rafmacalaba/datause-encoder-data huggingface dataset:
## **Figure 22.  Returns to Education in Latvian Labor Market by Source and Group of Employees, 2002**

(upper panel: general education ladder, lower panel: vocational education ladder) <!-- Start of picture text --> 0.80<br>0.70 higher vs secondary,<br>"reward"<br>0.60<br>0.50 higher vs secondary,<br>"access"<br>0.40<br>secondary vs basic,<br>0.30<br>"reward"<br>0.20<br>secondary vs basic,<br>0.10 "access"<br>0.00<br>0.35<br>0.3 postsecondary<br>vocational vs technical<br>0.25 secondary, "reward"<br>0.2 postsecondary<br>vocational vs technical<br>0.15<br>secondary, "access"<br>0.1<br>technical secondary,vs<br>0.05 basic, "reward"<br>0<br>technical secondary, vs<br>basic, "access"<br>all malefemaleLatviannon-Latvianpublicprivateurban rural<br>allmalefemaleLatviannon-Latvianpublicprivateurban rural<br>log points<br>log points<br><!-- End of picture text --> _Notes:_ ”Reward” component is returns to education when major group of occupations and firms size are controlled. ”Access” component is difference between returns without and with occupation and firm size controls. Returns are numerically smaller than in Appendix Tables 24 and 25 because they are measured in logarithmic points rather than percent to allow decomposition. Professional higher education is not separated from academic. Presented results refer to full-time employees. _Source:_ Calculation based on LFS data.

# **Experience and Earnings**

**Age–earnings profiles.** Cross-sectional age-earnings profiles describe how earnings of individuals differ across cohorts at a given point in time. Age is often taken as a proxy for the length of work experience in studies of returns to human capital. Thus age-earnings profiles give information about how experience is rewarded in the labour market. The typical industrialized country age-earnings profile for men in most education groups is rising up to about age 50-52, after which it decreases. For women it often peaks somewhat earlier.<sup>10</sup>

Compared to the socialist era, a significant decline in returns to experience in most transition countries has been well-documented. The decline is easily attributable to the fact that skills acquired under central

> 10  See e.g. Ehrenberg and Smith (2003), pp. 277 – 278.

32
|Firm Provides Training Y:1 N:0|0.019<br>(0.024)|0.017<br>(0.022)|0.018<br>(0.025)|0.042<br>(0.033)|0.000<br>(0.000)|0.000<br>(0.000)|0.000<br>(0.000)|0.000<br>(0.000)|
|---|---|---|---|---|---|---|---|---|
|Courts Obstacle Y:1 N:0|1.542|1.726|1.231|5.480*|0.008***|0.008***|0.007***|0.009**|
||(2.857)|(2.930)|(3.149)|(2.778)|(0.003)|(0.003)|(0.002)|(0.004)|
|Total hours of power outages|||||0.000|0.000|0.000|0.000|
||||||(0.000)|(0.000)|(0.000)|(0.000)|
|Firm Purchased Fixed Assets Y:1 N:0|5.304*|5.324*|6.298*|0.423|-0.001|-0.001|-0.000|-0.003|
||(2.747)|(2.924)|(3.327)|(3.035)|(0.003)|(0.003)|(0.003)|(0.004)|
|Time Tax|0.142*|0.141*|0.160*|0.058|0.001***|0.001***|0.001***|0.000|
||(0.078)|(0.078)|(0.090)|(0.091)|(0.000)|(0.000)|(0.000)|(0.000)|
|Population (logs)|4.654*|4.725*|4.858*|5.836**|0.004**|0.004**|0.004**|0.004*|
||(2.508)|(2.531)|(2.734)|(2.541)|(0.002)|(0.002)|(0.002)|(0.002)|
|Primary Education|-0.016|-0.013|-0.000|0.081|0.000|0.000|0.000|0.000|
||(0.293)|(0.292)|(0.311)|(0.206)|(0.000)|(0.000)|(0.000)|(0.000)|
|Freedom from Business Regulations|-1.177|-1.247|-1.487|-1.116|-0.001|-0.001|-0.001|0.001|
||(2.200)|(2.209)|(2.390)|(1.879)|(0.002)|(0.002)|(0.002)|(0.003)|
|Merchandize Trade to GDP ratio|-0.055|-0.052|-0.053|-0.052|0.000|0.000|0.000|0.000|
||(0.059)|(0.059)|(0.063)|(0.059)|(0.000)|(0.000)|(0.000)|(0.000)|
|Growth rate of GDP per capita (%,|-0.689|-0.651|-0.723|0.244|-0.002**|-0.002**|-0.002**|-0.001*|
|annual)|(0.426)|(0.415)|(0.463)|(0.399)|(0.001)|(0.001)|(0.001)|(0.000)|
|Inflation (GDP deflator)|0.049|0.062|0.086|-0.009|-0.000|-0.000|-0.000|-0.000|
||(0.338)|(0.336)|(0.373)|(0.317)|(0.000)|(0.000)|(0.000)|(0.000)|
|Rule of Law|25.019**|24.443**|25.389**|17.773**|0.002|0.002|0.001|0.002|
||(11.404)|(11.338)|(12.089)|(8.515)|(0.003)|(0.003)|(0.003)|(0.005)|
|Control of Corruption|-19.903**|-19.192**|-19.759**|-13.657|||||
||(9.125)|(9.084)|(9.525)|(8.630)|||||
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|-28.457|-31.202|-56.800|-106.662|-0.074|-0.075|-0.066|-0.123|
||(89.347)|(87.681)|(100.466)|(82.585)|(0.054)|(0.054)|(0.049)|(0.081)|
|Number of observations|18,781|18,781|14,148|4,633|22,230|22,230|17,037|5,193|

