rafmacalaba/data-use-mentions-extended huggingface dataset:
where _wi,j,t_ is the food budget share of household _i_ in region _j_ at time _t_ ; _PF,j,t_ is the true price of food items; _PN,j,t_ is the true price of non-food items; and _Pj,t_ is the true price of all goods, which is a weighted average of _PF,j,t_ and _PN,j,t_ ;<sup>_Y_</sup> _i_ , _j_ , _t_ is total household expenditure; **X** is a vector of individual household characteristics, and _u_ is the residual.

Hamilton (2001) decomposes any observed price level,<sup>_P_</sup> _j_ , _t_ , into the true price level and an error term, under the assumption that the error term is uniform across region _j_ , as follows: where<sup>_P_</sup> _j_ 0, is the true price at time _0_ ;  _,j,t_ is the cumulative percent increase in the CPI from year _0_ to year _t_ ; and _Et_ is the cumulative percent measurement error in the CPI from year _0_ to year _t_ .

Inserting equation (2) into equation (1) leads to the following: If cross-sectional household consumption data and a cross-sectional CPI for food, non-food and all items over the entire sample period are available, the following equation can be estimated: where _Dt_ is a dummy variable equal to 1 in year _t_ ; _Dj_ is a dummy equal to 1 for region _j_ ; and <sup>ˆ</sup> is the constant term from equations (1) and (3), plus the coefficients of the omitted time and the omitted region dummies.

The time dummy variables,<sup></sup> _t_ , are used to measure the CPI bias as follows: 9
and the Syrian crisis, the UNHCR and the World Food Program (WFP) have been conducting a variety of surveys as well as extensive home visits that allowed researchers to analyze refugee conditions as had never been done before.

The paper uses two data sets: the Jordan proGres registration system (PG for short) as of December 2014 and the Jordan Home Visits survey, round II data (HV for short) collected between November 2013 and September 2014. Both data sets were provided by the UNHCR in the context of the joint World Bank-UNHCR study on the welfare of Syrian refugees (Verme _et al._ , 2016). These comprehensive data sets have the distinct advantage that they can be linked by a common identification number. We can therefore trace the same individuals and households across the two sources of data.

The proGres registration system is what we consider the “census” of refugees. This data set has no information on consumption but contains socio-economic characteristics for all registered individuals and households. Variables available in the PG data include, among others, date of birth, place of birth, gender, date and reasons of flight, arrival date in Jordan, registration date, ethnicity, religion, education, professional skills, and occupations in the countries of origin and asylum.

The HV data have been collected in successive rounds since 2013 for the purpose of targeting refugees with cash assistance programs and they contain information on income and expenditure as well as a large set of individual and household socio-economic characteristics. Although this is not a sample survey, for the purpose of this study we will consider this data set as our hypothetical sample survey. The HV data we use cover about one-third of all registered persons in Jordan in 2014 and are therefore a sub-sample of the PG data. Our experiment is restricted to households present in both data sets, a total of approximately 40,000 households.

9
# _Overall results_

Survey data confirm the importance of investor protections and other measures to minimize political risk in attracting and retaining FDI. To explore the effects of political risk on investments, the GIC Survey 2019 asked respondents how they would respond to hypothetical political risk scenarios. Two in three existing investors would consider withdrawing investments or cancelling planned investment in the face of political risk exposure in host countries (Figure 3). These findings are consistent with the broader literature on political risk which consistently identifies political risk and regulatory uncertainty as major concerns for foreign businesses. They are also in line with other surveys on the topic.<sup>6</sup> Risks of expropriation and government breach of contract are likely to elicit particularly negative investment reactions, in line with findings by Kusek and Silva (2018). About 50 percent and 40 percent, respectively, of investors would consider withdrawing existing or cancelling planned investments when faced with these risks (Figure 3). Sudden legal changes, currency restrictions, and delays in obtaining permits and approvals elicit somewhat less severe reactions. Such risks are more likely to cause investors to delay investments rather than cancel or withdraw investments completely.

