rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 11043

# **Abstract**

This paper presents a fundamental reassessment of the global - human resources crisis in primary health care, using nation ally representative survey data from 7,915 health facilities across 10 Sub-Saharan African countries. The reassessment consists of three main parts. First, in contrast to a literature that posits pervasive health workforce shortages, the paper estimates that the median primary health care provider sees 10.9 patients each day and spends under two hours doing so. However, variation in patient loads across facilities implies that most patients visit busier facilities, and therefore the median patient experiences long wait times. Second, by combining caseload data with measures of medical competence for 14,367 individual providers, the paper demonstrates that provider caseload is very weakly correlated with medical competence. As a result, the most competent doctors in each system are nearly as likely to be underutilized as the least competent. Third, the paper assesses how much productivity is lost due to the low observed correlation between caseload and competence, by calculating potential quality improvements from matching the most competent providers to the busiest postings. Such transfers could increase the likelihood that a patient sees a provider who can correctly manage simple cases by 4.5 percentage points, or 12 percent, but with substantial variation across countries. The paper concludes that in half of the countries in the sample, there are substantial numbers of competent but underutilized providers; but in the other half, quality improvements will require a full overhaul of the training infrastructure and spatial distribution of facilities.

This paper is a product the Office of the Chief Economist, the People Vertical and the Office of the Chief Economist, Middle East and North Africa Region. 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 ayichang@worldbank.org; rgatti@worldbank.org; Jishnu.Das@georgetown.edu; and bd581@georgetown.edu.

_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. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Table 4** : **Regressions of Social Welfare Growth on Average Income Growth:  Min 5-Year Spells Disaggregated By Decade**

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|---|---|---|---|---|---|---|---|---|
|**Social Welfare Function**|**Bottom 10%**|**Bottom 20%**|**Bottom 40%**|**Bottom 90%**|**Atkinson Index (1)**|**Atkinson Index (2)**|**Atkinson Index (3)**|**Sen Index**|
|||||**Panel A: 1980s**|||||
|Avg. growth|1.120***|1.061***|1.016***|0.980***|0.995***|1.019***|1.049***|0.998***|
||(0.133)|(0.0991)|(0.0753)|(0.0388)|(0.0375)|(0.0689)|(0.0941)|(0.0468)|
|Number of Observations|80|80|80|80|80|80|80|80|
|R-squared|0.664|0.782|0.859|0.956|0.959|0.870|0.780|0.943|
|Variance decomposition|0.593|0.737|0.846|0.975|0.964|0.854|0.743|0.945|
|P-val. wald test,slope=1|0.369|0.542|0.835|0.616|0.904|0.785|0.604|0.961|
|||||**Panel B: 1990s**|||||
|Avg. growth|1.127***|1.062***|1.013***|0.983***|0.999***|1.025***|1.058***|0.998***|
||(0.0979)|(0.0757)|(0.0590)|(0.0297)|(0.0306)|(0.0559)|(0.0739)|(0.0349)|
|Number of Observations|198|198|198|198|198|198|198|198|
|R-squared|0.468|0.650|0.779|0.940|0.924|0.716|0.569|0.919|
|Share of variance due to growth|0.415|0.612|0.769|0.956|0.924|0.699|0.538|0.921|
|P-val. wald test,slope=1|0.196|0.417|0.824|0.579|0.977|0.662|0.436|0.950|
|||||**Panel C: 2000s**|||||
|Avg. growth|1.018***|0.996***|0.970***|0.985***|0.990***|0.991***|1.000***|0.982***|
||(0.119)|(0.0776)|(0.0505)|(0.0189)|(0.0261)|(0.0638)|(0.0935)|(0.0260)|
|Number of Observations|167|167|167|167|167|167|167|167|
|R-squared|0.326|0.517|0.699|0.931|0.890|0.589|0.416|0.895|
|Share of variance due to growth|0.320|0.519|0.720|0.945|0.899|0.595|0.416|0.911|
|P-val. wald test,slope=1|0.879|0.959|0.557|0.444|0.709|0.887|0.996|0.481|

Notes: This table reports the results from a regression of growth in the indicated social welfare function (in each column) on growth in average incomes, in the sample of nonoverlapping spells lasting at least five years. The three panels correspond to the indicated time periods. The regression for each decade includes all spells with at least one endpoint falling in the indicated decade. Spells are weighted by the fraction of the years of the spell falling in the indicated decade. Heteroskedasticity-consistent standard errors reported in parentheses. * (**) (***) indicate significant differences from zero at the 10 (5) (1) percent level. The last row in each panel reports the p-value associated with the test of the null hypothesis that the estimated slope coefficient is equal to one.

