rafmacalaba/data-use-mentions-extended huggingface dataset:
6

# III. **METHODS, DATA AND DESCRIPTIVE STATISTICS**

We model the effect of insurance on the probability of an individual incurring large annual out-of-pocket payments, with large being defined relative either to the sample average income or the per capita income of the individual’s own household.

Use of a basic probit or logit model on cross-section data could reasonably be criticized on the grounds that selection into health insurance is non-random and occurs in such a way as to generate a correlation between the insurance dummy and the error term. This could in principle apply to the CMS, GIS/LIS schemes and private/other schemes. In the case of the CMS scheme, it seems more likely that any selection is adverse to the scheme: people with unobservables that predispose them to a higher-than-expected risk of ‘high’ expenses are more likely to enroll, biasing the coefficient on insurance upwards. In the case of the GIS/LIS scheme, and possibly the private/other category as well, the bias seems likely to be in the opposite direction: the scheme’s members are likely to have a very low risk compared to the population at large, and we may only partially capture this by the observables that we enter in our probit model. We try to get round the possible endogeneity of insurance in two different ways, depending on the dataset. Two of the three household surveys we use are panels. These permit us to allow for an unobserved time-invariant fixed effect capturing unobserved risk, which could be correlated with insurance coverage; the model we use in these surveys is a fixed-effect logit model (cf. e.g. Wooldridge 2002). In the cross-section survey, we use instrumental variables (IV). We capture insurance coverage using two variables, the first indicating whether the individual is a member of a CMS, the second capturing membership of the GIS or LIS.<sup>5</sup> As instruments for our insurance variables, we use a variable capturing whether or not a CMS
The data have the advantage that recruits report not only their age and education but also where they resided before enrolling; of the 605 Tunisian recruits, we are able to map delegation of residence for 501, of whom 468 also have information on education and age. Our sample for the analysis thus consists of these 468 Daesh recruits.<sup>4</sup> The average number of recruits per delegation is 1.9, but the average masks a highly skewed distribution (see Figure 1a): recruits come from 94 delegations only (out of 263), whereas Bizerte, the northernmost city in Africa, had 30, the highest number. The Tunis area was also a recruitment hotspot. Of the 468 Tunisian recruits in our database, 128 are from the governorates of Tunis and Bizerte. In general, recruitment rates are much higher in urban areas.

Detailed information on the socio-economic characteristics of recruits (including their age and education), allows us to construct a variable of recruitment that counts the number of Tunisian recruits who resided in delegation _d_ , have education level _e_ , and have reported to be _a_ years old; we therefore created five-year age groups so that each cell (observation) in the dataset is characterized by the triplet _{d, e, a}_ . Table 1 provides both cell-level and delegation-level summary statistics for our variables of interest. The number of recruits in a given cell ranges from 0 to 8.

**Socio-economic data** The 2014 Tunisia Census of Population and Housing is the source of our main independent variables. The census collected information on demographics, educational attainment, marital status, unemployment, television and radio ownership, emigration, and

> et al., 2016a,b; Jayakumar and Sumpter, 2019; Morris, 2020; Evans et al., 2020; Brockmeyer et al., forthcoming), but to the best of our knowledge they have not been used for country case studies.

> 4Using available information on Daesh recruit age, education,
# **3. Estimating the Drivers and Determinants of Regional Poverty**

The estimation strategy to understand regional determinants of poverty is based on an existing literature associating growth composition, labor, and other regional variation to poverty reduction. Explanatory factors of the evolution of regional poverty has generally be classified as either “patterns of growth” or “initial conditions”. The methodology in this paper fallows closely previous literature (Ferreira, Leite, Ravallion, 2010; Suryahadi, Suryadarma, and Sumarto, 2009; Montalvo and Ravallion, 2010; Loayza and Raddatz, 2010).

Our data set comprises of 26 NUTS2 regions from 2006 to 2013. Descriptive variables at the regional level are not available for all years and depending on the specification, results will have fewer observations. In terms of patterns of growth, we consider regional and spatial variations in economic variables, shifts in sectoral growth, the composition of growth, fiscal spending. In terms of initial conditions, we control for demographic and infrastructure variables.

