rafmacalaba/data-use-mentions-extended huggingface dataset:
confidence in the central bank. To measure these elements, we use the Corruption Perceptions Index (Transparency International 2019a, 2019b), which scores 180 countries based on experts and business executives’ perceptions of the level of corruption in a country's public sector. It is a composite index combining 13 surveys and assessments of corruption collected by various institutions. The index has a scale of 0-100, where zero represents the highest level of perceived corruption and 100 the lowest level of perceived corruption.<sup>10</sup> Finally, we use the Governance Pillar Score (MSCI). This index covers 198 countries to "assess the extent to which a country's long-term competitiveness is affected by its institutional capacity to support long-term stability and functioning of its financial, judicial, and political systems, and capacity to address the environmental and social risks.”<sup>11</sup> The Governance Pillar Score is included in the MSCI assessment on the environment, social, and governance (ESG) risk of sovereign countries. The best score is zero; the worst is ten. The index is derived from third-party sources and focuses on political rights and civil liberties, stability and peace, control of corruption, and public financial management.

**_Macroeconomic Variables._** We then use the country risk score from the Economist Intelligence Unit's Country Risk Model to control for the macroenvironment. <mark>This score rates country risk on a scale from zero (no risk) to 100 (maximum risk), by taking a simple average of the sovereign, currency, and banking sector risk scores of a given country.</mark><sup>12</sup> <mark>W</mark> e included this variable in the analysis to assess the macroenvironment in which central banks operate. Alternatively, we use the number of currency crises and the current account balance as a percentage of GDP as another means of describing the macroenvironment in which the central bank operates.
CÔTE D’IVOIRE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE According to the UNICEF-WHO Joint Monitoring Program, Côte d’Ivoire is not on track to meet the MDG for sanitation. Based on household surveys, access to improved sanitation has been stagnant, increasing only 3 percentage points from 20 percent in 1990 to 23 percent in 2008 (table 7). Although approximately 18 percent of the population has shared access to improved sanitation facilities, this does not count towards the achievement of the MDG.A large share of the population—30 percent—still practices open defecation, although this is significantly lower than in the low-income benchmark group (table 11). Furthermore, access to flush toilets and improved latrines are four times and two as high as in the low-income group, respectively. On the other hand, use of traditional latrines is about half that found in the low-income peer group. Overall, a relatively large share of Côte d’Ivoire’s population has access to higher-end sanitation solutions, albeit in many cases on a shared basis. Looking ahead, an important area for action will be to move the large share of the population that continues to practice open defecation onto the first rung of the sanitation ladder.

**Table 11 Benchmarking sanitation access**

||Unit|Low-income<br>countries||**Côte d**|**’Ivoire**||Middle-income<br>countries|
|---|---|---|---|---|---|---|---|
|||Mid 2000s|**LSM88**|**DHS94**|**MICS00**|**MICS06**|Mid 2000s|
|Access to flush toilets|% pop|4.9|16.5|13.4|25.4|23.8|40.8|
|Access to improved latrines|% pop|9.9|
Domicílios_ are not representative at those levels.

A number of small-area estimation techniques have been developed to seek to address this missing data problem. In this paper, we rely on application of the “poverty mapping” approach developed by Elbers, Lanjouw and Lanjouw (2002, 2003). This approach typically involves a household survey and a population census as data sources. First, the survey data are used to estimate a prediction model for either consumption or
## **B Which Variables Account for Growth in Mean Welfare?**

Which variables in the model account for the bulk of the change in poverty? While this question is difficult to answer directly, indirect evidence is available by decomposing the growth in log per capita consumption predicted by the model. A natural framework for better understanding the contribution of individual variables is the classic Oaxaca-Blinder decomposition (Oaxaca (1973), Blinder (1973)). This technique decomposes the mean difference across two groups into a portion explained by differences in endowments and a portion due to differences in returns. In this case, the two groups are the households in the 2011-12 survey and the households in the 2014-15 survey. We decompose the difference between the model’s mean predicted per capita consumption in 2014-15 and 2011-12. Because the means from each year are generated by predictions from the same model, none of the difference is attributable to changes in the coefficients (returns), and all of the change is due to the mean of the predictor variables (endowments). These changes can easily be decomposed into the portion due to each individual predictor variable, which helps to identify the variables that account for the largest changes in the model.

