rafmacalaba/data-use-mentions-extended huggingface dataset:
25 rural and 27 percent in urban areas). Puzzlingly, changes in survey-based poverty lines and the CPI had largely mirrored each other in the surveys prior to 1997. Application of the SAE methodology is attempted here to check on the poverty numbers for Kenya that are based on survey-based price deflators (World Bank, 2008).

# **_Poverty Predictions in the Face of Uncertain Inflation Adjustments_**

According to the SAE prediction approach, poverty in Russia rose between 1994 and 1998, but by much less than official statistics would suggest. Table 6 shows that while official statistics indicate that the incidence of poverty rose from 11.4 percent in 1994 to 33.8 percent in 1998, the poverty prediction method based on a comprehensive model of non-consumption assets suggests that the headcount rose only from 11.4 to 14.1 percent during this five year period. Interestingly, a model specification that includes an indicator capturing households‘ subjective assessment of their wellbeing supports the finding of a relatively modest increase in poverty (Table 6, model 5).<sup>19</sup> The broad finding of a more muted increase in poverty (relative to what official statistics imply) is also evident separately for rural and urban areas, although in absolute terms the SAE approach based on the comprehensive assets specification does suggest that poverty increased appreciably in rural areas, from 13.1 percent to 22.4 percent. When the SAE approach is applied to track poverty between 1994 and 2003, the approach indicates that poverty in 2003 was only marginally lower than in 1994, while the official statistics suggest that poverty in 2003 remained roughly the same. The qualitivate

> 19 Gibson, Stillman and Trinh (2008) also find that subjective wellbeing indicated a much milder decline in living standards between 1994 and 2001 than official estimates had suggested.
days without rain for the growing season minus<br>the long run average<sup>_∗_</sup>|
|Share of rainy days|The percent of growing season days with rain<sup>_†_</sup>|
|Deviation in share of rainy days|The percent of growing season days with rain minus the long run<br>average<sup>_† ∗_</sup>|
|Intra-season dry spells|The maximum length of time (measured in days) without rain<br>during the growing season<sup>_†_</sup>|
|**_Panel B_**_: Temperature_||
|Daily average temperature|In Celsius|
|Daily maximum temperature|In Celsius|
|Mean|The frst moment of the daily temperature distribution for the<br>growing season<sup>_†_</sup>|
|Median|The median daily temperature for the growing season<sup>_†_</sup>|
|Variance|The second moment of the daily temperature distribution for the<br>growing season<sup>_†_</sup>|
|Skew|The third moment of the daily temperature distribution for the<br>growing season<sup>_†_</sup>|
|Growing degree days (GDD)|The number of days within bound temperature for the growing<br>season, following Ritchie and NeSmith (1991)<sup>_†_</sup>|
|Deviation in GDD|GDD for the growing season minus the long run average<sup>_† ∗_</sup>|
|Scaled deviation in GDD|The z-score for GDD|
|Maximum temperature|The average maximum daily temperature|

_Note_ : The table presents definitions for included weather variables and transformations from weather sources defined in Table 1. _†_ Growing season determined for each country following FAO crop calendar (see Table
.

- National Bureau of Statistics, 2021a. Basic Information Document: Nigeria General Household Survey– Panel 2018/19.

- National Bureau of Statistics, 2021b. Basic Information Document: National Panel Survey (NPS 20192020), Extended Panel with Sex-Disaggregated Data.

- National Statistical Office, 2020. Basic Information Document: Malawi Integrated Household Panel Survey (IHPS) 2019.

- Onis, M. de, Elaine Borghi, Onyango, A., Amani Siyam, Alain Pinol, 2006. Length/height-for-age, weightfor-age, weight-for-length, weight-for-height and body mass index-for-age; methods and development, WHO child growth standards. WHO Press, Geneva.

- Rencher, A.C., 2002. Methods of multivariate analysis, 2nd ed. ed, Wiley series in probability and mathematical statistics. J. Wiley, New York.

19
data from the Ministry of Trade.

