rafmacalaba/data-use-mentions-extended huggingface dataset:
# **7. Recent International Initiatives toward Introducing Output-based (Performance-oriented) Grants to Sustain National Minimum Standards for Merit Goods and Achieve Expenditure Needs Equalization**<sup>**2**</sup>

Results based finance has recently been used to sustain national minimum standards for merit goods and achieve expenditure need equalization in a few countries. Most of these practices are in the fields of education and health only but in a limited number of cases such finance has been used in other public goods such as infrastructure. The following paragraphs highlight these examples.

# **(a) Results Based Financing of Education**

Several countries have launched innovative programs to create results based accountability in financing sub-national education programs. These are reviewed in the following paragraphs: _Australia: National Schools Specific Purpose Payments, 2009_ The National Education Agreement specifies the following objectives for this grant program: _ensuring that all children are engaged in and befitting from schooling, with a goal of lifting the Year 12 attainment rate to 90 percent by 2015_ ; and _ensuring children meet basic literacy and numeracy standards and continuing to improve overall literacy and numeracy achievements_ .

These are equal per capita payments for both government and non-government schooling to all States. Out of this the government schools component for each state is based upon each State‘s share of full time equivalent student enrollments in government schools. The growth factor for government school component is the product of the growth in average government schools recurrent costs and the growth in full time equivalent in government schools. The grant funds are required to be spent on schooling but States have full budget flexibility to allocate funds as they see fit to achieve mutually agreed objectives. The program has not set any benchmarks for performance but as part of an overall reporting and accountability framework, states must provide performance data to the Commonwealth and to the general public. The COAG Reform Council will collate data for all states and will publish such statistics and analytical reviews of performance for government and general public use (The Australian Treasury, 2009)
manufacturing exports in 2018 were 4.5 times larger than Indonesia’s, despite a population less than half that of Indonesia.

# **6 Conclusion**

Most of the literature on energy prices and plants’ performance has focused on electricity. However, distinguishing between electricity and fuels is important because they power capital equipment of different types. In this paper, we test for the impact of both electricity and fuel prices, finding an asymmetric impact: fuel price increases have a positive effect on plants’ performance while electricity price hikes worsen performance in Mexico and have a more muted effect in Indonesia. We contend that this result is consistent with fuel powering old technology capital vintages, while new technology tends to be embodied in electric capital vintages. An increase in fuel prices induces plants to scrap old fuel-powered machinery and purchase new electric equipment. Since electric machinery is more productive and energy-efficient, the static negative effect of fuel price increases is more than compensated by the positive effect on technological upgrading, with a potentially positive net effect on performance.

Our analysis is based on large nationally representative samples of manufacturing plants in two of the largest developing countries (Indonesia and Mexico). As such, threats to external validity which typically affects single country empirical analyses are less of a concern in our framework. Our approach also implies that the results are not specific to a particular setting or identification strategy.

These results suggest that besides the environmental and fiscal costs, subsidization of fuel prices may also lead to firms using sub-optimal technologies to the extent that a decrease in subsidy may actually improve performance. This reinforces the arguments in favor of fuel subsidy reform and carbon taxes, particularly in developing countries.

Much as in the case of other technology, such as management practices (Bloom et al., 2013), the findings also hint at some (information or otherwise) frictions that prevent firms from adopting more efficient electricity powered technology despite the positive returns of adoption. This calls for further research on the barriers to adoption in developing countries,
either income or consumption per capita – from 142 economies in the developing world. This collection of survey data is then combined with complementary data on population, inflation, real economic growth, and Purchasing Power Parity (PPP) exchange rates.

Estimating poverty rates for different types of households requires additional data on individual characteristics, comparable across countries and regions, from the same household surveys used to calculate poverty. Poverty profiles typically utilize a set of variables that are relatively straightforward to obtain such as age, gender, education, and sector of work. Nonetheless, compiling these variables from diverse household surveys, which differ in the quality of their data and the nature of the questionnaires, and harmonizing variable names and codes across surveys, is a remarkable achievement. These heavy data requirements are the main reason why only a few empirical studies have examined patterns across such a large number of countries.

This analysis is based on the September 2016 vintage of the Global Micro Database (GMD), a collection of globally harmonized household survey data recently developed by the Data for Goals 4
million (Coale, 1981) to 45 million (Dikotter, 2010) individuals, mostly rural residents, died or failed to be born in the three year period.

