rafmacalaba/datause-encoder-data huggingface dataset:
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During the 1990s, debt to equity ratios of Thai corporates increased significantly: the median value rose from 1.6 in 1988 to 2.3 in 1996. During the same period, the maturity structure of debt shortened considerably. The share of long-term debt almost halved, decreasing from 58 to 31 percent over that period of time. At the same time, foreign indebtedness increased considerably. By 1996, the median value of foreign debt amounted to 80 percent in Thailand, and 42 percent (median value) of the Thai corporations' total debt was denominated in foreign currency, the bulk of which carried short maturities (30 percent).<sup>40</sup> Moreover, Alba et all 1998 show that firms with more concentrated ownership tended to have higher leverage ratios. These authors find that firms recording the highest increase in leverage-an average of 53 percent-between 1992 and 1996, were those where the top-five owners' concentration was 60 to 80 percent. In contrast, firms with more dispersed ownership (below 40 percent of shares belonging to the top five owners) show an increase in leverage of only 19 percent, on average. Furthermore, the authors find some support for their hypothesis that "firms with relatively worse performance got a disproportionately large share of financing in the period immediately preceeding the crisis." By end 1996, hence, Thailand's firms were highly susceptible to liquidity and interest rate shocks. As a result of high leverage, small shocks to interest rates, the exchange rate or operational cash flow would greatly affect the ability of these companies to service their debt. The high share of short-term liabilities made them fpirther vulnerable to sudden swings in international investors' confidence and to the possibility of being unable to borrow from international capital markets to roll over short-term debt or meet other current debt service obligation.

# **4. The Deterioration in the Risk Profile of Financial Institutions**

The risk profile of Thai financial institutions deteriorated significantly during the period 1990-96. In particular, Thai banks and finance companies became more exposed to credit risk as the quality of their portfolios deteriorated, in part because the lending boom and enhanced competition in the financial sector led to lending to riskier firms and without proper credit assessments. Credit risk also increased because of increased lending to risky sectors, and because borrowers became more exposed to foreign exchange and interest rate risks. Banks and finance companies also became more exposed to maturity mismatches. In more detail, this deterioration in the risk profile is explained below.

**_Impact of the Lending Boom._** The rapid credit growth that took place in the early 1 990s strained the expertise of financial institutions in screening, selecting and monitoring loans. As is common in many recently liberalized systems, the skills required to evaluate risky investment projects and monitor the borrower during the life of the loan may have been in short supply in a banking system in which directed and collateral based

40 Figures cited from Claessens, Djankov, Lang (1998).
To show the robustness of these results, we have tried three alternative strategies for each of the outcomes. In row 2, we drop the sample that lives 20 to 40 km away, since they might be affected by the mine; in row 3, we drop the sample that was surveyed two years prior to mine opening; in row 4, we add closest mine fixed effects; and in row 5 we cluster on the closest mine. The coefficients do not change much, even if some magnitudes become bigger and the estimates more significant. However, as in the results using DHS data, these estimates are not precisely measured – few are statistically significant because the standard errors appear large.

Women are 7.4 to 10.4 percentage points more likely to work in service or sales if they live close to a mine (depending on the estimations in panel A columns 4, only one statistically significant estimate). Women close to mines are 2.5 to 2.6 percentage points more likely to work in mining (only one statistically significant estimate).

Men, on the other hand, (results shown in panel B of table 12), are significantly more likely to work in mining, and insignificantly less in agriculture or service and sales. The likelihood that a man works in mining increases by 11.3 to 12.6 percentage points, which is more than a 400 percent increase in likelihood from the mean value which is 2.8%. For both men and women, the results are indicative of changes in labor force participation on the extensive and intensive margin. Fewer people work, as indicated by columns (1) and (2) (significant for men), but those who work, work more hours than before (column 3, albeit insignificant). It should be noted that the sample sizes are limited and these estimates may suffer from lack of power.

Annex figure 2 presents the results graphically and shows the spatial structure for a subset of the variables. The likelihood of a woman working in services decreases with distance from mine, and log wages are higher within 10 to 20 km of an active mine. Men are, intuitively, more likely to work as miners if they reside close to an active mine, and the correlation decreases with distance. Wages for men are also higher close to active mines (panel D). Beyond 40 km, the estimated effects are close to zero.

Table 12 shows that log annual wages are higher close to mines (column 1), and that most of the increase is driven by the increase in wage rates for women (column 2). Women, however, have lower wages before the mine, and a smaller share of women earn wages. Globally, it is considered that the historic expansion in service sector employment (which in this context increased significantly within 10 km) has played a pivotal role in reducing the gender wage and hour gap (Ngai and Petrongolo, 2017).

