rafmacalaba/datause-encoder-data huggingface dataset:
This paper contributes to the strand of the economics literature analyzing the effects of weather shocks by focusing on exposure during the gestational period on health outcomes at birth. Existing literature looking at the health effects of weather shocks while in utero has found no effects of excess rainfall on children’s height and weight in rural Mexico (Aguilar and Vicarelli, 2011; Conroy et al. 2011) or on adult height in rural Indonesia (Maccini and Yang, 2009). However, a drought in an arid African area has been shown to increase malnutrition and mortality for children under the age of 12 months (Kudamatsu et al, 2012). Rather than analyzing the effects of weather shocks _in utero_ on outcomes in childhood and adulthood, we focus on outcomes at birth. One related study shows that exposure to extreme hot temperatures during the second and third trimester of pregnancy leads to small reductions in birth weight (7 to 11 grams) in the U.S. (Deschênes et al., 2011). A second (unpublished) study provides evidence that a negative rainfall shock one year before birth worsens health at birth in the semiarid region of Northeastern Brazil; a 31% decrease in rainfall reduces birth weight by 1.9 grams and the likelihood of full-term pregnancies by 0.6 percentage points (Rocha and Soares, 2012).<sup>9</sup> We improve upon these two studies in two main ways. First, we consider a wider range of health outcomes at birth, namely birth weight, gestational length, length at birth and the Apgar score and find that previously unexplored outcomes are actually more responsive to severe weather events. Second, we study the effect of both extremes of precipitation and temperature shocks using nationally representative data from a developing country.

We identify the causal effect of weather shocks on birth outcomes by merging monthly temperature and rainfall data with unique, nationally representative, administrative data on birth records in Colombia from 1999 to 2008. We identify extreme precipitation and temperature events based on the magnitude of the deviation from the historical average monthly rainfall level in a given municipality. To gauge the causal effect, we rely on the exogenous variation in fetal exposure to these shocks by municipality and date of birth using a fixed effects design. Our identification strategy relies on the fact that temporary rainfall and temperature deviations from the historical average in the individual’s region of birth while _in utero_ are uncorrelated with latent determinants of health at birth. Our detailed data also allow us to explore how the effect of weather shocks varies according to the timing during gestation.

> 9 The effects are stronger during the 2nd trimester of gestation, for girls, and for children born during the dry season. The paper also finds evidence for an increase in infant mortality rate by 2.14 per 1,000 newborns with higher effects on infant mortality during the first 6 months of life, and on mortality due to intestinal infections and malnutrition.

5
advanced in age, between 45-60 years old. In order to see marked improvements in the cocoa sector there is a need to continue introducing better varieties of cacao and improving the techniques of producing cocoa (e.g., planting densities). There have been major renovation and rehabilitation efforts after Hurricane George, and this has led to changes in the cocoa sector to achieve these aims.

2) Recent activities by the government and by the donor community have been focused on the adoption of improved varieties to increase productivity, on cocoa fermentation practices to increase farmgate prices, and to improve access to markets that pay a premium for quality and specialty cocoa. As national cocoa production increases, it is important to continue these efforts to guarantee access to a larger market.

The major advantage for future competitiveness is agro-ecological conditions that allow for production of organic cocoa. Competing countries are less able to produce organically because of pests and diseases that are absent in the Dominican Republic.

As emphasized in the section on competitiveness of the coffee sector, comparisons based on average yields and costs of production are not particularly helpful, considering the available data. Nevertheless, comparing yields among several countries, the Dominican Republic seems fairly competitive in terms of average yields in the mid-1990s. As can been seen in table IV.17, average national yields fell dramatically over the past few years which is almost entirely due to the effects of Hurricane George. Dominican yields are relatively high because of the lack of diseases other nations contend with.

|**IV. 17 Intern**<br>|**ational Compar**<br>|**ison of CocoaY**<br>|**ields (kg/ha)**<br>|||
|---|---|---|---|---|---|
|**3 year**<br>**Avg.**|**Dominican**<br>**Republic**|**Brazil**|**Ecuador**|**Cote**<br>**d’Ivoire**|**Ghana**|
|**1999-01**|290|290|340|570|300|
|**1996-98**|450|390|210|570|330|
|**1993-95**|460|450|250|560|400|

Source: FAO website.