We examine two measures of famine severity at the county level. The first measure follows Meng, Qian and Yared (2015) and proxies the famine severity as proxied by the birth cohort size of survivors observed in 1990 census. The logic for this measure, as Meng, Qian and Yared (2015) argued, is that “famine increases infant and early childhood mortality rates and lowers fertility rates such that a more severe famine results in smaller cohort sizes for those born shortly before or during the famine.” Specifically, we define our variable “ _famine control_ ,” the opposite of “famine severity,” in a county as the ratio of the number of surviving births (per year) in the county during the period 1959-1961 relative to the number of surviving births (per year) in the same county during the period 1954-1957, as observed in the 1% sample of the 1990 China Population Census: The _higher_ the measure, the _less severe_ the famine was in county _C_ . Meng, Qian and Yared (2015) point out that birth cohort size measure of famine severity is not vulnerable to the misreporting because it is less influenced by the government’s desire to understate famine severity. However, it potentially suffers from the measurement error caused by differential death rates after the famine period across counties.

The second measure is the newly compiled county-level death rates, number of deaths per thousand (see Section 4).

Figure 3 plots the kernel density estimation of the distribution of the famine control proxied by birth cohorts (top panel) and death rates (bottom panel) among counties with FA3- and YRD-affiliated cadres (left), and counties with or without guerilla presence (right). The top panel of Figure 3 demonstrates that counties led by FA3-affiliated cadres had more severe famine as measured by birth cohort size than counties led by YRD-affiliated cadres
# **I. Introduction**

Access to reliable electricity has long been recognized as a driver of social and economic development. A growing body of literature shows that low access to and poor quality of electricity affect households’ living standards and social welfare (Chakravorty, Pelli, and Marchand 2014; Samad and Zhang 2016, 2017). Many studies examine the causal link between electricity and development in Bangladesh and India (Khandker, Barnes, and Samad 2012; Rud 2012; van de Walle et al. 2017). To the best of our knowledge, this is the first rigorous empirical study of Pakistan.

Previous analysis of Pakistan shows only correlation between electricity and income (Gellerson 1982; Yazdanie 2010; Majid 2013). Such correlations are often misleading, for at least two reasons. First, people with lower income either put off adopting electricity once it becomes available or are unable to afford the connection fees and other costs of electricity. Second, areas farther from urban centers and areas with limited access to roads and markets are more likely to stay off‐grid. People in these areas are also more likely to be poor. Because it fails to address the endogeneity of program placement and household adoption of electricity, simple correlation analysis cannot explain the direction of causality.

This study uses econometric techniques to determine the net effects of rural electrification on various household and individual welfare outcomes in Pakistan, after controlling for endogeneity using the propensity score matching method. The analysis is based on data from two household surveys. The first—the Pakistan Social and Living Standard Measurement (PSLM) Survey—is a two‐period panel household survey that was carried out in 2007/08 and 2010. It provides information on household demographic characteristics, income, expenditure, education, health, and electrification status. It did not collect information on the quality of electricity supply.

The second survey was conducted in 2014 under the Lighting Pakistan program of the International Finance Corporation (IFC 2015). It asked households to estimate the average duration
>Sources: Authors’ estimations based on SEDLAC (CEDLAS and World Bank), Human Development Report Office (United<br>Nations), and WEO (October 2018).<br>Index (2002=100) Index (2002=100)<br>Index (2002=100, inverted axis) Index (2002=100, inverted axis)<br>Index (2002=100) Index (2002=100)<br>Index (2002=-100, inverted axis) Index (2002=-100, inverted axis)<br><!-- End of picture text --> 20
firms had loans or lines of credit in 2008-2009, only about 38 percent of small firms did.<sup>8</sup>

> 7 Though BEEPS was conducted in 28 countries, our sample is comprised only of firms that were interviewed in all three BEEPS rounds. That sample spans 23 countries.

> 8 Data from www.enterprisesurveys.org based upon BEEPS data.  The regional averages are unweighted averages of the weighted country averages (i.e., all countries weighted equally but weighted averages are used for each country).

6
banks operating in Kenya along with their market share, and, when the bank is owned by multiple parties, allocating the ownership across the regions of our model. The database Bankscope was sufficient for this task in most cases, but websites of the banks had to be consulted to allocate ownership shares in several cases. The results, by region and sector, are presented in table 6. Documentation

> 24 Kenya agreed to implement zero tariffs on East African Customs Union imports as of January 1, 2005. See Michael-Stahl (2005).

23
in each industry. We assume that because of heterogeneity in intrinsic industry characteristics such as headquarter intensity and capital intensity, industries incur di¤erent bene...ts and costs from ...nancial integration and thus have di¤erent incentives to invest in subsidiaries. This assumption is consistent with the ...ndings of a growing literature led by Antras (2003) and Antras and Helpman (2004), who show that industries with higher headquarter intensity and capital intensity tend to have greater incentives to engage in integration and subsidiary investment.

