rafmacalaba/data-use-mentions-extended huggingface dataset:
|0.34|
||(0.06)|(0.02)|(0.07)|(0.02)|(0.07)|(0.02)||||

**Note:** Standard errors are shown in parentheses (List method estimates show robust standard errors). All regressions are adjusted for sampling stratification. *** p<0.01, ** p<0.05, * p<0.1. Columns 1-6 show the relevant covariate coefficients from separate regressions. Columns 7-9 show the p-values from a chi-squared test of the differences between these coefficients. The vulnerability index includes measures of food poverty and household economic insecurity, and wife's stress. The unequal village gender norms index includes beliefs about unequal gender attitudes held by others in the village. The relationship quality index includes measures of  trust, satisfaction, empathy and communication in the marriage. The public speaking confidence index includes measures of willingness to speak up in public. The progressive gender attitudes index includes measures of personal views on the acceptability of IPV and on gender roles.

increase in a woman’s perception of unequal village gender norms was associated with a 3 percentage point increase in the probability of experiencing IPV when violence was measured face-to-face, a 4 percentage point increase when measured using ACASI, and a 37 percentage point increase when using the list method. However, the positive conservative norms-IPV relationship was only statistically significant when IPV was measured using the list method. In Rwanda, the differences between the list and direct methods were statistically significant; in Nigeria, they were not.<sup>29</sup> Table A9 shows that the gender norms-IPV relationship difference between the methods was driven by the respondents who believed their village had the most conservative norms. There was no IPV reporting difference across the list and direct methods for respondents who believed their village had progressive norms. As with vulnerability, these results demonstrate that the relationship between community gender
-4 -

**II. The Data**

The basic information used for the analysis of representation was provided by the WTO/GATT Directories issued in 1982 and in August of 1987 and 1997. These Directories have a listing of all staff working in each member country Mission dealing with WTO/GATT matters as well as the location of the Mission. The listings contain only professional staff - secretarial and support staff (drivers etc.) are excluded; and are usually headed by an Ambassador, permanent representative of the country to the WTO/GATT. As noted above, the representation may be from a Mission in Geneva, from a Mission elsewhere in Europe or in a few cases from a Ministry in the capital of the country itself. They are all listed in the WTO/GATT Directories -- whose format has changed little over the years; they have just become thicker.

The period covered is of interest because it looks at the WTO/GATT representation at three different points in time, each associated with a distinct period in the organization's activity: 1982 is before the beginning of the Uruguay Round, 1987 is a year after the Uruguay Round was launched, and 1997 is the present -- ten years later, when the WTO has been established and the implementation of the Round is in full swing.

The Directories also contain a listing of the officers of the Organization and all subsidiary bodies, committees, working groups etc. as well as their country affiliation which is used in the discussion of participation in WTO activities. Chairmen have traditionally played a relatively active role in the GATT and the WTO -- their role has not been purely cosmetic. An organization like the WTO -- and previously the GATT -- which works with consensus despite the fact that the countries represented are very different in their economic size, presents complex challenges in designing decision making structures that result in an equitable representation of the interests of all participants. Chairmanships play a role in this effort to maintain a reasonable balance of
but no comparable increase in male employment (Dinkelman forthcoming).  Grogan and Sadanand (2009) obtain a similar estimate of a 9 percentage-point increase in female employment, versus no impact on male employment, from rural electrification in Guatemala.  Both studies attribute this largely to the fact that electricity frees up women’s time by increasing the efficiency of domestic chores, especially cooking.  These results are broadly consistent with other studies that draw on large samples of electrified and nonelectrified households and control for numerous potential confounders.  For example, ADB (2010), Barkat (2002), and Chowdhury (2010) find that electrification reduces the time that women spend collecting fuelwood (in Bhutan) and increases the evening time they allocate to income generating activities and the probability of employment (in Bangladesh).  It is also consistent with the conclusion that electrification has greater positive impacts on women when accompanied by effective social marketing and financing schemes for appliances that reduce the time required for domestic chores (e.g., ESMAP 2004) or enterprise services for women to take advantage of electricity services (e.g., project in Niger Basin cited by World Bank 2010a).**

# _4.2.2.1 Time allocation to domestic chores_ **Studies find some impact of electrification on women’s time allocation, different from the impact on men’s time allocation, but not in all dimensions or in all ways expected.**

