rafmacalaba/datause-encoder-data huggingface dataset:
a rise in power. I conjecture that individuals with high social characteristics benefited from stronger empowerment than others.

Hypothesis 5 Power winners have high social resources: <u>Opi at</u> > 0.

Finally, negative economic shocks are expected to lower welfare and power but have a larger impact on welfare than power. Hypothesis 6 Negative economic shocks lower welfare and power, and welfare more than power: ~~Owi~~ < ~~Opi~~ <0 for z” as negative economic shock. Ozer Oz 5.2 Covariates The modified framework of equations 4 and 5 implies a different specification of the (2°, z°, z°, 2°, z”) vectors compared to the level models of equations 2 and 3. Attention is restricted to time-invariant and pre-determined variables and shock or change measures.

e Basic characteristics 2°: Gender, age and parental education are time-invariant variables. The TLSS included modules on school enrollment back to 1998/99 and changes in household composition since the violence in 1999, covering members both joining and leaving the household. Using this information, I construct household composition and education levels as before the violence in 1999. About one in five households changed household size during 1999 to 2001, and one in sixteen individuals aged 15 and older underwent schooling since the violence in 1999.

e Economic characteristics z© Asset holdings and housing conditions are endogenous and likely to have changed since 1999. Households provided recall information on the levels of livestock holdings in 1999. About one in twenty families had an Indonesian bank account in 1999.

e Social characteristics z* Ownership of radios and television sets is timevariant, and the displacement since 1999 is likely to have affected the language composition of sub-district populations. Mother tongue is determined at birth, while ability to speak Tetun and Portuguese are unlikely to have changed substantially since 1999.

e Community characteristics z© While community infrastructure and leaders characteristics are likely to have changed since 1999, the indicator variables of regions and district and sub-district capitals are time-invariant.
and Girardin’s index leads to drastically different values for such constellations with larger values for smaller ethnic group holding state power at the exclusion of larger ethnic groups from state power. The authors find that their index is a statistically significant predictor of civil war onset. Unfortunately, due to data limitations, their index is currently only available for Eurasian and North African countries.

Table 1 provides a correlation matrix for the various measures of fractionalization and polarization used. There is clearly often strong correlation among the various measures, but they are far from identical. We conclude that it is important to test the various available measures if we want to ensure robustness of our results. Due to the limited country coverage of Cederman and Girardin’s (2005) _Ethexclusion_ measure, it is not included in this table. However, they report a bivariate correlation coefficient of .42 and .36 with _Ethfrac (Fearon & Laitin)_ and _Ethpol (Montalvo-R.Q.)_ , respectively, which are fairly low as one would expect, given its different conceptual basis.

# _The control variables_

Turning to control variables, there is an enormous theoretical and empirical literature that has accumulated on the causes and consequences of military spending (Gleditsch et al. 2000; Hartley and Hooper 1990). Most of these studies have focused on arms races between the superpowers, or are case studies of single countries over time. We rely primarily on two recent empirical studies addressing the determinants of military spending (Collier and Hoeffler 2002; Goldsmith 2003), namely Collier and Hoeffler’s (2004b) study of military expenditures in five-year averaged periods from 1960-1999 and Goldsmith’s (2003) study of military spending over the period 1886 to 1989, none of which addressed ethnic and other diversity, however.

We control for the level of per capita income in purchasing power parity ( _GNI p.c._ ) as well as its growth rate ( _Economic growth_ ), which are commonly used variables (Davoodi et al. 2001; Goldsmith 2003; Gupta, de Melo, and Sharan 2001). We log GNI p.c. to reduce skewness. Most find that income is positively related to higher expenditures, arguing that wealth allows governments the greater luxury of stronger defense (Collier and Hoeffler 2002). In economic terms, military spending is likely to be a normal good, that is a good with a positive income elasticity (Sandler and Hartley 1995). High economic growth rates might make it easier for governments to impose a greater defense burden on society. We use total population (logged) to control for country size because this influences both ethnic heterogeneity and militarization 8
Table C2: Total income in microdata (survey + tax records)