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BICP reports and other firm level information collected by the World Bank for its subsector studies and projects. Second, the effect of moving to a freetrade environment on different subsectors is analyzed with the use of a multisector computable general equilibrium model. The firm level effective protection estimates are compared with the simulations obtained from the CGE model. The CGE shows that the higher effective protection a subsector enjoys, the more negatively this subsector is effected by moving to free trade. In the third section of the study, a detailed analysis of the protected subsectors are undertaken to highlight more precisely the sources and structure of these inefficiencies.

# A. NOMINAL AND EFFECTIVE PROTECTION IN MANUFACTURING

4. There are serious problems in estimating effective protection rates in India. Many industries are effectively autarkic where, depending on the degree of domesti **c** ompetition, domestic prices might be below or above landed prices of imports (inclusive of tariffs). In this context, deregulation has a very important effect on domestic prices. Easing of entry barriers in many subsectors lead to a rush of investments and eventually to the creation of excess supplies. These excess supplies lead to lower prices than the tariff inclusive prices of imports. Thus for many product groups, domestic prices are much lower than landed (inclusive of tariffs) import prices.

5. Another reason for non-equalization of domestic and import prices is the implicit or explicit price controls and canalization. Public sector supplies a large portion of intermediate goods (especially inmetals and chemicals) and thus either directly controls their prices, or controls the prices of imports through public sector canalizing agencies. Supplies of many of these commodities are also rationed to actual users. Thus, firms needing more inputs (both imported and domestic) than supplied by the rationing system have to import the difference (either directly or through REP licenses) at a higher landed price. In other products, Government agencies import the products at high tariffs and sell at a price that is lower than the landed price, thus cross subsidizing the imports through charging other levies on domestic production (Pursell, 1988; Aksoy, 1991). This is prevalent in many petrochemical products.
Participation rates in off-farm employment in SSA do not only vary substantially across countries but also within countries. Recent data are provided by Davis et al. (2017) but their analysis is at the household-level and for rural areas. According to their estimates, on average, 18 percent is engaged in agricultural wage employment, 15 percent in non-farm wage employment and 34 percent in non-farm self-employment. McCullough (2017) reports individual participation rates in wage and self-employment across different sectors for rural and urban areas. The author finds that within rural areas the bulk of wage labor takes place in the agricultural sector, while the services sector is the most important sector in urban areas. Off-farm wage employment is less common than self-employment in rural areas for all sectors while in urban areas this depends on the type of sector and country. In general, men tend to participate more in off-farm activities (particularly in wage employment) since off-farm employment opportunities are more limited for women (Nix et al., 2016). However, other studies focused on West Africa have documented that rural non-farm entrepreneurship is more common among women than men (Rijkers and Costa, 2012; Ackah, 2013). Due to the ongoing structural transformation in SSA caused by globalization, income growth, population increase and urbanization, off-farm employment opportunities are assumed to evolve rapidly (Reardon et al., 2007). This has been demonstrated by Yeboah and Jayne (2018), who show that the share of labor force engaged in farming has experienced a sharp decline since 2000 in SSA, and that off-farm employment is expanding rapidly.

Men and women enter off-farm employment either out of necessity (push factors) or opportunity (pull factors) (Haggblade et al., 2010). The literature has defined push factors as negative factors that force households and individuals to seek supplementary income sources outside the farm while pull factors are defined as positive factors that attract people into off-farm employment to improve their welfare (Alobo Loison, 2015). Given that self-employment in agriculture is a high-risk, seasonal activity with a large underemployment rate and that rural labor markets are underdeveloped (McCullough, 2017), it is often argued that push factors play a more important role than pull factors in SSA.

Push factors entail different forms of shocks, lack of agricultural productive assets and social factors. Both idiosyncratic shocks (e.g. death or illness of a household member) and covariate shocks (e.g. extreme weather events, sudden price increases) might push people into off-farm employment to mitigate farm income losses (Kijima et al., 2006). Households who are constrained 4
provide another piece of information to policy makers interested in the use of financial education for increasing saving, whether for general purposes or for retirement / old age.

In addition to the topic of savings, numerous studies focused on credit and borrowing and used information on loan defaults after interventions as an outcome variable. The meta-analysis of these studies found the least evidence of impact from financial education interventions. Figure 13 presents this analysis in a forest plot. Even at the 90% confidence interval, the null hypothesis of no impact from the intervention cannot be discarded.