**Figure 3. Reactions to political risks**

<!-- Start of picture text --> Question: How would the risk of the following events affect your investments in this<br>country?<br>At least one of the below 39% 27% 16% 11% 4%2%<br>Expropriation 24% 23% 13% 23% 12% 5%<br>Gov. breach of contract 17% 22% 16% 28% 13% 6%<br>Sudden, adverse change in laws 12% 16% 25% 36% 7% 4%<br>Currency restrictions 9% 13% 29% 33% 12% 5%<br>Delay in permits and approvals 6% 9% 38% 34% 9% 4%<br>0% 20
Bound, J., C. Brown, and N. Mathiowetz (2001) “Measurement Error in Survey Data”, pp. 3705-3843 in J. Heckman and E. Leamer (eds.), Handbook of Econometrics Volume 5, Elsevier: Amsterdam.

Bound, J., and A. Krueger (1991) “The extent of measurement error in longitudinal earnings data: do two wrongs make a right?”, Journal of Labor Economics 12: 345-68.

Bowles, S., S. Durlauf and K. Hoff (2004) Poverty Traps, manuscript in press.

Carter, M. and C. Barrett (2005) “The Economics of Poverty Traps and Persistent Poverty: An Asset-based approach”, SAGA Working Paper 178.

Collado, M. Dolores (1997) “Estimating dynamic models from time series of independent cross-sections”, Journal of Econometrics 82(1): 37-62.

Dasgupta, P. (1997) “Nutritional Status, the Capacity for Work, and Poverty Traps”, Journal of Econometrics 77: 5-37.

Dasgupta, P. and D. Ray (1986) “Inequality as a Determinant of Malnutrition and Unemployment”, The Economic Journal Vol. 96, No. 384: 1011-34.

Deaton, A. (1985) “Panel data from time series of cross-sections”, Journal of Econometrics 30: 109-126.

Galor, O. and J. Zeira (1993) “Income Distribution and Macroeconomics”, Review of Economic Studies 60(1): 35-52.

Ghatak, M., M. Morelli and T. Sjostrom (2001) “Occupational Choice and Dynamic Incentives”, Review of Economic Studies 68(4): 781-810.

Jalan, J. and M. Ravallion (2004). “Household Income Dynamics in Rural China”, pp. 108-124 in S. Dercon (eds.) Insurance Against Poverty, Oxford University Press: Oxford.

Kniesner, T. and Q. Li (2002). “Nonlinearity in Dynamic Adjustment: Semiparametric
# **3 Data and Stylized Facts**

**Data** We combine multiple data sources to be able to empirically test the predictions of the theory present in this paper. Labor market indicators come from the Tunisian Government’s National Survey on Population and Employment (ENPE). Among other information, the ENPE reports individuals employment status by industry (if any), gender, and province for intermittent years between 2006 and 2016.

Trade data comes from two sources. Merchandise trade data comes from UNCOMTRADE while services trade numbers come from the WTO-OECD BaTiS database. We then used concordance tables to map HS and BOPS trade product codes onto ISIC industry codes, using concordance tables from WITS. Finally, we mapped ISIC industry codes onto Tunisian Economic Activity Nomenclature (NAT), which is a domestic economic activity classification based on NACE Revision 3. Additional details can be found in the Appendix.

We were then able to construct a provincial panel for labor market and trade indicators. Of primary concern, for each province, we can trace the evolution of employment by gender and sector as well as the provincial exposure to foreign demand shocks, which are defined later in this section.

