAIS across all years.

# **3 Empirical Strategy**

The racial composition of the workforce is likely influenced by a variety of firm and job characteristics, such as firms’ management practices, hiring policies, training and promotion opportunities, and local labor market conditions, which could also affect worker retention. Omitting such unobservable characteristics would thus bias the retention effects of coworker similarity. Focusing on changes in the workplace composition over time by controlling for workplace fixed effects in longitudinal data (Hirsch _et al._ , 2020; Leonard and Levine, 2006), may not be enough to mitigate these concerns, as these changes may also be driven by changes in confounding firm and job characteristics.

We exploit exogenous changes in the racial coworker composition that arise from unexpected deaths of workers. Specifically, we compare the responses of incumbent workers in peer groups that experience the death of a non-white worker with those that experience the death of a white

> 7Throughout the paper, we use the term ‘firm’ interchangeably to refer to establishments.

7