rafmacalaba/data-use-mentions-extended huggingface dataset:
(Lopez Acevedo 2002).

**_Training is not a substitute for good schools._** Firms overwhelmingly choose to train skilled (secondary education or above) workers. This is an exceptionally robust finding that is salient in cross-country household surveys, administrative databases, and firm panel data sets. Although the likelihood of training increases with all levels of education, the critical threshold seems to be completion of secondary education.. This most likely reflects the idea that investment in training requires a sufficient prior skill base in order to pay-off. This pattern is consistent with the pattern of returns to training.  Overall, firms rarely invest into basic skills of a worker, rather focusing on specific job related skills.

**_The efficacy of training differs by provider and modality_** . Studies of programs in Mexico find that on-the-job training in Mexico is more effective than classroom training. Further, private training centers seem to out-perform public training centers in the Caribbean, Colombia and Brazil (McArdler and Blom, forthcoming, Barrera and Higuera, 2004 and World Bank, 2004).

**_The majority of firms in the LAC region receive training from private providers._** In the LAC region, only 16% of enterprises use public institutes for external training (Batra, 2001b).  Private sector providers – firms themselves, industry associations, buyers and equipment suppliers, and private training institutes and colleges – are more important than government-run training institutions as sources of training. Naturally, there exists a large variation within the region. In Chile and Uruguay, employers rely mostly on private external training sources, whereas firms in other countries, such as Belize, Ecuador, Nicaragua, and Trinidad and Tobago, involve public training suppliers to a larger extent (Marquez, 2002; Cinterfor 2001, and De Ferranti et al., 2003). In almost

35 See background note by Andreas Blom and Gladys López-Acevedo 36 Gill, Dar, and Fluitman (1998) and Tan and Batra (1995b), McArdler and Blom (forthcoming)
90.0|37.8|46.8|0.0|38.1|
|Percent of carriers passing IATA/IOSA Audit|0|0|0|0|0|50|
|FAA/IASA Audit Status|No Audit|No Audit|No Audit|Failed|No Audit|No Audit|

Source: Bofinger 2008. Derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data Note: All data as of 2007 based on estimations and computations of scheduled advertised seats, as published by the Diio SRS Analyzer. This captures 98 percent of world-wide traffic, but a higher percentage of African traffic is not captured by the data. The Herfindahl-Hirschmann index is a commonly accepted measure of market concentration. It is calculated by squaring the market share of each firm competing in the market and then summing the resulting numbers. A HHI of 100 indicates the market is a monopoly, while a lower the HHI the more diluted is the market power as exerted by one company/agent. n.a. = not applicable.

# Water resources

Sierra Leone is well endowed with water compared to other West African countries. The country is considered water-abundant as it has 30,960 cubic meters per capita per year of renewable water resources, one of the highest in Sub-Saharan Africa. Sierra Leone can be divided into twelve river basins, of which five are shared with Guinea and two with Liberia. The most important ones, from west to east, are the Kolente (Great Scarcies), Kaba, Rokel, Pampana (Jong), Sewa, Moa, and Mano. With rainfall ranges from 1,900 to 4,000 millimeters per year, averaging 2,526 millimeters per year, Sierra Leone is one of the most humid countries in Africa.

But population growth, urbanization, and industrialization put pressure on water supply. Sierra Leone‘s population growth (at 3.4 percent annually) put a significant pressure on
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE prevents surplus power in the north being wheeled to the center and south of the country. This problem of regional imbalances in power supply and demand will only become more accentuated as new generation capacity comes on stream, underscoring the importance of improving the transmission network.

Power production costs, at $0.16 per kWh, are relatively high by the standards of neighboring countries in southern Africa (figure 4). The higher costs in Angola, particularly when compared to its neighbors, are partly explained by the country’s reliance on oil-based generation for about 40 percent of its production, at a cost of around $0.30 per kWh.

**Figure 4. The costs of power production in Angola are relatively high**

<!-- Start of picture text --> 45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Power Production costs (US cents per KWh)<br>Mali Burkina Faso Congo Benin Cape Verde Cameroon Rwanda Niger Angola Madagascar Kenya Tanzania Botswana Chad Ghana Senegal Namibia Cote d'Ivoire Lesotho Uganda Nigeria Malawi Mozambique Ethiopia DRC Zambia South Africa<br><!-- End of picture text --> Source: Derived from Briceño-Garmendia and Shkaratan (2010-); based on data from 2005–06. Angola costs derived based on IFC staff estimates and are for 2010.