|Year|Final Income|Wages|Mixed|Capital (total)|Capital (distributed)|Capital (Tax data)|Imputed rent|Corporate|
|---|---|---|---|---|---|---|---|---|
|2003|89.6|41.7|31.5|7.9|2.2|0.5|7.2|6.5|
|2004|99.0|51.7|29.0|8.9|2.5|0.6|8.0|7.3|
|2005|106.3|52.7|32.2|11.6|3.2|0.9|8.6|9.5|
|2006|130.4|68.3|38.3|13.0|3.7|1.0|10.3|10.3|
|2007|155.3|79.6|47.9|14.2|3.7|1.4|13.1|11.7|
|2008|182.8|93.4|52.2|20.3|10.9|0.9|16.4|10.3|
|2009|192.8|102.0|60.6|12.0|4.3|0.7|17.1|8.7|
|2010|207.6|109.1|64.0|14.9|6.1|0.9|18.0|9.9|
|2011|208.4|113.1|58.7|15.3|3.6|1.1|21.3|11.7|
|2012|205.1|111.5|55.6|15.1|3.2|1.4|22.9|11.9|
|2013|233.6|127.1|63.9|16.8|4.6|2.3|25.8|12.2|
|2014|251.9|140.3|64.8|18.9|4.5|2.1|28.0|14.4|
|2015|271.1|148.9|69.7|22.5|5.1|2.9|30.0|17.4|
|2016|299.9|168.2|71.6|27.1|5.9|3.5|33.0|21.2|
|2017|312.1|173.6|78.1|28.1|5.7|3.6|32.2|22.4|
|2018|331.1|189.4|80.1|29.1|6.9|4.1|32.5|22.2|
|2019|360.0|200.1|88.1|29.9|6.6|5.0|42.8|23.2|

_Note:_ This table presented total amounts of income components for each year in the final microdata. It includes survey and tax records after the merging of the two sources, but before scaling up components to macroeconomic aggregates. Values are in nominal billions of Lempiras.

# **D Income definition using tax data**

In this appendix we provide a detailed description of how we construct income variables in the tax records. We start by presenting the composition of total income, then discussing in detail each of its components:

**Total individual income** =

- _+ Capital Income_

- ++ Dividend income (DMR 113)

- ++ Interest income (DMR 115)

- ++ Rental income (DMR 106)

- _+ Wage income_

- ++ Wages (reported on yearly tax filings and/or DMR 111 )

- _+ Mixed income_

- ++ Income from service provision (yearly tax filings and/or DMR 112)

++ Income from commercial activities (yearly tax filings and/or other thirdparty sources)

- ++ Income from non-banking interest, commercial rent and other income (yearly

- tax filings)

- _+ Undistributed corporate profits_

56
in the Cournot model equal to 17.556 (s.e. = 0.14) and standard deviation term _σ_ = 1 _._ 394 (s.e. = 0.119). These numbers imply a distribution of annualized entry costs with mean of approximately US$42 million, and US$2.7 million at the 5th percentile and $647 million at the 95th percentile. These values appear consistent with capacity installation costs reported by industry sources. In 2011, installation costs were approximately US$150 million per megaton (million tons) of annual capacity (Cembureau, 2021).<sup>21</sup> The average market in our sample has 14.5 megatons of annual capacity per firm (measuring the number of firms, as in estimation, as the inverse of the Herfindahl index), which at this price will cost US$2,175 million to install. This implies that for a discount rate of $42 _/_ $2 _,_ 175 = 0 _._ 02 the annuity value of fixed costs equals the expected total cost of installing capacity in the average market.

Given the substantial variation in fixed costs observed, an important question is whether average fixed costs vary systematically across economies, for instance those with different institutions. Rule of law does not have statistically significant effects on fixed costs. Perhaps surprisingly, the coefficient on rule of law is positive. One might have expected that better legal institutions would lower fixed costs, for instance through less expropriation risk. The positive coefficient suggests legal institutions may have other countervailing effects that also raise entry costs, for instance higher costs of environmental compliance (see Ryan, 2012). An alternative measure of institutions, the (Z-score of) the procedures required to start a business as reported by the Doing Business indicators (World Bank, 2020a), has a positive and statistically significant effect on fixed costs. This result validates this de-jure measure of entry costs by showing it is related to observed fixed costs in the cement industry. The exchange rate, which may affect fixed costs to the extent that capital is imported, does not have an economically or statistically significant effect on fixed costs.

**Conduct.** The Cournot equilibrium concept appears to accurately characterize competition in the average market in the industry.