As for the safeguards, the sectoral breakdown is relatively different according to the type of instrument (Chart III.4). Most of the investigations under the WTO safeguard were made for textiles and apparel, and home appliances. Besides these two sectors, special safeguard investigations focus on chemicals and petrochemicals. In the case of the Andean safeguard, almost 60% of the cases are agricultural and focus on two products: rice and vegetable oils.

Summarizing, most of the dumping and safeguards investigations have involved industrial products. Additionally, the investigations on agricultural products have mainly concentrated on safeguards and in those few cases in which the application of anti-dumping duties were requested for this sector, they were denied. Finally, textiles, apparel, iron and steel products, and chemicals and petrochemicals are the sectors requesting more investigations, which is consistent with international patterns. In effect, data for dumping investigations in the western hemisphere shows that these tend to concentrate on chemicals, plastics, paper, textiles and basic metals.<sup>20</sup>

# **Chart III.4     SAFEGUARD INVESTIGATIONS BY SECTORS**

> and steel the products are steel bars, chrome plated sheets, iron or steel wire rods, billets, tin sheet and hotrolled steel.

> 20 Tavares et al (2001) Antidumping in the Americas.

25
country. The source of the expected real GDP growth data is World Economic Outlook by the IMF (IMF, April 2010). According to our results, this variable is not statistically significant. Deposit rate, on the other hand, is significantly positively correlated with the number of deposit accounts.

Among the infrastructure indicators, electricity consumption does not have a significant relationship with deposit penetration, whereas the number of land line and cell phone users is positively correlated with the number of deposit accounts. Financial sector infrastructure indicators, such as the number of commercial bank branches per 100,000 adults and per 1,000 square kilometer that measure the geographic and demographic outreach of the financial system, are positively and significantly related to deposit penetration.

The legal environment, measured by the index of the strength of legal rights for borrowers and lenders, is also closely related to deposit penetration. A favorable legal environment for lending may enable banks to operate more profitably through lending and to grow, eventually leading to an expansion of deposit services. The analysis of the correlation between deposit account penetration and other indicators of favorable credit environment also produces similar findings. Moreover, the availability of explicit deposit insurance scheme suggests higher deposit penetration.

A greater degree of competition, as proxied by the concentration ratio in the banking sector, is associated with greater deposit penetration. This is in line with earlier studies (see, for example, Demirgüç-Kunt and Huizinga, 1999) arguing that competition in the banking sector would increase efficiency, and in turn, would lead to a larger variety of products and services offered to a larger depositor base.<sup>5</sup> The analysis of the correlation between the interest rate spread (lending rate minus deposit rate) and deposit account penetration lends support to this argument (not shown in Table 6). Countries with lower interest rate spreads also show a higher number of deposit accounts per 1,000 adults.

Analysis of the volume of deposits normalized by GDP offers further insight into the factors affecting financial access
**Figure 7: Years of education, by gender and location**

<!-- Start of picture text --> 1940 1950 1960 1970 1980 1990<br>Women/Rural Women/Urban Men/Rural Men/Urban<br>12<br>10<br>Years of education 8<br>6<br><!-- End of picture text --> Source: Staff estimates based on HIES 2016.

Note: 1990s cohort refer to individuals born between 1990 and 1995. Average mean years of education by decade-cohort of individual’s year of birth; individuals in 1990 cohort may not have fully completed education at the time of survey (2015), and hence these individuals could attain higher mean years of education.

# 4.2.Defining Trade Policy Scenarios<sup>21</sup>

# 4.2.1. Baseline

**As mentioned above, an ex-ante evaluation requires the creation of a counterfactual simulation that will serve as a business-as-usual scenario with no policy change.** The model relies on the GTAP version 9 database benchmarked to 2011. The CGE model runs up to 2028, replicating the key historical macroeconomic aggregates from the World Bank World Development Indicators (World Bank, n.d.). Productivity growth in the baseline scenario is calibrated to achieve the historical and forecasted GDP growth rates up to 2021. Productivity growth is adjusted thereafter to be consistent with historical trends.<sup>22</sup> The baseline scenario also incorporates tariff reductions under existing FTAs. These are based on the data set provided by the International Trade Center, including all TPP members’ FTA commitments up to 2027 (International Trade Centre 2016). We also assume that Sri Lanka enjoys the Generalized System of Preferences (GSP) preferences on its exports to EU markets.

