rafmacalaba/data-use-mentions-extended huggingface dataset:
(2). Finally, the point estimates and their corresponding standard errors are obtained by computing respectively the average and the standard deviation over _R_ imputed poverty rates. Note that the standard errors of the poverty estimates are driven both by model error and idiosyncratic error. The contribution of the idiosyncratic error (the area- and the household error) will tend to zero as the size of the target population of a small area (in the population census) tends to infinity. Similarly, the contribution of the model error tends to zero when the sample size of the survey (which is used to estimate the model parameters) tends to infinity.

# **2.2 Remote-sensing-based small area estimation of poverty**

Relying on remote sensing data to estimate small area poverty rates alters the dimensions of the model with which we work. The unit of observation will be a geographic unit, think of a small administrative unit such as a village. The dependent variable in this case will be village level poverty rather than household level consumption expenditure. Furthermore, restricting the set of the covariates that can be derived from publicly available remote sensing data could conceivably increase the magnitude as well as the spatial correlation structure of the error term. Adopting a spatial error model will help fit this spatial structure (e.g. Anselin, 2001).

We use a spatial error model (henceforward SEM) that assumes iid normal shocks to local poverty spilling over to other locations depending on distance. It is assumed that village level poverty _q_ can be described (in matrix notation) by: where _X_ is the matrix with geo-covariates (rows indicating villages and columns indicating covariates), _u_ satisfies _u_ = _ρWu_ + _ε_ , where _ε_ is a vector of independent local errors satisfying _ε ∼ N_ (0 _, σ_<sup>2</sup> _I_ ) and _E_ [ _ε|X_ ] = 0, and where _W_ is a spatial weight matrix with elements _wij_ measuring the spatial correspondence between villages _i_ and _j_ (relative to
# **2.6 Treatment fidelity**

We first measure the share of adults in a village attending the community-wide meetings, which provided the forum for facilitated discussion under MEET, introduction of the CDP under SMS, and the electing of SMC members under ELECTIONS-CAPACITY **(Table 4)** .<sup>22</sup> Overall, on average the participation rate was 63 percent among treatment villages. Mirpurkhas had the highest participation rate with 75 percent of households, followed by 57 percent in Sanghar and 51 percent in Matiari. These district-level differences in participation rates are controlled in the proceeding estimations using district fixed effects.

The MEET and SMS groups have higher participation rates, on average 67 percent and 68 percent respectively, compared to 58 percent for MEET+ELECTIONS and SMS+ELECTIONS. This suggests that some aspect of the ELECTIONS-CAPACITY intervention actually reduced attendance at village meetings. The differences in attendance are particularly stark for Matiari district, where average participation rates were only 38 percent in groups receiving ELECTIONSCAPACITY, and 63 percent for MEET and SMS treatments alone.

For the SMS intervention, the other key aspects of treatment fidelity are registration and participation on the CDP platform. A total of 4,981 unique users registered on the CDP portal. The average registration rate in SMS villages (65 percent) was higher than that in SMS+ELECTIONS

> 22 The population was estimated from the household census conducted for the listing exercise. The take-up rate is measured as the number of attendees as a proportion of the number of adult individuals residing in the main settlement of each village. The project teams were instructed to revisit any village for which attendance was under 20 percent and hold a second meeting. Reported attendance is for the second meeting in these villages. As discussed in Section 2.2, the sample includes all main settlement schools and 15 percent of schools outside main settlements of sampled villages.

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Data on wages and salaries of Consolidated Central Government is taken from the IMF's Report No. SM/95/226 of September 6, 1995.

Jordan Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1993.

Data on Central Government Education and Health employment estimates are taken from Barbara Nunberg's Aide Memoire on Jordan dated December 1994. Non central Government employment represents a staff estimate for 1996 based on data taken from WB report on Local government finance sector study of April 20, 1990..

Military employment data do not include personnel of paramilitary units, i.e., the Public Security Directorate (10,000) under the authority of the Ministry of Interior, and the Civil Militia People's Army (200,000).

GDP at market price, and data on wages and salaries of Consolidated Central Government are taken from Government Finance Statistics and relate to 1993. In Jordan, Consolidated Central Government includes Education and Health services. Accordingly, employment figure includes Education and Health employment. Data on wages in manufacturing (monthly basis) are taken from the Intemational Labor Office's Yearbook of Labor Statistics 1995 and refer to 1993.