The Conduct model also reports estimates for Equation (9). The estimated value of the Conduct model constant, which corresponds to the Conduct in a market with the average concentration of urban centers and average multimarket contact, is _λ_ 0 = 0 _._ 688 (s.e. = 0.257). The hypothesis that this coefficient is equal to one cannot be rejected. Moreover, this value is one-fifth of the (concentration-adjusted) number of firms in the average market, 3.362, as reported in Table A1. Recall that for a cartel, all firms behave jointly as a monopolist and _λ_ = _Nit_ . The average market looks most like the Nash equilibrium of the Cournot model. Though firms have market power, and affect prices through their choices of quantities,

> 21Cembureau reports a euro value for 2021, which we convert to dollars in 2011 using the current exchange rate and the US GDP deflator.

22
**Table A1: Regression of computerization timing on initial values and changes in credit and transactions**

||**Panel A: Cre**|**dit**|||
|---|---|---|---|---|
|Credit in 1997|0.001<br>[0.001]||0.009<br>[0.005]||
|Credit in 1998||0.001<br>[0.001]|-0.008<br>[0.004]||
|Change in Credit 1998-1997||||0.001<br>[0.000]|
|Observations|387|387|387|387|
|R-squared|0.11|0.11|0.11|0.11|
||**Panel B: Transa**|**ctions**|||
|Transaction Volume in 1997|0.002<br>[0.0003]**||0.002<br>[0.0004]**||
|Transaction Volume in 1998||0.002<br>[0.0003]**|0.001<br>[0.001]||
|Change in Transaction Volume 199|8-1997|||0.0003<br>[0.0002]|
|Observations|242|242|242|242|
|R-squared|0.31|0.31|0.31|0.31|

_Notes:_ The dependent variable is 1 if a SRO computerized in 1999 and 0 if this happened in 2001 or 2005. Standard errors in brackets. Change in credit or transaction volume is the absolute change at SRO level. * significant at 5%; ** significant at 1% 21
- Fearon, James D., Jeremy M. Weinstein, and Macartan Humphreys. 2009. “Can Development Aid Contribute to Social Cohesion after Civil War? Evidence from a Field Experiment in Post-Conflict Liberia.” _American Economic Review: Papers & Proceedings_ 99(2): 287– 91.

- Fofanah, Mohamed. 2011. “Sierra Leone: Renewed Commitment to Local Government.” _IPS News_ . http://www.ipsnews.net/2011/04/sierra-leone-renewed-commitment-to-localgovernment/ (July 16, 2013).

- Gberie, Lansana. 2005. _A Dirty War in West Africa: The RUF and the Destruction of Sierra Leone_ . Bloomington, Ind.: Indiana University Press.

- Gelman, Andrew and Jennifer Hill. 2007. _Data Analysis Using Regression and Multilevel/Hierarchical Models._ New York: Cambridge University Press.

- Glennerster, Rachel, Edward Miguel, and Alexander Rothenberg. 2012. _Collective Action in Diverse Sierra Leone Communities_ . National Bureau of Economic Research. http://www.nber.org/papers/w16196 (April 30, 2013).

- Habyarimana, James, Macartan Humphreys, Daniel N. Posner, and Jeremy M. Weinstein. 2007. "Why Does Ethnic Diversity Undermine Public Goods Provision?" American Political Science Review 101(4): 709-725.

- ———. 2009. _Coethnicity: Diversity and the Dilemmas of Collective Action_ . New York: Russell Sage Foundation Publications.

- Hecock, R. Douglas. 2006. “Electoral Competition, Globalization, and Subnational Education Spending in Mexico, 1999–2004.” _American Journal of Political Science_ 50(4): 950–61.

- Hoffman, Danny. 2011. _The War Machines: Young Men and Violence in Sierra Leone and Liberia_ . Durham, N.C.: Duke University Press.

- Humphreys, Macartan, and Jeremy M. Weinstein. 2008. “Who Fights? The Determinants of Participation in Civil War.” _American Journal of Political Science_ 52(2): 436–55.

- Iversen, Torben, and Francis Rosenbluth. 2011. _Women, Work, and Politics: The Political Economy of Gender Inequality._ New Haven: Yale University Press.

- Jablonski, Ryan, Audrey Sacks, and Marco Larizza. “The Clientelism Tradeoff: How Discretionary Revenue Shapes Spending Choices.” Working Paper.

