rafmacalaba/datause-encoder-data huggingface dataset:
ASSET, SALE, EBIT, and CHAEBOL to be positive, as they should increase the probability of foreign investment. If foreigners buy only the better firms then the coefficient on FDEBT should be negative. Alternatively, over-indebted firms may be more likely to be vulnerable to takeover, suggesting the coefficient might be positive. We also include (2-digit SIC) industry fixed effects in all regressions, The first three columns of Table 4A report the coefficient estimates from this regression equation. They were estimated using both Logit and Probit. The results are very similar in both cases so we report only the Logit estimates. All three variables-size, profitability, and chaebol membershipincrease the likelihood of foreign investment. EBIT and DEBT are negatively correlated (the simple correlation coefficient is -0.37). When debt is not included the coefficient on profitability is positive and highly significant. Firms with greater debt are significantly less likely to receive substantial foreign investment, suggesting that highly indebted firms, although perhaps in greater need of foreign capital, do not attract foreign ownership.<sup>8</sup> Next, we look for evidence on capital shortages using data on chaebol affiliation. We include business group data since domestic investment in Korea often comes via the firm's group affiliation, as most chaebol have affiliated banks and non-bank financial companies. If groups have limited access to capital, then we expect the group debt variable to positively impact foreign ownership of their affiliates. We include the log of group assets (GASSET) and group debt-asset ratios (GDEBT). We expect group assets to negatively impact foreign ownership since these groups can subsidize firms more easily.

8Kang and Stulz (1997) evaluate the effect of firm characteristics on foreign ownership in Japan and find similar results for firm size and leverage. They interpret this as evidence that foreign investors do not hold the market portfolio. Our results suggest that some of the relation that they are picking up is because ownership data also contains merger and acquisition activity, which should not be expected to mirror the market portfolio.

14
Africa, and Asia found that roughly three-quarters of utilities in both sectors include some form of quantitybased subsidy in their residential tariff structures (Komives et al 2005).

Quantity-targeting can be thought of as a form of self-selection, in the sense that individual customers determine how much of a subsidy they receive based on how much water or electricity they choose to consume. Another form of self-selection is service-level targeting in which only those households who opt to use a particular lower-cost service level (e.g. public water taps) receive a subsidized connection or a subsidy for on-going service. The use of service-level targeting is quite rare in the electricity sector. In the water sector, it is not uncommon for utilities to provide free or reduced price water from public standposts.

Another form of explicit targeting involves the use of administrative decisions to determine subsidy eligibility. The decision could be to subsidize all customers in a particular demographic or employment group (e.g. categorical targeting of pensioners or war veterans) or those living in particular neighborhoods or regions (i.e. geographical targeting). For geographic targeting to effectively identify the poor, the location of the household must be a reliable proxy for poverty status. This may work well, for example, where poor households live together and isolated from non-poor households. Geographic targeting is often found combined with an IBT or VDT. The water tariffs in Panama City; in Merida, Venezuela; and in Managua, Nicaragua, for example, combine a special tariff for slum areas with a general IBT for residential customers (Walker et al 2000).

Means-testing is a more time consuming and data-intensive approach to administrative decision-making about targeting. It involves using a multi-criteria instrument to evaluate the income or poverty level of individual households in order to find those who are most in need of a subsidy. One example is the Georgian Winter Heating Assistance Program (GWHAP), put in place in 1998 as electricity prices were set to increase in Georgia. This scheme provides subsidy recipients with free electricity for a fixed volume of kilowatt-hours per year (Lampietti et al 2003) The Chilean water subsidy is another—and perhaps the most widely cited example — of a means-tested consumer utility subsidy. The program subsidizes between 40 and 70 percent of up to 15 cubic meters of water for poor households.

Our survey of utility tariffs and subsidies revealed little systematic information on the use of administrative targeting in water and electricity utilities (Komives et al 2005). It is clear, however, that many utilities in Latin America at least offer separate social tariffs or special discounts for disadvantaged customers. Use of categorical targeting to reach pensioners in particular is very common on Eastern Europe and Central Asia.

5
# **References**

Bailey, D.H., G.J. Duncan, F. Cunha, B.R. Foorman, and D.S., and Yeager. 2020a. "Persistence and FadeOut of Educational-Intervention Effects: Mechanisms and Potential Solutions." _Psychological Science in the Public Interest_ 21(2): 55-97.

- Bailey, M.J., B.D. Timpe, and S. Sun. 2020b. _Prep School for poor kids: The long-run impacts of Head Start on Human capital and economic self-sufficiency._ (No. w28268): National Bureau of Economic Research.

