rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

On September 25, 2015, countries appropriated a set of 17 Sustainable Development Goals (SDGs) to end poverty, secure the planet, and ensure prosperity for all (World Bank 2018b). The SDGs are a call for action by all nations—poor, rich, and middle-income—to advance prosperity while protecting the planet (World Bank 2018a). The idea is that ending poverty must run inseparably from efforts to boost economic growth and address a range of social needs, including education, health, social protection, and job opportunities, while being mindful of environmental change and ecological security. For the goals to be reached, all stakeholders—governments, the private sector, civil society, and individual citizens—must be actively engaged (World Bank 2018ab).

Concurrently, efforts by the international community to take action on climate change led to the establishment of the United Nations Framework Convention on Climate Change (UNFCC), which has held annual Conferences of the Parties (COPs) since 1995. In 2015 the Paris Agreement was adopted and ratified by 178 countries, committing virtually all countries to undertake ambitious efforts to combat climate change and adapt to its effects. One of the key requirements of the climate agreement is energy transition to promote greater use of renewable energy.

Sub-Saharan Africa (SSA) is particularly vulnerable to climate change. Its suffers from energy poverty,<sup>1</sup> as well as severe flooding, coastal erosion, prolonged droughts, strong winds, crop damage, and biodiversity loss. Many case studies (IIASA 2014; UNEP 2016) present evidence of the direct link between energy access and socioeconomic benefits and rural poverty reduction in Africa.

A regional study by the World Bank suggests that less than 43 percent of the population of SSA had access to electricity in 2016, with almost two-thirds of the population (more than 600 million people) living without electricity (80 percent in rural areas) (World Bank 2018b). The investment required to
rest of Europe, India, Japan,<br>the Republic of Korea, Middle East, Asia, the Russian<br>Federation, South Africa, South America, United<br>States)|
|**GRACE**|All economic activities in the world,<br>as expressed by national accounts<br>data|Base year (2014).<br>The model can be<br>solved yearly until<br>a future year, such<br>as 2050 and 2100<br>by updating<br>external variables<br>every year.|15 Regions|
|**IFs**|A multisector general equilibrium<br>economic model (which uses<br>inventories as buffer stocks and to<br>provide price signals so that the<br>model chases equilibrium over<br>time); it provides labor, investment,<br>and consumption information to<br>partial equilibrium energy and<br>agriculture models.|base year of IFs is<br>2015 and the<br>model runs<br>recursively in<br>annual time steps<br>through horizons<br>up to 2100.|186 countries|
|**PHOENIX**|27 production and investment<br>sectors|2000-2150|GTAP countries|
|**POLES**|Industry, services, agriculture|Base year: 2015,<br>time steps: Yearly,<br>horizon: 2015-<br>2100|66 regions (including detailed EU28, all OECD<br>countries and main non-OECD countries)|
|**WorldScan2**|16 sectors|2000-2050|16 regions|

13
microfinance organizations, and have sufficient resources and capacity for these additional functions. There is also a risk that promoter agency’s attention is diverted to developing the new functions before core functions of the SHGs and the federations are sufficiently established. Hence, it is advisable that SHG federations establish their competency as microfinance organizations before diversifying into other activities.

# **7.  Issues and Challenges: External**

## **Capacity of Promoter Agencies**

Limited organizational capacity of promoter agencies is perhaps the most crucial constraint in developing sustainable SHG federations. Capacity of the promoter agencies primarily determines the quality of SHG federations. Promotion of federations requires institution-building skills beyond that required in promoting SHGs. Very few promoter agencies –both NGOs and government agencies possess skills.

