rafmacalaba/data-use-mentions-extended huggingface dataset:
, and Dawn M. Upchurch, 1995, "Gender Differences in Child Health: Evidence from the Demographic and Health Surveys," PpuIation and Develoment Review, 21(1): 127151.

King, Elizabeth M, and M Anne Hill, 1993. Women's Education in Developing Countries: Barriers, Benefits and Polie. Baltimore MD.: Johns Hopkins Press.

Klasen, Stephan, 1996. "Nutrition, Health and Mortality in Sub-Saharan Africa: Is There a Gender Bias," Journal of I)evelopment Studies, 32(6):913-932, 944-948.

28
The combined and standardized data introduced in this paper is expected to keep growing as the World Bank's Enterprise Analysis unit is implementing similar surveys of informal businesses in an increasing number of cities and countries, making the data publicly available upon completion of each project. While most of the patterns we report in this paper are consistent with the existing literature, much remains to be further investigated to fully understand the industry dynamics of informal businesses and the underlying causes behind their lack of formalization. The main purpose of data collection efforts on informal businesses by the World Bank's Enterprise Analysis unit, and two companion papers, Aga et al. (2022) and this paper, is to enable and encourage this timely and much-needed further research through these cross-country businesslevel databases of informal businesses, which will eventually contribute to the development of economies through understanding and implementing ways of enabling businesses to escape what appears to be a rather dismal state of informality. In particular, the questions that are critical to understanding the informal sector and can be examined with these databases include, among many more: (1) what are the underlying factors that keep businesses informal, (2) what are the determinants of the differences in performance between formal and informal firms, and (3) what are the spillover effects between the formal and informal sectors.

31
may not have changed very much, the type of employment within sectors did. Wage employment of any type grew much faster than the labor force over the 15 year period and private-non agriculture wage employment increased by around 12 percent per annum between 2002/03 and 2005/06. This recent increase was one of the fastest growth rates in private wage jobs in Africa, second only to Ghana (World Bank, 2010).

**Figure 2: Non-wage employment accounted for most of the employment growth**

<!-- Start of picture text --> Net new primary jobs created in the labor market , 1992/93 -<br>2005/06<br>3,000,000<br>2,500,000<br>2,000,000<br>Agriculture<br>1,500,000<br>Industry<br>1,000,000<br>Services<br>500,000<br>-<br>Wage Non wage All<br><!-- End of picture text -->

**Source** : Authors calculations based on the IHS 1992/93 and UNHS 2005/06

Non-wage employment outside the agricultural sector _(self-employment, microenterprise owner, or family helper)_ grew even faster, dwarfing the increase in wage employment. Employment in a non-farm household enterprise accounted for the majority net primary non-agriculture jobs created between 1992/93 and 2005/06, absorbing many of those who came into the labor force without a primary education certificate and thus lack qualifications needed for most types of wage employment. The traditional employment, family farming, grew the slowest at a rate below the growth of the labor force. This was a transformation, but not at the productivity levels that 16
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absent in the 1980-82 period. When the government did finally intervene in 1982, it was too late.

# 3 - ADJUSTMENT IN THE CORPORATE SECTOR

It was seen in the previous section that problems in the banking sector began when an important portion of the loan portfolios of the banks became non-performing. In this section I would like to look at the other side of the coin and review the performance of the corporate sector--major borrowers of banks, especially in 1981 and 1982--and try to provide answers to the following types of questions: a) Did the shocks to the corporate sector simply consist of increased interest expenses or did adverse cost/demand conditions also play a role?

b) How did firms react to these shocks financially? Is there any indication that distressed firms actually increased their indebtedness during this

# period?

To answer these questions, a panel data set of firm-level financial statements will be analyzed. The source of data are the income statements and balance sheets of a sample of 91 firms registered at the Capital Markets Board (CMB) of Turkey. The same data set has been used by Ersel and Sak (1986) in a similar study. The data is available for the period 1979-84.