Table 9 displays the results. Each cell represents the percentage of the total change in mean log per capita consumption attributable to each variable. The time trend alone, for example, explains 83 percent of the increase in urban areas and 80 percent in rural areas. Notably, transport expenditure declined in real terms during this period. This slowed the predicted increase in per capita consumption, particularly in urban areas. The rainfall variables contributed to a relatively large share of the increase in urban areas. However, the total increase in welfare was much smaller in urban areas, meaning that in absolute terms rainfall’s contribution was similar in urban and rural areas. As might be expected, changes in the means of livelihood categories contribute a significant amount in urban areas, but much less in rural areas.
needs whilst upholding their livelihood strategy, and a Survival Threshold<sup>31</sup> , below which households are unable to meet food needs. As of 2015, the DRM TWG decided to discontinue the use of LIAS data, as it was recognized that LIAS baseline data are outdated for the highlands regions. At present, seasonal needs assessments are therefore mainly based on the use of checklists.

- (iv) **_Drafting of HRD Document:_** Based on compiled beneficiary numbers, the federal-level HRD Editorial Committee drafts the HRD document. The HRD Editorial Committee is composed of representatives of DRMFSS, the UN and NGOs. The Committee compiles the received assessments and presents these to the EWRD at DRMFSS.

29 The DRM Technical Working Group is a multi-sectoral forum led by DRMFSS, including GoE representatives, DPs and humanitarian partners.

30 The Livelihoods Protection Threshold represents the total income required to sustain local livelihoods, in particular: (i) ensuring basic survival, (ii) maintaining access to basic services, such as health and education expenses, (iii) sustaining livelihoods in the medium to longer term through purchases of seeds, fertilizer, veterinary drugs, etc., (iv) achieving a minimum locally acceptable standard of living through purchase of clothing, coffee and tea, etc. See USAID and FDRE Disaster Risk Management and Food Security Sector, MoARD: _The Livelihoods Integration Unit Uses of the Baseline Information and Analysis._ 31 The Survival Threshold represents the total income required to cover: (i) 100% of minimum food energy needs (2100 kcals per person), (ii) the costs associated with food preparation and consumption, such as salt, soap, kerosene, and/or firewood for cooking and basic lighting, (iii) any expenditure on water for human consumption. The Survival Threshold is the line below which intervention is required to save lives. See USAID and FDRE Disaster Risk Management and Food Security Sector, MoARD: _The Livelihoods Integration Unit Uses of the Baseline Information and
welfare vector for any area _c_ is defined as _yc_ = ( _yc,s_<sup>_T, y_</sup> _c,r_<sup>_T_)</sup><sup>_T_,whichcontainsthewelfareforsampled</sup> and non-sampled observations in area _c_ , denoted _yc,s_ and _yc,r_ respectively. Typically, the non-sampled population (of size _Nc − nc_ ) is much larger than the sampled population (of size _nc_ ). It may also happen that _nc_ = 0 for some areas, meaning that the area is not sampled in the survey. The empirical best (EB) predictor of _τc_ for an area _c_ that is sampled ( _nc >_ 0) is defined as the conditional expectation _E_ ( _τc|yc,s_ ; _θ_<sup>ˆ</sup> ). For an out-of-sample area ( _nc_ = 0), the empirical best (EB) predictor of _τc_ is _E_ ( _τc_ ; _θ_<sup>ˆ</sup> ), which is similar to the ELL estimator. Here, _θ_<sup>ˆ</sup> is a consistent estimator of _θ_ as the overall sample size _n_ tends to infinity. In contrast with this procedure, the traditional ELL approach does not require to link the census and survey observations.

When the expectation _E_ ( _τc|yc,s_ ; _θ_<sup>ˆ</sup> ) has no explicit form, the empirical best (EB) predictor of _τc_ can be approximated using a Monte Carlo (MC) simulation method. The MC approximation to the EB ˆ predictor _τc_<sup>_EB_</sup> = _E_ ( _τc|yc,s_ ; _θ_<sup>ˆ</sup> ), as described in Rao and Molina (2015), is obtained as follows:

1. Using the survey data, fit model (1) via any method providing consistent estimators. This yields the vector of parameter estimates:

Usual fitting methods under this approach are
86|<br>86|86|86|
|Number of instruments|41||38|36|53|41|37|36|52|
|Hansen test for over-|0.222||0.235|0.085<sup>[B]</sup>|0.296|0.159|0.129|0.093<sup>[B]</sup>|0.380|
|identifying restrictions||||||||||
|AB AR(1) test|0.000||0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|AB AR(2)test|0.969||0.880|0.932|0.885|0.972|0.967|0.997|0.985|

Notes:<sup>[A]</sup> GDP data are adjusted for international differences in purchasing power of the dollar.<sup>[B]</sup> When the p-value of the Hansen test is a borderline case, the instruments are further validated by the supplementary evidence from the Sargan test of the over-identifying restrictions. Robust standard errors in parentheses; asterisks indicate statistical significance at the ***1, **5, and *10 percent level. Data for the transition economies refer only from 1997 onwards. All equations include time period and group-specific dummies.

