rafmacalaba/data-use-mentions-extended huggingface dataset:
extreme poverty lines.<sup>25</sup> Eligibility for the beneficiaries is determined by a Proxy Means Test (PMT) based on household expenditure and consumption data, including 34 variables related to “characteristics of the household head (gender, health, and disability status), marital status (divorced, widowed), household size and composition (children, elderly), employment status, assets (cars, livestock, and property including condition and building materials), and health of household members” (Wong et al 2019, 12). Participating households receive cash transfers quarterly of between NIS 750 and NIS 1,800<sup>26,27</sup> directly into their bank accounts. In addition to the CTP, the MoSD administers a number of other cash assistance programs including cash assistance to orphans and distressed families, cash assistance to discharged government employees, and emergency cash assistance for women. Near-cash social assistance includes food assistance/daily rations to the beneficiaries.

# **2.4.2. Indirect subsidies**

**A fundamental characteristic of the West Bank and Gaza’s energy supply is its reliance on imports, particularly from Israel, Jordan, and the Arab Republic of Egypt.** Almost all fossil fuels are imported.

## **Electricity**

**Residents of the West Bank and Gaza rely on imported electricity, primarily from Israel.** The imported share is about 99 percent of total supply in the West Bank and 64 percent in Gaza.<sup>28</sup> The Palestinian Energy and Natural Resources Authority (PENRA) is responsible for provision of electricity and for energy policy formulation. There are five electricity distribution companies (DISCOs) and a single buyer of electricity, the recently established Palestinian Electricity Transmission Company (PETL). The PETL sources electricity from the Palestine Power Generation Company (PPGC), Israel, and other neighboring countries. In Gaza, the Palestine Electric Company (PEC) operate the Gaza Power
Zingales (1998; hereafter RZ), which has been used in a number of tests, is innovative and very much applicable for our empirical tests on the effects of competition in the banking system on economic growth in terms of the degree of access to external financing. Next, we provide more detail on the RZ approach.

The RZ model relates the growth in real value added in a sector in a particular country to a number of country- and industry-specific variables. In the case of RZ, the specific test focuses on financial development. The question of RZ is whether financially dependent firms can be expected to grow more in countries with a higher level of financial development. The authors use U.S. firm data as proxy at the industry level to derive the typical external financial dependence for a particular industrial sector. Their presumption is that the well-developed financial markets in the United States should allow U.S. firms to achieve the desired financing for their respective industrial sector. This offers a way of identifying the degree to which industries desire external financing anywhere in the world.<sup>6</sup> The innovation of the RZ approach is in positioning an interaction between a country characteristic (its financial development) and this (benchmark) external financial dependence of a given industry. It then investigates how industrial growth relates to this interaction term, thereby investigating whether industrial sectors that typically use more external financing grow faster in countries with greater financial sector development. This overcomes some of the identification problems encountered in standard cross-country growth regressions. The approach is also less subject to criticism regarding an

> 6 The advantage of this approach is that one does not need information on the actual external financing dependence for industries in different countries. The comparability of such data would be reduced because accounting practices differ around the world.  It does assume that there are technological and economic reasons why some industries depend more on external finance than do others, and that these differences, to a large degree, prevail across countries. This does not
parameter, and _ε_ is an error term. Here, we specialize to _y_ = _θ e_ + _a_ + _ε_ , where _θ_ is teacher ability, _e_ is teacher effort, and _a_ is household (student and/or parent) effort. That is, we enrich the specification of labour to allow for (additively separable) teacher and household inputs but abstract from the role of physical capital and school-level productivity.<sup>17</sup> Using a theoretical framework built around this education production function, we show that school management structures can impact student learning outcomes via the following three channels:

1. _Teacher selection_ : schools with high management scores offer compensation packages that select in more able (higher _θ_ ) and more intrinsically motivated (lower effort cost) teacher types.

2. _Teacher incentives_ : schools with high management scores offer compensation packages that extrinsically incentivize, and adopt practices that intrinsically motivate, more effort from any given teacher type that selects in.

3. _Household incentives_ : schools with higher management scores institutionalize a strong work ethic and culture of high achievement among students and encourage greater parental involvement within the school (higher _a_ ).