59
based on a classification of households in fourteen groups, of the impact of external shocks on poverty in Malawi. In IMMPA, the distributional and poverty effects of shocks are assessed in two ways: first by calculating a set of indicators (for income distribution) based directly on the model's simulation results; second, by linking IMMPA simulation results to a household expenditure survey.

Specifically, two measures of income distribution are generated directly from IMMPA: the Gini coefficient and the Theil inequality index (see, for instance Litchfield (1999) or Cowell (1998)).27 Both are based on the six categories of households that were identified earlier, that is, workers located in the rural traded sector, rural nontraded sector, urban (unskilled) informal economy, urban unskilled formal sector, urban skilled formal sector, and capitalists. Thus, these indicators allow the analyst to study changes in income distribution and poverty between groups, under the assumption of complete homogeneity _within_ groups (or representative households). Formally, they are defined as where _n =_ 6 is the number of household categories and _YH = YHi/n_ is the arithmetic mean level of disposable income for household categories. The Theil inequality index is measured as and other variables are as defined above. We also calculate these two indicators using consumption, instead of disposable income.<sup>28</sup>

> 27Other commonly-used indicators include the Atkinson index which, like the Gini index, ranges from 0 to 1. For a detailed analytical discussion of the pros and cons of various mneasures of income inequality, see Cowell (1998). For instance, the Atkinson index is sensitive to inequality changes in the lowest part of the income distribution; the Gini coefficient is sensitive to inequality changes around the median; and the Theil index and coefficient of variation are both sensitive to inequality changes in the top part of the income distribution.

> 28The initial values from IMMPA are 0.512 and 0.515 for the consumption-based and income-based Gini coefficients respectively, and 0.199 and 0.203 for the consumption-based and income-based Theil indexes.

50
Policy ReseaRch WoRking PaPeR 4495

# **Abstract**

The social and economic consequences of poor mental health in the developing world are presumed to be significant, yet are largely under-researched. The authors argue that mental health modules can be meaningfully added to multi-purpose household surveys in developing countries, and used to investigate this relationship. Data from nationally representative surveys in Bosnia and Herzegovina, Indonesia, and Mexico, along with special surveys from India and Tonga, show similar patterns of association between mental health and socioeconomic characteristics across countries. Individuals who are older, female, widowed, and report poor physical health are more likely to report worse mental health outcomes. Individuals living with others with poor mental health are also significantly more likely to report worse mental health themselves. In contrast, there is little observed relationship between mental health and poverty or education, common measures of socio-economic status. The results instead suggest that economic and multidimensional shocks such as illness or crisis can have a greater impact on mental health than overall levels of poverty. This may have important implications for social protection policy. The authors also find significant associations between poor mental health and lowered labor force participation (especially for women) and higher frequency visits to health centers, suggesting that poor mental health can have significant economic consequences for households and the health system. Finally, the paper discusses how measures of mental health are distinct from general subjective welfare measures such as happiness and indicate useful directions of future research.