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8. In Sri Lanka the observation has been made that the goverrment pay structure is significantly "out-of-line" with neighboring countries. A recent report, "A Policy Framework on Cadres and Salaries", by the Administrative Reforms Committee, presented the following data. The ratio of top to bottom salaries in six Asian nations was: Singapore, 62:1; Malaysia, 25.4:1; Pakistan, 19.4:1; India and Bangladesh, 12:1; and, Sri Lanka, 7.5:1. But, as suggested by Table 4 in the text, compression in the Sri Lankan pay structure may be due more to long term factors than short-term real wage movements.

9. Heller and Tsit (1983) reveal that even with the rapid growth of government employment in many developing economies, government employment per capita remains far higher in advanced economies. This may, in part, reflect the further development of the welfare state in rich versus poor nations. However, with regard to the development of domestic labor markets, public employment in developing versus developed nations represents a far higher percentage of total wage employment. This helps to explain greater concern in developing country settings over government pay and employment policy spill-over effects.

10. Lindauer (1987b) presents cross-country evidence on recent growth in government expenditures.

11. Egypt is frequently cited as a nation heavily committed to "employer-oflast-resort" policies. Ozgediz (1983) indicates that Cote-d'Ivoire, Mali, Mauritius and Sri Lanka have also pursued such policies in the recent past (p.6). Harris, Andoh, Evlo and Starr (1987) comment that Senegal, Togo, CAR, Guinea, Mali and Sudan, "...got into difficulty in the 1980s by having policies of automatic hiring of all university graduates as output of the educational system began to expand rapidly." (pp. 97-98) 12. Lindauer, Meesook and Suebsaeng (1987), p.<sup>19</sup> .

13. Klinov (1987) discusses this issue for Bolivia. She summarizes, Low-wage-high-employment rather than high-wage-low-employment is a preferred combination by workers, because of a conjunction of a small probability of regular salaried employment in the private sector, and the acceptability of moonlighting while working in the public sector. The insurance value of a public-sector job is underlined by the central role of health insurance in workers' compensation: all publicsector employees are insured, as against a very small percent in private-sector employment. When economic conditions in the private sector deteriorate, as they did in the eighties, the pressure for this combination increases. (p.5)

# 14. Ozgediz (1983), pp. 6-7.

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# **3.2. Estimation techniques**

The first outcome of interest that we study is whether children are currently attending school. As this is a binary outcome, we use maximum-likelihood to estimate a bivariate probit model, following Newey (1987), which we will follow common practice in referring to as the IV-Probit model.<sup>12</sup> The marginal effects of this model will then be compared to marginal effects from standard probit estimation. However, current school attendance does not allow for possible delays in starting schooling, catch-up and grade repetition. As seen in Figure 1, it appears that children in migrant households are slightly more likely to start school at age 5 than children in nonmigrant households. Therefore greater attendance in non-migrant households at older ages may just be the result of these late starters catching up.

Instead of school attendance, we therefore focus most of our attention on educational attainment, measured by the grade-years of schooling attained by children. Following Hanson and Woodruff (2003) we begin with two-stage least squares (2SLS) of the following equation: _Schoolingi =_ α _+_ β _*Migranti+_ δ _’Xi +_ ε _i_ (1), where _Schoolingi_ is the years of schooling attained by child _i, Migranti_ is a dummy variable taking the value one if a household has a migrant member and zero otherwise, and _Xi_ is a set of individual and community controls.

However, there are several features of education data that render OLS and 2SLS analysis inappropriate. Ideally one would like to observe the final level of schooling completed by individuals and relate it to the migrant status of the household in which they grew up. Here we face the problem that while schooling is complete for

> 12 Estimation was carried out using the IVProbit command in STATA version 9.

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significantly more productive than exporters-only. These findings are all verified for exporterimporters in the agribusiness sector whereas for exporter-importers in the apparel sector the findings on the likelihood of foreign ownership, on the higher capital intensity and the higher productivity are not verified.