Lebanon Civil Service numbers only refer to currently filled Civil Administration employees for 1994. In total, there were 110,000 people on the government payroll. Non central Government employment comes from the same source as above and relates to 1992. Teachers' data were taken from the Administrative Rehabilitation Project, Technical Annex of June 5, 1995. The note mentions 32,000 teachers. Health Sector employment data are taken from Annex 3 of the note based on a report prepared for the World Bank by Cristian de Clerq of the UNARDOL. It states that as of 1992, the Ministry of Health and Social Affairs employed 2,984 employees. NGOs employ four times as many people, or about 6,880 people, in the sector. Military employment data do not include paramilitary units, i.e.
The implementation of these new rules and regulations has been uneven across different countries in the region. Figure 17 below shows the percentage of countries in each ECA group that have implemented new capital and liquidity requirements according to the latest BRSS survey. Russia and Turkey, where some of the largest banks are located, have imposed new capital surcharges for banks deemed systemically important. Additional liquidity requirements are still to be implemented in the majority of ECA countries.

**Figure 17: Capital and Liqudity Requirements on SIFIs**

<!-- Start of picture text --> 100100 100<br>71<br>64<br>50<br>40<br>33 33 33<br>29<br>17<br>15<br>0 0 0 0 0<br>EU CE & BC WB SC CA Russia Turkey EE RoW<br>Additional capital requirements Additional liquidity requirements<br>100<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> Source: Bank Regulation and Supervision Survey conducted by the World Bank. Note: **EU** stands for Western Europe, Southern Europe, Northern Europe; **CE & BC** for Central Europe & Baltic Countries; **WB** for Western Balkans; **SC** for South Caucasus; **CA** for Central Asia; **EE** for Eastern Europe; **ROW for** the rest of the world **.** Another important element of new regulations has been to provide a robust framework for the resolution of systemically important banks. The bankruptcy of Lehman Brothers in 2008 highlighted the many difficulties in resolving large banks with multiple business lines in different countries subject to local regulations in those countries. Moreover, without a specific resolution regime for large/interconnected financial institutions, supervisory authorities lack the tools to intervene in the
a material proportion of cross-country income divergence (Comin and Hobijn, 2010; Comin and Mestieri, 2018). Both approaches, based on firm and country-level data, emphasize that structural improvements in productivity can diffuse across borders only over long time-lags, implying that structural measures of productivity synchronization are low.

Cross-country correlations provide an insight into the extent to which different measures of productivity are synchronized. This approach is applied to labor productivity growth and TFP growth, as well as the SVAR-identified technology measures contribution to labor productivity growth. These can provide important insights into how synchronized each measure is between country pairs, with average correlations providing a summary statistic within groups of economies (International Monetary Fund, 2013).

The average 10-year rolling correlations between all bilateral pairs for each measure of productivity growth suggest that global synchronization was very low prior to the global financial crisis (see Figure 11). During the crisis and its immediate aftermath, correlations rose for all measures of productivity growth. Correlations between those measures with sizable demand-driven cyclical components (labor productivity and TFP growth) were considerably higher than those for the SVAR-identified structural technology shocks, similar to previous findings for advanced economies (Huo et al., 2020; Imbs, 1999). These structural measures have returned to zero in advanced economies and EMDEs in recent years, while they remain high for labor productivity and TFP. Based on these correlations, productivity synchronization in both EMDEs and advanced economies appears to be a largely cyclical phenomenon. Advanced economies featured higher cross-country correlations of labor productivity and TFP than EMDEs. Since 2005, Low Income Country (LIC) productivity growth has been largely unsynchronized, even during the global financial crisis, plausibly reflecting limited trade integration and the effects of idiosyncratic shocks. The poorest EMDEs are largely unaffected by both global fluctuations in demand and technology spillovers.

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), and PROEXPORT (Colombia). They use rich and unique datasets for the six Latin American countries that combine firm-level customs data with covariates drawn from other national firm-level data sources, and constitute the first rigorous micro-based evidence of the effects of export promotion.<sup>32</sup> The picture emerging from Christian Volpe’s survey is that export promotion was effective in facilitating export expansion for firms in the LAC region, but primarily along the extensive margin. Firms exporting differentiated goods benefit more than those selling more homogeneous goods. Small and relatively inexperienced companies benefit more than larger and already established exporters. Finally, bundled services that provide support to firms throughout the export development process appear to be more effective than isolated actions.

> 30 This assumption is designated as ―ignorable treatment assignment‖ by Rosenbaum and Rubin (1983) which is the seminal study on propensity score matching estimation. The assumption means that program participation and outcomes are independent, conditional on a set of observed attributes.

> 31 The single-match method is called ―nearest-neighbor.‖ Alternatively, one can use _n_ nearest neighbors, or the entire sample of untreated firms with weights that decrease with distance from the treated firm’s propensity score. This latter method is called ―kernel matching.‖ Many other refinements are possible. See Caliendo and Kopeinig (2005) for details on propensity score matching estimators.

> 32 An alternative, more traditional route to the evaluation of export-promotion’s effectiveness is the aforementioned cross-country study of Lederman et al. (2010).