- Johns, Sheridan, and Richard Riley. 1975. “Local and District Councils - Should They Be Forgotten?” _The Journal of Modern African Studies_ 13(2): 309–32.

Keen, David. 2005. _Conflict and Collusion in Sierra Leone_ . New York: Palgrave.

- Kilson, Martin. 1966. _Political Change in a West African State: A Study of the Modernization Process in Sierra Leone_ . Cambridge: Harvard University Press.

32
WPS3851

# The Return to Firm Investment in Human Capital

Rita Almeida* The World Bank

## Pedro Carneiro

University College London, Institute for Fiscal Studies and Center for Microdata Methods and Practice

#### Abstract

In this paper we estimate the rate of return to firm investments in human capital in the form of formal job training. We use a panel of large firms with unusually detailed information on the duration of training, the direct costs of training, and several firm characteristics such as their output, workforce characteristics and capital stock. Our estimates of the return to training vary substantially across firms. On average it is -7% for firms not providing training and 24% for those providing training. Formal job training is a good investment for many firms and the economy, possibly yielding higher returns than either investments in physical capital or investments in schooling. In spite of this, observed amounts of formal training are very small.

Keywords: On-the-Job Training, Panel Data, Production Function, Rate of Return JEL Classification codes: C23, D24, J31

### World Bank Policy Research Working Paper 3851, February 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

*We thank conference participants at the European Association of Labor Economists (Lisbon, 2004), Meeting of the European Economic Association (Madrid, 2004), the IZA/SOLE Meetings (Munich, 2004), ZEW Conference on Education and Training (Mannheim, 2005), and the Econometric Society World Congress 2005. We thank especially the comments made by Manuel Arellano and Steve Pischke. We also thank the comments of an anonymous reviewer. Corresponding author: ralmeida@worldbank.org. Address: 1818 H Street, NW MC 3-348, Washington, DC, 20433 USA.
(right‐hand panel of Figure 2.6). Countries like Hungary, Bulgaria, Czech Republic and Poland had substantially higher services trade shares, although they are smaller economies that are part of the EU. Export sophistication also appears to be falling. Turkey has specialized increasingly in services exports typical for middle‐income countries, such as transport and tourism. It has developed fewer exports of financial, IT or professional services, which are considered more modern services with higher levels of value‐added (World Bank, 2014).

**Figure 2.6: Trade in GDP shares for Turkey and peers (2000‐2015)**

<!-- Start of picture text --> Trade in GDP Trade shares in GDP, 2015<br>150 157.4 158.1<br>100 104.6<br>85.2<br>50 56.5<br>32.1 24.5 22.6 36.9 39.8 32.9 30.0 27.5<br>17.6 18.3<br>0 9.9 9.7 7.0 5.9 5.9 5.4<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 HUN BGR CZE POL ZAF RUS TUR CHN IDN BRA IND<br>Services (left) Manufacturing (right) Services Manufacturing<br>Source: WDI; author’s calculations<br>50<br>11 45<br>40<br>35<br>9<br>30<br>25<br>7<br>20 Share of trade in GDP<br>Services trade share in GDP<br>15 Manufacturing trade sharein GDP<br>5 10<br><!-- End of picture text -->

**When taking services as inputs into account, Turkey performs in line with its peers.** Services are not only exportable in their own right, but also are critical inputs to manufacturing and GVCs, as seen above. The importance of services as inputs can be seen by examining the value added from domestic service inputs (domestic indirect) and foreign services inputs (foreign) in exports.<sup>7</sup> The left‐hand panel of Figure 2.7 shows domestic indirect services value‐added together with the exports of services firms (domestic direct, which is what is shown in Figure 2.6, but in value‐added terms).<sup>8</sup> Overall, Turkey is in the middle of the pack, although the domestic indirect share is well below the OECD average. The share of foreign value‐ added is also relatively smaller (see right‐hand panel in Figure 2.7). Turkey’s low rate of participation in GVCs and foreign inputs (Figure 2.5) is reflected in the relatively low foreign services value‐added in exports. _However, one critical omission in the data is that it does not include services produced in‐house by manufacturing firms, which is the focus of the following section._

> 7 Based on statistics from the OECD‐WTO Trade in Value‐Added (TiVA) database.