Figure 13 Papers testing loan defaults after intervention <!-- Start of picture text --> %<br>Authors Date Country Intensity Channel ES (90% CI) Weight<br>Agarwal et al 2010 USA 35 1 -0.09 (-0.12, -0.06) 27.54<br>Gine Mansuri 2011 Pakistan 46 1,2 -0.02 (-0.04, -0.00) 28.82<br>Bruhn Ibarra McKenzie 2012 Mexico 4 7 0.02 (-0.01, 0.05) 26.81<br>Berg & Zia 2013 South Africa 26 3 0.04 (-0.04, 0.11) 16.83<br>Overall (I-squared = 87.6%, p = 0.000) -0.02 (-0.06, 0.02) 100.00<br>NOTE: Weights are from random effects analysis<br>-.115 0 .115<br><!-- End of picture text --> While the results from the meta-analysis in figure 13 are significantly heterogeneous and do not show a significant overall effect size, they nevertheless provide interesting insights. Two substantive points of difference between the studies that did and did not show impact lie in whether the education intervention was targeted around a specific topic and whether there was an associated immediate opportunity to use the newly-acquired information.

The two studies in particular that showed positive impacts on loan behavior were Agarwal et al (2010) and Gine and Mansuri (2011). The study population in Agarwal et al (2010) consisted of low-income households with lower credit scores who voluntarily participated in a counseling program provided by a non-profit organization. In this sense, the participants in the financial education intervention gained targeted information (specific to home ownership) and had the immediate opportunity to apply their 22
# Figure 3: Korea’s Investment Rate and Capital-output Ratio

<!-- Start of picture text --> 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015<br>Year<br>IR Trend IR Actual<br>K/Y Trend K/Y Actual<br>.5 4<br>.4<br>3<br>.3<br>2<br>Investment Rate<br>Capital-Output Ratio<br>.2<br>.1 1<br><!-- End of picture text --> average investment rate for the entire sample period is 31.5%. However, the investment rate was not constant over time. It first increased sharply from 11.7% to 43% in 1979, and then gradually decreased to 30.4% by 2014 with fluctuation. The sharp rise of Korea’s investment rate in the 1970’s was mainly driven by the industrial policies in relation to export promotion and establishment of heavy-and-chemical industries.

The human capital per worker (which is the rate-of-return weighted total years of schooling index) monotonically increased throughout the sample period at the annual average growth rate of 1.52%, but in a concave way, i.e. the growth rate of human capital has decreased over time from 10
**_Electricity Sector Reform in Developing Countries, Jamasb, Mota, Newbery, Pollitt_**

<!-- Start of picture text --> 40 14<br>35 12<br>30<br>10<br>25<br>8 Generation (GWh) per employee<br>20<br>6 Capacity (MW) per employee<br>15<br>4<br>10<br>5 2<br>0 0<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>GWh / employee MW / employee<br><!-- End of picture text -->

**Figure 6: Labour productivity in Endesa Argentina generation Source: Endesa Annual Report 1999 and 2002**

Since Chile, several Latin American countries have embarked on extensive economic reform programmes with privatisation playing a central role. A review of the privatisation experience in Latin America suggests that ownership change, although competition and effective regulation has not always been present, has improved the financial and operating indicators of the sectors (see ESCAP, 2001).

Fisher, Gutierrez, and Serra (2003) addresses the effect of the privatising several sectors of the economy in Chile on efficiency of firms and social welfare. Although privatisation of the electricity sector was part of a larger reform program between 1985 and 1989, the proceeds from the sale of the assets in this sector accounted for about half of the total proceeds in this period. Between 1988 and 2000, the installed capacity of the system has grown from a small size of 4,016 MW to a medium size of 10,045 MW.

The study finds that since privatisation investments have risen, unit costs of declined, energy losses have fallen, labour productivity has increased and end-user prices have declined (although mostly due to lower fuel prices). However, it is noted that prior to privatisation, while the firms were making large losses around 1974, by the time of privatisation they had become profitable. The study finds a moderate profit increase in generating firms. The profits in the distribution firms, where regulation has been relatively weak, appear to have been stronger. The study concludes that regulators have been unable to pass all the benefits to customers.

The study shares a common shortcoming of multi-sector case studies. While it is useful to view the reform aspects in specific sectors in a larger context of economic reforms, there tends to be a trade-off in the form of limited in-depth analysis of individual sectors. The paper compares the before and after performance of the major electricity firms using t statistics. However, a counterfactual for the post privatisation period is not considered, and there is no formal analytical linkage between the performance improvements on the one hand, and the impact of price reductions on consumer welfare 30
term, effect sizes are often small (Shah et al., 2024) and, even when large, not sustained over time (Baird et al., 2013). As programming to address the substantial mental health needs of adolescent girls continues to evolve in LMICs, implementers and researchers must ensure that they consider the age-, gender-, and context-specific needs of this group in terms of measurement, targeting, and broader program design.