_Note:_ DRC = Democratic Republic of Congo; kWh = kilowatt-hour.

Meanwhile, tariffs, at $0.042 per kWh, are among the lowest in Africa, covering only a small fraction of costs (figure 5). Power tariffs in Angola are low even by the standards of other hydropower-dependent countries, whose power tariffs typically stand closer to $0.10 per kWh. Angola has not revised its power tariffs since 2004. These low power prices, although meant to benefit
data since many other changes (eg, general trade and investment liberalization) also impact agriculture in the period under review. Nevertheless, aggregate data provide a context for discussions about productivity and production for maize and other selected outputs.

# **4.1** Overview **of Agricultural Growth 1961 to 1990s**

Growth Indices: FAO indices for agriculture, crop, and livestock production in Turkey (see Figure 4.1) show little change in rate of growth from years before to years after 1980. For all three indices, average rates of growth for 1961-63 to 1978-81 fall in the range 2.2-3.2 percent per year, while from 1979-81 to 1991-93, growth rates ranged from 2.3-2.5 percent per year.

Inputs and Outputs: Aggregate data mask important changes in source of growth. Table 4.1 shows that fertilizer use and tractor numbers grew at double digit annual rates in the 1960s and 1970s, declining to 2.9 and 4.4 percent, respectively, from 1981. This suggests that new varieties and other new technology (improved planting equipment, new pesticides, better match of fertilizers to soil deficiencies) contributed relatively more after 1980 than before. Irrigation has not had a major impact on growth; latest figures report only 7 percent of cultivated area irrigation.

Aggregate data also mask important differences in rate of growth for various outputs. Table 4.1 shows production for maize, sunflower, soybeans, chickpeas, potatoes, and tomatoes growing significantly faster after 1980 than FAO's crop index. Among animal products, poultry meat and egg production grow much faster than FAO's livestock production index both before and after 1980 (see Figure 4.5).

Agricultural Terms of Trade and Other Factors: During the 1980s, government commitment to industrial development led to policies that squeezed agriculture, so that "agriculture was taxed in aggregate rather than subsidized as in the 1970s" (WB 1990, vol 1, p 3). Despite many liberalizing reforms,
. The data for US federal fund rate, domestic currency to USD exchange rate, broad money (M2, a proxy for money balance), domestic interest rate,<sup>19</sup> GDP, and CPI are obtained from the International Financial Statistics (IFS) published by the International Monetary Fund (IMF). Data for exports and imports are obtained from the Direction of Trade Statistics (DOTS) published by the IMF. Data on crude oil price is taken from the Primary Commodity Prices published by the IMF. For the dummy variable, it is 1 for 1997Q3-1998Q4 and 2008Q32009Q4, and zero elsewhere. Seasonal adjustments are applied on our quarterly data, except for the US federal fund rate and domestic interest rate, and CPI is used in converting nominal GDP into real GDP.

> 19 Domestic interest rate for both Malaysia and Singapore is the interest rate of government securities

> (i.e. treasury bills). Even if lending interest rate is used for both countries, we obtain similar impulse response results.

24
Table S1.5: ULTRA-POOR’S FOOD TRANSFERS WITH THEIR BASELINE NETWORK

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||_Fraction_<br>_sources_|_of baseline food_<br>_from whom tra_|_transfer_<br>_nsfers..._|_Fraction_<br>_recipien_|_of baseline food_<br>_ts to whom tra_|_transfer_<br>_nsfers..._|
||decreased|unchanged|increased|decreased|unchanged|increased|
|Treatment|-0.003|0.034|-0.031**|-0.053|0.065|-0.012|
||(0.036)|(0.035)|(0.014)|(0.050)|(0.050)|(0.008)|
|Mean in control|0.398|0.514|0.088|0.506|0.457|0.038|
|Adjusted R-squared|0.048|0.053|0.053|0.131|0.140|0.085|
|Observations|12180|12180|12180|3077|3077|3077|

