rafmacalaba/data-use-mentions-extended huggingface dataset:
factors that diminished hopes for recovery. Most alarming is the fact that a recovery in the employment situation has never been observed; in fact, as Japan's Economic Planning Agency (EPA) has stated, it remained severe and continued to deteriorate during the recent gradual recovery (see Figure 2).

Shigeru Otsubo is an Economist with the International Economics Department, the World Bank. Masahiko Tsutsumi is an Economist with the Economic Planning Agency of the Japanese government. Analyses provided here were based on data available at the end of 1995. The findings, interpretations, and conclusions are the authors' own and should not be attributed to the World Bank or the Economic Planning Agency. Research assistance by Yoshiaki Igarashi and Tetsuo Umemura is gratefully acknowledged. Editorial assistance was provided by Deborah Davis and Sarah Crow. ' According to the Economic Planning Agency's reference dates of business cycles, the bubble cycle had a 53-month expansion phase (from November 1986 to April 1991), followed by a contraction phase spanning 30 months (from April 1991 to October 1993). The average duration of Japan's post-war business cycle expansion has been 33.5 months, while contractions have averaged 16.9 months.

1
.

Source: Enterprise Surveys and authors’ calculations.

In MENA, green management quality appears to be more important for the firms’ decision to make green investments (and the number of different green investments) than credit constraints; the estimates are consistent with our hypothesis 2, but not with hypothesis 4. Estimates in Panel B indicate that compared with firms in ECA, firms in MENA appear to be less sensitive to credit constraints when deciding on the number of different green investments to make – none of the estimated average marginal effects is statistically significant. In both regions, the number of green investments firms make seems to depend more on the quality of green management practices. In the ECA region, the pattern looks similar to the one in Panel A. A unit increase in the quality of green management practices increases in magnitude with the number of different green investments. It is associated with an 18.2 and 3.5 percentage points decrease in the likelihood of making no or one green investments, respectively, and a 6.1 and 15.6 23
Laplante Rnd, Rilstone (1996; henceforth LR) have examined whether or not _inspections_ have an impact on the emissions of pulp and paper plants in the United States and Canada, respectively. MV (1990) have shown that inspections reduce permanently the level of emissions of plants by approximately 20%. LR (1996) have shown that not only inspections but also the threat of inspections reduce emissions by approximately 28%.4 These two analyses also find that inspections induce more frequent self-reporting of emissions by the regulated plants. Hence, not only do inspections have an impact on the actual level of emissions of the plants, but they also provide regulators with a more accurate pollution profile of the plants. More recently, Nadeau (1997) has shown that inspections significantly reduce the duration of firms' violation of air pollution standards in the pulp and paper industry in the United States. These results have clear policy implications as to the importance of a credible monitoring strategy to create incentives for pollution control.

If inspections have an impact on the behavior of polluters and partly explain improvements in their environmental performance, a second question of interest thus pertains to the determinants of the regulator's monitoring strategy. Deily and Gray (1991, 1996), and Dion, Lanoie and Laplante (1998) have shown that regulators are sensitive to local environmental damages in their decision to inspect specific plants and, other things being equal, that greater inspection effort is allocated towards those plants whose emissions are likely to generate a higher level of damages. While this may be a desirable behaviour,' these authors also show that the monitoring and enforcement activities of regulators are also a function of variables pertaining to local labour market conditions (such as unemployment

- 4 These two analyses examine the impact of inspections on water pollut on emissions as measured by biological oxygen demand (BOD) and total suspended solids (TSS).