**Stylized facts** Low female labor-force participation rates and high female unemployment rates characterize Tunisian labor markets. While Tunisia female labor force participation is higher than other Middle Eastern and Northern Africa (MENA) countries, it is about half of the rate of OECD countries and much lower than other developing regions such as Latin America. The gender gap in unemployment has increased substantially since the Arab spring, in 2011, and female unemployment rates have never converged back to pre-Arab spring levels. Sectoral employment is very segmented across gender lines and exports grew less in more femaleintensive sectors. In 2006, the female share of employment across different industries ranged anywhere from 1% to 75%. The most female-intensive industries are garments, social and cultural services, education, and banking and insurance. Importantly, industry contribution to export growth in the 2006-16 period
These measures have been validated for two countries (US and Brazil) against an index designed to reflect WHO healthy diet recommendations (2020) along with two additional recommendations from IARC (2018) and constructed using diet recall data (Herforth et al. 2020). Validation for a further eight countries is in the process of publication (Kennedy et al. 2022). The authors state that these metrics, combined with the MDD-W, can be used to assess current performance and track progress towards healthy diets. The only published study found to date that links these measures with outcomes is an observational study in China that assessed the association of the GDR scores with overweight and obesity in children 7-18 years of age, finding a positive association between the NCD-Risk score and obesity as well as a negative association between the overall GDR score and obesity (Wang et al. 2022).

The **Global Diet Quality Score (GDQS)** is a food-based metric composed of 25 food groups (16 healthy, 7 unhealthy and 2 unhealthy when consumed in excessive amounts) that are important contributors to nutrient intake and/or NCD risk (Bromage et al. 2021). It uses self-reported consumption based on a semiquantitative 24-hour recall. It is currently one of the only diet quality metrics that has been validated against both nutrient adequacy and diet-related NCD risk. A recent study found that the GDQS was better than other metrics (e.g. MDD- W and AHEI, the Alternative Healthy Eating Index from 2010) at predicting changes in weight and waist circumference among women in Mexico (Angulo et al. 2021). Another study for China found that the GDQS was inversely associated with metabolic syndrome and nutrient inadequacy or both (He et al. 2021). The use of the two submetrics allows one to distinguish between diets which are nutrient inadequate due to insufficiency in the diet, and diets which are nutrient-inadequate due to unhealthy food choices. However, this metric has not yet been widely integrated in national surveys and data availability for
adverse conditions in Tanzania through trade. For each trading partner, we report the percentage of Tanzania dry years in which that country does not also experience a dry year. We report these percentages for each decade of the 21<sup>st</sup> century.

- We calculate how often each of Tanzania’s key (or potential) trading partners experiences a dry year in the same year that Tanzania does not experience a dry year. This metric indicates the likelihood that Tanzania will experience non-adverse climate conditions in the same year as her trading partners experience adverse conditions, therefore offering the potential for Tanzania to benefit through trade. For each trading partner, we report the percentage of dry years in that partner-country in which Tanzania does not also experience a dry year. We report these percentages for each decade of the 21<sup>st</sup> century.

We combine the 22 GCM realizations from the CMIP3 archive by first performing the above calculations for each GCM realization individually, and then calculating the mean across the ensemble of GCM realizations.

# **4.2.2 – SEVERE DRY EVENTS IN TANZANIA**

Table 7 shows the number of dry years in Tanzania in each decade of the 21<sup>st</sup> century, along with the percentage of Tanzania dry years in which each trading partner does not experience a dry year. The decadal occurrences are reported as the mean of the decadal occurrences of the individual climate model realizations. With this measure, a dry year occurrence of 0.5, as reported for the decade from 2000-2009, indicates that across all of the GCM realizations in that decade, the occurrence of dry years in Tanzania was half as common as in the historical period (which was one year on average per decade given the way the dry year has been defined). In addition, the number of years in which Tanzania experiences a dry year but another trading partner does not is reported as a percentage of the Tanzania dry years in that decade
unlikely to be rectified in even the longer term. However, the importance of entrepreneurial activity for increasing household welfare may very well be fostered by steps in the short- and medium-term, perhaps including public works and micro credit programs. In Russia, where non-governmental organizations have been especially active (Nizhnyy Novgorod, Yekaterinburg), credit unions and small business incubators have been set up.