NABARD should identify SHG federation models that add value to the SHGs and take initiatives to build the capacity of SHG promoter agencies in promoting such federations. As in the case of SHG promotion, these initiatives could include exposure visits to such federations and structured training programs. Promoter agencies that have promoted such federations could be used as lead trainers. Additionally, new organizations that focus exclusively on capacity building of promoter agencies and SHG federations also need to be promoted in states with significant number of SHGs. Donor agencies could support the establishment of such organizations. An effort in this direction is the creation of Andhra Pradesh Mahila Abhivrudhi Society (APMAS with the support of DFID and CARE-India. APMAS works on quality assessment and enhancement of SHGs, SHG federations, and SHG promoter organizations.

## **Inadequate Knowledge Base on SHG Federations**

No estimates are available on the number of federations in existence, their outreach in terms of SHGs supported, the extent of the services provided by them, or the amount of financial resources handled by them. NABARD should play a leading role in improving the knowledge base. A database of SHG federations needs to be created. Assessments and impact studies of the different federation models, and development of performance standards and rating indices need to be carried
**Figure 6. Infant and Child mortality:  One year and five years survival probability, by birth cohort. From birth histories in the Demographic and Health Survey 2000 Cambodia.**

<!-- Start of picture text --> 1<br>0.95<br>0.9<br>0.85<br>0.8<br>0.75<br>0.7<br>70-74 75-79 80-84 85-89 90-94 95-99<br>Birth cohort<br>Percentage of children who survived the first year Percentage of children who survived the fifth year<br>Survival probability<br><!-- End of picture text --> 26
9

**Chart 2. Labor Force Activity Rates in Overall Economy and in Formal Sector 1995**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>50/ / \\ Male-overall<br>50% 0- Male-formal<br>\ \ \\ --- Female-overall<br>4v0% - -X - Female-formal<br>30%- l1 8<br>0%<br>0) st a) in o 0) Lo o 0) 6 +<br>('. e% e'~ t L U) Ct) (D (D ) U)<br>-N tN Cf) CU) ° L O U) co co o-<br>Source: World Bank household survey (1995)<br><!-- End of picture text --> Clearly, the economic transformation during the transition has led to significant changes in occupational choice and allocation of time within the household. The collapse of real wages in the formal sector has induced workers to allocate part - or all - of their working time to informal activities. Within households, the allocation of time is marked by a return toward a more traditional division of labor between men and women with women devoting a much higher proportion of their time to obtaining a secondary income, especially at young and old ages.
8 Before surveying existing studies, a few dimensions of the available data are worth noting.

The two major sources of government expenditure data are national income accounts and government buidgets, the former being more widely available and reported on in discussions of government size in developing countries. Standardized sources for national income accounts for developing nations tend to provide only government consumption (Gc), which differs from total government expenditures (Ge) by excluding government transfer payments (Gt) and government gross capital formation (Gi). By comparison, government budget data, widely available for OECD nations, tend to cover all components of Ge. Measures of Ge seem most appropriate for assessing the total resource cost of government activity. By comparison, Gc is a more limited index of the "resource burden" of government but may be a more precise measure of spending for public goods, per se.

One of the most standardized and comprehensive sources on Gc, in terms both of number of nations and years of coverage, has been provided by Summers and Heston(1984). This data set has been widely used by R. Ram, who has authored a number of recent empirical papers on the growth of government.8 The Summers and Heston series employs, so-called, 'international prices' in order to reflect truer purchasing power parity than is available by relying on official market exchange rates. These data are, therefore, deflated to a particular series of constant dollars. Given the differences between the Summers and Heston data and those employed by other researchers (i.e., most
# **C.4 Combined Instrument and Reduced Form Estimates, by Age**

The previous subsection, Appendix Section C.3, estimates the effect of the FPE and MTI reforms on schooling using five instruments. However, reduced form analysis across different ages would become cumbersome with the large number of variables; combining the impact of the two reforms would allow for easier graphical representation of the impact of the reforms across different ages. Cutting the data by age is only possible with the inclusion of both reforms, this allows for the use of the national sample and additional variation from the timing of the MTI implementation.