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temperature played a significant role for the spread of COVID-19 in 122 cities in China. Holtmann et al. (2020) also find that weather conditions affect the initial stages of the spreading of Coronavirus in 47 countries, with higher temperatures leading to slower spreading. While ambient temperatures may affect the COVID-19 survival in the environment, it is also possible that a population confined to indoor activities is more susceptible to the infection. We thus control for the minimum temperature ( _Temp_ ) recorded each day in each country and expect that higher temperatures may induce governments to delay the adoption of CCMs ( 8 < 0). **Given that most of the CCMs entail the curtailing of individual liberties and the** ββ **pandemic has a global nature, political regimes and external pressures or demonstration effects from outside may also influence the adoption of CCMs** . More democratic regimes may find difficulties in restricting individual liberties affecting mobility and social contact, while autocratic regimes tend to be more able to apply tighter restrictions to contain the pandemic. We control for a measure of democracy levels obtained from The Economist (2020) database and expect 9 < 0 _._ In addition to internal pressures to implement or delay the adoption of CCMs, authorities have faced international pressure and learned from the experience of other countries in dealing with the pandemic. In particular, the adoption of ββ stringency measures in Western European countries, which were among the first to suffer from massive contagion, influenced the speed at which other countries implemented CCMs. We use a measure of the stringency of CCMs in 26 European economies as a demonstration effect for the adoption of CCMs in the world and expect 10 > 0.<sup>4</sup> ββ

## **4.** Econometric Results

# _a. Adoption of CCMs_

**The econometric results of our estimations for the adoption of CCMs are presented**

**in Table 1.** We have grouped the results according to the two categories described above (those that mainly
in indicators in most years, we can say that seasonality is at play for financial intermediation. We present the monthly distribution of loan disbursement, loans recovered, and other indicators of program performance.

> 7 In 2010, Bangladesh Microfinance Statistics reported around 750 MFIs. Instead of PKSF data on its sectoral level portfolio, it would have been better if PO level data were available for analysis. Unfortunately, PO level data are not available.

> 8 Subsidizing funds for on-lending by POs by PKSF along with the grants for institutional development of MFIs has helped promote the viability of microfinance institutions in Bangladesh (see Khandker, Khalily and Samad 2016).

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global variables in the first four quarters, global shocks can affect country-specific variables (without any sign or zero restrictions).

The identification strategy is based on the following assumptions, combining sign and short-term restrictions as shown above: A positive global demand shock triggers a simultaneous increase in global output growth, global inflation, and oil prices.<sup>5</sup> A positive global supply shock leads to higher global output growth and oil prices but lower global inflation. A positive oil price shock induces an increase in oil prices and global inflation but a drop in global output growth. Finally, global shocks can have contemporaneous effects on domestic variables, but domestic shocks can only influence global variables with a lag.

A positive country-specific supply or demand shock increases country-specific output growth. However, a country-specific supply shock reduces domestic inflation, whereas a country-specific demand shock increases it. A positive interest rate shock (corresponding to a contractionary monetary policy) initially increases the domestic interest rate and results in an appreciation of the domestic currency, while it decreases domestic output growth and inflation. Finally, a positive exchange rate shock (corresponding to an appreciation of the domestic currency) only assumes an increase in the exchange rate, while its impact on other domestic variables is left unrestricted. All country-specific shocks are assumed to affect country-specific variables on impact through the corresponding sign restrictions, although the robustness checks also consider such restrictions lasting for an alternative number of periods.<sup>6</sup> The system is estimated on a country-by-country basis using quarterly data with two lags, as in Charnavoki and Dolado (2014). The Bayesian estimation used searches for 1,000 successful draws of at least 2,000 iterations with 1,000 burn-ins. The results shown in the paper are based on the median of these 1,000 successful draws and 68 percent confidence sets at the country level, although alternative presentation methodologies (for example, the median target, as in Fry and Pagan 2011) are considered as a robustness check.
Policy Research Working Paper 9440

# **Abstract**

This paper assesses the quality of health care across African countries based on health providers’ clinical knowledge, their clinic attendance, and drug availability, with a focus on seven conditions accounting for a large share of child and maternal mortality: malaria, tuberculosis, diarrhea, pneumonia, diabetes, neonatal asphyxia, and postpartum hemorrhage. With nationally representative, cross-sectional data from 10 countries in Sub-Saharan Africa, collected using clinical vignettes, unannounced visits, and visual inspections of facilities, this study assesses whether health providers are available and have sufficient knowledge and means to diagnose and treat patients suffering from common conditions amenable to primary health care. The study draws on data from 8,061 primary and secondary care facilities in Kenya, Madagascar, Mozambique, Nigeria, Niger, Senegal, Sierra Leone, Tanzania, Togo, and Uganda, and 22,746 health workers. These data were gathered under the Service Delivery Indicators program. Across all conditions and countries, health care providers were able to correctly diagnose 64 percent of the clinical vignette cases, and in 45 percent of the cases, the treatment plan was aligned with the correct diagnosis. For diarrhea and pneumonia, two common causes of under-five deaths, 27 percent of the providers correctly diagnosed and prescribed the appropriate treatment for both conditions. On average, 70 percent of health workers were present in the facilities to provide care during facility hours when those workers were scheduled to be on duty. Taken together, the estimated likelihood that a facility has at least one staff present with competency and the key inputs required to provide child, neonatal, and maternity care that meets minimum quality standards is 14 percent. Poor clinical knowledge is a greater constraint in care readiness than drug availability or health workers’ absenteeism in the 10 countries. However, the paper documents substantial heterogeneity across countries.

