rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 9334

# **Abstract**

To aid national and international efforts to support countries in enhancing their pandemic preparedness in the face of COVID-19, this paper draws from the World Bank’s Service Delivery Indicator surveys to highlight key aspects of health service preparedness in Kenya, Sierra Leone, and Tanzania. The results of this analysis paint a highly variable picture. At least 10 percent of lower-level facilities in all three countries have inpatient care capacity, suggesting that these lower-level facilities could help absorb surges in patient flow. Less than half of the facilities in the three countries have a fixed or mobile phone and less than a third have internet access, suggesting a likely challenge in communication and timely sharing of essential information. Concerningly, less than half of the facilities have appropriate handwashing facilities for patients, with even lower rates in rural areas. Between 80 and 95 percent of the facilities have a thermometer to diagnose fever, but availability of a thermometer, stethoscope, and blood pressure cuff together is variable (ranging from almost 90 percent of the facilities in Tanzania to less than 65 percent in Sierra Leone). The paper concludes by highlighting key innovations for future surveys to improve measurement of pandemic preparedness.

This paper is a product of the Office of the Chief Economist, Human Development 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 jsharma3@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
and γj allows for region-specific intercepts.

The variables contained in the vector Zi come from administrative and survey data. The 2014 Census provides information on Districts’ population and urbanization rates, while poverty rates are estimates for 2013 (World Bank, 2016b)<sup>21</sup> . We complement these socioeconomic data with average students’ score at the UCE in 2011, a nationally administered exam students take at the end of lower secondary school, as the best available proxy for education in the population.<sup>22</sup> Our survey also gathered data on the population of radio stations operating in Uganda at the time of the survey, from which we derive a variable of the number of radio stations headquartered in a district. Access to radio, and to media in a more general sense, has been shown to impact the public scrutiny of local politicians (Ferraz and Finan, 2008) and affect citizens’ investment in health and education (Khemani and Keefer, 2015; Khemani and Keefer, 2014). We also consider that the existence of local radio stations might proxy for other unobservables such as the dynamism of local markets.

In addition, we compile data on local vote shares from the 2011 elections, the last one before our survey. The main variables are the vote share of the presidential candidate of the NRM, the national ruling party (labeled "Voter Attachment to National Party"), at the district level, and voting Herfindhal Indices<sup>23</sup> for district politicians<sup>24</sup> .

The lower panel in Table 4 presents descriptive statistics of administrative data for the 75 Districts included in the survey. The median District in our sample has approximately 250,000 people and is overwhelmingly rural<sup>25</sup> . Poverty rates, at 23 percent in the median District, are only slightly higher than the national rate of 20 percent in 2013. The numbers also show the subdued level of political competition in presidential
> - 36 -

# 7. Ecopomic Assessment of VTE at the National Level

We begin the economic assessment of VTE in C8te d'Ivoire by presenting results of multivariate analysis at the national level. The next two sections of the paper will add regional and sectoral dimensions. We discuss first the effect of VTE on earninas (sec. 7.1) and on access to different kinds of work (sec. 7.2). Next, the structure of private and social costs associated with VTE is presented (sec. 7.3). This leads to the calculation of the interrial rate of return to VTE (sec. 7.4). Lastly, we discuss briefly the earnings of the self-employed (sec. 7.5), and summarize the main findings (sec. 7.6).

# 7.1 Earnings Analysis

The CILSS data allow the construction of two earnings measures:

-

- (1) Wi basic pre-tax cash earnings from the main job.

- (2) W2 - total pre-tax earnings from the main job - WI + bonus/commissions + wages-in-kind (food, housing, clothing, transport, other) from the main job.12/

- 12/ Respondents to wage questions in the CILSS could report either pre-tax -

- or post-tax income a follow-up question identified which they had reported. Unfortunately, information on taxes paid is only available at the household level. We pro-rated these taxes to reported individual incomes and added the amount of tax to incomes reported post-tax, so as to put all earnings figures on a comparable pre-tax basis. Some editing of the earnings data was done to recode outlying values. This involved less than 1X of the cases.
# **2.3 Health Calendar**

We collected daily information on symptoms, health consultations, and medications taken in weekly visits over the course of nine weeks from the child’s primary caretaker (usually the mother). Caretakers could use pictorial diaries (Figure B.1 in the online appendix) to mark off symptoms and health-related events. The surveyor then reconstructed the child’s health history during the visit. There was a concern that parents would respond to the observation by the surveyors, for example by watching their children more closely. Ultimately this drawback was outweighed by the benefits of detailed coverage without the problems associated with long recall periods (Das et al. (2011)). The surveyors were carefully instructed not to comment on the child’s health, and to emphasize that they had no medical qualification. While we cannot rule out all Hawthorne effects, we believe our precautions mitigated them, combined with the fact that the calendar data collection had been piloted over six weeks after the baseline in 2012, thus creating some habituation.<sup>11</sup>