Many studies compare the average time allocation of rural men and women in households and communities that have electricity vs. those that do not (e.g., ESMAP and EnPoGen studies in India and the Philippines). In the case of activities that are not possible without electricity (such as watching TV in regions where batteries are not used to power TV), the time allocated to the activity is clearly a result of having electricity. However, in general, it is not possible to conclude anything about the causal impact of electricity from these comparisons, because differences in
0.12|0.13|0.17|0.34|0.38|0.52|0.53|0.54|0.57|
|Household Characteristics||X|X|X|X|X|X|X|X|
|Urban/Rural|||X|||||||
|Survey Blocks||||X|||||X|
|Food Products|||||X|||||
|COICOP 2-dig||||||X||||
|COICOP 3-dig|||||||X|||
|COICOP 4-dig||||||||X|X|

This table shows the average slope of the Informal Engel curve across countries for different specifications under the robustness scenario assignment of places of purchase to formality. The robustness scenario differs from the central scenario by assigning specialized stores to the informal sector, in addition to maintaining corner stores, non brick and mortar and self-production in the informal sector. The slopes are estimated from: _Share In f ormali_ = _β_ . _ln_ ( _expenditure pc_ ) _i_ + Γ _Xi_ + _εi_ . The dependent variable is informal expenditure share and the main explanatory variable is log expenditure per capita. Controls include household characteristics (household size, age, gender, education of head), location indicators (urban/rural, survey enumeration blocks), and product codes for food vs all other purchases, 2nd, 3rd and 69
This raises an important but poorly understood issue concerning the space we choose to aggregate multiple dimensions. We must decide whether this is to be done in the space of attainments—the variables we use in assessing individual welfare for the purpose of deciding who is poor and how much—or in the space of deprivations—the assessments we make about whether one is poor in a given attainment space. While arguments can be made for and against each approach, I have questioned the main arguments made in favor of deprivation aggregation, as has become popular in the recent “multidimensional indices of poverty.” 17
# **_A Closer Look at the Impact on the Devaluation on Farm Households_**

This section of the paper discusses the trends in rural poverty evidenced by the household data. On the one hand, the data raise questions about the reliability of the survey results. On the other hand, they also suggest that many rural farm households may not have benefited -- at least in the short term from the devaluation -- to the extent commonly supposed (Goreux, 1995) because of export taxes imposed at the time of the devaluation.

The large increase in poverty in West Forest seems to be driven by the large decrease in home consumption. Consumption of home-produced food fell by 13 percent in East Forest and by 20 percent in the Savannah, compared to the 40 percent fall in West Forest (table 32). Breaking it down further, the fall was about 21 percent for food crop farmers in the West Forest, compared to 33 percent in the East Forest and 17 percent in the Savannah. For export crop farmers, the fall in consumption of home production was 43 percent in the West Forest, compared to only 6 percent in East Forest, and 13 percent in the Savannah (table 33). Thus, the huge decline in consumption of home production, particularly among West Forest export crop farmers, raises questions. The estimation of Engel curves shows that food shares drop significantly in the West Forest between 1993 and 1995, and not in the other rural areas (table 34).8 However, for West Forest, the dummy for 1995 becomes insignificant if a dummy is added for 1995 export crop farmers. The export crop-1995 dummy is significant and negative, indicating that in 1995, West Forest export crop farmers had significantly lower food shares than other West Forest residents.<sup>29</sup> Table **32 Mean Household Expenditure Per Capita** _(in 1985 CFAF per year)_

|||_199_|_3_|||_1995_|||
|---
cohorts. Both average years of education and, even more dramatically, the proportion of individuals who have at least some secondary education are much lower for individuals who were teenagers in 1975. This is especially marked for males (the differences are only statistically significant for males and for both gender together, not for females). Since, among the young adult males in 1975, the educated have been especially targeted by the violence and the mortality, as shown in figure 5, it is also likely that the difference between the teenagers in 1975 and the preceding birth cohorts would have been even larger in the absence of selective mortality of the highly educated.

As a consequence of the collapse of the school system, a large number of children were not allowed to attend secondary school, even though they might have been willing to. Once the reconstruction of the country and the school system started, they were older and had probably many other priorities. This constitutes an exogenous shock to educational attainment. Initially, the aim of this paper was to attempt to use this exogenous variation to identify the labor and non labor market returns to schooling. The preceding analysis has shown that, if the exogenous variation in education is only defined at the birth cohort level, the exclusion restriction, i.e. the condition that the birth cohort only affects the outcomes of interest, like earnings or health, through education, is not likely to be satisfied. Indeed, mortality, violent death, fertility, marriage and disability rates are all strongly affected by birth cohort effects. If in addition to the cohort related variation, I could rely on some exogenous geographical variation in schooling, this approach might be more appealing<sup>15</sup> . I will try to find and use data about school reopenings or reconstruction after the Khmer Rouge period to make progress on this strategy. However, this approach would only be valid if the geographical variation in education proves not to be correlated with geographical variations in mortality or other outcomes.