This paper is a product of the Health, Nutrition and Population Global Practice. It is part of a larger effort by the World Bank to provide open access to
heterogeneity however emerges across countries. While the gravity results suggest that TAs have partly raised cross-border government procurement, the CHB values remind us that government procurement remains vastly home biased, even if the trends show a slow opening up to trade.

# **6 Concluding remarks**

This paper estimates trade barriers in government procurement. In doing so, it analyses the role of trade agreements to see if and how much they have contributed to reduce those barriers.

Using new Inter-Country Input-Output (ICIO) tables from the TiVA database for 62 countries between 1995 and 2015, we obtain estimates of government purchases across sectors and countries of origin and use those in a standard gravity model. The estimates suggest that governments are significantly more local in their purchasing decisions than private firms. The border effect is large and significantly higher for public markets, especially in services. Yet, we find that trade agreements and the EU single market in particular have contributed to the opening up of government procurement, with the effect being robust in services. Non-discriminatory provisions specific to government procurement are driving the trade-creating effect of TAs in services, whereas the effects are overall weak in goods.

The estimates from the gravity model are then used to estimate indexes of home bias in government procurement and private markets. These measures capture the overall tendency to trade locally rather than internationally, and hence include also factors other than protectionist trade policies. Home bias is larger in government procurement than in private markets. Two mechanisms can account for this difference: (i) governments being on average more home biased than firms in purchases of both services and goods; and (ii) governments spending relatively more on sectors (goods and services) that are more home biased. We find that this difference is however shrinking over time: home bias in government procurement is declining faster than home bias in private markets.

Our results have implications for trade negotiations that touch upon government procurement issues. A strong result of our analysis is that national borders are relatively thicker in services, which absorb the
>Rural - Male<br>Urban - Male 4<br>2<br>0<br>Source: Estimates based on the Enquête<br>Nationale sur l’Emploi (ENE; Labor Force<br>Survey)<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text --> At the same time, the labor market has failed to create new opportunities for the large number of women and youth entering the workforce. Women constitute the majority of the inactive population. Female unemployment is high, which hints that inactivity might be due to women being discouraged from working. In 2018, female unemployment reached 14 percent, while male unemployment was 8.37 percent. For the youth aged 15-24, both inactivity and unemployment are high in comparison with other age groups. The “not in education, employment or training” (NEET) rate has remained among the highest in MENA, and those who were NEET in 2010 were still NEET even after 10 years (Alfani et al. 2020).

Unemployment slightly decreased, especially among men, before the COVID-19 pandemic.<sup>6</sup> Reaching an all-time low in 2011 of 8.91 percent, the unemployment rate declined from 13.4 percent in 2000 to 9.75 percent in 2018 (Figure 2.8). This decrease was driven mainly

> 5 Defined as the population aged 15-99 years old.

> 6 Data from the Labor Force Survey are available from 2000 to 2018.

6 | P a g e
Intra Censal Survey of 2015 (Encuesta Intracensal). The purpose of the simulation is to observe performance of the different methods under more realistic scenarios. The survey is carried out by the Mexican National Institute of Statistics and Geography (Instituto Nacional de Estadistica y Geografia - INEGI). The survey has a sample of 5.9 million households and is representative at the national, state (32 states) and municipal or delegation level (2,457 municipalities), as well as for localities with a population of 50,000 or more inhabitants.

The 2015 Intra Censal Survey questionnaire includes the following topics related to the housing unit: dwelling features, size and use of the dwelling, conditions for cooking, ownership and access conditions, access to water, sanitation facilities and sanitation, electric power, solid waste, equipment, appliances and automobile; and information and communication technologies (ICT). It also includes the following demographic information: total population and structure, birth registration, marital status, health services, ethnicity, education, economic characteristics, nonpaid work, migration, daily mobility, fertility and mortality, household composition, non-labor household income,

> 20  ̄ The correlation is also present between _x_ 5 _ac_ and other omitted deviations, _zach_ .

> 21The empirical average relative root MSE of the mean for the UC approach is slightly higher than that of the CensusEB.

> 22The unit context method also yields mean welfare estimators that are 38 times more biased than Census EB variants (not shown).

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