**Table 5. Performance Premia for Exporter-Importers based on EEA Data**

||**Log**<br>**(Employment)**|**Log (Age)**|**Foreign owned**<br>**dummy**|**Log (Real**<br>**Output)**|**Log (Capital/**<br>**Employment)**|**Log (Labor**<br>**Productivity)**|
|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|**f**|||**All Manufactu**|**ring Firms**|||
|**Dummy or Exporter‐**|||||||
|**Importers**|0.776***|0.232***|0.058***|0.837***|0.362***|0.086***|
||(0.049)|(0.023)|(0.008)|(0.050)|(0.045)|(0.024)|
|**log (Capital/ Employment)**||||||0.308***<br>(0.007)|
|**Year Fixed Effects**|Yes|Yes|Yes|Yes|Yes|Yes|
|**Industry (ISIC 3‐digit)**<br>**Fixed Effects**|Yes|Yes|Yes|Yes|Yes|Yes|
|**Observations**|5884|6071|6071|6045|5869|5667|
|**R‐squared**|0.133|0.174|0.132|0.206|0.243|0.482|
||||**Agribus**|**iness**|||
|**Dummy for Exporter‐**|||||||
|**Importers**|0.731***|0.241***|0.104***|0.873***|0.584***|0.259***|
||(0.183)|(0.074)|(0.024)|(0.150)|(0.147)|(0.088)|
|**log (Capital/ Employment)**||||||0.361***<br>(0.024)|
|**Year Fixed Effects**|Yes|Yes|Yes|Yes|Yes|Yes|
|**Observations**|631|647|647|644|629|606|
|**R‐squared**|0.037|0.02|0.035|0.079|0.03|0.299|
||||**Appa**|**rel**|||
|**Dummy for Exporter‐**|||||||
|**Importers**|1.157***|0.372***|0.011|1.019***|0.169|‐0.087|
||(0.175)|(0.071)|(0.014)|(0.135)|(0.141)|(0.067)|
|**log (Capital/ Employment)**||||||0.151***<br>(0.021)|
|**Year Fixed Effects**|Yes|Yes|Yes|Yes|Yes|Yes|
|**Observations**|622|642|642|641|616|600|
|**R‐squared**|0.114|0.078|0.006|0.125|0.02|0.087|

Notes: Robust standard errors in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively. Only exporting firms are included in all the regressions in Panels A and B.

# **6. Why Don’t Peruvian Exporters Import More? The Role of Policies**

In this section we conduct an exploratory analysis of some of the policies that may be preventing Peruvian exporting firms from importing more and thus from benefiting from those imported intermediate inputs, given the evidence provided in Sections 3, 4 and 5. The policies that we consider are nominal applied tariff rates, non-tariff measures (NTMs) on imports and the use of a special regime to clear import customs in advance (“despacho anticipado”). Our focus is on analyzing the correlations between these three types of policies and Peruvian firms’ import outcomes. The results presented in the analysis below indicate correlations between all the policies and the import performance of exporters. For a more sophisticated analysis of how these policies 17
this year) (World Bank 2012b). Given current population projections, a 6 percent growth rate in the GDP over the coming decade would represent a 4.9 percent rate for GDP per capita. (The compound rate of population growth over 2012–17 and 2012–22 is 1.1 percent per annum.) Although the recent growth has not been even across all regions, the three regions that account for the bulk of absolute poverty—East Asia, South Asia, and sub-Saharan Africa—have observed strong GDP growth rates in recent years: approximately 8 percent in East Asia, 7 percent in South Asia, and 5 percent in sub-Saharan Africa. Current expectations are that these rates will be maintained, although most forecasters acknowledge that serious risks persist that further crises emanating in the “rich world” will spill over significantly into economic growth and poverty reduction in the developing world; see, for example, the discussions in World Bank (2012a, b).

Let us turn now to the other key variable determining the extent of poverty, namely inequality. The concept of “global inequality” that is relevant to the measurement of global poverty pools all residents of all countries and measures the inequality among them as if they were one country. Figure 1 plots one such measure of inequality, the mean log deviation. The mean log deviation is a theoretically sound measure with the useful property of exact decomposability by population subgroups (Bourguignon 1979). Thus, we can cleanly separate the “between-country” component of total inequality from the “within-country” component. Figure 1 provides this breakdown of total inequality in the developing world.