> 8 To be precise, the exports picked up in Figure 2.6 cover in large part trade in services on a cross‐border basis, which is called Mode 1 services supply in WTO parlance. It also covers Mode 2 (i.e. consumption abroad) and part of Mode 4 (i.e. temporary movement of labor), but does not cover Mode 3 (sales of foreign affiliates). Mode 3 trade however is covered in Figure 2.7 under domestic services value‐added in gross exports, directly or indirectly when used as inputs into downstream industries’ exports. The TiVA database does not (yet) make a distinction between foreign and domestic affiliates from FDI.

10
Attitudes and Policies toward Refugees: Evidence from Low- and Middle-Income Countries Cevat Giray Aksoy<sup>∗</sup> Thomas Ginn<sup>†</sup>

**Keywords: refugees, integration, social cohesion, refugee policies.**<sup>1</sup> **JEL codes: F22, J15, J24.**

> ∗European Bank for Reconstruction and Development (EBRD), King’s College London, IZA Institute of Labor Economics, ( `aksoyc@ebrd.com` )

> †Research Fellow, Center for Global Development (CGD) ( `tginn@cgdev.org` )

> 1We thank Leon Bost, Saheel Chodavadia, Franco Malpassi, Reva Resstack, and Pablo Zárate for superb research assistance. We are grateful to Christopher Blair, Guy Grossman, and Jeremy Weinstein for sharing part of the Developing World Refugee and Asylum-Seeker Policy dataset and to Edgar Scrase for sharing UNHCR’s sub-national population data. We additionally appreciate comments from four anonymous reviewers at the World Bank. This paper was commissioned by the World Bank Social Sustainability and Inclusion Global Practice as part of the activity “Preventing Social Conflict and Promoting Social Cohesion in Forced Displacement Contexts.” The activity is task managed by Audrey Sacks and Susan Wong with assistance from Stephen Winkler. The program is funded by UK aid from the United Kingdom’s Foreign, Commonwealth and Development Office (FCDO), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. This work does not necessarily reflect the views of FCDO, the WBG or UNHCR. Financial support from the IKEA Foundation is also gratefully acknowledged. The views presented are those of the authors and not of CGD, EBRD or any other institution. All interpretations, errors, and omissions are our own.
# _b) Correlation between Personality-Trait Variables_

Table 6 presents a correlation matrix for all eight personality-trait variables. The patterns of correlation are generally consistent with the literature, except that higher levels of emotional stability (or lower levels of neuroticism) are somewhat negatively correlated with other big-five traits. Among the big five, extraversion is least correlated with the other four traits, while the other four traits are all positively or negatively correlated with one another. Conscientiousness is positively correlated with emotional stability, agreeableness, and openness. The three other personality indicators, grit, decision-making style, and hostile attrition bias, show broadly similar patterns of correlation. Agreeableness, openness, grit, and decision-making style are closely correlated at between 0.37 and 0.50. However, emotional stability is negatively correlated with all four of these traits. Conscientiousness and extraversion are neutral and show independent correlation patterns with certain other traits.

While these correlations are largely consistent with the findings of the international literature, there are some important contextual variations. The coefficients are generally larger in Bangladesh than in other cases, especially in comparison to a similar study in Colombia (Acosta _et al_ ., 2015). In Colombia the absolute size of the correlation maxes out at around 0.20, while in Bangladesh it reaches 0.50. Pierre _et al_ . (2014) report small to moderate correlations of between 0.1 and 0.4, so Bangladesh may be a unique case in this regard. The direction of the correlation is also unique. A study in Peru finds that all of the big-five traits were positively correlated with each other (Cunningham _et al_ ., 2016). Such a pattern is also shown by a meta-analysis of 212 psychological studies showing that big-five traits are generally correlated positively with each other (Van der Linden _et al_ ., 2010). This difference could reflect the specific context of Bangladesh, or it could be attributable to some methodological issue related to the STEP instrument. Other studies indicate that the reliability of the scale might not be very high among countries in the STEP pilot stage because of low literacy rates among respondents, their unfamiliarity with the concepts used in the survey, the presence of reverse-coded items, and the use of a four-point scale for scoring items rather than the original five-point scale (Pierre _et al_ ., 2014). There is also some debate among researchers regarding the questions used to measure big-five attributes, which are mostly derived from a factor analysis of English words that is translated into other languages, in this case Bengali.

13