This paper—a product of the Research Department—is part of a larger effort in the department to look at the determinants of mental health status in developing context. Policy Research Working Papers are also posted on the Web at http://econ. worldbank.org. The author may be contacted at edecastro@worldbank.org.

_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
climate controls (Somanathan et al., 2021). _Structural_ shifts imply a drastic switch in the sector, for instance, by transitioning out of agribusiness sector to those that are less exposed to climate risks (Colmer, 2021) through (gradual or sudden) reallocation of factors of production (Zhang et al., 2018). _Spatial_ transformation takes place by physically relocating (Linnenluecke et al., 2011; Khanna et al., 2021) or diversifying production across locations (Pankratz and Schiller, 2021). Firms can also adapt by establishing financial links to other regions which can act as an effective spatial risk sharing tool (Albert et al., 2021).

# **2.1 Sectoral and Structural Dimensions of Adaptation**

Consider a firm that has already chosen a production location close to final consumers. Suppose that the firm has perfect foresight concerning weather shocks. This firm anticipates when it will be very hot and when it will be very rainy or polluted. The firm is endowed with a production function and the owner of the firm understands how the firm’s production is affected by each possible weather realization. Consider a case where the omniscient manager knows that over the next two weeks the weather will be awful. This firm can adapt by cancelling production during that time or by investing in protective gear to protect the production facility. The firm could undertake the less drastic approach to adaption through _within sector shifts_ and accelerate its production before the onset of terrible weather and store the production in a dry shed. In these cases, the marginal cost of adaptation to the firm is the cost of renting the shed and paying the workers overtime for their long days during the dry time. If the firm cancels work and does not pay the workers for those lost days, then these workers bear the cost of the weather induced downtime. For those families who do not have buffer savings, they can suffer large consumption drops if the earner’s income plummets due to horrible weather.

The perfect foresight case highlights the importance of adjustment costs. A shock is more costly when a firm is unprepared. A firm’s ability to make sectoral shifts, that is, pivot and substitute how it produces, when it produces plays a key role in determining the costs it suffers from extreme weather. The losses from a shock may differ at the firm level versus the industry level. If one firm fails to prepare for an anticipated weather event and cannot produce output that day then this creates an opportunity for a firm that is better prepared. In this sense, the industrial organization of a sector exposed to climate change plays a crucial role. A local monopolist will suffer a lower long term profit loss from a short run disruption in output because consumers have few alternatives to switch to.

Heterogeneous firms differ along a number of dimensions that determine their ability to 4
Specifically, we assess how share price returns respond to news about president Wahid’s potential impeachment<sup>27</sup> by running regressions of the following form: i i Where i is the return on the price of security iiVV _I_ during episode _e,_ P i is a measure of political RR = αα+ ρρP LL + εε connectedness, notably the Suharto Dependency Index developed by the Castle Group, and i is an error RR LL term. The coefficient on _POL_ should be negative if political connections remained relevant. We also run εε regressions in which we augment the specification with an indicator of the return on the Jakarta Stock Exchange Composite Index net of broader Southeast Asian Effects (referred to as _NR JCI_ ) and its interaction with the indicator of political connectedness. _NR JCI_ serves as a measure of event severity. If the severity of an adverse rumor affects politically dependent more than less dependent firms the interaction term _NR JCI*POL_ should be positive.<sup>28</sup> To set the scene for the analysis Figure B1a shows the response of share prices, as measured by average daily returns, by level of political connectedness to rumors about Suharto’s health; connected firms experienced greater reductions in their share prices than firms less dependent on Suharto. Such a pattern is not present in Figure B1b, which examines share prices responses to six salient events leading up to Wahid’s impeachment.