In Section 3.2, we present a simple model that suffices to make the points above. In Section 3.3 and Section 3.4 we explain, intuitively, how the above selection and incentive effects are driven by people management and operations management. Then, in Section 3.5 we draw together these results and discuss implications for policy. We also briefly comment on how predictions would change in an alternative model featuring ‘low-cost private schools’.

# **3.2 The model**

We focus on a teacher who must decide whether to accept a job offer in her assigned public school, or decline it and apply to a private school or the outside sector.

> 17In principle, parents could play a further role by selecting between schools. Since our PISA data cannot speak to this issue, we leave the analysis of management-induced household selection
high in upper-middle and high-income countries (44 and 36 percent, respectively), possibly driven by a demand-side impediment, such as costly power generation that results in high electricity tariffs.

This paper estimates the impact of electricity consumption on the value added in the following three sectors: agriculture, manufacturing, and services. It uses panel data on 126 countries for 1996-2014 obtained from the WDI and the International Energy Agency (IEA). Choosing electricity consumption as an explanatory variable instead of other related indicators, such as electrification rates, outages, or electricity generation, helps capture both the supply- and demand-side constraints while also accounting for electricity import and export. At the same time, estimating value added-electricity consumption elasticities for individual sectors is important, given large sectoral differences in electricity intensity and the distinct sectoral compositions of GDP across countries.

The estimation approach uses two-step difference and system panel generalized methods of moments (GMM) techniques to address endogeneity and reverse causality problems. Estimating the impact of contemporaneous electricity consumption on value added growth in reduced form equations by ordinary least squares (OLS) might generate bias in the coefficients due to reverse causality or endogeneity issues. For example, increased electricity consumption in countries with high electricity gaps might raise countries’ value added, as firms would consume more electricity and expand their production of goods and services. At the same time, higher value added growth will generate higher demand for electricity, as firms will increase their electricity consumption along with their production. In terms of endogeneity, there might be other factors driving both electricity consumption and value added, such as the discovery of oil resources. To address these econometric problems, the paper applies the two-step difference and system panel GMM econometric approaches, which improve the OLS estimators by using the lags of regressors as instruments and address the bias from omitting country fixed effects in the estimations.

There are two streams in the literature that discuss the impact of electricity on economic growth: countryspecific studies that cannot be replicated across countries given data limitations and cross-country studies that mostly rely
# **Coverage of Informal Channels**

Despite the increasing number of financial products available in the marketplace to transfer money between countries, some migrants may choose for several reasons to send money to their home country through informal channels, such as:

- Sending cash with people traveling to their home country.

- Putting the money in an envelope and mailing it.

- Using the services of Hawala-type of money transfers.<sup>6</sup>

<!-- Start of picture text --> Chart 3. Informal Channels for Transferring<br>Remittances<br>Percent of Central Banks<br>that Collect Data from Informal Channels<br>Costa Rica<br>yes<br>Ecuador<br>25%<br>El Salvador<br>Honduras<br>Indonesia<br>Mexico<br>Moldova<br>no<br>75% Nicaragua<br>Peru<br>Russia<br>Data is collected through:<br>� surveys to migrants visiting their home country<br>� household surveys<br><!-- End of picture text --> As shown in chart 3, of the 40 countries covered in the survey 10 central banks have developed tools to measure the volume of remittances inflows that occur through informal channels. In these countries, information is normally collected through surveys of migrants visiting their home country and/or household surveys. Countries like Ecuador and Honduras conduct surveys to migrants at the country’s main points of entry (usually airports) every year, especially during holiday seasons when the inflow of migrant visitors reaches its peak (e.g. Christmas and New Year, Easter, Chinese New Year, etc.)<sup>7</sup> Other countries such as El Salvador conduct household surveys every year to estimate the total value that they receive in remittances either through formal or informal channels.

