rafmacalaba/datause-encoder-data huggingface dataset:
# **Why Measure Governance and Service Delivery in Health and Education?**

It has now been eight years since the launch of the _World Development Report 2004: Making Services Work for Poor People_ (World Bank 2003) _._ The report helped spark a flurry of World Bank operational and analytical work on governance and service delivery in health and education, at a time when academic researchers were also taking a greater interest in the issue. That work has focused on measuring service delivery at the point of contact between provider and client. By identifying gaps in the quality and quantity of ―street-level‖ service delivery, this measurement effort complements the essential work of measuring outcomes such as learning, educational attainment, and health status. At the same time, over the past decade, a shift toward more rigorous evaluation has increased the focus on measuring the impact of health and education interventions that seek to improve service delivery and outcomes by strengthening accountability mechanisms and incentives for providers. In education, these interventions include school-based management, teacher incentives, and the provision of information to users. Similarly, in health, a range of pay-for-performance schemes (and the associated accountability mechanisms) are being evaluated in numerous countries.<sup>3</sup> From the perspective of service delivery, governance can be understood as the set of incentives and accountabilities that affect the way provider organizations, their managers, and staffs behave, as well as the quality and efficiency with which they deliver services. From this

> 3 See http://siteresources.worldbank.org/EXTHDOFFICE/Resources/5485726-1239047988859/5995659- <u>1239048041095/Basic_Ed_Cluster_HDNCE.pdf and http://siteresources.worldbank.org/EXTHDOFFICE/Resources/5485726-1239047988859/59956591239048041095/P4P_in_Health_Cluster_HDNCE.pdf.</u>

3
of durables in anticipation of prolonged lock down, and also purchases of health-related goods, like facemasks.<sup>19</sup> To isolate the response of firms to only the supply side shock, we exploit the time varying nature of COVID lockdowns across countries. Define the supply-side lockdown exposure of firm j in time t as: Lockdown Exposurej,t i,j,t0 × (Lockdown stringency in country i at time t) = ∑s i where si,j,t0 is the share of firm j’s import volume (measured in twenty-foot equivalent container units, or TEU) from country i in t0, the quarter December 2019 to February 2020, and lockdown stringency in country i at time t is the Oxford University lockdown stringency index (Hale et al., 2020). Time intervals are three-month quarters, so the stringency index in each time period is the average for the quarter. Lockdown exposure is zero for all firms prior to March 2020, and then varies across firms and quarters after the pandemic begins.

To isolate responses to this supply side shock we estimate the regression: Net Separation Rate = αj + αt + β1(Lockdown Exposurej,t) + β2(Lockdown Exposurej,t × Wj) + ε j,t where αj is a firm fixed effect and αt is a quarter fixed effect. The quarter fixed effects control for changes in demand in the US economy during the pandemic. Wj stands for alternative firm characteristics hypothesized to shape resilience, namely: (i) the share of import volume (again in TEU) of goods that are differentiated, rather than sold on an organized exchange or with reference prices, according to Rauch (1999); (ii) the number of suppliers to the firm at t0; and (iii) the total volume of imports at t0.

Results are reported in Table 1. Across all columns, and in contrast to the aggregate pattern in Figure 5, supply-side lockdown exposure increases net separations. This confirms that the aggregate pattern of decreasing net separations is explained by demand factors.

> 19 Consistent with this, when replicating Figure 5 only using firms importing either: (i) facemasks, or (ii) electric car batteries, the spike in entry rates is somewhat larger in the quarter starting March 2020.

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prior expectations. It appears that for women the "care" effect of sickness dominates the "substitution" effe:ct, which would drive them to work more in income-generating activities. All other effects for rural men and women appear to be insignificant. The picture for the effect of sickness on time use in urban areas is somewhat different. Two effects are significant. First the sickness of an adult tends to drive both men and women to allocate a greater share of their time to self-employment activities. This implies that adult sickness possibly has a stronger incomereducing effect in urban areas where the "shock" to the household income of the sickness of an adult may be greater. Secondly, it appears that urban men do reallocate their time in response to sickness in the household-they are more likely to work as wage workers if a child is sick (column 12, table 3b) and less in that activity if an adult is sick. One explanation for this may be that male participation in wage work in response to sickness is opposite in sign to that of women (column 11, table 3b). Thus the sickness of a child drives the women out of the wage market and the men into it, whereas adult sickness has the opposite effect. One has to be cautious in buying this explanation however, since the estimated coefficients of sickness in the female regression (column I 1) are not significantly different from zero.