## **<u>Patterns of Growth</u>**

The starting point estimation is a first difference equation (Eq 1). The dependent variable is the headcount ratio based on the Foster-Greer-Thorbecke family of poverty indices. The headcount ratio or poverty rate is based on imputed annual household income per capita and international poverty lines. The change in a poverty index is regressed onto changes in the value added of sector _s_ in region _r_ and at time _t_ . The primary sector is agriculture ( _s_ =1), the secondary sector is industry ( _s_ =2), and the tertiary sector is services ( _s_ =3). This is the most basic specification for estimating the impact of the “pattern of growth”.

Since the equation is a first difference, any time-invariant regional level fixed effects are removed. From the poverty convergence results earlier, it is evident that the impact of growth patterns by sector will be uneven regionally. Therefore, it is also necessary to interact the regressors with
How Much Teachers Know and How Much It Matters in Class: Analyzing Three Rounds of Subject-Specific Test Score Data of Indonesian Students and Teachers Joppe de Ree<sup>_∗†_</sup>

# _JEL Classification_ : I21, I25, H42, O15

_Keywords_ : Education, value-added modeling, teachers, subject matter knowledge, Indonesia

> _∗_ World Bank, email: joppederee@gmail.com

> _†_ The research presented in this paper was generously supported by the Dutch Education Support Program (DESP) funded by the Government of the Kingdom of The Netherlands. Special thanks go to Dedy Junaedi (and his teams of field workers, data managers, etc.), Susiana Iskandar, Titie Hadiyati (and team), Amanda Beatty (and team), Halsey Rogers, Menno Pradhan, Karthik Muralidharan, Andy Ragatz, Samer Al-Samarrai, Ai Li Ang, Husnul Rizal, Laura Wijaya, and other colleagues at the World Bank office in Jakarta, as well as to colleagues at the Indonesian Ministry of Education and Culture, such as Yendri Wirda Burhan, Simon Silisabon (and the team at _puslitjak_ ), Rahmawati, Yani Sumarno (and the team at _puspendik_ ).

1
, particularly Google mobility and search terms, predict a respectable amount of the variation in changes in wellbeing. The signs – positive or negative correlation – are often intuitive, especially when explaining variation over time. Real-time indicators were better able to predict cross-sectional variation in initial crisis impacts, measured as retrospective changes, than intertemporal variation in outcomes over repeated waves of survey data. In addition, the real-time indicators were better suited for predicting income losses than gains. The importance of different indicators also varies widely by context, highlighting the benefits of training models that 5
Table 6: Impact on high school graduation by performance in ENLACE 2009 (math)

|Outcome variable|Lowest|tercile|ENLA<br>Middle|CE (Y/N)<br> tercile|Highest|tercile|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Treatment|0.004|-0.003|-0.004|-0.005|0.040**|0.037**|
||(0.020)|(0.020)|(0.018)|(0.018)|(0.017)|(0.017)|
|Controls||Yes||Yes||Yes|
|N|2,486|2,452|2,449|2,418|2,386|2,350|
|Mean Dep. Control group|0.57|0.58|0.72|0.72|0.76|0.76|
|SD Dep. Control group|0.49|0.49|0.45|0.45|0.43|0.43|
||*** _p <_0|_._01, ** _p <_|0_._05, * _p_|_<_0_._1|||

The full set of controls include age, dummies for sex and area (urban-rural) and test scores on Enlace 9, one for spanish and other one for mathematics. ENLACE (Y/N) takes the value of 1 if the student took the 12th grade exam in 2012 and 0 otherwise. The three groups are formed using the terciles of performance in ENLACE math 2009.

# **5.
The central labor adjustment in microenterprises with workers is instead an upward shift in short term labor demand for workers in response to blackouts. Each additional blackout weekday is associated with 0.32 additional non-owner work days and 3.13 additional non-owner work hours in a workweek. On a daily basis, firms with workers statistically significantly increase the number of workers staffed and total worker hours on blackout days, on days that are the second of two consecutive blackout days, and on days with power that follow a blackout day. In addition, total weekly wages paid decrease by 20% for each blackout day in a workweek, but the analysis fails to reject that outages have no effect on within-firm worker hourly wages.