**The baseline contemplates continuation of recent trends using standard closure rules for macroeconomic accounting.** The scenarios incorporate three closure rules. First, government expenditures are
830)|(0.977)|(1.421)|(0.856)|(1.023)|(1.489)|
|Constant|108.2***|97.91***|94.64***|101.5***|89.78***|86.84***|
||(0.289)|(0.456)|(0.653)|(0.298)|(0.477)|(0.684)|
|Observations|40,684|16,236|8,578|40,684|16,236|8,578|
|R-squared|0.0650|0.0730|0.0909|0.0796|0.0939|0.105|
|Mean dep. Var.|115.8|106.0|104.2|110.2|99.57|97.67|
|Mean dep. var. control 2013|68.59|71.51|76.06|61.34|64.80|70.44|
|Mean dep. var. treated 2013|134.8|133.2|126.1|130.6|128.1|120.4|

_Source:_ Own computation based on LAA’s land administration database and digitized records as well as hand-digitized plot size from 2013 and 2016 aerial photography as described in the text.

24
ge - all countries**|35.1|10027|2489|61|81%|5.9|2.8|38%|36%|43%|
|**Media**|**n - all countries**|5.7|4420|1830|37|82%|5.6|2.6|38%|35%|43%|

Note: the figures shown are based on measures in the country-year level dataset (file CY.dta) averaged across the period 20062008 for each country. * indicates exceptions to the sample period: for Kuwait averages are taken across the period 2009-2010 and for Portugal averages are taken across the 2003-2005 period. Total exports are obtained as the number of exporters multiplied by the average exports per exporter.

20
<br>1,619|1,466|1,460|
|**Panel C: Neig**<br>|**hbors sample retention**<br>|||
|All|72.1%<br>–|–|–|
|Treatment|74.7%<br>–|–|–|
|Control|69.5%<br>–|–|–|
|p-value (T-C)|0.421<br>–|–|–|
|N|2,017<br>–|–|–|

_Notes:_ This table shows panel retention and compliance with treatment assignment. The p-values are obtained by estimating _yic_ = _β_ 0 + _β_ 1Treatment _c_ + _εic_ . Standard errors are clustered at the locality-level. Retention is defined as survey retention, meaning that the participant was located and surveyed in that round. These numbers only include people who were successfully surveyed. Panel A shows retention of the 2,017 households that were sampled for the study. Panel B shows treatment take-up for the sub-sample of households that were interviewed at midline (1,619), endline (1,466), and follow-up (1,460), and Panel C shows retention for the neighbors of the 2,017 refugees sampled. In this case, retention is an indicator for whether a Jordanian neighbor was located near the pre-randomization home location of the refugee household. The neighbor was selected using a randomized algorithm starting from the original home of the refugee participant, with a median distance between the selected neighbor and the original refugee participant’s home of 63 meters. Cases of non-retention could result from an inability to identify the refugees’ home location based on midline data, or the inability to locate Jordanian neighbors during field visits to the location.

42
of the firm size distribution based on limited information. In further revisions, we expect to include more countries and characterize the differences in the percentiles between formal and informal.

# **3.4 Number of workers by size and formality**

By dividing the number of employees by the number of firms, it is possible to estimate the average firm size in the economy (26 workers per firm in Africa, 33 in the rest of the world). For all the countries for which this calculation is possible, the elasticity to income measured with GDP per capita is 0.13. Bento and Restuccia (2021) estimate an elasticity of 0.3 for an average firm size that includes all people involved in business activities (but only manufacturing and services). In our setting, this would be equivalent to redefining people working for a business as the sum of employees and own-account, and also redefining firms as the sum of employers and own-account. This second definition yields and average firm size of 1.9 workers for the African continent and 7.7 elsewhere. The elasticity to income is 0.48, higher than the one initially estimated, and conceptually closer to the one estimated by Bento and Restuccia (2021).