6 Empirical studies indicate that the choice of mechanism used is not just related to
**Graph 9 ‐ Priorities for developing SALM**

<!-- Start of picture text --> 10<br>9<br>9<br>8<br>8<br>7<br>7<br>6<br>5<br>5<br>4<br>3<br>3<br>2<br>2<br>1 1<br>1<br>0<br>Extending Extending Measurement Compiling Analysis Institutional None Other<br>perimeters of perimeters of data capacity framework<br>liabilities assets<br><!-- End of picture text --> Source: Responses to SALM survey 29
25

|University|0.004|0.067|0.004|0.061|0.767|
|---|---|---|---|---|---|
|**Lived in Dar es Salaam all their life**|0.750|0.433|0.669|0.471|0.000|
|**Marital status**||||||
|Never married|0.721|0.449|0.796|0.403|0.000|
|Divorced/separated|0.243|0.429|0.169|0.375|0.000|
|Currently married|0.013|0.115|0.016|0.124|0.668|
|Widowed|0.013|0.111|0.015|0.120|0.662|
|Cohabiting|0.010|0.099|0.005|0.068|0.146|
|**Has at least one child**|0.744|0.437|0.668|0.471|0.000|
|**Neighborhood poverty ranking (1-**||||||
|**5)**|2.682|0.684|2.687|0.684|0.872|
|**Self-ranked social status (1-7)**|3.720|1.526|3.715|1.530|0.931|
|**Income**||||||
|Total income (30 days) Tsh - mean|256200|303441|252414|346987|0.788|
|Income from sex work (30 days)<br>Tsh - mean|238419|280312|235261|228049|0.
Azzimonti, Marina, Marco Battaglini, and Stephen Coate. "The costs and benefits of balanced budget rules: Lessons from a political economy model of fiscal policy." _Journal of Public Economics_ 136 (2016): 45-61.

Bergman, U. Michael, and Michael M. Hutchison. "Economic stabilization in the post-crisis world: Are fiscal rules the answer?." _Journal of International Money and Finance_ 52 (2015): 82-101.

Bianchi, Javier, Pablo Ottonello, and Ignacio Presno. "Fiscal stimulus under sovereign risk." _Journal of Political Economy_ 131.9 (2023): 2328-2369.

Caselli, Francesca, and Julien Reynaud. "Do fiscal rules cause better fiscal balances? A new instrumental variable strategy." _European Journal of Political Economy_ 63 (2020): 101873.

Daehler, Timo, Joshua Aizenman, and Yothin Jinjarak. "Emerging markets sovereign CDS spreads during COVID19: Economics versus epidemiology news." _Economic Modeling 100_ (2021): 105504.

Davoodi, Hamid, Paul Elger, Alexandra Fotiou, Daniel Garcia-Macia, Andresa Lagerborg, Raphael Lam, and Sharanya Pillai. "Fiscal Councils Dataset: The 2021 Update." _International Monetary Fund_ (2022a).

Davoodi, Hamid, Paul Elger, Alexandra Fotiou, Daniel Garcia-Macia, Xuehui Han, Andresa Lagerborg, and Raphael Lam. "Fiscal Rules and Fiscal Councils: Recent Trends and Performance during the COVID-19 Pandemic." _International Monetary Fund_ (2022b).

Esquivel, Carlos, and Agustin Samano. "Expansionary Fiscal Consolidation Under Sovereign Risk." _Working paper_ (2023).

Eyraud, Luc, Mr Xavier Debrun, Andrew Hodge, Victor Duarte Lledo, and Ms Catherine A. Pattillo. "Secondgeneration fiscal rules: Balancing simplicity, flexibility, and enforceability." International Monetary Fund (2018).

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followed in 2004 by a second wave of liberalization, whereby tariff rates were simplified and further reduced. See Table 1 for a list of Egypt’s Regional Trade Agreements currently in force. Reforms to improve the business environment and exchange rate accompanied these changes (Zaki 2016; Zaki et al. 2018). In November 2016, the Central Bank of Egypt announced the floatation of the Egyptian pound, which depreciated by 45 percent against the US dollar, reaching EGP 13, at the decision and reached EGP 18 soon thereafter (Zaki et al. 2018).