What rationalizes an increase in worker hours and a reduction in wages paid, without any measured effect on average hourly wages at the firm level? The evidence in this paper, though speculative, suggests that firms employing unskilled workers are increasing worker labor inputs and that firms employing skilled workers are driving the reductions in wages paid. The composition of the workforce in this industry is consistent with this interpretation; 91% of the workforce are unskilled (and poorly paid), while the tiny fraction of skilled workers take home 61% of total wages paid.

This paper is divided into six sections. Section 2 introduces the context of the study, providing background on Ghana’s electricity crisis and the garment sector. Section 3 describes the sample and data. Section 4 discusses estimation. Section 5 presents the findings and discusses possible interpretations. Section 6 concludes.

# **2 Context**

## **2.1 Dumsor**

In Ghana, as in many low-income countries, electricity demand often outstrips supply, and power is erratic and frequently unavailable. A recent survey of electricity consumers in twelve African countries found that in all but Rwanda more than half of respondents reported at least one outage per day; in Ghana, that figure is 68% of electricity consumers ( **?** ). Despite
# **1 Introduction**

Technology is the key driver of productivity differences across countries (Comin and Hobijn, 2010; Comin and Mestieri, 2018a; Easterly and Levine, 2001; Hall and Jones, 1999). Some estimates suggest that cross-country differences in the timing of adoption of new technologies account for at least a quarter of per capita income disparities (Comin and Hobijn, 2010). Despite the importance of technology in explaining productivity and income differences and the fact that technology is a buzz word for many policymakers, good measures of the use of technology at the firm level are scarce and confined to a few technologies and sectors. This data gap is especially large in developing countries, which makes it challenging to examine whether firms in developing countries use better technologies that already exist and why. Therefore, a deeper understanding of the meaning of new technologies, their use, and the obstacles to adoption requires appropriate tools and methodologies to measure their diffusion.

This paper describes the results of the “Firm-level Adoption of Technology” (FAT) survey implemented in Senegal.<sup>1</sup> It identifies some of the key obstacles for the adoption of new technologies faced by those firms. The paper describes with large granularity the level of technology use across firms and sectors, the relationship between technology use and performance, and provides some cross-country benchmark. The FAT survey developed by Cirera, Comin, and Cruz 2020 is an innovative tool to measure the use of technology at a granular level. The survey implemented in Senegal includes a nationally representative random sample of about 1,800 firms with 5 or more employees, both formal and informal, in agriculture, industry and services from the latest establishment census for Senegal.<sup>2</sup> In the paper, we analyze the adoption and use of technologies in Senegal through three key angles: (1) standard measures of technology related to general purpose technologies; (2) use of technologies applied to general business support functions; and (3
|**(10)**<br>**N**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Demographic and Socio-economic Characteristics**||||||||||||
|What do you think is recorded as the population of [INSERT WOREDA] according to official administrative data in the<br>Census of 2007 Gregorian Calendaar (1999/2000 Ethiopian Calendar)?|Census 2007|99,605|61,868|1.19|||||||740|
|What do you think is recorded as the percentage of rural inhabitants of [INSERT WOREDA] according to official<br>administrative data in the Census of 2007 Gregorian Calendar (1999/2000 Ethiopian Calendar)?|Census 2007|0.84|0.26|-2.66|||||||726|
|What do you think is recorded as the unemployment rate of [INSERT WOREDA] according to official administrative<br>data in the Census of 2007 Gregorian Calendar (1999/2000 Ethiopian Calendar)?|Census 2007|4.07|5.63|2.21|||||||740|
|What do you think the primary (grades 1-8) enrolment numbers are for [INSERT WOREDA] according to official<br>administrative data?<br>**Education indicators**|EMIS 2006/07-2013/14|21,929|14,020|1.06|0.61|0.05|-12.36|0.00|-2.34|-2.63|705|
|What do you think the primary (grades 1-8) pupil-section ratio is for [INSERT WOREDA] according
# **Figure 1 Commodities: price volatility and importance for exports and revenues**