- Baird, S., F.H. Ferreira, B. Özler, and M. Woolcock. 2014. "Conditional, unconditional and everything in between: a systematic review of the effects of cash transfer programmes on schooling outcomes." _Journal of Development Effectiveness_ 6 (1): 1-43.

- Baker, M., J. Gruber, and K. Milligan. 2015. _Non-cognitive deficits and young adult outcomes: The longrun impacts of a universal child care program._ (No. w21571): National Bureau of Economic Research.

- Baker, M., J. Gruber, and K. Milligan. 2008. "Universal child care, maternal labor supply, and family wellbeing." _Journal of Political Economy_ 116 (4): 709-745.

- Banerjee, A,, Sh. Cole, E. Duflo, and L. Linden. 2007. "Remedying Education: Evidence from Two Randomized Experiments in India." _Quarterly Journal of Economics,_ 122 (3): 1235-1264.

- Belfield, C., A.B. Bowden, A. Klapp, H. Levin, R. Shand, and S. Zander. 2015. "The economic value of social and emotional learning." _Journal of Benefit-Cost Analysis_ 6 (3): 508-544.

- Berkes, J., A. Bouguen, D. Filmer, and T. Fukao. 2019. _Improving Preschool Provision and Encouraging Demand: Heterogeneous Impacts of a Large-Scale Program._ Washington D.C.: The World Bank Working Paper Series.

- Berlinski, S., S. Galiani, and M. Manacorda. 2008. "Giving children a better start: Preschool attendance and school-age profiles." _Journal of Public Economics_ 92 ((5-6)): 1416-1440.

- Bernal, R., and S.M. Ramírez. 2019. "Improving the quality of early childhood care at scale: The effects of “From Zero to Forever”." _World Development_ 118: 91-105.

- Bernal, R., O. Attanasio, X. Pena, and M. Vera-Hernández. 2019. "The effects of the transition from homebased childcare to childcare centers on children’s health and development in Colombia." _Early Childhood Research Quarterly_ 47: 418-431.

- Bouguen, A., D. Filmer, K. Macours, and S. Naudeau. 2018. "Preschool and parental response in a second best world evidence from a school construction experiment." _Journal of Human Resources_ (The World Bank Working Paper Series) 53 (2): 474-512.

- Brinkman, S.A., A. Hasan, H. Jung, A. Kinnell, and M. Pradhan. 2017. "The impact of expanding access to early childhood education services in rural Indonesia." _Journal of Labor Economics_ 35 (S1): S305-S335.

- Brodeur, A., N. Cook, and A. Heyes. 2020. "Methods matter: P-hacking and publication bias in causal analysis in economics." _American Economic Review_ 110 (11): 3634-3660.
**Table 1. Definitions of variables in firm-level analysis**

Variable Definitions FRN Share of foreign ownership largest owner Ownership shares by the largest shareholder. ln(firm age) The logarithm of firm age. Loss_crime The share of losses in sales due to crime. Ln(GDPpc) Ln(GDP per capita of the country) Firing_cost Firing costs in terms of weeks of wages, from WDI. Interest_spread Interest spread, from WDI. Govt_efficiency Government effectiveness index, from ICRG. Control_CR Control of corruption, from ICRG.

# **Table 2.   Summary statistics at firm level for the SADC region as a whole**

|||**Foreign fir**<br>|**ms**<br>|**D**|**omestic fi**<br>|**rms**<br>|
|---|---|---|---|---|---|---|
||mean|p50|sd|mean|p50|sd|
|Ln(L)|3.395|3.091|1.619|2.530|2.398|1.333|
|Ln(GDPpc)|7.013|7.230|1.201|6.852|6.951|1.181|
|Firing_cost|61.230|53.200|41.204|53.537|32.000|41.339|
|Interest_spead|10.583|7.587|10.571|12.157|8.720|12.880|
|Govt_efficiency|-0.176|-0.306|0.746|-0.273|-0.334|0.754|
|Sales growth|0.405|0.078|1.370|0.285|0.093|1.008|
|TFP|0.079|0.008|0.630|-0.057|-0.098|0.522|
|TFPtran|0.091|0.023|0.581|0.001|-0.030|0.492|
|Exporter|0.207|0.000|0.406|0.077|0.000|0.267|
|FRN|0.834|1.000|0.264|0.000|0.000|0.000|
|Largest owner|0.711|0.700|0.245|0.839|1.000|0.241|
|Ln(firm age)|2.139|2.079|0.984|2.036|1.946|0.968|
|Loss_crime|0.012|0.000|0.038|0.021|0.000|0.514|