# *
# **Technical Appendix**

## **A.1 Data sources**

measured respectively by imports as a share of global exports, capital outflows as a share of global inflows, and patents as a share of global patents. The imports measure corrects for reexports for the major entrept economies of Singapore and Hong Kong, and for intra-monetary union trade using bilateral trade flows data. The capital outflows measure includes FDI and portfolio capital, but excludes derivative transactions. The patents measure utilizes patent approvals to all national patent bodies reporting to the World Intellectual Property Office. The expanded polarity measure additionally includes weights for the migration channel, as measured by immigrant stock as a share of global immigrants.

_Population growth_ was measured as the growth rate of the total population, _investment_ was gross fixed capital formation share of GDP, _enrollment_ was the primary school enrollment rate, _income per capita_ was real per capita GDP measured in 2000 U.S. dollars, _health status_ was the life expectancy of the population, the _dependency ratio_ is ratio of the population younger than 15 or greater than 65 relative to the working age population, and _government size_ was the government expenditure share of GDP. Alternative measures for enrollment, health, and dependency used were the secondary school enrollment rate, the under-5 mortality rate, and the old-age dependency ratio. Additional controls: _Technology diffusion_ was proxied by mobile cellular subscriptions coverage, _infrastructure_ by road density, and the regional dummies included _Africa_ , _Latin America_ , and _East Asia_ .

The measure of _institutional quality_ was a composite measure obtained by taking the proportion-weighted sum of the first three principal components of 11 subcomponents of the ICRG political risk measure: government stability, corruption, law and order, bureaucracy quality, socioeconomic conditions, investment profile, internal conflict, external conflict, military in politics, religious tensions, and ethnic tensions (only the first three components obtained an eigenvalue greater than unity, justifying their inclusion). Alternative institutional quality measures applied: (a) inclusion of democratic accountability to the subcomponents used
turnover), profits (operating surplus), employment, net exports, and capital expenditure. The supply of enabling infrastructure also creates additionality in the future, by facilitating future competitiveness of the economy. Similarly, innovation and investment in research and development have long-term additionality that may not be immediately evident but is necessary for long-term sustainable economic growth.

_Sources:_ NTMA 2015a, 2015b.

# **6. Public Capital Multipliers**

Publicly available data suggest that SIFs generate a wide range of public capital multipliers. Box 6 contains a brief description of the methodology used for calculating public capital multipliers and its application to a select number of SIFs for which data are publicly available. Although multipliers do not reflect the ability of SIFs to operate efficiently, or the wider social and economic impacts of their investments, they do provide a useful indication of a fund’s ability to crowd in external capital. However, the following important caveats apply:

- _Definition._ Multipliers may refer to different dimensions (for example, different ratios, financing instruments, definitions of public and private capital, definitions of commitments or investment, and so on). For example, the external capital may come from other government institutions, or multilateral development institutions, instead of the private sector.

14
between plus and minus 100, and so, it is less affected by extreme values. For our baseline sample, the mean value of the growth rate is -2.36 and the standard deviation is 25.96.

We also experimented with the more commonly used growth measure, which is the annual change in the log of labor productivity (log difference). All the results discussed throughout the paper hold for this alternative growth measure and are available on request from the authors.

# 3.4 _Main explanatory variable_

Our primary explanatory variables are the country’s level of ethnic fractionalization, a measure of firm size, and the interaction term between the two. Ethnic fractionalization describes the extent of ethnic diversity and division in a country. A more ethnically fractionalized location is one in which the likelihood of two randomly drawn individuals belonging to the same ethnic group is lower. We use the ethnic fractionalization measure from Alesina et al. (2003), which is reproduced in Table A3 in Appendix A. The measure is based on the ethnic composition of countries in the early to mid-1990s. Because of the large gap between the reference year for the ethnic fractionalization index (early to mid-1990s) and the ES (2007–2019) and the lack of a year-byyear match between the two, it is critical that the ethnic fractionalization index be relatively stable over time. The stability of the ethnic fractionalization index is discussed in detail by Alesina et al. (2003). They argue and provide evidence that the index is stable over at least a 30-year time horizon, which is adequate for our case.