# Poverty Equations

Given the difficulties of conducting household surveys in the FSU (high refusal rates, extremely high reluctance to reveal sensitive information on income and alcohol consumption), it is not surprising that measurement error is a significant concern for the data sets on Estonia, Russia, and Kyrgyz Republic. Additionally, the lower levels of goodness-of-fit for the FSU countries noted above suggest that a binary dependent -variable (poor/non-poor) estimated by probit techniques might perform better and would provide valuable information about some of the variables included. In this sense, lack of consistency between the probit and OLS results would demonstrate problems either with measurement error or in specification.

The probit results in Table 15 suggest an acceptable fit, but not a particularly good one. The model serves to predict correctly the poverty status of about three-quarters of Estonian households, but only 60-65 percent of Russian and Kyrgyz Republic households. As was the case with the OLS results, not all coefficients were found to be significantly different from zero at a 90 percent confidence level, and a small subset of the coefficients which were significant in the OLS were not significant in the probit (Estonia: primary 76
**Table A1.4: Data on debt betas**

|**Company**|**Bond**|**Premium (basis points)**|**Debt betaa**|
|---|---|---|---|
|BAA|£250m, 8.5%, 2021|85|0.14|
|Electrobras|$50m, 10%, 1999|431|0.72|
|Telebras|Lira 350 billion, 13%, 1999|345|0.58|
|BT|£500m, 7.125%, 2003|16|0.03|
|BT|£229m, 12.25%, 2006|35|0.06|
|Nynex|$150m, 8.75%, 2004|76|0.13|

_Note:_ **a** A simplification has been made by assuming that the equity risk premium is constant across countries at 6%.

_Sources: Financial Times_ and Datastream.

As can be seen from the table, debt betas can vary from close to zero, for large utility companies in developed countries, to close to one, for utilities in developing countries. Of course, the assumptions underlying the debt beta calculation have an important impact on the value found and so these figures are being used purely for illustrative purposes rather than precise measures of the actual debt betas. Table A1.5 provides an estimate of the asset beta for two of the companies with debt betas. These are then compared to the asset betas calculated on the standard assumption.

**Table** A1.5: Asset beta estimates

|**Company**|**Equity beta**|**Debt beta**|**Gearing**|**Asset beta**|
|---|---|---|---|---|
|BT
treatment effect heterogeneity can be effectively leveraged to fine-tune optimal policy targeting.

Several caveats are in order regarding the limitations of this work. <mark>The most important one is the use of a broad definition of migration for our migration outcome variable, which is driven by limitations in the existing data, as discussed in Section 3. To overcome this constraint, we stress the need for longitudinal household surveys to collect more comprehensive migration data. This includes details about the nature and duration of migration episodes, as well as the characteristics, history, and purposes of migrant individuals. Achieving this goal requires equipping household surveys with improved migrant tracking tools and protocols. Ideally, efforts to enhance the design of longitudinal surveys should be also harmonized and standardized across countries to ensure comparability of migration data and enable researchers to study potential differences in countryspecific relationships.</mark> At the same time, for half of the migrant-sending households, we observe more than one household member migrating across waves, so even if it were possible to distinguish different types of migration, accounting for intra-household heterogeneity in the estimation approach would not be straightforward. A complementary strategy to investigate resourceconstrained immobility would be to look at the dynamics of migration intentions and aspirations rather than actual migration, but the available information on these variables is limited to crosssectional surveys (e.g., Gallup World Poll) which come with their related econometric challenges. Regarding internal versus international migration, while our results apply predominantly to internal migration, we suspect that external validity with respect to international migration is high, as the key finding regarding the importance of endowments and liquidity constraints should hold even truer when it comes to costlier cross-border movements.