We see that there has been a trend decrease in total inequality; over the period as a whole, there is a small but statistically significant negative trend, at −0.002 per year (standard error = 0.001), compared to a mean log deviation of 0.57. However, the bulk of the decline in overall inequality was in the period until the late 1990s. There is an indication of rising overall inequality since 2005.<sup>6</sup> The future evolution of overall inequality will be crucial to the trajectories of overall poverty measures. It might be conjectured that higher rates of economic growth will (at least initially) put upward pressure on inequality within low- and possibly middle-income developing countries, as predicted by the famous Kuznets Hypothesis (Kuznets 1955). However, this conjecture is not consistent with past evidence, which indicates that inequality within growing

> 6 Note that these are aggregate indices for the developing world as a whole. Different regions have observed different patterns in how average inequality across countries has been evolving; Ravallion and Chen (2012) provide details by regions.

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# **Figure 5 – Breakdown of virtual-C** **<u>exports from China (percent)</u>**

<!-- Start of picture text --> China<br>E15<br>22%<br>RoW<br>35%<br>USA<br>RUS 20%<br>2%<br>CAN<br>JPN<br>2% MEX<br>13%<br>2% eit<br>2% IND<br>2%<br><!-- End of picture text --> Note: virtual carbon exports have been estimated using the BTIO approach. Source: authors 25
Our analysis indicates that actual health impairment status, pesticide toxicity, average number of hours spent in the shop per day, as well as the interaction between the most harmful pesticides and training received in pesticide use and handling, are significant factors impacting traders’ health risk perception of pesticides. Health impairment status is significantly affected by pesticide toxicity, exposure in terms of number of years spent in pesticide business, trader’s age, and the interaction between the most harmful pesticides and training received in pesticide use and handling.

The present study contributes to the literature in several ways. Firstly, studies on the misperception of pesticide toxicity tend to be quite rare. Secondly, to the best of our knowledge, studies of the health effects of pesticide exposure on traders are also nonexistent in the literature, with most concentrating only on farmer health.<sup>2</sup> Thirdly, although many may consider the relationship between a traders’ perception of pesticide risks and health status rather obvious, this study provides a more rigorous test of that hypothesis.

The remainder of the paper is organized as follows. In Section 2 we describe the trend in pesticide use and the associated problems in Bangladesh. In Section 3 we present the survey, and in Section 4 a description of the dataset. Models, along with econometric findings, are documented in Section 5. In Section 6 we conclude and offer the implications of our findings.

# **2. Pesticide use in Bangladesh at a glance**

As in many developing countries, Bangladesh has promoted the use of pesticides to expand agricultural land and increase output per acre.<sup>3</sup> As a consequence of this expansive policy, pesticide use has more than doubled since 1992, rising from 7,350 metric tons to 16,200 metric tons in 2001. An FAO analysis of pesticide composition

> 2 This is particularly true in developing countries where even pesticide-related health studies are few in number (Ante and Pingali, 1992; Crissman et al., 1994; Dung and Dung, 2003; Pingali et al., 1994). 3 Promotional activities have included extension services and significant subsidies (Rasul and Thapa, 2003; Hossain, 1988).

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and similar numbers of children on average. The household income of the potential business owners is about 1100 Rs less per month than the households of current business owners, and they are a bit less likely to own refrigerators or sewing machines. In terms of household assets that could potentially also be used for business purposes, 53 percent live in households with a sewing machine and 41 percent in households with a refrigerator, but only 25 percent have a gas cooker and only 7 percent an oven.

# **2.2 Randomization into treatment and control groups**

Our goal is to determine the impact of business training for these women, and to see whether this training has more effect when coupled with a capital grant. We therefore randomly selected 400 current owners and 400 potential owners to be offered business training; half of these were pre-selected to receive a grant of 15,000 Rs (US$129) conditional on finishing the training. We wanted to use the possibility of receiving a grant as an incentive to attend training but did not want the selection of people into training to be affected by the grant. Therefore at the time of offering the business training, individuals were told only that half of those who completed the training (evidenced by receiving the training completion certificate which requires 80% attendance) would be randomly chosen to receive a 15,000 Rs grant.