**Source:** Author’s analysis based on original survey data. **Notes:** The table reports ITT estimates based on specification (1) estimated with OLS. The sample includes observations from ultra-poor households surveyed at the midline and endline surveys. All specifications control for survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level (unit of randomization). The dependent variable in column 1 (2) is a dummy variable equal to 1 if the amount of food
growth that is typically associated with production. Additional concerns arise in hydrocarbon projects where delays may increase the risk of “stranded assets” as global efforts to tackle climate change induce a shift towards less carbon-intensive technologies and greater energy efficiency (Stevens et. al. 2015, Carbon Tracker Initiative 2004, McGlade and Ekins 2015).<sup>5</sup> Such stranded assets pose financial and growth risks to the companies that own or operate them and the governments that back them.

This working paper discusses the evolution of macroeconomic vulnerabilities during the development of major resource discoveries, the impact of slowing commodity prices on development times, and policies to shorten these times. The analysis rests on a data set for gold and copper discoveries worldwide since 1950 (proprietary to MinEx Consulting). Over this period, gold and copper discoveries have accounted for two-thirds of non-ferrous discoveries.

> 2 “Giant” fields are conventional fields with recoverable reserves of 500 million barrels of oil equivalent or more. Despite the increasing importance of unconventional shale oil and gas fields, current and future oil and gas supply is dominated by conventional giant fields (Bai and Xu 2014). 3 The drop in industry investment has partly reflected growing concerns about misallocation of capital expenditures into exploration over the past decade (McIntosh 2015).

> 4 Coal projects in Mozambique are reportedly losing money, because of the slump in coal prices, and inadequate infrastructure (Almeida Santos, Roffarello, and Filipe 2015).

> 5 “Stranded assets” refer to resource capacity, specifically for hydrocarbons (coal, oil, gas), that remains unused as the world reduces its hydrocarbon consumption in order to reduce risks arising from climate change (Carbon Tracker Initiative, 2004, McGlade and Ekins 2015).

2
# **Introduction**

Economists explain the closure of firms during recessions with Schumpeter’s (1934) creative destruction theory, where during downturns small and less efficient firms are the ones to exit the market. In times of extreme economic distress, however, firms in every country are reeling from the inability to do business as usual. To make things worse, many sectors see collapsed demand and economic uncertainty stretching months, if not years. In the current pandemic, governments rightly focus on dealing with the health aspects first, and only then on the recovery of the economy once the immediate danger of the pandemic is over.

In the meantime, businesses are rapidly running out of cash. In the United States, half of small firms – those with fewer than 500 employees – have cash reserves for less than a month, and another quarter of businesses may run out of cash in two months. For service industries, the period to illiquidity is even shorter. Restaurants, for example, have less than a month of cash in hand (Didier et al., 2020).

This breathing period is extended with government programs already in place to support worker retention through subsidizing jobs, freezing interest payments on loans, and extending new bank credit. This extension differs across industries – it helps labor-intensive sectors more, and firms with established lines of credit benefit more as well. Still, other payments – like rent and cost of materials – are weighing on businesses. Exporters are unable to ship goods due to disrupted transport links. Even when transport is possible, new trade restrictions may apply or demand has simply collapsed.

New analysis on 12 high-income and middle-income economies across Africa, Central Asia, Europe, Latin America, and the Middle East reflects similar patterns. The analysis uses a sample of 6,345 firms from the World Bank’s Enterprises Surveys in Colombia, Greece, Italy, Jordan, Kazakhstan, Kenya, Morocco, Peru, Portugal, the Russian Federation, Turkey, and Ukraine. All surveys were
Table 3b: Tests for discontinuity on selected covariates: survey data