Creating a single instrument that measures exposure to both the FPE and MTI reforms, and predicts changes in schooling, requires a simplifying assumption from the five-instrument model used in Appendix Section C.3. The necessary baseline assumption is that the magnitude of the effect of an additional year of FPE on schooling is the same as each additional year of MTI, in either a positive (no script change) or negative (with script change) direction. For simplicity, it is also assumes that there is no interaction between the two reforms, only one of the six interaction terms in the first stage of Appendix Tables C.7 to C.9 has a p-value below 0.15. These assumptions yield a combined intensity measure that can be expressed using the following equation: _Izy_<sup>_MT I−T_</sup> is zero for all non-Tigray regions, and is shown to be positive in Appendix Tables C.7 to C.9. Similarly, _Izy_<sup>_MT I_</sup> , is only non-zero for MTI regions other than Tigray (as described in Appendix Section C.2), and shown to be negatively associated with schooling in Appendix Tables C.7 to C.9.<sup>24</sup> Additionally, the differences in the estimated effect of each intensity measure can also be statistically tested. Using the estimates from the DHS
# **7. Conclusion**

Malnutrition is a pervasive problem in Ethiopia and it is no doubt a direct consequence of widespread poverty. Analyzing the impact of different factors on children’s malnutrition is further complicated by Ethiopia’s vulnerability to frequent and severe droughts. Using demographic and health survey data, this study examines on the impact of access to basic environmental services, such as water and sanitation, on the probability children are stunted and underweight. The focus is on the impact of externalities associated with access to these services. Therefore, controlling for household access to these services, we look at how the proportion of households with access to these services in the community affects children’s nutritional status.

We find that child’s age, mother’s height, household wealth and mother’s education are important determinants of a child’s nutritional status, which is consistent with the findings of most studies in this literature. The impact of mother’s education, however, is small and probably mostly a reflection of the very low level of female education in Ethiopia. With respect to the environmental variables, we find that there are indeed significant externalities associated with access to water and sanitation at the community level. The external impacts of the community level of access to these services are an important determinant of the probability a child is underweight. Our results also show that the impact of access to water is larger for children living in rural areas.

As the proportion of households in the community with access to water and sanitation increases, the external impact on children’s nutritional outcomes diminishes, as expected. The external impact of community access to water and sanitation on the probability a child is stunted is only significant when the proportion of households with access to these services is low. As the level of access increases, however, the coefficients on these variables become small and no longer significant. Stunting is a chronic condition and is likely to persist even after elimination of conditions that contribute to it—these results
is significantly improved in Round 2 in both treatment arms (Panel B). As previously, the effect in the UCT arm is significantly stronger than that in the CCT arm. Together, the graphical analysis and the alternative outcome measures show that our results are robust to the use of alternative indicators of psychological distress.

# _Dimensions of Mental Health Affected_

In Table 5 we investigate the impact of the intervention on the three dimensions of mental health measured by the GHQ-12 (Graetz, 1990): anxiety and depression, social dysfunction, and loss of confidence. For this analysis, we again linearly rescaled the three dimensions such that they take values from 0 to 100 so that the magnitude of the impact on each dimension is comparable to the others. Panel A shows that among _baseline dropouts_ the CCT intervention did not affect any of the three components of mental health. Among _baseline schoolgirls_ (Panel B), on the other hand, we do find a substantial impact of the intervention on two of the components. First, there is a significant 3 percentage point improvement in the Round 2 social dysfunction scale among respondents in the CCT arm, which appears to have persisted after the end of the intervention. Second, both the anxiety and depression scale and the social dysfunction scale improved by more than 8 pp among respondents in the UCT arm in Round 2. The impact on the anxiety and depression scale had weakened (but was still statistically significant at the 90% confidence level) by Round 3, whereas the program effect on social dysfunction had entirely dissipated.