It should be noted at the outset that the sample is not representative of all private sector firms in Turkey. First, all of the firms are issuers of either stocks and/or bonds to the public or have at least 100 shareholders. Second, the average scale of the corporations in the sample is large, so that the firms in the data set can be taken as representative of large corporate sector only. The following table, taken from Ersel and Sak (1986, p.93), compares the average total assets of firms in the CMB sample sample and those
gauged by ENE survey from 2003 and the quantile regression methodology is applied (see Appendix B for details on the quantile methodology). This methodology characterizes the distribution of wages in more detail than traditional ordinary least squares (OLS) and two stage least squares (2SLS) regressions, as it makes it possible to break down the wage determination process across the entire wage distribution. Additionally, workers are allocated in different groups with different characteristics. Wages are compared across workers organized by gender, education and skills, labor status, and location. Findings for rural areas with more than 15,000 inhabitants are presented in Table 3.1. This section analyzes for each quantile whether the impact of various individual characteristics on wages is homogeneous across the wage distribution. Findings indicate that wages are by no means determined in the same way for high and low paid workers.

|**Table 3.1: Wage Determinat**|**ion in R**|**ural**|**Mexic**|**o (Qu**|**antile**<br>**Quan**|**Regre**<br>**tile**|**ssion**|**s), 200**|**3**||
|---|---|---|---|---|---|---|---|---|---|---|
|**Log monthly labor income**|**0.1**||**0.2**|**5**|**0.**|**5**|**0.7**|**5**|**0.**|**9**|
|**Worker Characteristics**|Coeff|SE|
it is easier to ward off premature death than morbidity, the associated improvement to **d** 2 = (0 _._ 015 _,_ 0 _._ 03 _,_ 0 _._ 06) in period 2 is a bit less dramatic than that in mortality.

# **3.3 Setting up the sequence under perfect foresight**

The system must be set up in such a way that it satisfies two requirements. First, the household must choose a plan that is in keeping with what we observe in the present. The key variable here is the level of investment in education, _e_ 1. Children in India’s rural areas typically start school at 6 years of age and complete about 6 years of schooling on average. Hence, with up to 12 years of schooling available, and noting that morbidity reduces the endowment of productive time, the model must be set up so as to yield (1 + _τ_ ) _e_<sup>0</sup> 1<sup>= (6</sup><sup>_/_12)(1</sup><sup>_−d_1</sup> 1<sup>).A furtheradjustmentis neededfor the</sup> number of days lost due to bad weather, especially in the monsoon. The Orissa survey yields an estimated 8.6 and 9.4 days a year, on average, for primary- and secondaryschool children, respectively, involuntary absences that were mainly attributable to their teachers’ failure to arrive (Bell and van Dillen, 2011). Assuming a school year of 180 days, the required condition becomes (1 + _τ_ ) _e_<sup>0</sup> 1<sup>=(1</sup><sup>_−_9</sup><sup>_/_180)(6</sup><sup>_/_12)(1</sup><sup>
age 65 (-)|-0.0122<br>(0.009)|-0.0218**<br>(0.011)|-0.0115<br>(0.008)|-0.0206*<br>(0.011)|
|16. Total Dependency Ratio (-)|0.0627<br>(0.082)|-0.0457<br>(0.057)|||
|17. Youth Dependency Ratio (-)|||-0.0012<br>(0.092)|-0.0348<br>(0.065)|
|18. Elderly Dependency Ratio (-)|||0.266*<br>(0.153)|-0.4966<br>(0.578)|
|19. constant|-1.1082***|0.1958|-1.0647***|0.2461|
||(0.406)|(0.183)|(0.41)|(0.183)|
|R-squared|0.8703|0.8305|0.8724|0.8344|
|Adjusted R-squared<br>FSttiti|0.8389<br>5099|0.7859<br>3568|0.8408<br>4694|0.7900<br>3616|
|-asc<br>F-Stat df 1|.<br>51|.<br>65|.<br>52|.<br>66|
|F-Stat df 2|211|251|210|250|
|<br>Number of Countries|20|35|20|35|
|Number of Observations<br>Standard Errors are in parentheses.<br>*, **, *** denotes significance at the 90, 95 and 9
4.5|6.5|1.48|NA|
|Average liquid bulk handling charge<br>(US$/tonne)|NA|0.5|1|3.5|2.68|NA|1|

Source: Ocean Shipping Consultants 2009. Derived from AICD ports database downloadable from http://www.infrastructureafrica.org/aicd/tools/data TEU = 20-foot equivalent units.

