rafmacalaba/data-use-mentions-extended huggingface dataset:
# WPS3645

## **Efficiency of Public Spending in Developing Countries: An Efficiency Frontier Approach**<sup>**1**</sup>

Santiago Herrera Gaobo Pang

## _Abstract_

_Governments of developing countries typically spend between 15 and 30 percent of GDP. Hence, small changes in the efficiency of public spending could have a major impact on GDP and on the attainment of the government’s objectives. The first challenge that stakeholders face is measuring efficiency. This paper attempts such quantification and has two major parts. The first one estimates efficiency as the distance between observed input-output combinations and an efficiency frontier (defined as the maximum attainable output for a given level of inputs). This frontier is estimated for several health and education output indicators by means of the Free Disposable Hull (FDH) and Data Envelopment Analysis (DEA) techniques. Both input-inefficiency (excess input consumption to achieve a level of output) and output-inefficiency (output shortfall for a given level of inputs) are scored in a sample of 140 countries using data from 1996 to 2002. The second part of the paper seeks to verify empirical regularities of the crosscountry variation in efficiency. Results show that countries with higher expenditure levels register lower efficiency scores, as well as countries where the wage bill is a larger share of the government’s budget. Similarly, countries with higher ratios of public to private financing of the service provision score lower efficiency, as do countries plagued by the HIV/AIDS epidemic and those with higher income inequality. Countries with higher aid-dependency ratios also tend to score lower in efficiency, probably due to the volatility of this type of funding that impedes medium term planning and budgeting. Though no causality may be inferred from this exercise, it points at different factors to understand why some countries might need more resources than others to achieve similar educational and health outcomes._

## World Bank Policy Research Working Paper 3645, June 2005

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange
Table 3: DiD estimates for the effect of terrorist attacks against civilians in Mogadishu

||Log of core<br>consumption<br>(1)|Log of food<br>consumption<br>(2)|Poverty<br>status<br>(3)|Poverty<br>gap<br>(4)|Experienced<br>hunger<br>(5)|Police<br>competence<br>(6)|
|---|---|---|---|---|---|---|
|Diff-in-diff<br>coefficient (β)|-0.326***<br>(0.118)|-0.415**<br>(0.159)|1.617***<br>(0.597)|0.115***<br>(0.039)|0.813<br>(0.659)|-0.881**<br>(0.365)|
|Wave|Yes|Yes|Yes|Yes|Yes|Yes|
|Exposed/control|Yes|Yes|Yes|Yes|Yes|Yes|
|District fixed<br>effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Characteristics of<br>household & head|Yes|Yes|Yes|Yes|Yes|Yes|
|Dwelling<br>characteristics|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1,532|1,532|1,532|1,532|1,516|1,498|

Source: Authors’ calculations based on data from the SHFS and ACLED. Note: Estimated coefficients from an OLS or probit regression. Characteristics of household refer to size and receiving remittances, while those from household head refer to age,
<!-- Start of picture text --> 10056<br>BO 7a<br>7a<br>aot rit)<br>Parents who would<br>6076 a7 Parents who would — transfer their child to<br>409% keep their child in private school if free<br>public school<br>ars 30%<br>40%<br>31%<br>26%<br>20% 18% ish<br>am 8% 7 a<br>on<br>CSEE pas rates cChildcanstilllve Motivated Smaldassies Schodlreputation Adequate Located near the Low school fees<br>in the village teachers Materials hore:<br><!-- End of picture text -->
6 -4 -2 0 2 4<br>Georgia Ghana Kenya<br>-6 -4 -2 0 2 4 -6 -4 -2 0 2 4 -6 -4 -2 0 2 4<br>Serbia Ukraine Vietnam<br>-6 -4 -2 0 2 4 -6 -4 -2 0 2 4 -6 -4 -2 0 2 4<br>Males Females<br>Kernel densities of standardized scores<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>0 0 0<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>0 0 0<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>0 0 0<br><!-- End of picture text --> <!-- Start of picture text --> Conscientiousness (Z score)'<br>Males and females aged 25-54<br>Armenia Bolivia Colombia<br>-6 -4 -2 0 2 4 -6 -4 -2 0 2 4 -6 -4 -2 0 2 4<br>Georgia Ghana Kenya<br>-6 -4 -2 0 2 4 -6 -4 -2 0 2 4 -6 -4 -2 0 2 4<br>Serbia Ukraine Vietnam<br>-6 -4 -2 0 2 4 -6 -4 -2 0 2 4 -6 -4 -2 0 2 4<br>Males Females<br>Kernel densities of standardized scores<br>.4
as defined in table G1. The regressions have enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), refugee household-level controls (shelter program, baseline number of children, baseline number of children plus adults), the Marlowe–Crowne scale, and respondent gender, education, and age. In the parentheses are robust standard errors clustered at the locality level. Statistical significance represented by * (10%), ** (5%), and *** (1%).