**A. Commodity price movements since 2020**

**B** . **Share of EMDE exports of key commodities**

<!-- Start of picture text --> Percent Percent of exports<br>15<br>70<br>10 60<br>5 50<br>0 40<br>30<br>-5<br>20<br>-10<br>10<br>0<br>Oil Copper Coffee Natural<br>gas<br>C. Resource revenues as share of total fiscal D. Duration of commodity booms and slumps<br>revenues<br>Percent of fiscal revenues Months Booms Slumps<br>70 80<br>60<br>60<br>50<br>40 40<br>30<br>20<br>20<br>0<br>10<br>0<br>Energy Metal Agriculture<br>exporters exporters exporters<br>Oil Natural gas Coffee Rice Wheat Cotton Aluminum Copper Nickel Zinc<br>Energy Agriculture Metals<br><!-- End of picture text --> _Sources_ : UNU-Wider; UN Comtrade (database); WITS (database); World Bank. _Note_ : EMDEs = emerging market and developing economies; LICs = low-income countries.

A. Bars show the average month-on-month change in commodity prices from January 2020 to November 2023. Whiskers show the interquartile ranges. The commodities used (from the World Bank’s Pink Sheet database) are crude oil (average of West Texas Intermediate, Brent, and Dubai); natural gas index; coffee, Arabica; rice, Thai 5 percent; wheat, U.S. hard red winter; cotton, A Index; aluminum; copper; nickel; and zinc.

B. Panel displays the median export share of oil, copper,
<!-- Start of picture text --> 90,000 0.45<br>80,000 0.40<br>70,000 0.35<br>60,000 0.30<br>50,000 0.25<br>40,000 0.20<br>30,000 0.15<br>20,000 0.10<br>10,000 0.05<br>0 0.00<br>Hydro Geothermal Other renewables<br>Coal Oil Gas<br>Biofuels and waste Nuclear Total final consumption<br>Fuel type concentration index<br>GWH<br><!-- End of picture text --> Source: International Energy Agency The security of supply can be judged also by the amount of load shedding and outages that have been experienced. By 2015 the World Bank’s Enterprise Survey indicated that the Philippines’ performance indicators for electricity supply reliability were well above those for East Asia (Table 4), although the extent of outages and the ownership and use of self-generation still pointed to a weakness in the supply of electricity.

**Table 4: Reliability of Electricity Supply**

|Indicator|Philippines|East Asia|
|---|---|---|
|percent of firms experiencingoutages|39.9|45.9|
|Number of outagesper month|0.1|4.9|
|Average duration of outage(hours)|3.0|4.3|
|Average sales lost to outages for those experiencing outages<br>percent|0.8|3.2|
|percent owning generators|42.7|32.5|
|percent of electricity from generators for those owning a<br>generator|38.9|25.8|

_Source_ : World Bank Enterprise Survey 28
this possibility directly, in SI Section S7 we use DHS survey data to demonstrate that rates of both in- and out-migration are no different in refugee-hosting districts vs. neighboring districts that do not host refugees.

# **6 Policy and Program Implications**

In this section, we summarize the paper’s findings and discuss the implications for policies and programs. Using a DiD research design and a newly constructed geocoded panel dataset of core service provision—the locations of health centers and schools as well as the quality of roads—we find that host communities near refugee settlements in Uganda experience positive externalities. Our findings with respect to service provision are consistent across three key domains and are robust to alternative measures of proximity, and to different samples based on distance to settlements, which increases the in our results.

Using individual-level surveys, we find little evidence that proximity to refugees causes a backlash within host communities against them or related policies. These results are consistent with findings from other contexts such as Jordan (Ferguson et al., 2021) and the DRC (Pham et al., 2021). While we cannot directly assess this argument, we maintain that positive spillovers in the form of service delivery improvements likely help reduce tensions between refugees and host communities, and thereby contribute to social cohesion. These findings advance an emerging literature that assesses how the arrival of forcibly displaced people affects host communities, particularly in the goods and labor markets as well as through health channels. Nevertheless, our understanding of the economic impacts of forced migrants on host communities is in its infancy and requires pushing the research agenda forward across many other, particularly low-income, contexts.