Institutional reforms can also contribute significantly to easing capacity constraints. The more efficiently a port is operated, the more throughput can be accommodated within the physical capacity of its infrastructure. It is therefore critical to accompany investments with institutional reforms that increase the efficiency of port operations. A first key step would be to move toward the adoption of the internationally preferred landlord model of port management, whereby the public sector provides port 10
0.04|
||[-0.33]|[-0.12]|[0.59]|[0.76]|[-0.34]|
|2018-2019 Recession X Distance|-1.18**|0.63|-0.02|0.24|0.19|
||[-2.57]|[0.90]|[-0.04]|[1.63]|[1.25]|
|Constant|4.54***|13.08***|-6.04***|6.22***|3.66***|
||[9.77]|[16.49]|[-5.10]|[40.80]|[13.94]|
|Observations|1,134|1,134|1,134|1,134|1,134|
|R-squared|0.24|0.47|0.04|0.41|0.40|
|Number ofprovinces|81|81|81|81|81|

Notes: t-statistics based on robust standard errors (clustered by province) in parenthesis *** p<0.01, ** p<0.05, * p<0.1. All regressions use data on all 81 provinces and employ province fixed effects. Distance to major a port is computed as the shortest distance between the province centroid and the centroids of four provinces that have major ports (Istanbul, Izmir, Bursa and Mersin). This is measured in units of 100 km.

22
loans made to firms by every bank in Mexico.<sup>20</sup> All business loans, regardless of their size, have to be reported.<sup>21</sup> For each loan in the CNBV data set, we have information on the issuing bank, the borrower (firm), the outstanding amount, the annualized-loan spread, and the start and end date of the loan (maturity). There is information regarding each borrower, such as its identifier, location, industry and number of employees. Since loans are tracked every month, we are able to see their evolution until their maturity. Specifically, we can observe whether the debtor obligation is being fulfilled, and in case it is not, by how much and for how long the loan has been under-performing.<sup>22</sup> The final sample covers three legislatures LIX-LXI from October 2003 to March 2012.<sup>23</sup> For each loan, we keep the information at the initial period when the loan was extended. However, we also record whether the loan is more than 90 days in arrears within the 36 months after origination. Some borrowers had more than one loan outstanding with the same bank in one quarter. Therefore we adopt a similar approach as in La Porta et al. (2003) and aggregate individual loans at the firmbank-quarter level and report loan characteristics such as loan spread and maturity at origination using a weighted average by loan volume. The only exception is loan volume, which is the sum of the value of all new loans that a firm initiated from a certain bank in a given quarter. Our final data set contains 594,534 loans from 88,117 firms from 21 banks.

During the first month of each legislative session, committees are formed with one senator appointed as chairman. For each borrower and quarter, we define the dummy variable _chairman_ that equals one if the borrower is headquartered in a state with at least one senator serving as the
Girma et al., 2007).<sup>18</sup> The popularity of the Cox model is due to its convenient estimation of the effects of plant characteristics on survival using a partial likelihood approach and making no assumptions on the shape of the baseline hazard function.<sup>19</sup> The effect of plant characteristics on survival is specified as a proportional shifter of the baseline hazard function. However, a major caveat of the Cox proportional hazard model is that it requires survival time to be a continuous variable and plants to be ordered exactly regarding their failure time.<sup>20</sup> This does not apply to our data which groups plant survival times into discrete intervals of one year, as is the case for most survival studies that use annual plant-level census data. While we know which plants and how many plants exit from year to year, we are unable to order plants‟ failure times within a year, so there are „ties‟ among plants. In the presence of a sizeable fraction of tied survival times the coefficient estimates and standard errors of the Cox model can be biased (Cox and Oakes, 1984).<sup>21</sup> This caveat applies also to continuous-time hazard models with a parametric baseline hazard function. Thus, discrete-time hazard models are more appropriate for our analysis.

Another major caveat is that the Cox model does not allow controlling for unobserved plant heterogeneity, namely the fact that plants may have differing duration distributions even after controlling for a rich set of plant characteristics, due to computational difficulties.<sup>22</sup> A failure to account for unobserved heterogeneity will lead to biases in the estimated effects of plant characteristics on survival (Van den Berg, 2001) and to spurious negative duration dependence of the estimated Cox hazard function (Heckman and Singer, 1984).<sup>23</sup> Unobserved plant heterogeneity can however be controlled for in the discrete-time hazard models discussed below, as well
# **[Table 3 about here]**

# **5.2 Opportunity-Benefit Incidence Analysis (Opp-BIA)**

Three patterns of education spending—the distribution of monetized benefits accruing to children attending public schools in Liberia per beneficiary (―gross unitary public transfer‖), the distribution of private out-of-pocket household spending incurred as a result of attending schools per beneficiary (―unitary private spending‖), and the resulting difference between public benefit and private contribution towards school attendance, ―net unitary benefit‖, again on a beneficiary basis—are now compared (Figure 2). World Bank (2010) reports total resources in the public education system of US$ 77.2 million, accruing from three sources: US$12.2 million by the government, US$27 million by households and US$38 million by international donors. Gross unitary public transfers are obtained by dividing government‘s and donors‘ educational spending by level of education among enrolled students: US$ 6.9 per student enrolled in primary education and US$ 84.5 per student enrolled in secondary education. Unitary private spending is obtained directly from household‘s reports on their actual spending on children attending school. These patterns of average public resources spent per student and households‘ average private contributions towards education are compared against the distribution of household wealth<sup>8</sup> and the distribution of educational opportunities, proxied by the estimated probability of attending school conditional on their circumstances (see Table 1 above). The former generates traditional BIA, while the latter an ―Opportunity BIA‖.