# 4.3.2 HETEROGENEITY OF PROGRAM IMPACTS

As we have shown above, while cash transfers were beneficial in improving mental health among _baseline schoolgirls_ in both treatment arms, the program impact was significantly larger in the UCT arm than in the CCT arm. In this sub-section, we examine the elasticity of mental health 17
Consistent with household survey data in many developing countries, our calculations confirm the lack of representativeness of households in the upper tail of the income distribution. This is reflected in the degree to which we can capture the tax base and thus evaluate the allocation of the tax burden across the income distribution and the true implications of the tax and benefit system for redistribution. For example, there is no one in the data with earnings high enough to be paying salary tax in the highest tax bracket. Similarly, underrepresentation of the highest income households suggests some caution is needed in the interpretation of inequality measures under different income definitions and changes in inequality level over time.

Several elements of the tax system are simulated relatively well when compared to administrative statistics. For example, the exercise captures about 96 percent of employment injury insurance and 99.2 percent of property tax.

Our method of allocating education benefits allows us to assign 99.2 percent of education expenditure and 93.1 percent of health care costs. In the case of education, this is because transfers per student are given their value based on the official statistics. For health transfers, this validates the adopted approach to imputation of the value of health care benefits and indicates that the source of health costs used for the valuation of various procedures is very accurate. It is also worth noting that in this case underrepresentation of top income households may be less damaging for the precision of our simulations since many of the highest income households are likely to opt for the use of private health care.

Table 3: Simulation results and validation

||**Amount simulated,**<br>**(billion KHR)**|**Macro data,**<br>**(billion KHR)**|**Proportion of**<br>**simulated to macro**<br>**(%)**|
|---|---|---|---|
|Direct
aster|controls. (<br>isks; ***p<|2) All es<br>0.01 **|timates<br>p<0.05 *|weighted by<br>p<0.10|house|hold sampl|ing weight. S|tandard|errors ar|e clustered|at the enu|meration|area level. Si|gnificant|

Tables 7 and 8 present the results from the estimations of equations 1a and 1b separately for women and men, respectively, across the same time use categories reported in Table 6. The findings across equations 1a and 1b are overall consistent. In Tables 7 and 9, we see that labor force participation, as opposed to staying at home, has the largest association with time use. For off-farm (wage or NFE) work, the negative association with unpaid work is particularly large. Looking at the household fixed effects estimates for women (Table 7), working in an NFE is associated with roughly 50 minutes less time spent in in childcare; for wage workers, the decline is nearly 120 minutes. For women agricultural workers, however, the household fixed effects estimates do not show a significant association with unpaid work. The more muted association of agricultural work, compared to off-farm work, with time use is consistent with other findings discussed in this section. In general, we also find that labor participation has a much stronger association with women’s time use as opposed to men’s — for example, larger negative effects of off-farm work on unpaid domestic and childcare; and greater effects on off-farm and agricultural activity. The coefficients on other variables are also larger for women as compared to men, including significant negative effects of having a larger share of annual earned income in off-farm activities on unpaid work, particularly domestic (cooking and cleaning) activity; a negative effect of being in an older age group on unpaid work, an increase in domestic work from having other elderly household members
711|711|711|711|711|711|711|711|
|N2 (target survey)|698|698|698|698|698|695|698|698|
|**True poverty rate in TA 1**<br> <br>**Note**: The standard errors in parentheses are adjust<br>linear regression model with the theoretical distri|ed for comple<br>bution of the e|x survey desi<br>rror terms an|gn. All estimat<br>d Method 2 us|1<br>(<br>es are obtain<br>es the empir|7.7<br>1.9)<br>ed with the pop<br>ical distribution|ulation weigh<br>of the error|ts. Method 1<br>terms. Both m|uses the normal<br>ethods employ|