24
where _Yi j t_ is the outcome if individual (household) _i_ , in locality _j_ , surveyed in year _t_ . _Catchment Area j_ is a dummy variable set equal to 1 if the community is located within 0-5km from a coffee mill and 0 otherwise. _Postt_ is a dummy defined equal to 1 if the coffee mill is operational at the time of the survey and 0 otherwise. _Catchment Area j × Postt_ is essentially a dummy set equal to 1 for those living within 5km from a coffee mill that is operational during the survey year and 0 otherwise. _λt_ represent location and survey year fixed effects respectively. All other variables remain as previously defined.

# **6.1 Income**

The underlying premise of this paper is that the opening of a coffee mill leads to an income shock to residents within the catchment area of the mill, due to the value-addition of coffee washed in these mills relative to those washed using traditional methods. While the direct effect of the income shock will be borne by coffee farmers, the indirect effect on the local economy cannot be overemphasized.<sup>19</sup> To provide evidence on this channel, we leverage the wealth and asset poverty measures from the DHS dataset, due to the absence of income measures from the DHS dataset.<sup>20</sup> Table 7 presents results on the association between coffee mills and household income/welfare using proximate measures of household poverty status and wealth score from the DHS dataset.<sup>21</sup> The results show a positive income effect of coffee mills on treated households. Households living within the catchment area of coffee mills experience a 0.05 std increase in their wealth score (column 1), and a 4 percentage points (pp) decrease in the probability of being (asset) poor (column 3).

# **6.2 Child Health**

Conditional on the positive income effect of coffee mills in the catchment area, a proximate outcome is an improvement in the health of children living within the catchment areas. We test

> 19However, disentangling these effects is challenging due to data constraints.

> 20Rwanda’s Integrated Household Living Conditions Survey (EICV) has direct measures of household income, however, the dataset is not geocoded hence limiting its usage in the context of this study, especially as it pertains to identifying households in treated and control communities. In fact, the only location identifier in the (publicly available version) of the dataset is at the district level.

> 21The DHS measure of poverty is a measure of the relative (asset) wealth of a household. A household is classified as poor if its wealth score is within the two lowest quintiles of the wealth distribution in the country.

17
20 Tabale5. SAFE MOTHERHOOD INITIATIVE: MORE LIMITED EFFORT MODEL: SUMMARY OF ADDITIONAL ANNUAL OPERATING AND CAPITAL COSTS FOR MATERNAL HEALTH AND FAMILY PLANNING SERVICES District of 1,000,000 population (US$)

|Annual Operating Costs||
|---|---|
|Staff|$150,000|
|Transport|100,000|
|In-service training & supervision|50,000|
|Equipment and supplies|75,000|
|Healt;!<br>education|25,000|
|Monitoring and evaluation|20,000|
|Contingencies|30Q.00|
|Total Annual Operating Costs|S450.000|
|Annual oDerating cost Der canita<br>CaRital Cast|S0.45|
|Training|100,000|
|Conscruc..tion<br>and upgrading|340,000|
|Vehicles|160.000|
|TOTAL|$600,000|
|Assuming 50% of capital costs||
|attributable to maternal health&||
|family planning;|$300,000|
|Annualized cgaital cost (10-year||
|de2recLation)|S 30.000|
|Annualized cost per caRita|S0.03|
|ANNUALIZED TOTAL COSTS*|S480.000|
|ANNUALIZED TOTAL COST per capita|S0.48|

* Annual operating plus annualized capital costs.

Source: Herz and Measham, 1987.
95 the outcome of **a** unified demand system needs to be relaxed to accommodate bargaining over conflicting individual interests. This will not entail weakoning the emphasis it has assigned to the separate prices of women's, men's, and children's time **as** critical factors explaining the family's allocation of time, determination of fertility, and demand for market goods. But the bargaining model also emphasizes the ownership and control of nonearned income and the context in which individual members of the family work and are compensated for their labor. New data **are** needed to test and to exploit the distinctive features of this cooperative Nash-bargained framework, such **as** who owns and controls nonearned income in the householl. Labor supply studies will be needed to categorize "job types" in which women's "control" of their output varies, ruch **as** wage jobs, own account wora at home or outside of the hQme, and unpaid family work. This classification process may be aided by parallel studtes of family human capital investment in children and other intrahousehold resource allocations for which society has a vested interest. Research could then assess whether the intrafamily distribution of resources varies depending on whether women are engaged in different "job types.' A clearer distinction is needed as to what is the approprlate model for studying family behavior in various regions of the vorld. What are the m- pirical consequences of female control of resources for hor production and consumption decisions? Evaluation of public policies affecting households will then be more sharply focused and precise. This should lead to sound policies that increase she output of market and nonmarket goods while raising the productive capabillties of women. Anthropological studies of gender differencesi in family production and
-63-