# **5. Foreign Savings and Growth Channels**

# _<u>How Do Foreign Savings Support Growth in Emerging Europe?</u>_

So far we have established that foreign savings support growth in many—but not all—emerging European countries. In a growth equation, the interactions between current account balances and two of the EU-specific country groups mentioned before have a negative and statistically significant point estimate. The
women have equal access to the scheme, when they need it? Again, we cannot give a direct answer from the survey data. However, the patterns in the inter-state data are suggestive. We observe a negative correlation between the female share of work and the overall rationing rate (Figure 8). It might be conjectured that this correlation actually reflects differences in the extent of poverty. Women may well be less aware of their rights and less empowered to demand work in poorer states. For example, when other work is scarce, they may get crowded out by men. However, the negative correlation between the female share of work and the rationing rate persists when we control for the poverty rate, as can be seen in Table 7, which gives regressions for the female share of employment on MGNREGS against both the rationing rate and the

> obvious reason to question their veracity for measuring the gender composition of the work provided. 28  We estimate that the share of women in the total person days of casual labor in 2009/10 was 23.3%, based on the 2009/10 NSS.

16
Figure 1a: FDI inflows to Africa in millions of current USD over 1970-2013 <!-- Start of picture text --> 25000<br>20000<br>15000<br>10000<br>5000<br>0<br> Eastern Africa Middle Africa Northern Africa<br> Southern Africa Western Africa<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012<br><!-- End of picture text --> Source: Authors’ computation based on UNCTAD (2016) Figure 1b: BRICS’ versus G7’ FDI inflows to Africa over 2003-2012 (in percent) <!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br>BRICS/G7 Stock BRICS/G7- Flow<br><!-- End of picture text --> Source: Authors’ computation based on UNCTAD (2016) 3
only modestly less accurate when using predictions from the geospatial model for all villages. We therefore conclude that, although predictions from the census model predict the benchmark exactly in partial registry villages, this is not a major factor in explaining the improved performance of the partial registry predictions. Instead, the partial registry provides richer data with which to train a more accurate geospatial model.

# 4. Main Results

This section shows the results of the four alternative targeting methods. We compare welfare predictions generated using each method to the benchmark predictions using rank correlations, AUC coefficients, and R-squared coefficients.

Table 6 presents the metrics for each method. The partial registry is clearly the most accurate method for targeting the poor villages in the ten UBR districts in Malawi. The second best is using the RWI to rank villages. However, this method performs only moderately well, with an AUC coefficient close to 0.60 and a 0.2 rank correlation. On the other hand, using the PMT scores as a proxy for welfare does not show promising results; the PMT scores show zero correlation with our benchmark welfare and has an AUC coefficient equivalent to guessing poor villages at random. The IHS plus geospatial variable model shows the second-lowest coefficients, only slightly higher than the PMT scores in terms of AUCs. This may be due to the limited sample size. Because the sample size is restricted to the bottom half of households within UBR districts, there are only an average of about 8 households per village available to train the model. One indication of this is that performance improves noticeably when predicting average welfare across all households in the village, as noted below.

**<u>Table 6.</u>** <u>Rank correlations, AUC, and R</u><sup>2</sup> <u>of the targeting methods</u>

|**Ran**|**k correlations**|**AUC**|**R-squared**|
.

Previous evidence suggests that poorer regions tend to be more corrupt and are more likely to have weak institutions (Olken and Pande, 2012; Ferraz and Finan, 2008; Besley and Burgess, 2002). The paper explores whether incumbents are more likely to win in poorer versus richer municipalities. The classification of municipalities as rich or poor is based on the 2006 municipal-level poverty estimates provided by the Philippine Statistics Authority.<sup>15</sup>

> 15These data are available at `https://data.humdata.org/dataset/`

14
marginalized, have a greater likelihood of being excluded from public services and economic opportunities.

# **_4.1 National institution index_**

First, we look at Gallup's National Institution Index, which measures the percentage of adults expressing confidence in a variety of institutions, including the national government, military, judicial system, and financial institutions. On average, only 38 percent of adults in Iraq express confidence in national institutions, which is well below the level of comparator countries (figure 5). The data find no statistically significant gaps between men and women. Interestingly, the gap between the rich and poor has reversed over the last decade. Initially, wealthier adults were more confident in national institutions; by 2019, the poor were about 16 percentage points more likely than the rich to have a higher score for national institutions.