**Symptom Calendar.** The list of symptoms was designed in collaboration with Mali Health staff and based on the C-IMCI guidelines by the WHO (see also below). The C-IMCI is designed for use by CHW who have no prior medical experience. It consists of simple rule charts that use important and easy-to-spot symptoms to broadly classify a child’s illness and establish need for care. These properties mean we can collect symptom reports from mothers and surveyors who are not medically trained. The survey explicitly asks about convulsions, fits, or spasms; lethargy or unconsciousness; inability to drink; vomiting; coughing; difficulty breathing; more than three loose stools; blood in the stool; sunken eyes; and unusually hot (cold) skin. We constructed four more symptoms from the “other” category: skin conditions (e.g. spots, rash, itch), cold symptoms, ear pain,
workers with secondary education and workers with primary education, which was stable during the 1990s and then narrowed after 2002.

Both panels of figure 10 plot the estimated variations in the returns to schooling for the 17 countries in Latin America, plus Russia, South Africa, Turkey, and the United States. In particular, the left panel shows the annual changes in the country-specific labor earnings premiums of workers with secondary education relative to workers with primary or less education, and the right panel shows the annual changes in the country-specific labor earnings premiums of college-educated workers relative to workers with primary or less education. The variations have been truncated at 20 percent in absolute terms to control for outliers.

Figure 10 Annual growth rate of the education premium and labor income inequality, selected countries, 1990–2003 and 2003–10 _Sources:_ Seventeen Latin American countries: SEDLAC database; Russia: the Russia Longitudinal Monitoring Survey; Post Apartheid Labor Market Series: South Africa; Turkey: I2D2-LFS; the United States: I2D2-IPUMS. See annexes A.1 and A.2 for details. _Note:_ The underlying data represent the change in wage inequality and the change in the earnings premiums for each country and circa period. The years selected for each country-circa combination may be different depending on survey availability and to assure, to the extent possible, within-country comparability for each period; see annex B.1 for details. In the case of the y-axis, the growth rate of the education premium is plotted. For the x-axis, the growth rate of the labor income Gini is plotted. The educational categories—college, high school, and primary education or less (reference category)—follow country-specific education systems; see section 3 and equation 1 for details. The sample covers full time, wage, and self-employed workers 15–64 years of age. The values of the 1st and 100th percentiles of the earnings distribution were trimmed by each gender-education cell. The size of the bubbles represent the population of the country.

Our results show that
’s estimations incorporate years of schooling as a proxy for human capital in order to control for its effect on productivity growth per worker. Years of schooling are also interacted with the working age population share in order to capture information related to quality of labor supply.

Since the demographic determinants of growth may also affect savings and poverty, we simply replace the GDP per capita growth dependent variable with changes in the domestic savings as a share of GDP and poverty rate, in order to analyze the effects of demographic change on these on savings and poverty.

# **4. Data, trends, and descriptive statistics**

Several data sources covering the 1950-2010 period are combined in order to analyze the effect of demographic change on growth per capita and savings. First, the UN World Population Prospects 2015 Revision is used to provide cross-country information on population by different age groups. We use information on GDP per capita from the World Bank (WDI) and the Penn World Table (version 8.1). We also use average years of schooling by country, provided by Barro and Lee

> 15 Murtin (2013) suggests that increasing access to primary education leads to a reduction in the fertility rate.

11
exports of green goods, we assess the relationship between the imports and exports of green goods in the labor markets. This is done using trade data and labor force survey data at the industry level for the Philippines.

The paper finds that the Philippines’ share of green goods trade is relatively low, partly due to the high incidence of NTMs on imports of green goods. The paper finds 5 measures out of about 90 as reform candidates, which are costly and have a higher incidence on energy transition and other types of green goods and technologies. These measures are Labelling requirements (B31), Non-automatic import-licensing procedures other than authorizations covered unde (E1), Technical barriers to trade regulations on transport and storage (B42), Testing requirements (B82) and Traceability requirements not elsewhere specified (B859). These measures are reform candidates, where the underlying regulations are recommended to be reviewed.

As these reforms would increase the imports and exports of green goods, especially since green goods imports are linked with higher green good exports, we also assess the possible effect on workers. We find that higher green goods exports negatively correlates with the share of high-skilled workers within industries. In contrast, green goods imports are associated with an increase in the share of female workers within industries. Green goods imports are positively correlated with workers’ earnings across industries but not within industries. Overall, NTM reforms likely lead to labor reallocations across industries. Since we do not address endogeneity issues, these results on labor do not necessarily reflect causality but provide insight into possible relationships and the need to address potential adverse effects on affected workers when reforming trade policy in line with environmental policies.