Following these reforms, Egypt’s weighted mean applied tariffs fell from 20.0 percent to 6.6 percent between 2002 and 2016, before rebounding to 10.4 percent in 2019 (Figure 1). Egypt’s trade rose significantly, almost tripling between 2000 and 2020, with exports increasing by 196 percent while imports soared 231 percent (Figure 2). The increase in trade flows coincided with the depreciation of the Egyptian pound, which made exports relatively less expensive for foreign customers (Figure 2). Despite this increase in trade volume, the export sector remained a relatively small share of the Egyptian economy. In 2020, Egypt’s export sector accounted for a mere 13 percent of its GDP, as compared to 35 percent in Morocco, 38 percent in Tunisia, 61 percent in Malaysia, and 52 percent in Thailand (World Development Indicators).

Labor market outcomes tend to be better for workers in exporting firms compared to those in non-exporting firms. Consistent with both economic theory and results from other countries, Egypt’s exporting firms tend to pay higher wages, provide better jobs, demand more skilled workers, deliver higher job security, and hire more women (Saad 2020). They also tend to be few and relatively larger relative to non-exporting firms (ES data). According to the Egyptian Enterprise Census in 2017, which contains all enterprises, only 1 percent of firms export. The average sizes of exporting and non-exporting firms
exception of Model 2 using the 1991/92 data where the "female" dummy in the non-farm equation is not significant (perhaps due to the inclusion of the dependency ratio for females only, discussed below). The participation patterns of members residing in female-headed households is not clear from Model 3; the inclusion of that dummy is insignificant in all but agriculture participation in 1987/88 where they are found to be significantly less likely to participate.

Higher level of education has a negative effect on participation in agriculture, and it has the opposite effect on non-farm participation. This is true of both years, and it is a common finding in the literature (Lanjouw, Deininger and Okidi, and others). The effect of primary schooling is more nuanced: it has a positive but insignificant effect in the agriculture equation in 1987-88 (except for Model 3 where the effect is significant) and a positive and significant effect in the 1987-88 non-farm equation. But by 1992, primary schooling has a negative and significant impact on agriculture participation, and it has a positive, insignificant impact on non-farm participation in 1991-92. Since the excluded dummy is those with no schooling, this suggests that in 1988, there was more of a mixture of people with no school and primary school in agriculture, and that by 1992, those with primary schooling had declined. These patterns indicate that over time some of those with no education have moved toward non-farm, while as a group, those with no schooling became the main group in agriculture.

Distance to markets is an important determinant of growth prospects of the rural economy. Non-farm activities require a certain proximity to trading centers or small towns, while agricultural production gets an added impetus from close proximity to markets. As a proxy for "distance to market" we used a combination of distance traveled 23
, B. R. (2021): “Big business and the inequality trap in Latin America: Taxes, Collusion, and Undue Influence,” _UNDP LAC Working Paper, 5, 2021, Background paper for the Regional Human Development Report for Latin America and the Caribbean 2021_ .

- Schultz, T. P. (1990): “Women’s changing participation in the labor force: a world perspective,” _Economic Development and Cultural Change_ , 38, 457–488.

- Tybout, J. R. (2000): “Manufacturing Firms in Developing Countries: How Well Do They Do, and Why?” _Journal of Economic Literature_ , 38, 11–44.

- U.S. Census Bureau (1943): “Employment and Personal Characteristics,” 1940 Census of Population: The Labor Force (Sample Statistics).

- ——— (1953): “General Characteristics,” 1950 Census of Population: Volume 2. Characteristics

- of the Population.

23
measure in this paper. While it is possible that this is because the program is ineffective, it is crucial to qualify that it was designed to overcome obstacles that are less relevant in a dynamic (even chaotic) state of disequilibrium. In particular, efforts to invest in the future (whether via skills, capital, or social relationships) by definition will not bear fruit over a few months, and furthermore may be substantially less relevant in times of crisis. Our longerterm endline surveys with the full sample will allow us to test whether positive effects appear on that timescale, as well as evaluate any continued overall recovery from the pandemic and the long-run impact of the business grants.

One limitation of our study is that despite the fine-grained data and timing, we cannot distinguish the direction of causality between the two main categories of outcomes: economic performance and well-being. That being said, almost certainly it is self-reinforcing and goes in both directions. What we can confidently say in the bigger picture is that both elements matter and that grants can be instrumental in ameliorating negative effects along these two key dimensions in times of crisis. This is especially important for a population of youth, such as ours, for whom both upward and downward trajectories can have long-lasting impacts. Moreover, our results suggest that reacting quickly and investing at the individual level, especially in the absence of other support services, can offer a vital lifeline to youth that can facilitate recovery in the short-run and open doors for productivity and growth in the longrun.