Table (G17) Jordanian Neighbor Primary Outcomes Heterogeneity by: Age Group 18-25

||(1)<br>Social Attitudes<br>and Perceptions|(2)<br>Economic Attitudes<br>and Perceptions|(3)<br>PolicyPreferences|(4)<br>Altruism to Syrians|
|---|---|---|---|---|
|Treat|-0.268*|0.119|0.183|0.134|
||(0.138)|(0.168)|(0.165)|(0.183)|
|Treat*Neighbor 18-25|-0.716|-1.280*|-0.208|0.927|
||(0.624)|(0.713)|(0.525)|(1.241)|
|Neighbor 18-25|0.094|0.153|0.181|0.169|
||(0.130)|(0.150)|(0.116)|(0.213)|
|p-value: T + T*Het|0.11|0.09|0.96|0.38|
|Control Mean|0.09|0.09|0.09|0.09|
|Control SD|0.29|0.29|
nzaa168.

Horton, Sue, and Richard H. Steckel. 2013. “Malnutrition: Global Economic Losses Attributable to Malnutrition 1900–2000 and Projections to 2050.” In _How Much Have Global Problems Cost the World?_ , edited by B. Lomborg, 247–72. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9781139225793.010.

- International Agency for Research on Cancer (IARC). 2018. _Red Meat and Processed Meat IARC Monographs on the Evaluation of Carcinogenic Risks to Humans_ . _IARC Monographs on the Evaluation of Carcinogenic Risks to Humans_ . Vol. 114.

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urbanism concepts are prioritized, in line with a greater awareness of vulnerability in cities; as well as a scenario in which the virtualization of work leads to a decline of the urban center and of the attractor areas of forced mobility.

# **3. Methodology**

Mobile phone data refers to anonymized Call Detail Record (CDR) data (metadata), observed for every call/text made or received with timestamp, with GPS coordinates of the tower from where the call is made. Such data allows measuring mobility across entire cities or even countries at very fine spatial and temporal levels. Example outputs from mobile data that are relevant for understanding the changes in urban mobility introduced by the COVID-19 pandemic include: (i) dynamic population mapping – _How many people are at_ 3
Policy Research Working Paper 6421