------------------------------------------------------------------------

__---__--------

|Value Added (US Census Conc<br>as a percentage<br>of Gross|**T**<br>ept.<br>Pres<br>Value o|**ABLE4.6**<br>ent<br>and<br>f Output.|Former**Nati**<br>1975.**in n**|**onal**<br>Acco<br>**ational**<br>**c**|unts<br>Conce<br>**urrencies**|pt)|
|---|---|---|---|---|---|---|
|**---------------------------------------------------------**|**-------**|**---Mexico**<br>**-**|**__------**<br> <br>**----------**|**------------__--------**<br>**-------**|**--Brazil--**|**---------**|
||US<br>census<br>concept|present<br>national<br>accounts<br>concept|former<br>national<br>accounts<br>concept|US<br>census<br>concept|present<br>national<br>accounts<br>concept|former<br>national<br>accounts<br>concept|
|Total Manufacturing|45.79|35.02|32.81|39.20|33.63|32.83|
|Grain Mill Products|28.41|21.72|18.42|25.83|21.95|21.34|
|Sugar**&**Confectionery<br>Products|48.76|40.67|34.19|38.26|29.34|27.89|
|Malt and Malt Beverages|62.65|41.12|39.06|58.97|51.39|50.66|
|Tobacco and Tobacco Products|47.22|34.39<br>|30.59|52.49|49.32|48.82|
|Textiles|46.70|40.05|36.98|34.83|30.26|28.87|
|Footwear<br>and Leather Products|51.64|39.68|38.34|46.11|39.98|38.21|
|Pulp and Paper|42.98|33.01|30.61|43.31|36.45|35.47|
|Soap and Detergents|42.68|30.01|29.17|38.04|34.30|33.90|
|**a:Uis**|46.16<br>|32.69<br>|31.75<br>|39.28<br>|34.22<br>|33.42<br>|
|A^gricultural<br>Fertilizers|46.48|23.10|21.02|29.94|25.55|24.85|
|Petroleum<br>Refining<br>and Product|s30.08|19.86|17.05<br>|26.38|24.78|24.53|
|**Tires**and Inner Tubes|59.45<br>|45.98|45.45|43.82|39.39|38.89|
|Cesent|61.07|47.40|43.71|54.63|50.53|50.17|
|Bricks|52.98|45.76|43.91|72.97|58.59|57.30|
|**Iruoi**<br>**and**Steel|41.22|34.21|31.81|35.09|29.49|28.76|
|Radio and TV Receivers|52.85|39.11|35.81|40.39|37.04|36.47|
|MotorVehicles|36.88|27.94|25.65|24.94|21.75|21.41|
|Weighted<br>average17industries|42.33|32.04|29.74|34.05|29.70|29.02|

Source: US census concept ratios derived from table 3.10; present national accounts ratios derived from tables 1 for industries **Al** to A17 of the Statistical Appendix; former national accounts ratios derived from tables 4.1 and 4.5
results of the participants with considerably varying degrees of success. Although we should be cautious about generalization, these results are highly consistent with the results of earlier studies in Latin America. It seems safe to conclude that entrepreneurs in developing countries can improve the productivity of their MSEs by learning management techniques.

In earlier studies, the estimated training effects were economically large but statistically insignificant or only marginally significant. Our results suggest that such weak estimates come from the heterogeneity of the participants, in our case, between the fabricator group and the machinist group and within each of these groups. Probably, entrepreneurs’ managerial abilities are more difficult to improve than workers’ skills. Unlike vocational training, a management training program may improve the managerial abilities of only a few participants. Nonetheless, it may be worth providing from the social welfare point of view. This is because the quality improvement phase of industrial development is led by a few innovative entrepreneurs, and their success contributes to the overall social welfare through external effects including imitation by many other entrepreneurs, as the literature on industrial clusters attests to.