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is classified as health-related has important implications for population as well. For example, the study of aspects of health finance or of pharmaceuticals has implications for family planning programs, which are usually closely associated with health programs.

In addition to the population research conducted by the Policy and Research Division of PHN, the Bank also contributes to the work of WHO in the development of new contraceptive technologies. This contribution is administratively outside of PHN. Modest amounts of population related -search are also undertaken in the Education Department and in other parts of the Bank.

The general theme of current research in the Policy and Research Division is the internal efficiency of alternative policy and program strategies. The current focus of research in population falls into five areas:

- -* effectiveness of alternative family planning delivery systems,

- the role of the private sector in contraceptive distribution,

- incentives for small family size,

- populations projections for use in the sector work of other divisions in PHN and by the rest of the Bank, and research with a special focus on Africa.

In addition, "health" related research on cost-recovery and on drug procurement and utilization have relevance for the population sector. This agenda for research was established in 1984 and represents an attempt to change the orientation of the Policy and Research Division to be more supportive of the operational work of other divisions.<sup>6</sup> The outout of the Policy and Research Division is impressive. By the standards of outside research organizations, there is little doubt that the productivity of the Bank staff is distinguished. Two questions that have been raised in our review deal with the extent to which the division contributes to the work of the operating divisions, and the extent to which the topics chosen for emphasis are those most calculated to contribute to long run objectives of Bank work in population.

It is notable that in our discussions with PHN staff outside of the research areas, there was little mention of the contributions of the division to the ongoing work of developing and implementing projects. The policy study Population Growth 6Internal Memorandum, "Population: ideas regarding future research and policy work," June 15, 1984.

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PAYING THE PRICE FOR UNRELIABLE POWER SUPPLIES: GENERATION OF ELECTRICITY BY PRIVATE FIRMS IN AFRICA and the avoided damage to equipment that would have been caused by a power failure. Under profitmaximizing conditions, the expected marginal gain from a self-generated kWh is also the expected marginal loss from the kWh that is not supplied by the utility. Therefore the marginal cost of selfgenerated power may serve as an estimate for the marginal cost of an outage.

The cost to the firm of generating its own power consists of two elements. The first is the yearly capacity cost of the generator and other capital outlays. Following earlier literature, that cost will be denoted by _b(Kg)_ , where _Kg_ is the generator’s capacity measured in kW. The second is the variable cost per kWh—chiefly fuel cost, which is practically constant.<sup>22</sup> If the generator is used to capacity during power cuts, the variable cost per year will then be _v_  _H_  _Kg_ , where _v_ is the fuel cost per kWh, and _H_ is the expected total duration of outages, measured in hours per year. The total expected yearly cost per kW of backup generating power is then The expected respective marginal cost is and the expected marginal cost of a kWh generated is simply given by Applying equation (4) to the enterprise survey data allows us (using reasonable assumptions) to estimate the (marginal) cost of own generation from observed information about the acquisition and running costs of in-house generating capacity, and from data on the frequency of power outages.<sup>23</sup> For these purposes, values for _b’_ , _H_ , and _v_ must be obtained.

The operating cost, _v_ , is calculated as a product of the unit cost of fuel and the generator’s fuel efficiency (fuel consumption per kWh). Assuming that most firms in the enterprise survey dataset rely on thermal generation, the unit cost of fuel is approximated by an average price per liter of diesel fuel.<sup>24</sup> Fuel efficiency data was obtained from the Web sites of leading manufacturers of generators.<sup>25</sup> Fuel efficiency improves sharply after graduating from the smallest generators but becomes almost flat once capacity reaches 100MW (figure 23). The estimated operating costs and capacities of in-house generators in Africa, gleaned from the enterprise surveys, are summarized in appendix 1, table 3.

> 22 This measure does not account for other variable costs, such as maintenance, wages, and salaries.