**Note** : The standard errors in parentheses are adjusted for complex survey design. All estimates are obtained with the population weights. Method 1 uses the normal linear regression model with the theoretical distribution of the error terms and Method 2 uses the empirical distribution of the error terms. Both methods employ cluster random effects. Models 3.1 and 4.1 employ non-standardized variables. Models 3.2 and 4.2 employ variables in TA 3 standardized by those in TA 1. Food and non-food expenditures, household size and age in both surveys are transformed to normality using the Box-Cox method before standardization. The imputed poverty rates for TA 3 use the estimated parameters based on the TA 1. 1000 simulations are implemented. The true poverty rate for TA 1 is the estimate directly obtained using the consumption data in TA1 and the poverty line in NPS 2019/20 deflated to 2022 prices with the annual WB deflator. The estimates shown in bold or with an “a” respectively fall within the 95% CI or one standard error of the
# **1. Introduction**

The increasingly prominent threats of climate change have inspired a significant body of economic research on a variety of outcomes, such as agriculture (Deschênes and Greenstone, 2007; Schlenker and Roberts, 2009), labor productivity (Somanathan _et al._ , 2021), human health (Deschênes and Greenstone, 2011), and crime and conflict (Burke _et al._ , 2015a; Heilmann _et al._ , 2021). In particular, since climate change could reduce economic growth (Dell _et al._ , 2012; Newell _et al._ , 2021), this might, in turn, translate into slower progress with poverty reduction. Furthermore, climate change may unevenly affect different countries and population groups with the most harmful consequences of income decline being borne by less affluent groups; these would likely result in increasing inequality both across and within countries (Diffenbaugh and Burke, 2019; Hsiang _et al._ , 2019).

A possible explanation for the lack of empirical evidence on the impacts of global warming is the challenge of obtaining appropriate measures of poverty and inequality. While household surveys—the main source of official poverty statistics—have become increasingly available, these surveys are still unavailable or infrequently collected in many countries, particularly in poorer regions.<sup>1</sup> Another explanation is that poverty and inequality can widely vary within (and across) countries. Consequently, ignoring subnational variations could easily mask the dynamic relationship of these outcomes with climatic conditions, which have long been known to be location specific. Indeed, recent studies suggest that analysis using spatial aggregation of data at the country level may not reveal the true effects of climate change on economic growth, which can be improved with analysis using more disaggregated data at the subnational level (Damania _et al._ , 2020; Kalkuhl and Wenz, 2020).

> 1 A recent survey by Beegle _et al._ (2016) indicates that just slightly more than half (i.
The work presented here builds on a large literature on macro-level growth accounting and TFP measurement (Hulten 2010). However, it is important to note that there is also a rich literature on measuring economic efficiency at the sector or firm level, using methods and data that are much more fine-grained than what will be presented here. The focus on the macro scale has advantages, in particular the ability to pick up, however indirectly, the effects of policy and institutional reforms that can have macroeconomic consequences, in addition to technical progress. Using the World Bank _Wealth of Nations_ data also means that we can do broad comparisons of country, regional and income-class performance on TFP growth over time.

The _Wealth of Nations_ data include estimates of fixed capital stocks (based on the Penn World Table), human capital, and stocks and flows of natural resources in both quantity and value terms. To produce the estimates of TFP growth below, the World Bank data are augmented by UN national accounts data on the composition of value added, total remuneration of labor from the Penn World Table, as well as the number of persons employed aged 15 or more from the ILO. We also employ indices of real agricultural output from the FAO.

We begin section 2 by considering the general approach to productivity measurement and the specific issues associated with the incorporation of natural resources into growth accounting, followed by a detailed specification of the methods and data used to measure TFP growth. While it may seem at first glance that adding another factor to the calculation would reduce TFP growth estimates, we will demonstrate that is not always the case. Results of the TFP growth calculations and key findings are highlighted in section 3. The final section sums up the results, identifies how new measures of TFP could be used for policy work in the World Bank, and suggests areas for further work.

# **2. Methods and Data**

TFP growth is not a particularly intuitive concept for many people, but a simple formalization may help. If we compare