Our main measure of firm size is a dummy variable equal to 1 if the firm is a small or medium enterprise and 0 otherwise ( _SME_ ). We follow the definition used by Enterprise Surveys for survey stratification, whereby an SME is a firm with fewer than 100 full-time permanent workers and the rest (100 or more workers) are large firms. The firm size categories are determined 16
sample. The error term, , is assumed to be of a truncated normal distribution, with mean zero and truncated at ii ), such that remains non-negative. θθ JJ jj=1 jj iijj ii −(γγ+ ∑ δδ ZZ μμ Technical efficiency and the parameters from Equation 3 are estimated jointly using maximum log likelihood. The model, which substitutes Equations 4 and 5 into Equation 3, is estimated four times for each of the MAPS-based and AFSIS-based soil suitability measures: (i) including all plots and controlling for soil suitability with the inclusion of the distance measure from suitability class S1; (ii) including only plots classified as highly suitable (S1); (iii) including only plots classified as moderately suitable (S2); and, (iv) including only plots classified as marginally suitable (S3). Analysis is implemented on a sub-sample basis, rather than in a single model with suitability indicators, as it allows for the estimation of suitability-class specific production frontiers. Technical efficiency (TE) scores are computed based on the conditional distribution of given , following Battese and Coelli (1988), whereby the technical efficiency on plot _i_ is defined as: μμ εε (6) T ii<sup>= TT[exp (−μμ</sup> ii<sup>)|εε</sup> ii<sup>]</sup>

The technical efficiency scores are then used to compute potential production and productivity for the given level of inputs.<sup>15</sup>

# **5. Results**

# **5.1.Maize-Specific Suitability Measures**

The fuzzy set membership method described above was implemented using both the MAPS plotlevel soil samples and the AFSIS geospatially-derived soil data. The results of the maize-specific soil suitability classification exercise are summarized in Table 3. Using the MAPS plot-level soil samples as the basis for classification, 13 percent of plots are considered highly suitable, the majority (75 percent) considered moderately suitable, and the remaining
# **Too Rare to Dare? Leveraging Household Surveys to Boost Research on Climate Migration**

Gero Carletto _Development Data Group, The World Bank_ <u>gcarletto@worldbank.org</u> Marco Letta _Sapienza University of Rome and Global Labor Organization (GLO)_ <u>marco.letta@uniroma1.it</u> Pierluigi Montalbano _Sapienza University of Rome and University of Sussex_ <u>pierluigi.montalbano@uniroma1.it</u> Adriana Paolantonio<sup>*</sup> _Development Data Group, The World Bank_ <u>apaolantonio@worldbank.org</u> Alberto Zezza _Development Data Group, The World Bank_ <u>azezza@worldbank.org</u>

**Keywords:** migration, climate change, household surveys, LSMS

# **JEL classification:** O15, Q54, R23, C83

**Acknowledgments** : We are grateful to Mauro Testaverde for valuable comments and suggestions on an earlier draft of this work. We also thank participants at the IFAD 2022 Conference on “Jobs, Innovation and Value Chains in the age of Climate Change” where this work for presented for the first time.

* Corresponding author.
.

# **Evidence from Other Countries**

Costing and simulation models are fairly common in education sector planning. However, because of data availability and consolidation challenges, ECE is often not included as its own education level. In most cases, ECE is grouped together with primary education. However, there are examples of countries that have been able to achieve significant milestones through better planning and budgeting. For example, Uzbekistan developed dedicated strategies and costing simulations for preschool education as part of its Education Sector Plan 2019-2023 (Government of Uzbekistan, 2019). The preschool enrollment rates increased from less than 30 percent in 2017 to 69 percent in 2022 (Ministry of Preschool Education, Uzbekistan & UNICEF, 2022). In addition, more than 12,000 non-state family preschools were established between 2018 and 2021 using public-private partnership models.

Given the need to estimate ECE costs, a variety of different ECE costing and simulation modeling tools have been developed and utilized in various periods and contexts. Some well-known tools

> 3 The approach used a benefit–cost ratio from Engle et al. (2011), data on the population aged 3–5-years from the 2020 Pakistan Census, government expenditure per primary student from World Bank (2015), and GDP from World Bank (2020). The benefit-cost ratio was estimated at a conservative 6.4:1 and a discount rate of 6 percent was applied based on a study modeling the impact of increased ECE attendance on school attainment and subsequent wage changes in 73 low- and middle-income countries (LMICs) (Engle et al., 2011).

3
hinders access” points to a widening of the net for the information gathering beyond the users of financial services to the providers and their management, organization, strategies, cost structures, and operating environment. Key national basic indicators here could include measures of the cost of selected standard services, and availability of information/credit infrastructures.

# _Alternative sources of information_

The discussion clearly points to the need for a range of different sources of information. The needed data collection exercises can conveniently be grouped by the type of respondent, i.e. the person or organization which is supposed to have the information being collected. Essentially there are three, namely _users_ and _providers_ of financial services, and _experts_ of which the first can be subdivided into two classes, _household users_ and _business users_ , giving four types overall. Increasingly the build-up of national statistical collection capability implies that, in many cases, the primary surveys of these

> 9 Certain observers stress psychological aspects such as the values and attitudes of the household as additional types of determinant (the relevance to Islamic households is evident, see also the discussion below of Southern African surveys).

9