# _Employment changes and Time use_

Unexpected changes in the employment of household members can alter time-use patterns of men and women. One such change is employment. Our interest is to see if these effects are symmetric-ie. if the layoff of males affects female time use in a manner similar to the layoff of females. If women in the household put a higher premium on the household's consumption smoothing than do men, then they are more likely to a) sacrifice their leisure, and/or b) increase the share of income-generating work in their total time whenever there is an employment shock to the opposite sex. Our approach in measuring changes in employment is to develop a cluster-level indicator of rnale and female unemployment rates. As stated above only the rural subsample is used to estimate this effect. The results in tables 2a and 2b show that both men and women 16
assessing the vulnerability of an economy to a BOP crisis, each of which point to a high degree of vulnerability.

# **Table 3**

# **Indicators of Vulnerability to a BOP Crisis in Tajikistan**

||2006|2007|2008|
|---|---|---|---|
|Current account balance as percent of GDP|-2.8|-11.2|-8.8|
|Trade balance as percent of GDP|-34.2|-48.2|-51.7|
|Inflation (percent, period average)|10.0|13.2|20.4|
|Growth rate of bank lending to the private<br>sector (percent)|76.8|58.7|21.3|
|Unencumbered<br>international<br>reserves<br>as<br>percent of reserve money|36|26|44|
|Unencumbered<br>international<br>reserves<br>in<br>months of following year’s imports|0.7|0.5|1.2|
|Foreign currency deposits as percent of total<br>deposits|68|76|64|

Sources: IMF and World Bank Tajikistan ran current account deficits of 11 and 9 percent of GDP in 2007 and 2008 respectively, despite the large remittance inflows which are classified as a current account transaction. The trade deficit in goods and services exceeded 50 percent of GDP in 2008. Inflation has risen to double digit levels. Private sector credit growth has been 14
# attainment and health outcomes (proxied by life expectancy).

# Education

While secondary school enrollment rates are near advanced-economy levels in the average EMDE, tertiary school enrollment rates (40 percent) and secondary and tertiary school completion rates (27 and 10 percent, respectively) were less than two-thirds of the advanced-economy average in 2013-17 on average. In addition to expanding access to education, such as captured by these measures, improving the quality of education to improve education outcomes is critical (World Bank 2017c).

There are a number of policies that can improve education outcomes. At the national level, these include policies targeted at better training for teachers, greater teacher accountability, and performance incentives (Evans and Popova 2016). The development of metrics to assess and accelerate progress toward learning goals is a prerequisite for policy actions to improve educational outcomes (World Bank 2017c).<sup>18</sup> At the studentlevel, useful policies include efforts to tailor teaching to the requirements of students (Kremer, Brannen, and Glennerster 2013). Grants may encourage school attendance by disadvantaged students (Glewwe and Marulidharan 2015). Better early childhood nutrition and cognitive development improve students’ capacity to learn (Tsimpo Nkengne, Etang Ndip, and Wodon 2017).

In a stylized policy scenario, education-related policy indicators—secondary and tertiary enroll-ment and completion rates—are assumed to rise over 2018-27 in each EMDE by as much as their largest historical improvement in any ten-year period. This would imply that EMDEs, on average, would raise secondary school completion rates by 5 percentage points and secondary and tertiary enrollment rates by 7 percentage points, on average, during the next decade. In EMDE regions that have made particularly large strides in improving education outcomes but still have ample room for further improvements, such as SAR, secondary school completion rates could rise as much as 16 percentage points over the next decade.

Rapid technological and a more interdisciplinary perspective may also require new education strategies that allow workers to be flexible and adapt their knowledge and expertise throughout their careers. More than a third of the skills demanded in today’s world have been estimated to be no longer desirable for future occupations by 2020 (World Economic Forum 2016). Hence, the ability to mobilize knowledge and rapidly adapt skill sets may be a critical competency for workers to meet demands of future labor markets (OECD 2018a).