# **5 Acknowledgements**

We thank colleagues at StrongMinds, StrongMinds Uganda, BRAC USA and BRAC Uganda for their collaboration on this project. We thank seminar and conference participants at the University of Pennsylvania HP/CHIBE seminar series, 2020 AEA Annual Meeting, and the 2021 International Conference on Development Economics. We thank everyone who provided this project with great fieldwork and research assistance and are too numerous to list individually. We thank the JDE Pre-Results Review process for feedback early on in this study that improved our study design. Most importantly, we are so thankful to the young people, who generously shared their time and experiences for the purpose of this research. We gratefully acknowledge funding from Wellspring, CEGA, and the J-PAL Gender and Economic Agency Initiative (GEA). Ethical review committees at Makerere University Higher Degrees, Research and Ethics Committee (Uganda, Protocol 552), Uganda National Council for Science and Technology (Uganda, Protocol HS318ES), and George Washington University (USA, Protocol 180580) approved the study design. The trial is registered at AEA RCT Registry (AEARCTR-0004208) and ClinicalTrials.gov(NCT03966833). The findings, interpretations, and conclusions expressed are entirely those of the authors.

# **References**

- Baird, S., De Hoop, J., and Ozler,<sup> ̈</sup> B. (2013). Income shocks and adolescent mental health. _Journal of Human Resources_ , 48(2):370–403.

- Baird, S., McIntosh, C., and Ozler,<sup> ̈</sup> B. (2019). When the money runs out: Do cash transfers have sustained effects on human capital accumulation? _Journal of Development Economics_ , 140:169–185.

- Baird, S., Panlilio, R., Seager, J., Smith, S., and Wydick, B. (2022). Identifying psychological trauma among Syrian refugee children for early intervention: analyzing digitized drawings using machine learning. _Journal of Development Economics_ , 156:102822.

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<!-- Start of picture text --> Public School Performance and PEC Program<br>3<br>8<br>cocowo Evaluation of the Program<br>8§<br>Sg<br>Es<br>— ———<br>ones ===><br>“8 —<br>88<br>BE ea<br>@8]_ ae SS = ee eee SS<br>Sg ==<br>go So<br>zz<br>2-—<br>FrsT<br>3ss<br>2005 2006 2007 2008 2009 2010 2011 2012<br>Year<br>———— NO PEC ————— PEC ——-—-—- All Schools<br><!-- End of picture text -->
grade, and less than one percent repeat more than one. As the initial enrollment age varies, a more pressing concern for identification is whether errors in estimated start time, and therefore the year of the enrollment decision, will lead to failure of the exclusion restriction. We return to this concern in the discussion of our identification strategy below.

# **_C. Identification Strategy_**

Estimates of equation (2) using OLS would yield biased estimates because our proxy for the cost of migration, the share of the village labor force employed as migrants outside the county, reflects factors that influence both the demand for and supply of migrants from the village. A persistent disruption to the local economy, for example, could decrease the ability of parents to pay tuition while raising the relative return to migrant employment, inducing a negative relationship. On the other hand, a positive correlation between the number of migrants from the village and unobservables affecting high school enrollment could exist if increases in household wealth or expanded high school capacity (and lower test scores required for competitive admission) occurred simultaneously with growing access to migrant employment. To identify the effect of the migrant network and the higher net return from migration that comes with referral, we must find an instrumental variable correlated with the level of migration from the village but unrelated to unobserved individual, household and village factors affecting high school enrollment.

We make use of two policy changes that, working together, affect the strength of migrant networks outside of a home county but are otherwise unrelated to the demand for and supply of schooling. First, a new national ID card ( _shenfen zheng_ ) was introduced in 1984. While urban residents received IDs in 1984, residents of most rural counties did not receive them immediately. Where IDs are available, residents were able to apply for an ID after their 16th birthday. Second, a reform of the residential registration system in 1988 made it easier for migrants to gain legal temporary residence in cities, but a national ID was necessary to obtain a temporary residence permit ( _zanzu zheng_ ) (Mallee 1995). While some rural counties made national IDs available to rural residents as early as 1984, others distributed them in 1988, and still others did not issue IDs until several years later. The RCRE follow- up survey asked local officials when IDs had actually been issued to rural residents of the county. In our sample, about 11