To measure the degree of intra-...rm ...nancial linkages in each industry, we use the ratio of investments in subsidiaries relative to total assets for the median ...rm of each industry. This ratio captures the extent of ...nancial integration by the industry of parent ...rms. Given that the information required to compute the variable is not systematically available for private ...rms around the world, the variable is computed based on a dataset of publicly listed companies obtained from the Global COMPUSTAT. The measure is averaged for 2000-2004 to smooth out temporary ‡uctuations.

While identi...cation of our analysis does not require industries to have exactly the same ...nancial linkage levels over time and across countries, it does rely on the ranking of industries to be relatively stable. Otherwise, the proxy could potentially bias us against ...nding any relationship between ...nancial linkages and establishment responses to economic crises. For this reason, we perform two checks on the variable. First, we construct the measure using data from di¤erent time periods and ...nd the ranking of industries to be relatively stable over time. This observation suggests that industries’ranking in the degree of intra-...rm ...nancial linkages is mainly dependent on characteristics innate to each industry and thus unlikely to change structurally in response to short-term idiosyncratic shocks. Second, we compute the
# **6. Code availability statement**

The Random Forest Classification machine learning model is open-source and was used through the Python package scikit-learn: <u>https://github.com/scikit-learn/scikit-learn. Sensitivity</u> analysis was performed using the Python application programming interface of the Gdal package: https://gdal.org/.

# **7. References**

- Ahmed, B., 2021. The root causes of landslide vulnerability in Bangladesh. Landslides 18, 1707–1720. https://doi.org/10.1007/s10346-020-01606-0

- Ahmed, B., Rahman, M.S., Islam, R., Sammonds, P., Zhou, C., Uddin, K. and Al-Hussaini, T.M., 2018. Developing a dynamic Web-GIS based landslide early warning system for the Chittagong Metropolitan Area, Bangladesh. ISPRS International Journal of GeoInformation, 7(12), p.485.

- Ahmed, B., 2015. Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh. Landslides 12, 1077–1095. https://doi.org/10.1007/s10346-014-0521-x

- Ahmed, B., Rahman, M.S., Rahman, S., Hug, F.F., Ara, S., 2014. Landslide Inventory Report of Chittagong Metropolitan Area, Bangladesh.

- Ayalew, L., Yamagishi, H., 2005. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65, 15–31. https://doi.org/10.1016/j.geomorph.2004.06.010

- Breiman, L., 2001. Random Forests. Machine Learning 45, 5–32. https://doi.org/10.1023/A:1010933404324

- Chisty, K.U., 2014. Landslide in Chittagong City: A perspective
The four treatments implemented in Andhra Pradesh were: (i) whether the household is visited by an insurance educator; (ii) whether the educator is endorsed by an LSA, (iii) whether the educator presents the additional education module, and (iv) whether the visited household receives a high reward (Rs. 100 rather than Rs. 25). Because endorsement took place in twothirds of villages, we include as an additional treatment the interaction of whether the village was one in which endorsements took place and whether the household received a visit, to identify spillovers from endorsement.

Results are presented in Table 5. We use data from all 1,047 households, and since treatment compliance is not perfect, the results should be interpreted as intent-to-treat effects. The unconditional insurance take-up rate is 28%. Basic treatment effects are reported in Columns (1)-(3). Column (1) includes only the treatment variables. Column (2) also includes village fixed effects, while Column (3) includes both village fixed effects and a set of household covariates (specific controls are listed in the table notes).<sup>17</sup> In each of these three columns, being assigned a household visit, even if not combined with other treatments, increases take-up by 11.5 to 17.2 percentage points, while a high reward increases take-up by 39.4 to 40.8 percentage points. Each of these estimates is statistically significant at the 1% level. Individual LSA endorsement alone is positively signed and marginally statistically significant (t-stat between 1.5 and 1.7). However LSA-endorsement and the village endorsement variable are jointly significant at the 2% level in columns (2) and (3), which control for village fixed effects. Finally, the effect of the education module on demand is economically small and statistically insignificant.

Columns (4)-(6) interact these treatments with three household variables in turn: an indicator for whether the household
school premium which would raise a student’s test score 0.19 to 0.27 standard deviations. Using district-level and village level access instruments generally results in similar estimated effects, although the effect ranges from 0.15 to 0.41, depending on the sample and the instrument. Because village-level access instruments were only available for a selected subsample of non-movers, we view the estimates based on these instruments with caution. Nonetheless, the similarity of the magnitudes of the OLS and the districtlevel instrumental variable estimates suggest that in total, the endogeneity bias resulting from parent’s choice of school type does not invalidate the qualitative conclusions drawn from the OLS and fixed-effect estimates. Furthermore, the consistent finding of a positive public school premium across all estimation strategies is strong evidence that public junior secondary schools, on average, provide superior preparation for the national exam.