# Health policies

At 71 years on average in 2013-17, life expectancy in EMDEs is still below that in

> Restrepo 2017b and 2017c). In addition, automation may expand labor demand by creating new tasks for which labor has a comparative advantage (Acemoglu and Restrepo 2016).

> 18 Other measures such as reducing student-teacher ratios or additional years of schooling have effects that differ widely depending on country circumstances (Evans and Popova 2016; Hanushek and Woessman 2008).

14
When rising demand for biofuels is introduced into the model, agricultural producers dramatically accelerate the output of biofuel crops by shifting resources away from other agricultural activities. This is illustrated in Figure 7, which shows the contribution of each agricultural activity in the model to the total increase in agricultural output. The production increases vary substantially by country and type of grain (Table 5), with the largest gains realized in countries with relatively more abundant land, higher initial demand (e.g., the legislative mandates adopted in the US and the EU), and the existing penetration of biofuel technologies (e.g., Brazil is more competitive in sugar-base ethanol than other producers). At the same time, the supply expansion is limited by the amount of additional land that may be brought under cultivation—which we assume is limited in the six-year horizon of the model—as well as the additional labor that may be attracted to the agricultural sector, which is limited by the large and persistent wage gaps between rural and urban incomes in the developing world.<sup>12</sup> Therefore, output of other agricultural goods—such as rice, other crops, and livestock—declines relative to baseline as farmers find it more profitable to focus on biofuels. Given that many biofuels crops use land intensively, the returns to land rise substantially, ranging from above 40 percent in Brazil to just under 4 percent in Japan. The returns to unskilled labor rise substantially less: for developing countries as a whole, unskilled wages increase by 11 percent while land prices go up by 16 percent.

**Figure 7 Impact of biofuels on global agricultural production**

<!-- Start of picture text --> 7<br>Rice Wheat Corn Oil seeds<br>Sugar cane Other crops Livestock Agriculture<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>-1<br>2005 2006 2007 2008 2009 2010<br>Source : Simulations with World Bank’s ENVISAGE model.<br>Percent difference in real output relative to baseline<br><!-- End of picture text --> 12 In other words, although higher prices of agriculture contribute to a faster closing of rural-urban wage gaps in developing countries (relative to the baseline scenario) and reduce the incentive to migrate at the margin, an average agricultural worker still finds it advantageous to move to an urban area where earnings tend to be much higher. This labor market rigidity limits the supply response in developing countries.

18
Table 12: Firm-level BOS wage premium, 2016-2020.

|||All sample||Com|petitive sec|tors|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|BOS|0.450***<br>(0.00963)|||0.409***<br>(0.0115)|||
|BOS * 2020|0.105***<br>(0.0268)|||0.141***<br>(0.0369)|||
|Public||0.234***<br>(0.0131)|||0.182***<br>(0.0135)||
|Mixed||0.504***<br>(0.0171)|||0.446***<br>(0.0194)||
|Participated||0.722***<br>(0.0187)|||0.918***<br>(0.0283)||
|Public * 2020||0.217***<br>(0.0546)|||0.189***<br>(0.0596)||
|Mixed * 2020||0.0871*<br>(0.0515)|||0.0689<br>(0.0695)||
|Participated * 2020||-0.117***<br>(0.0379)|||-0.169***<br>(0.0595)||
|Ownership 10-_<_25%|||0.594***<br>(0.0339)|||0.747***<br>(0.0527)|
|Ownership 25-_<_50%|||0.709***<br>(0.0284)|||0.974***<br>(0.0517)|
|Ownership 50-_<_100%|||0.567***<br>(0.0165)|||0.527***<br>(0.0197)|
|Ownership 100%|||0.251***<br>(0.0134)|||0.199***<br>(0.0140)|
|Ownership 10-_<_25% * 2020|||-0.0861<br>(0.0628)|||-0.141<br>(0.0944)|
|Ownership 25-_<_50% * 2020|||-0.108*|||-0.129|
||||(0.0596)|||(0.114)|
|Ownership 50-_<_100% * 2020|||0.0608<br>(0.0461)|||0.0509<br>(0.0639)|
|Ownership 100% * 2020|||0.219***<br>(0.0532)|||0.195***<br>(0.0586)|
|Firm size|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm age|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector2d|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Year|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|10605699|10605699|10605476|9685870|9685870|9685705|
|R-squared|0.163|0.163|0.163|0.157|0.157|0.157|

_Note:_ The table presents the results of firm-level regressions of the average wage on ownership status, controlling for firm size, age, 2-digit industry and year fixed effects. Data from RAIS, from 2016 to 2020. Robust standard errors in parentheses. Significance levels: * 10%, ** 5%, *** 1%.