In the absence of mitigating policies, refugees may impose an economic burden by straining local social services and infrastructure, and introducing economic competition (Whitaker, 2002). The unequal provision of humanitarian aid to refugees can also provoke resentment (Jacobsen, 2005; Dryden-Peterson and Hovil, 2004). However, aid and infrastructural development targeted primarily at refugees can generate positive externalities for
19

# 5.  Remittances and Poverty

Now that per capita household expenditures have been predicted in the two situations – excluding and including remittances – for the three groups of households, it is possible to examine the impact of these financial transfers on poverty in Ghana. This is done in Table 6.

Table 6 is based on a poverty line of 684,401 cedis/person/year, which is equivalent to the poverty headcount index of 39.5 percent that is cited as the 1998/99 poverty line for Ghana (Ghana Statistical Service, 2000: Table 2).<sup>21</sup> Using this poverty line, Table 6 reports three different poverty measures. The first measure -- the poverty headcount -- shows the percent of the population living beneath the poverty line. However, this headcount index ignores the “depth of poverty,” that is, the amount by which the average expenditure of the poor fall short of the poverty line. The table therefore also reports a second measure, the poverty gap index. This index measures in percentage terms how far the average expenditures of the poor fall short of the national poverty line. The third poverty measure -- the squared poverty gap index – shows the “severity of poverty.” The squared poverty gap index possesses useful analytical properties, because it is sensitive to changes in distribution among the poor. In other words, while a transfer of expenditures from a poor person to a poorer person will not change the headcount index or the poverty gap index, it will decrease the squared poverty gap index.

Columns (1-4) of Table 6 report the results for the different poverty measures. With only one exception, all of the measures show that that the inclusion of remittances – either internal or international – in household expenditure reduces the level, depth and severity of poverty in Ghana.<sup>22</sup> However, the size of the poverty reduction depends very
Policy Research Working Paper 9235

# **Abstract**

Facing a fiscal crisis, the Islamic Republic of Iran decided to increase gasoline prices at the end of 2019. This paper estimates the impact of the price increase on household welfare and government revenue, using the most recent Household Expenditure and Income Survey conducted by the Statistical Center of Iran in March 2018–March 2019. The paper looks at the direct and indirect impacts of the reform and quantifies the compensatory cash transfer program the government instituted. Despite very regressive gasoline subsidies benefitting the rich the most, the increase in gasoline prices is found to affect the poor to a greater extent due to larger negative indirect impacts as well as their relatively low incomes. In total, poverty is estimated to increase by about 2.9 percentage points, with the direct impact accounting for a third of this increase. The proposed government scheme, if targeted perfectly to the poorest 18 million households, would fully compensate the poorest bottom 50 percent of the population and reduce poverty to below pre-reform levels. The annual cost of the program will be around 338 trillion rials, which accounts for 77 percent of the estimated total savings from the subsidies reform (439 trillion rials).

This paper is a product of the Poverty and Equity 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 authors may be contacted at aatamanov@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
The calculations in Sections 4 - 5 indicate that providing roads to villages in the hilly states is probably not socially profitable at the discount rate of 10 percent. Yet the net returns in the other participating states easily make good these shortfalls, even without an appeal to ‘growth effects’ generated by the larger economy, or to the existence of net, positive external effects produced by rural roads on the larger economy. On balance, Section 6 suggests that there are such effects, but the magnitude of the net benefit is shrouded in uncertainty.

The formal estimates of the benefits generated in the spheres of education and health are substantial in size, though the method used to arrive at them has its weaknesses. The respondents in the Orissa survey ranked these benefits roughly on a par with the commercial ones, as did many of the villagers with whom I spoke on my field-trip. When a passable road may well be decisive in life-and-death emergencies, people will indeed value it highly. At all events, it is reassuring that two such radically different 25