# **DIFFERENT TYPES OF PRIVATE SCHOOLS**

We now turn to comparing the average effect of attending different types of private junior secondary schools on test scores. We regressed students’ normalized score on the junior secondary school test on an indicator of school type that distinguishes between public Madrassah, private secular, private Madrassah, private other Muslim, and non-Muslim religious schools, controlling for the student characteristics listed above. Because the type of private school cannot be identified using the village data from the IFLS or school census data from the Ministry of Education, instruments for this regression were unavailable and only OLS and fixed effects results are presented. Table 5 presents these results. In the full sample, the disadvantage in test scores relative to public 25
Table A7: Impacts of Extended Provider Training and Patient Information on Other Aspects of Care

||(1)|(2)<br>Clinic Survey|(3)|(4)<br>Home|(5)<br> Survey|
|---|---|---|---|---|---|
||Number of<br>Medications<br>Prescribed|Number of<br>Medications<br>Prescribed<br>Excluding<br>Antimalarial|Prescribed<br>Antibiotic|Time Waiting<br>at Clinic|Time<br>Consulting with<br>Provider|
|Extended Training|-0.0698|-0.0853|-0.0200|-0.707|-1.081|
||(0.165)|(0.123)|(0.0474)|(1.471)|(0.822)|
|Patient Infomation|0.101|0.106|-0.0410<sup>_∗_</sup>|0.479|-0.363|
||(0.0728)|(0.0688)|(0.0209)|(0.983)|(0.627)|
|Mean (Basic Training, No Pat. Info.)|3.78|3.00|0.64|18.25|13.42|
|N|1971|1971|1971|1427|1427|

_Notes_ : Robust standard errors clustered at the clinic-pair level in parentheses. All regressions include strata and survey date fixed effects. Additional covariates are selected using double lasso, from the set of all covariates listed in Table 1 and all individual-level characteristics in Table A4 (excluding predicted malaria risk and characteristics only measured at the home survey) as well as the square of patient age and illness duration. We also
equals 0.16 meters. Scaling up, we got the share of productive land which is lost for one meter of SLR, labelled _LR_ . Data on coastline length are provided by the CIA database (www.cia.gov); data on the fraction of coast suitable for agricultural activities have been obtained from UNEP (2005).

_LRT_ The percentage change in the land stock by year and country, , is computed by multiplying the percentage of effective land change by meter of SLR, _LR_ , and the predicted adjusted SLR, as follows: Notice that the impact function (3) has four parameters. Two parameters (α _,_ β) are common across all regions, two other parameters ( _LR_ and _VR_ ) are country/region specific.

Table A1 in the Appendix shows, for each GTAP9 region, the percentage loss of land by meter of SLR, corresponding to the parameter _LR_ in (3), and the vertical land motion (VLM), corresponding to the parameter _VR_ .

Table A2 in the Appendix illustrates the percentage losses of productive land endowments for +1, +2, +3, +4 and +5 °C increases in average temperature, at the years 2050 and 2100, for all 140 countries and regions. As one can see, relevant physical effects of SLR are concentrated in a few countries, in particular: small island states of Oceania, Central America and Asia, Hong Kong SAR, China, Japan, Singapore, Jamaica, Puerto Rico, Trinidad and Tobago, Cyprus, Croatia, Bahrain, Kuwait, Qatar, United Arab Emirates and Mauritius.

# **3. Climate change impact #2: Variation in crop yields (agricultural productivity)**

Climate change is expected to bring about higher temperature, higher concentration of carbon dioxide in the atmosphere, and a different regional pattern of precipitation. These are all factors affecting crop yields and agricultural productivity. Not surprisingly, effects of climate change on agricultural production volumes are perhaps the most studied area
||At-risk-ofpov|ertyrate (60% of|mediandisposabl|eincome)|
|---|---|---|---|---|
|Overall|34.2%|17.1%|25.1%|-9.2%|
|Single adult|25.8%|9.4%|14.1%|-11.7%|
|Single parent|33.6%|25.1%|35.7%|2.1%|
|Couple without children|30.5%|11.9%|18.1%|-12.5%|
|Couple with 1 child|10.0%|10.6%|16.1%|6.1%|
|Couple with 2 children|10.8%|14.7%|22.6%|11.8%|
|Couple with 3+ children|26.6%|28.0%|41.0%|14.3%|
|Single retiree|96.3%|11.1%|20.0%|-76.3%|
|Couple of retirees|95.5%|2.5%|5.9%|-89.5%|
|Mixed|33.6%|20.8%|29.5%|-4.0%|

<mark>Source: Own estimates based on HBS 2014.</mark>

# **V. Progressivity, Marginal Contributions, and Pro-poorness of Taxes and Transfers**

How did each of the fiscal interventions contribute to the observed changes in poverty and inequality? Figure 4 presents the distributional impact of different components of the tax and benefit system as a share of market income (including pensions). Most components of the system are progressive, with the bottom 50 percent of the distribution being net