The 51 surviving firms we use for our analysis of share prices responses to political news in the post-Suharto era do not seem to be systematically more or less connected than the original sample of 79 firms used by Fisman as is shown in Table B1 below. Since these 51 firms operate in different sectors, as is shown in column 2, the stock market data also allow us to examine sectoral heterogeneity in both the prevalence and valuation of political connections, which helps shed light on the external validity of our 27 Specifically, we focus on six salient events. (i) On February 1 Wahid received his first parliamentary censure because of two financial scandals, “Bulogate” and “Bruneigate”. The “Bulogate” scandal involved the alleged theft of $2 million USD from state food company Bulog in the name of Wahid by his personal masseur. “Bruneigate” resulted from Wahid’s failure to make public a gift of 2 million USD from the Sultan of Brunei intended to provide assistance in Aceh. (ii) On February 13 parliament made its first call on Wahid to share power with Megawati Sukarnoputri. After several rounds of protests, (iii) on March 21 Wahid’s defense minister claimed that the president would be ready to step aside if there were constitutional reasons for doing so. (iv) Wahid received a second censure on May 1, 2001. While he rejected this censure, (v) on May 14, 2001 Megawati announced that impeachment proceedings against Wahid were “unstoppable”. (vi) on May 30, 2001 the parliament decided to start impeachment proceedings against Wahid.

> 28 Note that we anticipate _NR JCI_ to be negative such that a positive coefficient on the interaction _NR JCI*POL_ implies bigger losses for firms that are characterized by higher political dependence.

43
# **3 Quantitative Analysis**

The framework in section 2 establishes a relationship between the sectoral distribution of the workforce and the five variables outlined before. In this section, I will use the data to perform a set of counterfactuals to highlight issues related to the industrial share of employment in the Latin American economies.

## **3.1 Cross-country variations in employment shares**

The first exercise pertains to the stark pattern of premature deindustrialization observed in Latin America. The idea is to stack the Latin American countries against the economies that have attained a high industrial share of employment and gauge the relative importance of the aforementioned variables in explaining the gap across the two. Given that the sectoral share of employment varies systematically with development, it is desirable if the comparator countries have similar levels of economic development. My selection of comparator countries follows these two principles subject to the availability of data.

The input-output data from the OECD database (OECD, 2017) are available for the period 1995–2010/11 which defines the universe against which to compare the Latin American economies. Of more than 40 countries that feature in the GGDC 10-Sector database (Timmer _et al._ , 2015), the peak industrial share of employment exceeded 30 percent in 8 countries during this period. Mauritius is the only country among these 8 for which the input-output data are not available. I also include China as a comparator country as its industrial share of employment was just under 30 percent in 2011. China also merits inclusion as it is often argued that the development process in China has been different from many countries. It will be useful to see if China serves as an outlier benchmark relative to others in the present context.

Table 1: Comparator Countries

|Country|CHN|ESP|ITA|JPN|KOR|MYS|SGP|TWN|
|---|---|---|---|---|---|---|---|---|
|Peak Industrial Share (%)|29.5|30.6|31.1|31.6|33.5|35.8|34.3|37.5|
|Year|2011|2001|1995|1995|1995|1997|2000|1995|
|||GDP|per cap|ita relat|ive to L|atin A|merica||
|1995|1.24|3.39|3.48|3.99|2.31|1.59|4.88|3.15|
|2011|0.72|1.95|2.01|2.30|1.33|0.91|2.81|1.81|

The table reports the peak industrial share for the seven countries in the GGDC 10-Sector database for which the share breached the 30 percent mark in any year beginning 1995. China is also included as its peak is just short of the 30 percent threshold. The last two rows report the GDP per capita for the eight countries in the year they achieve the peak share relative to the average GDP per capita of the seven Latin American economies in 1995 and 2011.

6
Most existing literature using firm-level corporate governance provisions has studied exclusively US and OECD firms (see Shleifer and Vishny (1997) and Maher and Andersson (2000) for comprehensive surveys). For example, a recent paper by Gompers, Ishi and Metrick (2001) uses differences in takeover defense provisions to create a corporate governance index of US firms and finds that firms with stronger shareholder rights have better operating performance, higher market valuation, and are more likely to make acquisitions. An exception is Black (2000), which finds that the governance practices of Russian corporations are strongly related to implied value ratios. However, a remaining gap in the literature -- that our paper attempts to fill -- is a study of firm-level corporate governance practices across emerging markets and a greater understanding of the environments under which corporate governance matters more.