||(1)<br>**Full**|(2)<br>(3)<br>**2011-2012**<br>**2013-2014**|
|---|---|---|
|||**Panel A: Community survey**|
|Ln(km to any road)|0.228*|0.322<br>0.305*|
||(0.138)|(0.250)<br>(0.171)|
|Control Mean|7.613|7.648<br>7.588|
|Ln(km to major city)|0.154**|0.085<br>0.181**|
||(0.070)|(0.126)<br>(0.085)|
|Control Mean|4.601|4.664<br>4.557|
|Ln(members with rights to comm. land)|0.138|0.201<br>0.253|
||(0.137)|(0.263)<br>(0.166)|
|Control Mean|4.641|4.616<br>4.659|
|Ln(poverty index 2010)|0.148*|0.340**<br>0.057|
||(0.081)|(0.161)<br>(0.102)|
|Control Mean|0.079|0.015<br>0.124|
|Ln(average slope in degrees)|0.133**|0.092<br>0.124|
||(0.060)|(0.110)<br>(0
normal distribution and _f_ ( _._ ) is a polynomial of the variables. I also include a full set of year and industry effects in the equation. The panel dataset allows me to implement a Differencein-Differences (DID) matching estimator as suggested by Heckman et al. (1997). Blundell and Costa Dias (2000) note that combining matching with DID allows for the possibility of unobservables affecting participation if they can be represented by separable firm specific and /or time specific components.

Matching treated and control groups on propensity scores requires two assumptions. The common support assumption requires the possibility of a nonparticipant analogue for each participant. It is given by _P_ ( _W_ = 1) _|P <_ 1, where _P_ is the propensity score.

The conditional mean assumption requires that the expected change in outcome of the treated firm had it not been treated should be the same as the expected change in outcome of the control firm i.e., _E_ ( _Yt_<sup>0</sup><sup>_′−Y_</sup> _t_<sup>0</sup> _−_ 1<sup>_|P, W_=1)=</sup><sup>_E_(</sup><sup>_Y_</sup> _t_<sup>0</sup><sup>_′−Y_</sup> _t_<sup>0</sup> _−_ 1<sup>_|P, W_=0).Here,</sup><sup>_t −_1denotesthetimeperiod</sup> just prior to export entry, _t_<sup>_′_</sup> = _t_ + _i_ where _i_ = 1 _,_ 2 _,_ 3 _,_ 4 _..._ denote time periods after export entry and ( _Yt_<sup>0</sup><sup>_′−Y_</sup> _t_<
21 period up to 12 months of age (exclusive). Neonatal mortality covers the period from birth to 28 days of age (exclusive). (World Health Organization 2005) Cumulative measures of neonatal, postneonatal, and infant mortality are calculated as the number of deaths that were observed in the respective time period per 1000 live births. The calculations are based on the actual survival data of the individuals. Neonatal and postneonatal mortality rates for a cohort sum up to the infant mortality rate. These definitions were chosen so as to be compatible with standard measures, such as those used in official Demographic and Health Survey reports. However, the use of the number of live births, rather than the population surviving to 28 days (the beginning of the postneonatal period) as the reference group for postneonatal mortality generates odd properties for the postneonatal mortality measure. (For example, the upper boundary for the postneonatal mortality rate is less than 1.) To examine the sensitivity of the results, the postneonatal analysis was repeated conditional on survival to at least 28 days. These results (not shown) are very similar to those using the unconditional measured used in the paper.

# **Definitions of explanatory variables used in the analysis**

Due to data unavailability on ITN usage for deceased children, we use a dummy variable on ITN ownership by the household at the time of the interview as a proxy of ITN usage by the child.

Vaccination records are not available for deceased children. Thus, we use immunization rate at district level as an approximation of the individual immunization status. The immunization rate is calculated as the fraction of fully immunized children at district level in our full sample (4827 observations). The definition of full immunization was developed by the World Health Organization (WHO) and is applied by the DHS. A child is considered fully immunized if it received a BCG vaccination (against TBC), three doses of the DPT-HepB-Hib (also called Pentavalent), three doses of polio vaccines (excluding polio vaccine given at
End of picture text --> <!-- Start of picture text --> Note : Data is extracted from the World Bank Enterprise Surveys. Number of Observations: 6,395<br><!-- End of picture text --> Firms in Colombia and Peru are the most negatively affected by the hypothetical loss in external financing. Both countries see a reduction of their median survival time by over one month, going from about 4 months (16 and 15 weeks respectively), down to 11 and 12 weeks (Figure 8). Kazakhstan, which has the lowest trade exposure of about 7 percent and has among the highest proportion of working capital financed through retained earnings (85 percent), remains with a median survival time of 7 weeks under this scenario. Italy, Russia, and Jordan see a reduction of their median survival time by about 1 week relative to the baseline scenario in Figure 3.