- _5_ Economists have greatly criticized environmental regulations that impose on polluters uniform environmental standards since such standards ignore that plants impose non-uniform environmental damages per
provide for others (for example). As Bargain (2022) notes, applying this assumption in developing countries is problematic because “it is not common to find people living alone.”<sup>12</sup> The approach taken in this paper instead exploits household demographic composition and economic information to guide estimates of which household activities benefit the most (on net) from fiscal policies and which individual household members fill those roles. It also falls squarely within the “accounting” approach to fiscal incidence analysis which attempts to map fiscal facts (e.g., the taxes collected and the expenditures made) to the behaviors observed in a cross-section of individuals. This analytical choice also limits us to a description of how total economic activity at the household level is supported or hindered by fiscal policies and for whom within the household that such support or hindrance will matter most. These limitations are sufficiently different from those relevant for the intrahousehold bargaining literature, and we do not propose our method as a substitute for those approaches.

The methodology we propose is generalizable to most fiscal, socioeconomic, and cultural contexts for the following reasons. First, we include a broader set of activities and points of interaction with the fiscal system, such that individuals who spend most of their productive hours working without remuneration can nonetheless be made more productive by, or face burdens from, fiscal policy. Second, we propose procedures for a gendered fiscal incidence analysis where only the household-level data required for standard CEQ assessments is available and suggest modifications to those procedures when richer individual-level data is available.

# 3 Gender Inequality, Social Reproduction, and Fiscal Policy

Gender is a normative social construct defining and differentiating the roles, rights, entitlements, responsibilities, and social obligations of women and men. Gender is also a set of expectations providing positive and negative incentives for broad-based compliance with those roles, responsibilities, and obligations. Gender relations are embedded in markets and institutions and are integral to how an economy functions. Economic institutions, policies, and the
SOUTH SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE underpricing to 57 percent (table 15). In terms of utility size, the SSEC encounters one of the highest hidden costs in Africa (figure 18). If SSEC is to expand access, redressing these hidden costs must be its first urgent priority.

**Table 15. Operational indicators for the SSEC**

|||System|Implicit collection|Average total|Average|Total hidden|Total hidden|
|---|---|---|---|---|---|---|---|
||Net generation|losses|ratio|cost|effective tariff|costs|costs|
||(GWh/year)|(%)|(%)|($/kWh)|($/kWh)|($ million/year)|(% revenues)|
|2006|39|50|40|0.37|0.23|8.7|187|

_Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009); Southern Sudan statistical handbooks 2006–08; World Bank (2011).

**Figure 18. Benchmarking the SSEC’s high hidden costs due to inefficiencies**

<!-- Start of picture text --> 300<br>250<br>200<br>150<br>100<br>50<br>0<br>Nigeria Sudan Liberia Niger Mali Sudan Ghana Burkina Cote Benin Cape Senegal South<br>(South) (North) Faso d'Ivoire Verde Africa<br>Collection Inefficiencies Underpricing Losses<br>Percentage of revenues<br><!-- End of picture text --> _Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009); Southern Sudan statistical handbooks 2006–08; World Bank (2011).

Improving South Sudan’s electricity situation requires a phased approach
region. In terms of income groups: 10 countries are in the low-income group, 6 countries are in the lower middle-income group, and two countries are in the upper middle-income group.

The typical LSMS household survey contains questions on expenditures on private transport and transport services during recall periods. The questions on expenditures ask whether the households purchased any vehicles or paid for transport-related goods or services during the following periods of time: i) 7 days, ii) 1 month, iii) 3 months, iv) 6 months, and v) 1 year, and how much money was spent. Usually, short recall periods are used for transport expenditure on nondurables, such as public transport fares, fuels and lubricants, and maintenance and repairs, while the longer periods are used for transport expenditure on durables, such as purchases of vehicles, spare parts, and accessories.

Other household characteristics contained in LSMS surveys are used in the reduced-form regression part. Information on the total amount of expenditure, the size of the household and

> 3These surveys mainly fall into two categories; 10 of the 18 country surveys are from the LSMS collection included in the World Bank Microdata Library. The other 8 country surveys are from other multi-topic household surveys labelled as “LSMS-tagged” surveys.