> 23 This measure of marginal cost does not account for incomplete backup that may result in additional losses such as destruction of raw materials and damage to equipment. These losses are inversely related to the percentage of backup and the reliability of the firm’s backup equipment.

> 24 The fuel prices came from GTZ International Fuel Prices 2005, available at http://www.gtz.de/fuelprices

> 25 These manufacturers included Wärtsilä (http://wartsila.com) and Cummins (http://cumminspower.com).

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# **1 Introduction**

The past decade has witnessed a slowdown in global flows of foreign direct investment (FDI). This has had and will continue to have profound implications for global value chains, international trade centered around multinationals and technology transfer across international borders. However, there are also less visible implications, such as the impact of FDI inflows on domestic exporters in host countries, which is the topic of this study.

Foreign affiliates affect the performance of local firms in host countries in a variety of ways. For instance, their presence in the upstream (input-supplying) industries may have a direct effect on domestic firms by providing them with higher quality intermediates and capital goods, which results in increased productivity and higher product quality.<sup>1</sup> The presence of FDI may also have an indirect effect: by increasing the level of competition in the input-supplying sector, it may induce local input producers to become more efficient or upgrade their production processes and thus offer more technologically advanced inputs.<sup>2</sup> Moreover, the presence of foreign affiliates in the downstream (input-sourcing) industries may boost production quality among their local suppliers. This might be achieved, for example, by knowledge transfer from foreign affiliates to their local suppliers to facilitate production of cheaper or higher quality inputs.<sup>3</sup> Finally, domestic firms may learn from foreign affiliates that operate in the same industry. This could be happen either by observing the practices of foreign affiliates or hiring workers trained by them.<sup>4</sup> In this way, local firms may

> 1For instance, Javorcik et al. (2008) report that small Mexican producers meet with their input suppliers (usually foreign affiliates) every six months to learn about the possibilities of upgrading their products. Suppliers provide the necessary inputs and often prepare a new formula for the product based on these inputs.

> 2Several studies show that when firms have access to imports of high-quality and diverse inputs, there are significant gains in productivity (Amiti and Konings, 2007), export quality (Bas and Strauss-Kahn, 2015), and product scope (Goldberg et al., 2010).

> 3 See a survey by Javorcik (2008) on Czech manufacturing firms showing that about 40% of domestic suppliers receive some kind of assistance from their multinational customers. Interestingly, becoming a supplier was the incentive to improve product quality for about half of these domestic firms. Javorcik (2004) also shows spillovers from FDI to the supplying industries. In addition, Javorcik et al. (2018) use the Hidalgo and Hausmann (2009) product complexity indicator to show that domestic firms are more likely to produce more complex products as a result of the increased FDI presence in the supplying sectors.

> 4Recent evidence from the Russian Federation show that firms with foreign affiliates have better management and better performance (Grover and Torre, 2019).

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# **_Table A4.9c Relationship between ESW/ASA and IEG outcome rating by project group, weighted by original commitment (in millions, constant 2015 $)_**