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In the next section, we describe the data source. The methodology for data analysis is described in Section 3, and empirical results are presented in Section 4. We briefly conclude in Section 5.

# **2. Data source, description and preliminary analysis**

The data used in this study were collected in a survey conducted in 2005 of firms operating in the Republic of Korea over the period 1990 – 2000. Given the purpose of this study, only firms traded on the stock market were included in the survey. In total, 49 firms were contacted by way of questionnaires. The latter were divided into two sets. The first set included firms whose names had been associated with adverse environmental news in the printed media, while the second set included firms whose names had not been publicly associated with such news. In both cases, questions were asked pertaining to ownership information, production, employees and sales information, environmental information (wastewater), environmental performance per se (overall environmental performance, environmental management), constraints, incentives and sources of pressure experienced by firms to improve environmental performance, and the impact of media reporting.

The model seeks to explain the determinants of the change in overall environmental performance over the period 1990 to 2000. This change in environmental performance was captured by the response to the question “How do you compare your overall environmental performance in 2000 relative to 1990?” Choices of answer to this 5
Table 1: Industry Details

|Industry Code|Before<sup>†</sup>|After<sup>∗</sup>|Industry Name|
|---|---|---|---|
|311-312|3453|3244|Food Manufacturing and Food Products|
|321-322|1827|1735|Manufacturing of Textiles, Apparel except Footwear|
|331|1157|1092|Manufacturing of Wood and Cork Products except Furniture|
|341-342|710|646|Manufacturing of Paper, Paper Products, Printing, Publishing|
|381|1305|1212|Manufacturing of Fabricated Metal Products except Machinery|

> <u>†</u> Total number of observations in the original dataset. <u>∗</u> Number of observations used in the analysis after the exclusion of observations due to missing variables and industry switches.

present a structural model incorporating optimizing firms.

# 1.2 Dataset

In this study, I use data from Chilean Manufacturing Census (Encuesta Nacional Industrial Anual, ENIA) which is provided by Chile’s National Statistics Institute (INE). The dataset is an unbalanced panel of all establishments with 10 or more workers from 1979 to 1997. I use data on eight biggest 3-digit industries which are described in Table 1. Data is at the establishment level. In Chile, most of the firms had single establishments; hence the distinction between a firm and an establishment is not very crucial. Hsieh and Parker (2007) note that in 1984 only 350 establishments were associated with multi-establishment firms<sup>4</sup> . In the original dataset there were 8452 establishments and after excluding observations due to missing variables and industry switches, I used 7929 establishments in the analysis. Roughly the same 3-digit industries used here are analyzed in a study by Bergoeing, Hernando, Repetto (2005) who note that these industries represent around 60% of total value added in the Manufacturing Census. Table 2 shows the number of establishments of different entry cohorts observed during the span of the study.

> 4Moreover, Caves (1998) points out that most of the results on firm growth and turnover which form the main discussion in this paper, have been insensitive to the distinction between establishment and firm.

6
# _Differences-in-differences Estimates Compared to Simple Differences_

In addition to reporting the numbers and standard errors behind the figures, Tables 1–3 juxtapose two simple estimates of the MVP’s effects. The second-to-last column of each table shows the difference between the indicator values at three years and the baseline values, using the information presented in MVP (2010c). This before-versus-after estimate of the MVP’s effects is shown as the “simple difference within Millennium Village.” The last column of each table shows “differences-in-differences” estimates of the MVP’s effects, based on the MVP data along with DHS data. These estimates show the change in each indicator between the MV evaluation years, minus the change in each indicator in rural areas of the surrounding region between the same years. The values for rural areas of the surrounding region for the MV starting year (2005 in Kenya, 2006 in Ghana and Nigeria) are linearly interpolated using the two DHS values. For Ghana and Nigeria, the 2009 values are linearly extrapolated from 2003-2008 trends. Figure 10 summarizes these results, graphically comparing the before-versus-after (or “simple difference”) estimates compared to the differences-in-differences estimates for all three countries and all nine indicators (with the exception of the two indicators not reported in MVP [2010c] for Sauri, Kenya).