8
# **Figure 15. Donor support for small states and other EMDEs**

_Reflecting their significant development challenges, small states receive substantially more official development assistance, in relation to GDP, than other EMDEs. Larger shares of small states are grant-reliant and eligible to access IDA financing than other EMDEs._

## **A. Official development assistance, 2011-23**

## **B. Shares of grant-reliant and IDA-eligible countries**

<!-- Start of picture text --> Percent of GDP Percent<br>16 70<br>Small states EMDE excl. small states<br>14 60<br>12<br>50<br>10<br>40<br>8<br>6 30<br>4<br>20<br>2<br>10<br>0<br>Small states EMDEs excl. small 0<br>states Grant-reliant IDA eligible<br><!-- End of picture text --> _Sources:_ WDI (database); World Bank.

_Note:_ EMDEs = emerging market and developing economies; IDA = International Development Association.

A. Average official development assistance as a percent of GDP. Sample includes 28 small states and 104 EMDEs excluding small states.

B. Share of countries that are grant reliant, and those that are classified as IDA eligible as of 2023, as detailed in table 1.

48
||
|_C_|3.15|1.66|-2.20|_C_|1.31|1.05|-0.74|
|_P_|2.15|0.66|-4.06|_P_|0.31|0.05|-5.95|
|_FGT0_|0.69|0.47|-1.34|_FGT0_|0.38|0.08|-5.20|
|_FGT1_|0.45|0.21|-2.56|_FGT1_|0.14|0.03|-5.84|
|_FGT2_|0.32|0.12|-3.36|_FGT2_|0.07|0.01|-6.06|
|Watts|0.85|0.34|-3.18<br>|<sup>Watts</sup>|0.20|0.04|-5.91|

**Notes:** This table reports the global estimates of W(y, z), I(y, y̅), C(y, z), and P(y, z) and selected comparators. The welfare measure, W, is reported using the $6.85 per person per day threshold in 2017 $PPP (Panel A). C and P are reported for the $6.85 and $2.15 thresholds (per person per day in 2017 $PPP). The _Avg Annual Change_ column reports the average annual log differences. Abbreviations: Aε – Atkinson Social Welfare Index with ε= 1 or 2; FGT – Foster-Greer-Thorbecke poverty measure with α= 0, 1, or 2; AIε  – Atkinson Inequality Index with ε= 1 or 2.

17
The three sectors, especially garment and footwear, are also dominated by women workers with three quarters of total workers being women in the garment and footwear sectors and 53 percent of total workers being women in textiles.

It is also interesting to note that most workers are in the large scale companies (Table 3). Of course the industry survey does not include the very small scale and cottage industries (defined as employing less than 20 workers). Thus, in the formal sector most workers are in the medium-large companies.

The growth of women workers in the industrial sector has a number of implications. Firstly, it means the participation of women in paid activities results in an increased strengthening of the women's position in the family and in society due to their increased financial capacity. This in turn contributes to the development of the economy. On the negative side, the participation of women in the work force corresponds with a marked deterioration in working conditions, the emergence of more low-skilled jobs and an inadequate opportunity for
23 Decree 43 yet. Instead the question asked whether the facility had experienced different levels of income growth due to Decree 43, with codes from zero to one or missing. To identify whether Decree 43 had been implemented, we considered any positive number to indicate implementation. In addition, hospitals with zero or missing information were coded according to whether any previous year data indicated that the facility had been autonomized.

Some inconsistencies existed in the way autonomization was reported between years of the hospital inventory, and between the inventory and the data gathered from provinces. We attribute these differences to some ambiguity in how questions about autonomization were asked or how they were understood. If a facility was asked about implementation of Decree 43, they may have indicated their timing starting in 2006 or later, even if they had implemented Decree 10 earlier. Therefore we took the earliest year that autonomization was reported for any given facility between the two datasets as the starting point for autonomization in that facility and all later years were coded as autonomized.