We employ this framework to impute average firm size by size group and formality status using a model that links firm size in any of these groups to log GDP per capita, log GDP, firm

> 10For countries with no WBES data we use the average relative positions across countries covered by WBES.

> 11The broad assumption is that all countries are re-scaled with the same scaling parameter (momentspecific). It is possible to expect that moment differences between formal and informal vary by country. A further refinement to explore is making the scaling parameter dependent on country’s income level.

14
a lack of spatial input data. One specialist carried out the entire procedure. The significant labor, time, and cost savings achieved by developing an automatic pre-census sample frame could be invested in various aspects of national surveys and census preparation.

Despite limitations, the method was successfully implemented, and a national sampling frame was produced for Armenia. From financial, timeframe, and technological perspectives, the method performed better than the conventional manual techniques. Delineating a nationwide sampling frame by hand has historically required years and massive financial resources. In addition, the manual method is prone to several geometric issues, including gaps, overlaps, pockets, and disjoints since it is carried out by humans. These geometric problems could lead to bias being introduced into the frame and eventually into the collected data. Nevertheless, there are no geometric problems with the automatic method. This approach has several benefits over the gridded population sampling frame. Several researchers have directly employed gridded population data as a sample frame (Thomson et al., 2017; Cajka et al., 2018; Qader et al., 2020). The design of the sampling units is the primary distinction between our approach and the gridded population sampling frame. Houses and other structures are cut because the grid’s outline is not in line with features that can be seen on the ground. In contrast, the preEA tool creates pre-EA boundaries that adhere to observable elements like natural borders and roads.

> 23 A digital map of the enumeration areas also provides a significant amount of spatial information, such as settlement areas with residents within the PSU and their centroids, which can be used, for example, as starting points for random walks. If the household listing is possible for each pre-EA and all household addresses are available, a random walk procedure and thus starting point information are unnecessary. However, complete household listing is not available or hard to obtain in some developing countries, and the household listing operation—visiting each of the selected PSUs and listing all residential households in those PSUs
|Linear|Yes|Yes|Linear|Yes|Yes|
|Malawi|Linear|Yes|Yes|Linear|Yes|Yes|
|Mali|Linear|No|No||||
|Mozambique|Linear|Yes|Yes|Linear|Yes|Yes|
|Namibia|Linear|Yes|Yes|Linear|Yes|Yes|
|Niger|Linear|Yes|Yes|Linear|Yes|Yes|
|Nigeria|IBT|Yes|No|IBT|Yes|Yes|
|Rwanda|Linear|No|No|Linear|No|No|
|Senegal|TOU|Yes|No|TOU|Yes|No|
|South Africa|IBT/Linear|Yes|No|TOU|Yes|Yes|
|Tanzania|Linear|Yes|Yes|Linear|Yes|Yes|
|Uganda|TOU|Yes|No|TOU|Yes|Yes|
|Zambia|Linear|Yes|No|DBT|Yes|Yes|
|Zimbabwe|Linear|Yes|Yes|Linear|Yes|Yes|

_Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database _Note:_ IBT – increasing block tariff; DBT – decreasing block tariffs; TOU – time of use tariff. _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database Time-of-use tariffs (TOUs) are only practiced in a minority of cases, and are twice as prevalent among industrial tariff structures as among commercial tariff structures. TOUs allow power consumption to be associated with hours (peak/off-peak/night) and/or seasons (summer/winter, dry/regular), creating incentives for more efficient use of the power network. One-third of industrial and one-fifth of commercial tariff regimes in Africa incorporate TOUs.

12
# Firms Operating under Infrastructure and Credit Constraints in Developing Countries.