# **Abstract**

This paper investigates the differential impacts of foreign ownership on wages for different types of workers (in terms of educational background and gender) in Vietnam using the _Vietnam Household Living Standards Surveys_ of 2002 and 2004. Whereas most previous studies have compared wage levels between foreign and domestic sectors using firm-level data (thus excluding the informal sector), one advantage of using the Living Standards Surveys in this paper is that the data allow wage comparison analyses to extend to the informal wage sector. A series of Mincerian earnings equations and worker-specific fixed effects models are estimated. Several findings emerge. First, foreign firms pay higher wages relative to their domestic counterparts after controlling for workers’ personal characteristics. Second, the higher the individual workers’ levels of education, the larger on average are the wage premiums for those who work for foreign firms. Third, longer hours of work in foreign firm jobs relative to working in the informal wage sector are an important component of the wage premium. Finally, unskilled women experience a larger foreign wage premium than unskilled men, reflecting the low earning opportunities for women and a higher gender gap in the informal wage sector.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at efukase@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.
||in both the public and private systems, but we cannot<br>analyze expenditure differences between individuals<br>(Households with at least one member going to public<br>schools and another to private school include some<br>10.5% of all households with at least one member in<br>public school under Scenario1, and 17.1% under<br>Scenario 2).||
|---|---|---|
|4|Even after these assumptions there are still students<br>for which we do not have schooling information for<br>2009.<br>(2.5% of subjects age 7-31 do not have schooling<br>information for 2009)|**Main assumption**: All subjects for whom we do not have<br>complete information about schooling, school level or type<br>of school are assumed not to be attending school.|
|5|Official data on per-student public expenditure for<br>colleges reported by the Ministry of Education refers<br>only to teachers‘ colleges, excluding trade colleges,<br>technical schools, agricultural colleges or nursing and<br>paramedical schools.<br>(In 2009 combined public spending on technical<br>colleges and agricultural colleges was 3% higher than<br>spending on teachers colleges; the number of students,<br>however,is not available.)|**Main assumption:**Public expenditure per student in all<br>colleges is the same as the expenditure per student in<br>teacher colleges.|
|6|Outlier observations<br>(0.1% of households)|**Main assumption:**Expenditures on education larger than<br>the mean log-expenditure plus three times the standard<br>deviation (considering only positive values of expenditure)<br>are considered unreliable and are excluded from the
round such that the final standardized score has an overall mean of zero and standard deviation of one.<sup>15</sup> Table 2 reports regional means and standard deviations of the test scores, calculated at each step. The first (column I) are the weighted means of the raw competency tests (before any standardization); column II reports the age-standardized versions; and column III reports the final measures. As can be seen, movement from the second to the third metric constitutes a simple monotone transformation.

The test scores reported in Table 2 refer to the final sample used in the subsequent analysis, which pools all survey rounds. This is a slightly reduced sample of the original Uwezo data. Specifically, observations have been dropped that can be perfectly predicted using either household or school fixed effects – i.e., all singletons were removed. The objective of restricting the data in this way is to mitigate upward bias of the variance contribution estimates. As per the methodological discussion of Section 2.2, the analytical focus is on the variance components of the test score; and there is no evidence to suggest these dropped observations are distributed in a systematic pattern over regions or districts.<sup>16</sup> The (sample) standard deviations of the test scores, which can be directly interpreted as normalized measures of educational inequality (Van de Werfhorst and Mijs, 2010; Hanushek and Wößmann, 2006), are reported in parentheses in the table. It merits noting that the rank position of each region according to its test score variance is largely preserved, regardless of the transformation applied.

To implement the variance decomposition, the household and school indexes must be defined. The former is trivial – unique indexes are ascribed to all sibs in the same household in each year.<sup>17</sup> The school effects are less straightforward. In the present data, detailed information about the particular school each child attends is not provided. Nonetheless, we can identify the
given that the various categories of large expenditure items were treated differently (being included in some surveys but not in others). Where information is available, food items, both purchased and nonpurchased, are deflated by a cost-of-living index defined for foodstuffs in order to remove geographical and temporal variations in the cost of living. Details of deflation procedures are included in the notes below on each country The datasets are divided into five population quintiles based on monthly per capita expenditure levels derived from the above definition of total household expenditures. Each quintile contains the same number of _individuals_ and not households. As well as national quintiles, separate urban and rural quintile groups are defined by drawing households from the national quintile, depending on whether they resided in an urban or rural area. The numbers of people in these quintile groups are no longer the same and depend on the relative numbers of urban and rural households in the nationally defined quintile. Both the quantity and implicit price data (for the first six countries) are examined for outliers and households with extreme values are excluded from analysis.