18
Kaldor-Hicks efficiency criterion can be defended even if it produces undesirable or negative outcomes for some individuals or groups, so long as the overall benefits are greater than the overall costs to society (Manning, 2008) . However, we propose that the conceptual foundation of CBA (Kaldor-Hicks Criterion) does not adequately address the issue of heterogeneity in program impacts. We argue that an important consequence of accounting for heterogeneity is that international development assistance-driven justice sector gains, net of costs, disproportionately target those in extreme poverty and those in the bottom 40% of the population.

# **_Average and quartile treatment effects_**

The availability of data now makes obtaining average treatment effect (ATE) easier, particularly when random control trials (RCT) are employed. In short, the average treatment of the treated group (ATT) equals the ATE (for the population). The RCT lends itself to a simple nonparametric analysis of the treatment effect, given by 1 0 where, 1 is, for example, the average number of episodes of political violence (using the ATT = ATE = YY<sup>�</sup> −YY<sup>�</sup> Armed Conflict Location and Event Database (ACLED)) for the experimental group, while YY<sup>�</sup> 0 is the average number of episodes of political violence for the control group. The average effect of the intervention of interest is simply the difference between the two averages. As YY<sup>�</sup> stated above, however, the shortcoming of ATE is our lack of understanding of the distribution of effects. In addition, the mechanisms that make the prevention of conflict (economically) successful.

It is inherently difficult to measure the policy impact for each individual, since, by definition, the counterfactual for each individual under the policy may be unobserved depending on the possibility for appropriate matching of experimental and control groups. Often, even when these groups are matched, the heterogeneity of policy impact is
# **5 Concluding remarks**

This paper starts from the observation that in our study area of Kagera, Tanzania, much larger shares of the rural population tend to migrate to towns than to cities, despite greater welfare gains from moving to the latter than from moving to the former. With many of the rural migrants originally also poor, town migration contributed more to poverty reduction than city migration. To shed light on this seemingly suboptimal destination choice of ruralurban migrants, we draw on an extension of the Todaro model which explicitly considers different destinations (towns and cities) as well as spatial friction/migration costs to motivate an empirical specification to examine the drivers of this equilibrium.

In particular, using a dyadic regression structure applied to our two decade long panel of individuals from Kagera, augmented with the wealth to be expected at the different possible destinations from the census, we estimate the differential effect on destination choice of migration costs and expected wealth gain at destination controlling for individual characteristics (i.e. selection) through individual fixed effects. Migration costs were proxied by distance to the destination, following a longstanding (though recently somewhat ignored) tradition in the migration literature (Lucas, 2001). Acknowledging the well-documented importance of selection in migration, individual heterogeneity in the anticipated deterrent effect of distance and the expected pull effects of expected wealth at destination were further explored through interaction with the migrants’ poverty status, education level, and age before migrating. Simulations were finally conducted to explore the empirical strengths of the different factors in explaining the migration flows.

The findings underscore the continuing relevance of distance for destination choice (rural and urban); in our sample, expected wealth at destination would have to increase by 5.7 standard deviations on average to offset a 1 standard deviation increase in (log) distance. In this, it is consistent with Ravenstein’s first law of migration, put forward in 1885, stating that most migrants move only a short distance. When confined to urban destinations only, the
_Source_ : Data for the study comes from a survey of farmers in northern Uganda. The survey was conducted by Innovations for Poverty Action.

Note: This table presents OLS regression results for equation (1). For each column, the outcome variable is (an increasing) categorical variable (1-4) which measures the openness to vote for a political party, namely, the Democratic Party (DP), the Forum for Democratic Change (FDC), the National Resistance Movement (NRM), and the Uganda People’s Congress (UPC). _Work experience as enumerator_ is the number of months previously worked as an enumerator. _Want people to know how good can be at work; Strongly motivated by the wage can earn; Enjoy handling new problems; Enjoy trying to solve complex problems; Curiosity is a driving force for your actions_ are categorical variables taking value from 1 to 4 (decreasing). Each regression includes community fixed effects and a constant. For all regression, the sample includes enumerators who have completed at least 24 interviews. Robust standard errors in parenthesis are clustered at the enumerator level. *** p<0.01, ** p<0.05, * p<0.10.

37