# Outcome variables

The hospital inventory data contains data on inpatient admissions, average length of stay, and outpatient visits, as well as the number of beds – from these we can compute the number of inpatient days and the bed-occupancy rate. The inventory also includes data on revenues and costs; these were much messier, and apparently reflected inconsistent data entry across years for a given hospital, and a departure from the instructions within a year for a given hospital. Substantial efforts went into the cleaning of the financial data. In the end we constructed total revenue from its components having first cleaned them carefully. The components we included are: user fee (i.e. out-of-pocket) revenues; reimbursements from the health insurance agency (these include reimbursements associated with coverage provided by the government for children under the age of six and poor and otherwise disadvantaged
classes completed and ability through linear regressions, lack of effort measures might bias the estimates. We investigate this issue in Table 8. We begin with the regression of log classes completed, ln( _xt_ ) _,_ on log ability using the observed data (column 1) and the simulated baseline (column 2). The coefficients on log ability in columns (1) and (2) are very close, which is additional evidence of our good fit. In both cases, log ability explains about 20 percent of the variation in log classes completed.

**Table 8: Classes Completed Per Year.**

||Actual data|Si|mulated Dat|a|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|ln(ability)|0_._166<sup>_∗∗∗_</sup>|0_._156<sup>_∗∗∗_</sup>|0_._090<sup>_∗∗∗_</sup>|0_._085<sup>_∗∗∗_</sup>|
||(0_._015)|(0_._005)|(0_._005)|(0_._000)|
|ln(efort)|||0_._854<sup>_∗∗∗_</sup>|0_._915<sup>_∗∗∗_</sup>|
||||(0_._004)|(0_._000)|
|ln(shock to classes completed)||||1_._000<sup>_∗∗∗_</sup><br>(0_._000)|
|Constant|2_._060<sup>_∗∗∗_</sup>|2_._748<sup>_
_(i) Prioritizing direct reporting_ . Rather than linking indirect reporting to funding conditionalities, MDBs could introduce incentives to improve direct disclosure.<sup>4344</sup> Public and direct access to these records would be a strong accountability mechanism. As shown in Section 2, citizens in several countries have to rely on indirect sources to get a sense of their own government’s debt portfolio. Solid and accessible direct databases would also improve efficiency for exchanging data with external agents. Finally, future debt restructurings can have a stronger and long-lasting impact on debt transparency if the main players (e.g., borrowing country, Paris Club, IMF/WB, etc.) commit to ensuring that the necessary data reconciliation process with creditors is reflected in direct reports.

(ii) _Targeting a core of global, achievable, and enforceable direct reporting standards_ . As shown in Section 3, few developing countries fully comply with standards and definitions (e.g., nominal value rather than face value; external debt by residency; total public sector debt coverage). This paper shows that requiring high standards without consideration of local limitations, leads to divergent levels of compliance which perpetuates cross-country comparability issues and distrust over debt data. In this context, the focus on direct reporting should be supported by _interim_ standards aiming at consolidating debt statistics based on simplified definitions (i.e., external debt by currency; face value) and achievable coverage levels which should be clearly indicated (from central government to general government to public sector). This needs to happen in conjunction with building DMOs’ capacity and developing technical solutions to foster disclosure under best international standards.

(iii) _Standardizing and coordinating templates among external agents_ . Ongoing indirect reporting efforts to expand sectoral or instrument coverage, as in the LIC-DSA framework, should be encouraged. External agents should also seek to build their work on direct sources, rather than on multiple _ad-hoc_ data requests, which overburden DMOs. This would reinforce the incentives for stronger direct reporting.
schools)<sup>9</sup> to 12,924 schools in 2011 (14.5 percent of state schools and 4.4 percent of municipal schools).

The aim of this paper is to evaluate the short- and medium-term impacts of _Mais Educação_ on school outcomes. We exploit publically available data from _Prova Brasil_ and from the annual Brazilian school census. We also exploit administrative data on program participation provided by MEC. Because the program was rolled out over time, we compare the average educational outcomes of students in participating and in nonparticipating schools between 2007 (before the program was implemented) and 2011 (when the program reached a total of 6.2 percent of Brazilian public schools).