All the surveys contained some missing observations. Table A2.1 provides the numbers of observations at three levels:

1. The total number of households interviewed (full sample).
%|3.3%|
|6|Probit|Shadow employees|2.8%|2.8%|2.9%|3.0%|3.1%|3.3%|
|7|Logit|Shadow workers|3.6%|3.6%|3.6%|3.7%|3.7%|3.8%|
|8|Probit|Shadow workers|3.7%|3.7%|3.7%|3.8%|3.8%|3.8%|
|Narrow|definitio|n of shadow employment|(without t|hose with o|ral agreem|ents only)–|all variab|les used in|
|<br>estima|<br>ted models|<br>|||||||
|9|Logit|Shadow employees|0.5%|0.6%|0.6%|0.7%|0.7%|0.7%|
|10|Probit|Shadow employees|0.5%|0.6%|0.7%|0.7%|0.8%|0.7%|
|11|Logit|Shadow workers|1.6%|1.6%|1.6%|1.6%|1.6%|1.5%|
|12|Probit|Shadow workers|1.6%|1.6%|1.6%|1.7%|1.6%|1.5%|

Source: Authors calculations based on LFS data 2003-2008 and CASE 2006 survey database.

The resulting general trend estimates for 2003 -2004 from probit models for shadow workers (including employers) are presented on Chart 1 to better illustrate the results obtained. Only results of the probit models are presented for the
arsenic, salinity and declined groundwater storage combined that covers 31% to 44% of the land area of Bangladesh. Given the proven public-health risk to elevated arsenic and salinity in drinking water and considerable threat to food security associated with depleted water storage, our national-scale multi-hazard groundwater risk maps have the potential to guide current practices and policies towards achieving the UN’s sustainable development goals in Bangladesh.

# **5. Conclusions**

We draw the following main conclusions from this first-ever, national-scale multi-hazard groundwater risk mapping for Bangladesh: 12
position<br>80<br>60<br>40<br>20<br>0<br>-20<br>-40<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Quarters<br><!-- End of picture text -->

# C. Government Debt Gap

## Private Consumption

## CDS

<!-- Start of picture text --> 0.3 60<br>0.2<br>40<br>0.1<br>0.0 20<br>-0.1<br>0<br>-0.2<br>-20<br>-0.3<br>Weak fiscal position Strong fiscal position<br>Weak fiscal position Strong fiscal position<br>-0.4 -40<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Quarters Quarters<br><!-- End of picture text --> Note: The panel C is based on government debt gap (deviation from group mean) as conditioning variable. Strong fiscal positions are defined as government debt in the 10th percentile and weak fiscal positions are defined as government debt in the 90th percentile in the sample. These are based on estimates from the IPVAR model, where model coefficients are conditioned on fiscal position, with government debt as a percentage of GDP is the measure of fiscal position.

22
# **Firm Characteristics Used to Study Differences in Savings-related Measures**

In addition, rich ICA data allow us a number of firm characteristics and proxies for access to finance. We use these firm characteristics to study how our five savings-related measures differ across firms. Specifically we break down our sample based on the following characteristics:

- **Firm size** (we define small as firms with fewer than 50 employees and large otherwise). About 62% of our sample is classified as small firms and only 38% are defined as medium or large.

- **Industry** (manufacturing or services).<sup>7</sup> About 81% of our sample is in manufacturing.

- **Foreign ownership** , firms with over 10% of equity owned by foreigners. In our sample 6.5% of firms are classified as foreign firms according to this definition.

- **Government ownership** , defined as 10% of more of the firm owned by the government. In our sample 3.2% of firms are classified as government owned. Over 90% of government owned firms are in a manufacturing industry.<sup>8</sup>

- **Exporter** , defined as 10% of more of the firm sales were exported directly or through a distributer. In our sample about 23% of firms are classified as exporters.

In addition, we calculate two productivity-related measures that we can use to test whether firms that are more productive also have higher cash flows or investment. We calculate **total factor productivity (TFP)** as a residual from a regression of log sales on log fixed capital and log of wages. Unfortunately this measure is only available for manufacturing firms since we don’t have total value of fixed capital for service firms. In addition, we calculate **sales per employee** as a measure of labor productivity.

As discussed above, availability of external finance is an important factor
of the countries and survey waves included in our analysis, while Figure 1 shows their geographical distribution.