In a recent report, Credit Lyonnais Securities Asia (further referred as CLSA) produced corporate governance rankings for 495 firms across 25 emerging markets and 18 sectors.<sup>2</sup> The descriptive statistics presented in the report show that companies ranked high on the governance index have better operating performance and higher stock returns. We use the governance rankings produced by CLSA to further investigate the relationship between governance and performance using multivariate regression analysis. Unlike the CLSA report that concentrates on returns, we use Tobin's-Q to measure the market valuation of assets and return on assets (ROA) as a measure of operating performance.<sup>3</sup>

> **2** Credit Lyonnais Securities Asia, report titled "Saints and Sinners: Who's got religion", April 2001. 3 We do not use returns as a performance measure for several reasons. The first reason is the extreme volatility in returns over the last several years in emerging markets overall and particularly in Asia, which constitutes 70% of our sample. Second, even though the CLSA report suggests that firms with bad corporate governance have lower returns than better governed firms, the interpretation of this finding has to rely on market inefficiency arguments- instead of considering poor governance as a source of risk (and therefore requiring additional compensation for this risk in the form of higher return -i.e. negative correlation between governance and performance) investors underestimate the degree of agency costs resulting from poor

3
# TABLE 11.1 VENTURE CAPITAL FIRMS EVALUATION CRITERIA

|WE<br>Sample: Eight vent<br>(Perso|LLS (1974)<br>ure capital firms<br>nally interviewed)|POINDEXTER (1976)<br>97 venture capital firms<br>(MaiLed questiornaires)|TYEBJEE & BRUNO (Study1-19<br>46 venture capitaLists<br>(Telephone survey, open-en|84)<br>ded<br>questions)|
|---|---|---|---|---|
|Factor Av|erage Ueight|Investment<br>Criteria by<br>Rank Order of Importance|Factor|X of Respondents<br>Mentioning|
|Management<br>Comitment|10|1. QuaLity of Management<br>|1. Management SkilLs<br>&History<br>|89<br>|
|Product|8.8|2.Expected Rate of Return<br>|2. Market Size/Growth<br>|50<br>|
|Market|8.3|3. Expected Risk<br>|3. Rate of Return<br>|46<br> <br>|
|Marketing Skill|8.2|4. Percentage Equity Share<br>of Venture<br>|4. Market Niche/Position<br>|<br>20<br>|
|Engineering Skill|7.4|5. Management Stake in Firm<br>|5. Financial History<br>|11<br>|
|Marketing Plan|7.2|6. Financial Provisions for<br>Investor Rights|6. Venture Location<br>|11<br>|
|Financial SkilL|6.4|7. Venture Development Stage|7. Growth Potential<br>|11<br>|
|Manufacturing Skit|6.2|8. Restrictive Covenants<br>|8. Barriers to Entry<br>|11<br>|
|References|5.9|9. Interest or Dividend Rate|9. Size of Investment<br>|9|
|Other Participants|<br>5|10. Present Capitalization|10. Market/Industry<br>||
|in Deal|||Expertise<br>|7<br>|
|Industry/TechnoLog|4.2|11. Investor Control<br>|11. Venture Stage<br>|4<br> <br>|
|Cash-Out Method|2.3|12. Tax Shelter Consideration|12. Stake of Entrepreneur|<br>4|

# PREMUS (1984)

|Factor|Mean|(a)|
|---|---|---|
|Management Team<br>||9.7<br>|
|MarketNiche with high growth potential<br> <br>||8.2<br>|
|Technical<br>assessment of product<br> <br>||7.5<br>|
|Price of equity<br>participation<br>||7.2<br>|
|Timing of presumable positive cash flow<br> <br>||6.2<br>|
|Market type (for ex. technology<br>or services<br>||6.2<br>|
|Percent of equity ownership<br>||5.5<br>|
|Patent and Legat considerations<br>||3.9<br>|
|Others||7.1|

Note: (a) A value of 10 (high)<sup>to 0 (low) could be assigned to indicate the inportance</sup> of each factor in the valuation<sup>proces</sup> Sources: Adapted from Tyebjee and<sup>Bruno (1984), and Premus (1984).</sup> ti-