> 8 There is no direct interpretation of the values of the wealth index. The wealth index is constructed with principal factor analysis (PCA) of information on assets in the household. The source data are a series of dummies where 1 is assigned if the household has the asset (television, car, radio, etc). We construct z-scores of the data and project them onto a new basis using the
# **3. Empirical strategy**

To estimate whether mangroves can reduce hurricane damage, we divide the Caribbean region into 1 square kilometer grid cells and construct a cell-year panel for the 2000–12 period. The panel combines measures of economic activity, potential hurricane damage, and mangrove protection.

First, to assess the impact of hurricanes on economic activity, we estimate the impact of a damage function on our nightlight data set, which is our proxy for economic activity. Specifically, we follow Del Valle et al. (2020) and consider the following two-way fixed effect equation: N i i i ii ii = αα+ ββff + ππ + μμ + εε Where N i is nightlight intensity (in logarithms) of cell in year , i is the damage index,<sup>17</sup> are year fixed effects, are cell fixed effects, and i is the error term. The inclusion of cell fixed effects aims to ii capture the fact that certain areas in the Caribbean may have been exposed to more frequent and greater ii tt ff ππ ii incidence of hurricanes, leading to the reallocation of segments of the population—and thus economic μμ εε activity—or to the implementation of disaster prevention actions. Issues related to time-varying common shocks and nightlight time comparability are addressed by including year fixed effects. The estimation method is based on Correia (2016), which accounts for linear models with many levels of fixed effects (as in our case).

Second, to measure the level of protection, we use two data sets with different calculations of mangrove width and test for a potential nonlinear relationship. Our interest is to study the effectiveness of this potential mitigation property as the level of mangrove protection—that is, mangrove width—increases.

# **3.1.Modeling the windstorm model**

We use the wind speed data to obtain a damage index using the transformation and parameters proposed by Emanuel (2011) in the following expression: Where i represents the wind speed
Bank, 2014, Fig 7.10).

Controlling for additional characteristics of the region does not change results. Expanding estimates from Table 7, the addition of controls were added such as schooling enrollment rates, higher education staff, physicians per capita, female labor force participation, and employment shares by education type. The addition of these variables did not significantly change the role of central government spending or sectoral growth.

Table 8 illustrates complementary results from Equation (3) using local per capita expenditures. Local expenditures are not as explanatory of poverty changes as central spending; the R-squared from these regressions are much lower than regressions with central expenditures. Moreover, poverty is higher in the East when there is higher local expenditures. Local expenditures may not be as explanatory as central spending simply due to its smaller size and smaller impact on the local economy. Figure 27 and Figure 28 illustrate average central and local expenditures respectively and local expenditures are about a fourth or fifth the amount of per capita central spending.

26
Policy Research Working Paper 10348

# **Abstract**

Reliable small-area estimates of economic welfare significantly inform the design and evaluation of development policies. This paper compares the accuracy of wealth estimates obtained from the empirical best predictor (EBP) of a linear nested error model, Cubist regression, extreme gradient boosting, and boosted regression forests. The evaluation draws two-stage samples from unit-level household census data in seven developing countries, combines them with publicly available geospatial indicators to generate small area estimates of assets for all seven countries and poverty for two, and evaluates these estimates against census-derived benchmarks. Extreme gradient boosting and Cubist regression generally produce more accurate predictions than traditional EBP models. A proposed two-stage residual bootstrap procedure slightly underestimates confidence intervals, but leads to higher coverage rates than the parametric bootstrap approach used for EBP predictions. These results demonstrate that, given a sufficiently large sample of enumeration areas, predictions from extreme gradient boosting or Cubist regression with a two-stage residual block bootstrap generally provide more accurate point and uncertainty estimates for generating small-area welfare estimates.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at dnewhouse@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank
# **REFERENCES**

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Ravallion, Martin