As noted by Besley and Mueller (2018), there is a trade-off in using the WBES. While in some countries the information in the WBES dataset is less detailed than that found in national-level manufacturing surveys, the WBES offers several clear advantages. It provides harmonized information across a large and diverse set of countries, covers multiple sectors, and includes data on specific obstacles and market characteristics—such as informal competition—that are typically unavailable in other surveys. Moreover, given the focus of our analysis and the empirical strategy employed, which requires longitudinal firm-level information, the WBES offers better coverage than any other global dataset, including ORBIS Bureau van Dijk.<sup>8</sup> A typical firm in our sample is domestically owned and operates in the private sector. As shown in Table A2, the median firm has 22 employees and has been operating for 16 years. It mainly serves the domestic market—only 16% of firms are exporters—and generates $334,700 in annual sales and $271,800 in annual profits (in 2000 constant USD).<sup>9</sup> All variables are defined in Table C1.

**Selection, attrition, and missing values** As discussed in Section 3, our estimating strategy relies on the subset of firms with multiple observations over time, i.e., those included in the panel component of the WBES. This may raise concerns about potential selection issues affecting our estimating sample. In particular, one might worry about the selfselection of firms that are re-interviewed in subsequent survey waves, vis `a vis firms that, for various reasons, are interviewed only once. If such selection is correlated with our main

waves: 42 have two waves, 45 have three, and two have four. Across these country-waves, the sample includes 40,884 firm-year observations, corresponding to 16,608 firms observed twice and 2
18 programming conforms better to their tastes and linguistic preferences than do broadcasts from radios headquartered outside of their region (Buckley et al, 2008).

Much of the literature on media access must concern itself with the multiple sources of information that households have. In Benin, radio is essentially the only media to which most citizens have access. According to the nationally representative Afrobarometer (2005) survey of Benin, 25 percent of households own televisions. In our sample of 4200 households from northern Benin, only eight percent own a television, but 84 percent own radios. Newspapers are underdeveloped in Benin; the largest has a circulation of 7,000. They have little or no penetration in the areas surveyed here, although interviews with radio station staff indicate that radio news reports about Benin are often prepared based on reports from newspapers in the capital city, Cotonou.

# **5. Identification—the “natural experiment” in north Benin**

The central challenge in identifying media effects is the possibility that unobserved factors influence both media access and outcomes of interest, such as the provision of education services. The fragmentation of the radio market in Benin present circumstances akin to a natural experiment, giving us a different strategy to control for the endogenous placement of radio stations than in the received literature.

Previous research has relied on geological features that obstruct broadcasts from one or a few large stations as the source of identification of media impact (Strömberg, 2004; Olken, 2009; and Yanagizawa, 2009). Most debate centers on the exclusion assumption that these features have no direct effect on outcomes of interest. To the extent that topographical differences between communities with more and less access are significant, the exclusion assumption is more controversial. For example, geological obstacles that leave communities with less radio access might also render them significantly more isolated in all respects compared to communities with more radio access. The effects of radio access are then entangled with those of remoteness. Issues of
Services trade, including travel and tourism, has played an increasingly important role in the global economy. For example, since 2000, global travel and tourism revenues have nearly tripled, with the sector in 2021 accounting for 10 percent of global GDP, 30 percent of global services trade, and 10 percent of all jobs worldwide (World Bank 2020b).

Spillovers through global value chains are likely to have amplified the fall in world trade associated with the COVID-19 pandemic (Cigna, Gunnella, and Quaglietti 2022). Companies increasingly turned to digital technologies and diversified suppliers and production sites to mitigate disruptions caused by the pandemic (Saurav et al. 2020). In 2021, strains in global supply chains worsened significantly. The rapid recovery in global goods consumption from mid-2020 put acute pressure on the trade-intensive manufacturing sector. At the same time, COVID-19 outbreaks continued to disrupt production at many points along complex global value chains, creating significant obstacles to final goods production. COVID-19 outbreaks have also shut down some key port facilities, disrupting ocean shipping and air freight and leading to an unprecedented lengthening of supplier delivery times (figure 4). Regression analysis that controls for the effect of demand conditions suggests that global trade could have been 3.5 percent higher in 2021 had it not been for supply chain strains (figure 4).<sup>5</sup> In 2022, these supply 5 The impact of supply bottlenecks is estimated in an ordinary least squares regression of global trade on the manufacturing PMI for new export orders, the manufacturing PMI for supplier delivery times (a