Secondly, it must be emphasized that conducting this type of research is not possible without leveraging panel surveys that are multi-topic and interoperable in nature. Unfortunately, such surveys remain uncommon in many developing contexts, which hinders the possibility of studying climate migration dynamics in those regions. Relatedly, even in countries where panel data are available, these are often of
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with the approach followed with growth incidence curves. They both look at the growth of an indicator at a given percentile in the distribution. They both can control for background characteristics. However, growth incidence curves are typically calculated from repeated cross-sections. They typically cannot provide information on the distribution of performance of those households or economic actors who began at a given percentile. In contrast, student growth percentiles or conditional growth charts (often of children’s height or weight) typically look at repeated observations of the same individual and looks at where the performance of that individual falls in the full distribution of performance of a comparable group. For the issue of target setting, one is typically concerned with repeated observations of the same agents, so the approach of the student growth percentiles or conditional growth charts would appear to be the most relevant.

# 4.  An illustration of target setting using School Education Data from Minas Gerais, Brazil

This section uses data on student achievement scores across different schools in the state of Minas Gerais, Brazil to illustrate some of the implications of using different approaches to set performance targets. The primary source of data for this exercise is from the PROEB (Program of Evaluation of Basic Education), which carries out Portuguese and math tests at the pupil level for municipal and state school students in 5<sup>th</sup> , 8<sup>th</sup> and 11<sup>th</sup> grade in the state of Minas Gerais. This analysis focuses only the State schools (which

> 7 See Betenner et al (2011).  See also the primers on student growth percentiles put together by the states of Colorado and Massachusetts. ((http://www.cde.state.co.us/cdedocs/Research/PDF/Aprimeronstudentgrowthpercentiles.pdf) and ( <u>http://www.doe.mass.edu/mcas/growth/InterpretiveGuide.pdf</u> )

> 8 They use unconditional quantile regressions
be calculated using at least 25 tax IDs. In sum, the merged data set allows us to study real and financial aspects of both the credit and the employment program covering the universe of firms. For most of the analysis, we use the 2018–2020 period of these data sets.

To construct the sample of firms we use, we start from all legal and formally registered firms in the economy (602,874 firms) that have a tax ID, which we call _formal firms_ .<sup>5</sup> We then build several samples of the merged data that we use in different parts of the paper. Each sample has a different size and coverage (Table 1). The first sample, which we call _active firms_ , is constrained to include only firms with positive sales in 2019, which amounts to 449,615 firms. We use this sample to conduct the aggregate analysis of the paper and the

> 5We exclude natural persons who use their personal tax ID to borrow as a firm. For these natural persons, we do not have the same scope of information as we do for active firms, and we exclude 818,572 tax IDs for this reason. These natural persons are only included in our aggregate analysis when we report the total value of the program and in our estimate for total expected credit loss (Table 11).

7
was observed in Bangladesh for the Helen Keller sample, which include both urban and rural children, where the disparity was 25 percentage points. In the IFPRI sample which only includes rural children, there was no difference between the two groups. In Bolivia, Cambodia, Nepal and Zimbabwe, the difference between the two groups was between seven and four percentage points. These findings are consistent with the fundamental role of nutrition‐related behaviors and caring practices and the critical need for nutrition knowledge across all income groups. In Ethiopia, Indonesia and Peru both resource‐rich and resource‐poor children had similar access to adequate food as the differences were only one percentage point.

<!-- Start of picture text --> Figure 5c: Adequate Food Security by Household Wealth<br>Prevalence of Adequacy of Food<br>By wealth<br>67 68<br>53<br>47 46 47<br>38 37<br>34 34<br>32<br>29<br>25 25<br>18<br>13 12 13<br>BGD (HK, '10) BOL ('08) IDN ('10) NPL ('11) ZWE ('10)<br>BGD (IFPRI, 11) ETH ('11) KHM ('10) PER ('12)<br>Resource Poor Resource Rich<br>Source: Author estimates.<br>Note: (1) Bangladesh (HK) include food security but not information on minimum acceptable diet.<br>(2) Indonesia (RKD) only has information on household level diatery diversity, nothing else.<br>100<br>80<br>60<br>40<br>20<br>Percentage of children who meet criteria<br>0<br><!-- End of picture text -->

# **3.2.2 Adequate Care Practices**

More than 80