# The Case of Power Generators

### Philippe Alby<sup>1</sup> , Jean-Jacques Dethier<sup>2</sup> & Stéphane Straub<sup>3</sup>

Revised Version - December 7, 2010

## Abstract

_Many developing countries are unable to provide their industrial sector with reliable power and many enterprises have to contend with electricity that is insufficient and of poor quality. Because of these constraints, firms in developing countries opt for self-generation even though it is widely considered a second best solution. This paper develops a theoretical model of investment behavior in remedial infrastructure when physical and credit constraints are present. It then tests econometrically some implications from this model using a large sample of enterprises from 87 countries from the World Bank Enterprise Survey Database. After showing that these constraints interact and have non-linear effects depending on the industrial sector’s degree of reliance on electricity and size of firms, the paper draws differentiated policy recommendations. Credit constraints appear to be the priority in sectors very reliant on electricity to spur entry and convergence to the technological frontier, while in other sectors, firms would benefit more widely from marginal improvements in electrical supply._

> 1 Toulouse School of Economics, ARQADE. 2 World Bank, Development Economics Department. 3 Toulouse School of Economics, ARQADE and IDEI.

> We are grateful to Alexis Bonnet, Taryn Dinkelman, Vivien Foster, Farid Gasmi, José Luis Guasch, Franck Lecocq, Lucio Monari, Jon Stern, Bernard Tenenbaum and Clemencia Torres de Mastle, as well as the participants in the 2010 International Conference on Infrastructure and Development in Toulouse, for helpful comments. Mistakes remain ours.
is a dummy indicator variable for the global financial crisis of 2008‐09, and _Post_Crisis1011_ is a dummy indicator for the years 2010 and 2011.<sup>12</sup> Our coefficients of interest are μ0 and μ1 since these capture the behavior of firms’ capital structure during the global crisis and subsequently.<sup>_f_</sup> _i_<sup>is a set of firm fixed effects and</sup>  _ijt_ is the firm level residual that is assumed to be potentially auto‐correlated of order one and correlated in the cross‐section at the country level as well.<sup>13</sup> Indeed, the firm capital structures are quite persistent over time. In our complete sample, the correlation of the ratio of total debt to total assets, long‐term debt to total assets, and long‐term debt to total debt with their own lags are respectively 0.82, 0.84, and 0.85. Because of this persistence, standard OLS or fixed effects estimators will be biased. To correct the resulting bias, we use a feasible generalized least square estimator where the error terms are assumed to be serially correlated (Prais‐Winsten estimator). Specifically, the error terms are assumed to follow a first‐order autoregressive process  _ijt_   _ijt_  _ijt_ where  _ijt_ is the error term and  is the autocorrelation coefficient. The equation (1) is transformed by using the autocorrelation coefficient from the Durbin‐Waston statistics. We also cluster observations of the transformed model at the country‐year level. This

> 12 This period also coincides with the crisis in the euro area, which started with the first bailout of Greece in May 2010.

> 13 Various papers in the recent cross‐country comparative literature on corporate capital structures have relied on firm specific data, including Booth et al (2001), Fan et al. (2010), Beck et al. (2008), or
Policy Research Working Paper 10225

# **Abstract**

The paper uses a global vector autoregression model with quarterly time series data from 1994 to 2016 to investigate the spillover effects of Chinese trade and growth shocks on 10 Association of Southeast Asian Nations countries. Time varying trade weights are used to construct the foreign variables in individual country models and structural generalized impulse response functions are used to conduct the dynamic analysis. The results show that a positive shock to Chinese trade and growth has a positive effect on the growth of Brunei, Malaysia, the Philippines, Singapore and Thailand. The effect is much weaker and statistically insignificant for other countries.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. 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 author may be contacted at nkhan12@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 or the governments they represent._ Produced by the Research Support Team
in educational outcomes for individuals aged 14 or younger, between the 2001 DHS and the 2006 DHS. At such a young age, voluntary migration is extremely unlikely to have occurred.

# **Education**

# a. <u>Specification 1: Plain difference-in-difference</u>

This specification corresponds to the most traditional case of difference-in-difference. I define two cohorts, one not exposed to conflict (“control”) and one exposed to conflict (“treated”), and compare the difference in primary attainment between these two cohorts and across districts characterised by different levels of conflict intensity.

An inspection of the data shows that, although the legal age for the start of schooling is 6 years old and there are five years of primary schooling, a sizeable proportion of children is enrolled in primary school before 6 (70.1% at 5) and until 14 (16.1%) in the 2006 DHS, with 14