15
**Table 2: Heterogeneity by access to divorce**

||(I)<br>Physical<br>Violence|(II)<br>Sexual<br>Violence|(III)<br>Any<br>Violence|
|---|---|---|---|
|A: Equal access to divo|rce|||
|ln(Uf)|1.433***|1.526***|1.211***|
||(0.087)|(0.019)|(0.051)|
|ln(Um)|-0.789***|-0.815***|-0.662***|
||(0.045)|(0.009)|(0.023)|
|Observations|95040|35794|35736|
|X controls|Y|Y|Y|
|Countryand Year FE|Y|Y|Y|
|B: Women lack equal a|ccess to divorce|||
|Ln(Uf)|-0.517***|-0.290**|-0.631**|
||(0.107)|(0.131)|(0.243)|
|Ln(Um)|0.562***|0.200|0.597***|
||(0.077)|(0.121)|(0.191)|
|Observations|253125|232379|230781|
|X controls|Y|Y|Y|
|Countryand Year FE|Y|Y|Y|

_Source:_ Authors’ analysis based on data from the Demographic and Health Surveys (DHS).

# _Notes:_

* p<0.10, ** p<0.05, *** p
**Figure 3: Impact of increase in overall corruption on employment growth for firms that face low vs. high levels of financial constraints**

<!-- Start of picture text --> 0.4<br>0.34<br>0.3<br>0.2<br>0.1 0.06<br>0<br>Low Corruption High Corruption Low Corruption High Corruption<br>-0.1 -0.07 Low financial constraint High financial constraint<br>-0.2<br>-0.3<br>-0.33<br>-0.4<br>Employment growth rate (%, residuals)<br><!-- End of picture text --> Source: Enterprise Surveys. Based on authors’ own calculations.

Notes: The figure is a partial scatter plot of the residuals of growth rate of employment plotted against residuals of overall corruption and residuals of financial constraints variable. The residuals of employment growth are obtained by regression employment growth on country fixed effects, industry fixed effects and initial employment at the firm. Residuals of overall corruption are obtained by regressing overall corruption on country fixed effects, industry fixed effects, initial employment at the firm and financial constraints variable. Residuals of financial constraints variable are obtained like for overall corruption with the control for financial constraints replaced by overall corruption variable. The cut-off used for low vs. high financial constraints groups in the graph is the median value of the residuals of financial constraints variable. For overall corruption, the cut-off used is its median value within each of the two financial constraints groups shown. All variables are as defined in the main text.

33
and 0.39 respectively. Indeed, many of the households with the highest consumption levels reside in urban areas, with better access to markets and opportunities.

**Figure 7: Cumulative consumption distribution by state.**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>WRP<br>70%<br>NBG<br>60%<br>WBG<br>50%<br>LKS<br>40%<br>WEQ<br>30%<br>CEQ<br>20%<br>EEQ<br>10%<br>US$1.90 PPP<br>0%<br>0 20 40 60 80 100 120 140 160 180 200<br>Total cons. per capita per day (Oct. 2016 SSP)<br><!-- End of picture text -->

**Source: Authors’ own calculations based on HFS 2016 data.**

Poverty in 2016 is generally high but it is higher in former states that were more exposed to the conflict. The incidence of poverty reached extremely high levels in the former states of Eastern Equatoria, Northern Bahr el Ghazal, and Western Bahr el Ghazal, where about 9 in 10 people live under the international poverty line (95, 90, and 90 percent, respectively). In the former states of Lakes and Central Equatoria, the poverty headcount is slightly lower at about 8 in 10 people, though still extremely high by international standards (84 and 80 percent, respectively). One notable exception is the former state of Western Equatoria, as it was less affected by the conflict compared to the other states and has benefitted from high fertility and favorable weather conditions. Indeed, Western Equatoria, in the “green belt” of South Sudan, was the only state to record a consistent cereal production surplus in the years from 2014 to 2016 (FAO