For the current owners, we dropped four enterprises with either missing profits or profits above 10,000 Rs.<sup>5</sup> Randomization to treatment was then stratified by D.S. (10) and by whether or not there were children under age 18 for whom there was no other person to look after them while the mother worked (54%). Within each stratum we then formed triplets of firms on the basis of baseline profits, and randomly allocated two out of three firms in each triplet to receive training, and one of these two to get a grant conditional on completing training, subject to a quota of 40 firms per D.S. being chosen to be offered training.<sup>6</sup> For the potential business owners, in addition to D.S., we stratified on whether or not they had taken any steps in the past year towards starting a business (50% said they had), and whether they had never worked at all before, even as an unpaid family worker (18% had not).

> 5 The screening survey asked about profits in a “normal” month, while the baseline survey asked about profits in a specific month. All women in the sample had stated that their profits were below 5000 Rs in the screening survey, but 17% of these reported profits above 5000 Rs in the more detailed baseline survey referring to December 2008. Most of these were in the 5000-7000 Rs range. 6 This form of randomization is done to increase power (see Bruhn and McKenzie, 2009). We then always control for the randomization strata in our analysis.

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**Table 8: The effect of the US Monetary policy on Emerging Economies Financial Variables—Controlling for FOMC meetings during the 2013 Tapering event**

||E<br>k=1|xchange rate<br>k=2|s<br>k=3|k=1|Equity prices<br>k=2|k=3|k=1|Bond yields<br>k=2|k=3|
|---|---|---|---|---|---|---|---|---|---|
|2yr Treasury yield|0.039***|0.045***|0.043***|-0.053***|-0.087***|-0.090***|0.054***|0.072***|0.068***|
||[6.12]|[6.53]|[7.65]|[-6.67]|[-9.68]|[-8.91]|[3.59]|[5.51]|[5.11]|
|2yr T-yield* Tapering||||||||||
|Dummy|0.064***|0.049***|0.049***|-0.127***|-0.096***|-0.143***|0.270***|0.286***|0.387***|
||[5.19]|[3.92]|[3.81]|[-6.14]|[-5.32]|[-6.13]|[6.27]|[5.36]|[6.19]|
|Constant|-0.044***|-0.008|-0.011|0.321***|0.333***|0.461***|-0.129***|-0.144***|-0.112***|
||[-5.28]|[-0.96]|[-1.53]|[32.99]|[29.62]|[36.54]|[-7.39]|[-8.28]|[-6.01]|
|Observations|1,300|1,300|1,300|1,194|1,200|1,197|1,049|1,052|1,053|
|R-squared|0.099|0.081|0.069|0.059|0.072|0.077|0.096|0.101|0.122|
|# of countries|20|20|20|19|19|19|17|17|17|

_Notes:_ Dependent variables are percent change in exchange rate, equity prices, or bond yields, between the day of FOMC announcements and k days after that, as per Equation 1. The independent variable is percent change in 10-year Treasury yield between t and t-1, t being the day of the FOMC announcements. Tapering Dummy takes the value 1 for the three FOMC meetings on June 19, July 31 and September 18, 2013, and 0 for all other days. Regressions include country fixed effects. Robust t statistics are in parentheses. *, **, *** indicate that the coefficients are significant at 10, 5, and 1 percent level.

Results, in Table 8, show a larger spillover of the FOMC announcements for a given change in Treasury yields during the period of the tapering event. The average impact across all FOMC announcements, however, does not seem to be driven solely by these meetings—since the coefficients of changes in 2-year Treasury yield remain significant, with the same sign as before, even after controlling for their interaction with the dummy for tapering. Even though the empirical exercise conducted here reaffirms that emerging economies reacted significantly to US policy outside of the tapering event; the event exemplifies the kind of disruptive reaction one could see in emerging economies to a large surprise policy announcement in the US.

# **5.2 Data releases and other information**

Next, we strive to ensure that there are no other policy announcements, data releases, or developments that could affect the estimates of the spillover of FOMC announcements on financial markets in emerging economies.

In deciding the stance of monetary policy, the Federal Reserve Board is guided by its dual mandate of price stability and employment. Hence it is possible that the US non-farm payroll and the CPI data releases may shift expectations about monetary policy outside of FOMC meetings. Comfortingly, however, nearly all FOMC meetings are scheduled on Tuesdays and Wednesdays and are unlikely to overlap with the employment reports which are released on Fridays (see Wright 22