31
claims. Since sickness spells can and do frequently end without treatment, selection bias is pervasive.<sup>6</sup> For example, we show that children with the subsidy who visit the clinic have been actually sick weakly _longer_ , because the treatment succeeds at bringing children to the clinic whose parents tend to wait longer before seeking care.<sup>7</sup> In a companion paper (Dean and Sautmann, 2023) we report data from a follow up survey conducted in 2014 (see above). Unlike the 2013 data we use here, the 2014 survey did not include detailed health diary data, and so cannot be used to study healthcare demand conditional on health state, the central point of this paper. Instead, it focused on measures of health knowledge, use of preventive care, evidence of malnutrition, and diarrhea incidence and care. Using this broader set of measures, Dean and Sautmann (2023) do identify positive CHW effects, including complementary effects of CHW and subsidies, as we discuss in section 5.3.

Section 2 describes the policy environment and the RCT intervention and survey design. Section 3 discusses our use of the IMCI measures. Section 4 introduces a framework for demand for curative care and the effects of subsidy and information policies and section 5 describes the empirical results. Section 6 concludes with a brief discussion of policy implications.

# **2 Study Background and Data Collection**

The Malian health care system builds on a network of community health clinics or _centres de sant ́e communautaires_ (CSCOMs). A CSCOM has typically one or two trained doctors on duty and sells prescribed medications through an attached pharmacy. At the time of

> 6In most other spell data economists use, e.g. unemployment spells, there are no equivalent sources of censoring.

> 7One of the few data sets we are aware of that has panel spell data, the 1987 National Medical Expenditure Survey for the United States, includes only spells that lead to medical care use
7.574<sup>∗∗∗</sup>|7.787<sup>∗∗∗</sup>|7.511<sup>∗∗∗</sup>|7.705<sup>∗∗∗</sup>|7.585<sup>∗∗∗</sup>|7.792<sup>∗∗∗</sup>|
||(0.360)|(0.507)|(0.366)|(0.500)|(0.352)|(0.502)|
|Controls|No|Yes|No|Yes|No|Yes|
|Province FE|No|Yes|No|Yes|No|Yes|
|Observations|969|958|969|958|969|958|
|_R_<sup>2</sup>|0.368|0.413|0.369|0.413|0.366|0.411|
|F-test (η1 =−β1) _p_-value|.6|.84|.57|.84|.27|.42|
|F-test (η2 =−β2) _p_-value|.48|.66|.81|.96|.6|.48|
|F-test(η3 =−β3) _p_-value|.93|.78|.90|.81|.96|.84|

_Notes:_ Authors’ analysis based on data described in the text. In this table, we compare the three definitions of demobilization, with and without controls and province fixed effects. The table reports OLS estimates. The unit of observation is a household. Total consumption is the sum of consumption expenditures and consumption taken from households’ stocks per adult equivalent. In column (1) and (2), _Di_ only included ex-combatants cross-checked with official registers. In column (3) and (4),
call. The operators introduced themselves as surveyors working on behalf of researchers from UCLA and asked participants if they were willing to participate in a short survey about their economic expectations. If operators were unable to reach a respondent on the first attempt, they were instructed to make at least one further attempt at a later time.

## **4.3 Information Experiment**

We integrated our experiment into this credit card customer survey administered by our partner bank. The survey instrument, which is available in C, can be divided into five parts: (i) collecting baseline information, (ii) eliciting prior beliefs, (iii) providing information to a random subset 10
# **3. THE PENSION SPENDING EFFECT OF HIGH WAGE AND PENSION CLAIMS**

A key to the 1991-92 budget deficits and subsequent increases in the tax wedge can be sought in pension expenditures (this section), possibly adding other income-related expenditures (section 4). Because of the entitlement nature of the social security system, it cannot be disconnected from the evolution of wages. There can also be, and there were, spending pressures coming from autonomous changes in pension claims of the population.

The first link between pensions and wages is through labor shedding. Higher wages imply less employment for a given labor demand. First, enterprises and workers try to avoid putting too many people on the dole, instead exploiting first the pension system. Second, people who enter the pension system with a higher reference wage earn higher pensions. Third, pensions and government wages are, by Polish law, indexed to the wages in enterprises. Fourth, high wages attract new entrants into the labor force, most of whom first spend some time as registered benefit-earning unemployed.

Table 4 shows counterfactual pension spending data to quantify the effects of the main determinants of pension spending. Line 1 of the counterfactual ZUS expenditure eliminates the effect of the personal income tax on pensions from 1992 onward. The two main causes of the rising net pension expenditure are the number of pensioners (line 2) and average pensions (lines 3, 4 and 5).