There are two clear patterns in this comparison. First, in many cases the intervention sites perform somewhat better than the surrounding area. Second, the differences-in-differences estimates—which take account of trends outside the intervention sites—are frequently about half as large as the simple before-and-after differences in Kenya and Ghana. In a few cases for those two countries the simple difference shows an important improvement in the indicator while the differences-in-difference shows no relative improvement or even a relative decline. This pattern differs in Nigeria, where while in some cases the differences-in-differences are smaller than the simple differences, the reduction due to removing trends in the surrounding area is less than half of the magnitude of the simple difference. An exception to this pattern in Nigeria is access to improved sanitation, where the simple difference shows a large increase but the differences-indifference shows no relative increase. Overall, the differences-in-differences estimates show 16
> Table 2. Moeconnomic inditors dirning adjustment A comparison with other countries, period averagts for 197841, 198W5, and 198648

|CountiylGNP<br>per capita||GDPgowth||In|veatment/G|DP|Rca|l exchange|ae|(debt|Debt/GDP<br>-service raio|,%)||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||197841|1982-85|1986-88|1978-81|1982-85|1986-88|1978-8|1982-85|198688|1978I|1982-85|<br>1986-88||
|Cte d'lvoire (S1,180t|3.9|0.2|0.5|28.0|16.8|12.9|1.05|1.23|1.10|0.47<br>(24)|1.01<br>(40)|1.14<br>(44)||
|Morocco (S930)|2.3|3.9|3.9|23.7|22.2|20.3|1.00|1.15|1.25|0.50<br>(35)|0.93<br>(38)|1.10<br>(36)||
|Primary expouten (45 countries)|2.8|1.4|2.4|21.4|18.4<br>_|17.2<br>|1.03|1.13|1.39<br>_|0.35<br>(15)|0.53<br>(20)|0.61<br>(29)||
|Chile ($2,100)|7.5|-1.5|6.3|19.8|12.1|16.2|1.01|1.12|1.61|0.37<br>(46)|0.83<br>_(51)_|0.93<br>(29)|4|
|Malaysia ($1,690)<br>fi|7.6<br>MsnuGle|4.8<br>tunag cxpolten|4.6<br>|30.3|34.1|25.7|0.95|0.86|1.01|0.23<br>~~~~~|0.49<br>~~~~~~<br>(15)|0.65<br>~~~~~~<br>(18)|~~~(8)|
|Manuacturng exporters<br>(23 countris)|4.8|2.4|4.9|26.2|23.5|21.8|1.03|1.12|1.39|<br>0.21|<br> <br>0.35|<br><br>0.40||
|Ecuador ($1,260)|5.2|1.7|2.0|25.8|19.6|21.8|0.96|0 Jl|i.43|0.38<br>(35)|0.57<br>(40)|0.81<br>(33)||
|Indonesia ($470)|7.3|2.9|4.0|26.1|27.2|24.1|0.92|1.00|1.52|0.26<br>(18)|0.30<br>(20)|0.58<br>(36)||
|Fuel expozters (15 countics)|6.6|2.0<br>|0.9<br>_________|27.9|24.9<br>.__|19.4<br>_____|1.0|0.95|1.35|0.34<br>(18)|0.44<br>(25)|0.63<br>(39)||

Note: AUl data are unweighted period averages. For definition of country groupings see tabk 1. Sample of 83 countries with population cxceeding I milion in 1980.

a. Thc real xchage rate index is the ratio (expressed in common currency units) of a weighted sum of trading patemns' wholse index (1980 = 1.00). An increase in the value of the index indicates a deprction (increased competiiveness). b. In 1980 U.S. dol"an.

price indexes ovcr the domestic consumer price Sjou: For comparator rigures, Faini and de Melo (1990).