|||**By lendi**|**ng source**|**By count**|**ry group**<sup>**2**</sup>|
|---|---|---|---|---|---|
||**Overall**|**IBRD**|**IDA**|**FCV**|**UMIC**|
|Dummy for any ESW/ASA in the<br>corresponding GP within 3 years<br>before project approval<sup>1</sup>|0.161*<br>(0.091)|0.052<br>(0.126)|0.319***<br>(0.066)|0.247*<br>(0.141)|-0.207*<br>(0.117)|
|Observations|2,374|1,031|1,343|301|324|
|Adjusted R-squared|0.090|0.097|0.084|0.034|0.030|
|Note: Robust standard errors clustered at<br>All regressions are estimated using OLS, w<br>CPIA score of project country at approval<br>project and the exit year. Sub-sample of p<br>the first row consisting of: International B<br>Association (IDA); fragile, conflict and viol<br>Finance and Institutions (EFI); Human Dev<br>and Sector Work/Analytical and Advisory<br>country. (2) Country Groups are not mutu<br>reference. Designation as FCV is determi<br>Fiscal Year of Approval.|the country lev<br>eighting by orig<br>, change in the C<br>rojects within e<br>ank for Reconstr<br>ence settings (F<br>elopment (HD);<br>Services (ESW/A<br>ally exclusive no<br>ned by project c|el are reporte<br>inal commitm<br>PIA score ove<br>xit fiscal year<br>uction and D<br>CV); upper-mi<br>Sustainable D<br>SA) work exe<br>r collectively<br>ountry being|d in parentheses (<br>ent and controllin<br>r project lifetime,<br>2004-2016. Specif<br>evelopment (IBRD<br>ddle income coun<br>evelopment (SD).<br>cuted by the same<br>exhaustive, with F<br>on the WB Harmo|***p<0.01, **p<br>g for region, se<br>original commi<br>ic project group<br>); International<br>tries (UMIC); E<br>(1) Considers o<br>Global Practic<br>CV and UMIC s<br>nized List of Fr|<0.05, *p<0.10).<br>ctor, IBRD/IDA,<br>tment amount of<br>s displayed in<br>Development<br>quitable Growth,<br>nly Economic<br>e (GP) for a given<br>elected only for<br>agile Situations in|

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**Table 1: Investment decisions**

|Decision #|Mutual F<br>**X**<br>Return|und<br>Risk|Mutual<br>**Y**<br>Return|Fund<br>Risk|
|---|---|---|---|---|
|1|12|20|**13.0**|20|
|2|12|20|**12.5**|20|
|3|12|20|**12.0**|20|
|4|12|20|**11.5**|20|
|<br>5|12|20|**11.0**|20|
|Phase I:<br>Dlii<br>6|12|20|**10.5**|20|
|ecnng returns<br>(fund Y)<br>7|12|20|**10.0**|20|
|<br>8|12|20|**9.5**|20|
|9|12|20|**9.0**|20|
|10|12|20|**8.5**|20|
|11|12|20|**8.0**|20|
|12|12|20|**7.5**|20|
|13|12|20|12|**19.0**|
|14|12|20|12|**19.5**|
|15|12|20|12|**20.0**|
|16|12|20|12|**20.5**|
|<br>17|12|20|12|**21.0**|
|Phase II:<br>Ii ik<br>18|12|20|12|**21.5**|
|ncreasng rs<br>(fund Y)<br>19|12|20|12|**22.0**|
|<br>20|12|20|12|**22.5**|
|21|12|20|12|**23.0**|
|22|12|20|12|**23.5**|
|23|12|20|12|**24.0**|
|24|12|20|12|**24.5**|

Subjects are informed that they must invest all 5,000 tokens but can split them between the two funds however they wish. Following Barreda-Tarrazona et al. (2011), subject earnings are based on the amount invested in each fund, and the decision number (which controls the risk and returns levels of each fund). One decision is randomly selected to be paid; the earnings from investing in each fund for this decision is generated across a normal distribution with associated expected return and risk (i.e., standard deviation) levels. The return distributions for Fund X and Y are independent, as are those across decisions (subjects are informed of this).

The instructions walk subjects through the fundamentals of investing, including information on what returns are, what risk is, what mutual funds are, and the different types of mutual funds available. Subjects are also walked through earnings calculations to ensure that there is a base level of understanding. This way we ensure that those with no understanding of finance have at least a baseline level of working knowledge.

It is important to note that fund X is fixed across the entire experiment at a return of 12% and a risk of 20. The initial values were selected based on the historical performance of the Karachi Stock Exchange (KSE-100) Index, which acts as the primary benchmark for the performance of the main stock market in Pakistan. This means that the actual return of fund X is determined by
Hanson and Spilimbergo 1999), again, not so far out of sample.<sup>11</sup> The cost of a _Coyote_ or smuggler/guide appears to have held steady in real terms since the 1960s at around $350 ($2075 $US2002; Donato, Durand and Massey 1992)<sup>12</sup> although Crane et al (1990 cited in Hanson and Spillimbergo 1999) suggest that in 1993 only 8.3 percent of those apprehended by the INS had employed one.