# 3.1. The pensions of 1989 pensioners

The counterfactuals can be read horizontally or vertically. Horizontal comparisons are made according to the 1989 benchmark. Vertical comparisons show the effect of the various correction coefficients in a given year. It is useful to start from net pension spending (line 1). Line 2 then gives 15
The key explanatory variables in our analysis are indicators of financial structure that are drawn from the World Bank Database on Financial Development and Structure, which was updated in November 2010.<sup>7</sup> We rely on two primary variables that are intended to capture different aspects of each country‘s financial structure – the ratio of private credit to GDP and the ratio of stock market capitalization to GDP. From previous research on the finance-growth nexus, we would expect growth in employment to be positively linked to both of these measures. However, the new structural economics predicts that the association between firm growth and private credit relative to GDP, which is a bank-based measure of financial development, should be stronger for countries in early stages of development. By contrast, the association between employment growth and stock market capitalization (relative to GDP) is likely to be stronger for countries in more advanced stages of economic development.

Another aspect of financial structure that could impact firm growth is the nature of ownership—the extent to which the banking sector is state-owned or foreign. As we shall later show, however, neither state nor foreign ownership of banks explains variation in labor growth in our sample. The ratios of private credit to GDP and stock market capitalization to GDP are therefore the focus of the empirical analysis that follows.<sup>8</sup> Perhaps the first thing to notice from Table 2, which provides summary statistics for our four indicators of financial development, is that country coverage is broader for the private credit 7 More detailed descriptions of the dataset can be found in Beck, Demirguc-Kunt, and Levine (2000) and Beck and Demirguc-Kunt (2009). The permanent URL for accessing the dataset is: http://go.worldbank.org/X23UD9QUX0. 8 In banking systems characterized by a high degree of ownership by the government, banks‘ credit growth, portfolio quality, profitability, and productivity tend to suffer (Barth, Caprio, and Levine 2001; 2004; La Porta,
---|---|---|
|Count of Non-Adult Dependents in Household|-0.07|(0.18)|[1.00]|3.30|(2.02)|1,422|
|Student Child School Attendance in Household (=1)|0.18|(0.12)|[1.00]|0.51|(0.50)|760|
|Student Child School Attendance in Household (0-5)|-0.13|(0.30)|[1.00]|3.80|(1.29)|760|
|Child Hunger in Household (Days Last Week)|0.12|(0.18)|[1.00]|0.47|(1.15)|1,269|
|Any Child Vaccinated (=1)|0.09|(0.12)|[1.00]|0.84|(0.37)|509|

**_Notes_** :

- The table shows the regression results on pre-specified household-level child outcomes using the 2021 in-person data. Each row is its own dependent variable.

- The outcomes that require definitions are: Child Hunger is defined as the number of days in the last 7 days on which any children in the household had to go to sleep hungry because there was not enough food.

- The independent variable of interest is the TOT treatment indicator, which is the predicted value from a first-stage regression of treatment implementation on treatment assignment.

- The regressions also have assessment month-by-year fixed effects, enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program, baseline number of children, baseline number of children plus adults, respondent gender, and respondent age). In the parentheses are robust standard errors clustered at the locality level. Regressions are weighted by
reports the regression of the natural logarithm of hourly earnings<sup>9</sup> on a range of HE operators‟ characteristics (using this specification means that the coefficients can be interpreted as the percentage effect on earnings).

**Figure 3.3:   Normalized Earnings by Employment Type and Gender, 2006**

<!-- Start of picture text -->
1.5<br>1.25<br>1<br>0.75<br>Male<br>0.5 Female<br>0.25<br>0<br>Public wage non- Employer Private wage  Household  Wage agriculture<br>agr. non-agr. enterprise non<br>agr.<br><!-- End of picture text -->

_Note: Reference category = public wage employment, non-agriculture_ _Source: Calculations based on the ILFS 2006 data._ 31. Not surprisingly, education is found to have a strong effect on earnings, although there are clear diminishing returns. HE operators without complete primary education are clearly disadvantaged.<sup>10</sup> For example, for a male HE operator, completing primary education increases earnings by 23 percent at the mean, but completing ordinary secondary education adds only one percent more. This is not surprising, as few HE operators have reached this level of education. The returns to highest levels of education are significantly greater, but very few HE operators have education beyond primary school (less than one percent has gone beyond ordinary secondary). Age (a proxy for experience) adds much more to the earnings of male HE operators than of female HE operators. Migration status, however, has no effect on earnings.

> 9 Hourly earnings are obtained by taking the net earnings of enterprises divided by the hours reported by all those who worked in these enterprises.  One concern regarding the measure of hourly earnings for the self-employed is that earnings are qualitatively different from wages, i.e.,