The results of this paper warrant considerable further research. The longer-term training effects should be examined in future. Our conjecture is that only a small number of participants will continue to improve their business performance based on the knowledge acquired in the training. Another direction to explore is to provide advanced training. By providing elementary training, we have confirmed that entrepreneurs in the survival phase know little about management. Advanced training will allow us to explore what factors help industrial clusters enter the quality improvement phase successfully.

23
FTC Privacy Protection: Expenditures and Number of Employees <!-- Start of picture text --> == Privacy and Identity Protection == Number of Employees<br>15 80<br>m7 — = 60 68<br>5 10 8<br>= ro<br>5 so. Og<br>2®<br>53<br>3 20 5<br>eT<br>2016 2017 2018 2019 2020<br>Fiscal Year<br><!-- End of picture text -->
destructive fishing practices, such as the use of poisons in closed creeks or canals; increased water pollution; reduction in the freshwater flow of the river system; and intrusion of salinity. Significant threats from human actions are likely to continue in the future, and the stress on fisheries in the region may be further aggravated by climate change. Among climate-related threats fisheries in this region will face,<sup>1</sup> one of the greatest challenges will be increased aquatic salinity from sea level rise and climate-induced changes in temperature, rainfall and riverine flows from the Himalayas (Dasgupta et al., 2014; Gain, Uddin and Sana, 2008). These changes will adversely affect many fish species, with significant impacts on their reproductive cycles, reproductive capacities, suitable spawning areas, feeding, breeding, and longitudinal migration. Fishing communities are among the poorest of the poor in Bangladesh, so understanding these impacts is critical for ensuring the future sustainability of fishing-dependent households.

Within the southwest coastal region, Sundarbans ecosystem supports a wealth of fish diversity,<sup>2</sup> provides a refuge for fish from predators, and serves as a nursery for the larvae and juveniles of 90 percent of commercial fish and 35 percent of all fish in the Bay of Bengal (USAID 2010). In 2008, Gain, Uddin and Sana studied the impact of river salinity on fish diversity in the southwest coastal region near the Sundarbans. Their research area included highly saline conditions in Paikgacha _upazila,_ Khulna district, and moderately saline conditions in Rampal _upazila,_ Bagerhat district. The researchers analyzed river salinity data monitored by the Bangladesh Water Development Board (BWDB) for the Sibsa river in Paikgacha and the Passur river in Rampal, and found a significant increase in salinity from 1975 to 2004. After surveying

> 1 Other threats include increased water temperature, changes in cyclonic storm patterns, change in surge heights.

> 2 According to IUCN, water bodies in the Sundarbans (ruvers, streams and canals) covering 1,874 sq. km and marine zones covering 1,603 sq. km upport 27 families and 53 species of pelagic fish, 49 families and 124 species of dermal fish, 5 families and 24 species of shrimps, 3 families and 7 species of crabs, 2 species of gastropods, 6 species of pelecypods, and 8 species of locust lobster. See Shah et al., 2010 for details.

3
**Figure 1: Russia: Evolution of Public Debt and Real GDP**

<!-- Start of picture text --> 140<br>Real GDP, 1990=100<br>120 Domestic currency denominated debt<br>Foreign currency denominated debt 120<br>100 110<br>100<br>80 90<br>80<br>60 70<br>60<br>40<br>50<br>40<br>20<br>0<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>Source: World Bank.<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008<br><!-- End of picture text -->

**_But, as with most unsuccessful exchange-rate based stabilization programs, Russia’s program’s ultimate failure was traced to the lack of supporting fiscal adjustment and structural reforms._** The pervasive non-cash settlements and barter nonpayment system resulted in de facto “soft budget constraints” on firms and led to a complete breakdown of financial discipline, delaying the fiscal adjustment process. The financial indiscipline with massive implicit subsidies amounting to about 10 percent of GDP resulted in declining tax collections, rising government debt, higher interest rates, and poor growth performance (Pinto, Drebentsov, Morozov 2000). These factors reinforced each other, contributing to the general financial and currency crisis in 1998. The lack of fiscal adjustment resulted in a renewal of inflationary pressures and real appreciation right at the time when external variables, in particular the price of oil, moved against Russia.

**_The consequent real appreciation presaged the ultimate currency crash._** The real appreciation that resulted from an attempt to maintain an overvalued nominal exchange rate in the face of high and rising domestic inflation masked underlying negative debt dynamics but was clearly unsustainable. As so many exchange rate based stabilization programs that were not backed up by credible fiscal adjustment, this program, too, ended in a crash, a crash that was probably brought forward by the contagion effects from the Asian crisis (Figure 2).

7