Despite the differences that there are, the overall magnitudes of migration to the US vs to other states are not “too different.” Of total migration, roughly 2/3 is internal and 1/3 is to the US. As a very crude first approximation, even if we make no adjustments for any likely covariates such as distance or relative wages, a simple ?<sup>2</sup> statistic suggested by Bickenbach and Bode (2003) cannot reject that the same Markov process is driving both US and intra Mexican migration for 12 out of the 32 states.<sup>13</sup> To generate our “back of the envelope” calculations, we first note that total emigration from any state is the sum of migration to other states plus the US: Total migration in Mexico is

> 11 Inflated from 1985 figure with CPI.

> 12 This showed little change with the Immigration Reform and Control Act of 1986 suggesting that it is a fairly robust number. Figure was reached by inflating the 1960 figure by the CPI.  Coyotes only get paid for successful crossings.

> 13 These results do rely on dropping Mexico state and the Federal district since they are effectively the same unit and have very high flows between them.

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# **Enterprise Isolation Programs In Transition Economies:** Evidence from Romania

# Simeon Djankov*

JEL code: F 13; Keywords: Bankruptcy, transition economies, financial discipline

* World Bank. Correspondence: sdjankov(worldbank.org; tel: 202-4734748. Address: World Bank, 1818H St. NW, Washington DC 20433. I am grateful to John Nellis, Melinda RothAlexandrowicz and Patrick Tardy for helpful suggestions, and Kosali Ilayperuma for excellent research assistance.
# _4.5. Impacts on Livestock, Agriculture, Demographics, and Welfare_

In Table 5, we analyze the impact of the cash transfers on household livestock and agricultural outcomes, demographics, and various measures of household welfare. We measure livestock ownership in column 1 using tropical livestock units (TLU)<sup>21</sup> and in column 2 we investigate more specifically the ownership of chickens. In round 3, we find significant positive impacts of cash transfers to fathers on overall livestock holdings (Panel A, column 1) and specifically chickens (Panel A, column 2). The effects are significantly larger for cash given to fathers than mothers. Both conditional and unconditional cash transfers increase livestock holdings in round 3 (Panel B, column 1), and there is no statistical difference between the types of transfers. There is no impact of any transfer types on livestock in round 2.

Columns 3 and 4 focus on measures of household agricultural production. Column 3 analyzes the transfers’ impact on the annual production of peanuts, while column 2 focuses on production of sorghum and millet. Peanuts are the main cash crop in the region, while sorghum and millet are staple crops that are more likely to be used for own consumption. Giving transfers to mothers reduces peanut production significantly in round 3 (results in round 2 are not statistically significant), while transfers given to fathers works in the opposite direction (the coefficient for CTF is statistically significant in both rounds). In round 3, UCT also leads to lower peanut production. For sorghum and millet production, unconditional cash transfers lead to higher production than conditional transfers in both rounds, and the impact is significantly greater than zero in round 2.

One emerging tendency from the Table 5 results is that when the transfers are given to fathers this leads to more investment in livestock and the cash crop peanuts. This might be one of the mechanisms explaining how giving cash to fathers rather than mothers seems to be more effective in improving children’s nutritional status (at least in round 2) as reported in Table 4.

In column 5, we measure an individual’s self-perception about their social position within society in Burkina Faso using the MacArthur Scale of Subjective Social Status. The indicator takes the value 1 if the respondent rates himself above the average social status in Burkina Faso.

> 21 The concept of tropical livestock units (TLU) provides a common metric for quantifying a wide range of different livestock types in a standardized manner. To do this, an "Exchange Ratio" was developed to convert different species into a common unit. Specifically, we used the following equation: TLU = #horses*0.80 + #cattle*0.70 + #camels*0.70 + #donkeys*0.50 + #pigs*0.20 + #sheep*0.10 + #goats*0.10 + #rabbits*0.05 + #guinea fowl*0.01 + #chickens*0.01. For more details, see: http://harvestchoice.org/maps/total-livestock-population-tlu-2005

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