The main challenge, however, is that participating schools, and their students, are likely to have different abilities and come from different socioeconomic backgrounds and thus have different outcomes than nonparticipating schools. Actually, our evidence shows that, on average, participating schools had worse outcomes than schools not participating in 2007. This is partly a consequence of the selection criteria into the program: Targeting is aimed at schools with a low _Índice de Desenvolvimento da Educação Básica_ (IDEB) and/or located in areas of social vulnerability, and within schools at students with learning deficits. Hence, to assess the impact of the program we cannot simply compare participating and nonparticipating schools. Instead, we compare the performance over time in participating schools and nonparticipating schools that are as similar as possible to participating schools both in observable and unobservable characteristics.

We exploit propensity score matching to select schools that do not participate in _Mais Educação_ and ensure that they are very similar to treatment schools. We use this method to attempt ultimately to control for the selection bias by selecting a control group of schools that are very similar in a wide set of observable characteristics to those participating in _Mais Educação_ . The differences-in-differences estimator (comparing the changes in outcomes for the same schools before and after the program) allows us to control for unobserved characteristics of schools,
<mark>Dieleman, M., Cuong, P. V., Anh, L. V., & Martineau, T. (2003). Identifying factors for job motivation of rural health workers in North Viet Nam.</mark> _<mark>Human resources for health</mark>_ <mark>,</mark> _<mark>1</mark>_ <mark>(1), 1-10</mark> Doctor, J. N., Nguyen, A., Lev, R., Lucas, J., Knight, T., Zhao, H., & Menchine, M. (2018). Opioid prescribing decreases after learning of a patient’s fatal overdose. Science, 361(6402), 588-590.

Dupas, P. (2011). Health behavior in developing countries. Annu. Rev. Econ., 3(1), 425-449.

<mark>East, K. A., Hitchman, S. C., McDermott, M., McNeill, A., Herbeć, A., Tountas, Y., ... & Vardavas, C. I. (2018). Social norms towards smoking and electronic cigarettes among adult smokers in seven European Countries: findings from the EUREST-PLUS ITC Europe Surveys.</mark> _<mark>Tobacco Induced Diseases</mark>_ <mark>,</mark> _<mark>16</mark>_ <mark>East, K. A., Hitchman, S. C., McNeill, A., Thrasher, J. F., & Hammond, D. (2019). Social norms towards smoking and vaping and associations with product use among youth in England, Canada, and the US.</mark> _<mark>Drug and Alcohol Dependence</mark>_ <mark>,</mark> _<mark>205</mark
migrants to rural areas the stochastic dominance was already present at baseline and gets exacerbated by the end line (except for consumption per capita values to the far right of the distribution, where one would unlikely want to place a poverty line). There is a clear urban gradient to poverty, just as in Ferre et al. (2012) and Lanjouw and Marra (2018).

# **2.3 Census**

The KHDS survey informs us on the plight of the average Kagera migrant at the different destinations. This section considers the general population characteristics across the whole of Tanzania. In 2002, about halfway between the baseline and endline KHDS rounds, the Tanzania Population and Housing Census was conducted. The Integrated Public Use Microdata Series (IPUMS) provides access to the long-form census data on over 3.7 million individuals in the country, which can be disaggregated by district, and within each district by rural and urban areas, giving a total of 254 geographical areas to potentially consider (Minnesota Population Center, 2014).

The long-form sample contains basic census information, such as the age and sex of all members, from which we can derive household size and a dependency ratio. Information on housing characteristics includes the number of bedrooms in the dwelling and the materials out of which the roof, walls and floors are made. Further details on utilities allow us to calculate who has electricity, piped water and a flush toilet. We further know about asset ownership. For each individual there is information on their employment status and the industry or sector they work in. The location means for each variable are presented in Table 3.

Many of the variables collected reflect the wealth of the household. Lacking weights, such as prices, to meaningfully add up the various wealth components available in our data, we reduce the dimensionality of our wealth data by extracting the first principal component of a number of housing, utility and asset outcomes of sampled households.

We use the following variables for the principal component analysis: number of