**Figure 5: National Institutions Index, 2019 (% of adults)**

<!-- Start of picture text --> Oil exporting economies 55<br>Upper-middle income economies 46<br>Arab world economies (excluding Iraq) 42<br>Nigeria 45<br>Malaysia 74<br>Kazakhstan 62<br>Iraq 38<br>Indonesia 76<br>Colombia 34<br>Azerbaijan 73<br>Score 0 (lowest) to 100 (highest)<br><!-- End of picture text --> Source: Gallup World Poll, 2019.

Note: Data are unavailable for Djibouti, Oman, Qatar, Somalia, Sudan, Syrian Arab Republic. Oil exporting economies, Arab world economies, and Upper-middle income economies are defined using World Bank classifications (available at: https://datahelpdesk.worldbank.org/knowledgebase/topics/19280-country-classification). Arab world economies exclude Iraq.

9
years are, however, somewhat mixed. FHHs without children had the strongest upward mobility for Egypt and Tunisia, but had similar upward mobility as FHHs with children for Mauritania and the West Bank and Gaza (Appendix A, Figures A.5-A.8). Finally, we plot the results for upward and downward mobility for FHH types, with and without children considered together, in Figures A.9 and A.10 (Appendix A). These figures show that FHHs have higher upward mobility and lower downward mobility than MHHs across all FHH types and countries, except for Jordan.<sup>16</sup>

# **5. Conclusions and Policy Implications**

The dramatic events of the Arab Spring and the following decade of structural reforms and sectoral developments _inter alia_ have brought to the fore the importance of better understanding gender inequalities. We offer new analysis on the feminization of poverty as related to FHHs, using 20 survey rounds spanning the past two decades for six countries across the Arab region— namely Egypt, Iraq, Jordan, Mauritania, the West Bank and Gaza, and Tunisia—an understudied set of countries. We propose and evaluate a new typology of FHHs consisting of four main types (and several sub-types) of FHHs with a new focus on the presence of children, which offers policyrelevant insights regarding the trends and dynamics of poverty feminization. We assemble and harmonize the available cross-sectional data and construct synthetic panels to address the lack of actual panels.

We find that different FHH types display clear differences regarding poverty levels and trends. In particular, self-reported FHHs, potential FHHs, and most-educated-female-adult FHHs are less likely to be poor than non-FHHs for the six countries, while the opposite holds for majority-female-

> 16 While Milazzo and Van de Walle (2017) find self-reported FHHs to be generally poorer, they also find these households to contribute more to the overall decline in poverty in Africa.

25
Consistent with the overall expenditure figures, Uganda’s share of social spending is lowest among the benchmark group. At 3.9 percent of GDP (or 4.2 percent if we include public pensions), the country spends less on direct transfers, education, and health than Tanzania (6.3 percent), Ghana (7.4 percent) and Kenya (9.5 percent), a result driven by low education expenditure (see Figure 4a). Social spending is also lower than Ethiopia (at 7.2 percent), driven by education and in direct transfers.

_Figure 4. Public expenditure in different countries_ _a. Public expenditure as percent of GDP b. Composition of social public expenditure as percent of GDP_ <!-- Start of picture text --> 55% 20%<br>45%<br>15%<br>Tanzania South 4.1%<br>35%<br>Africa 10%<br>Ghana<br>2.0%<br>25% Kenya 1.2% 1.7% 7.0%<br>5% 1.6%<br>15% Ethiopia 4.6% 5.7% 3.8% 4.6% 1.3%2.4% 6.4%<br>Uganda 0% 1.3% 1.1%<br>5% Ethiopia Ghana South Tanzania Uganda Kenya<br>0 5,000 10,000 15,000 20,000 25,000 (2010) (2012) Africa (2011) (2016) (2015)<br>(2010)<br>GDP per capita (2011 PPP)<br>Direct transfers Education Health<br>% of GDP % of GDP<br><!-- End of picture text --> _Source: CEQ Institute database, World Bank WDI for GDP 2011 PPP per capita numbers. For Kenya,
# **Labor Demand and Trade Reform in Latin America***

Pablo Fajnzylber Universidade Federal de Minas Gerais Belo Horizonte, Brazil William F. Maloney World Bank Our thanks to Leonardo Villar, Cristina Fernandez, Shane Greenstein, Pravin Krishna, Eduardo Ribeiro and Luis Serven for helpful discussions, and to Matheus Magalhaes for research assistance. Also, our appreciation to Jim Tybout for offering the three panel data sets and to Marcelo Tokman, the Ministerio de Hacienda, and the Instituto Nacional de Estadistica, Santiago Chile for help updating the data series for Chile. They of course, bear no responsibility for erroneous analysis or conclusions.
in the Appendix clearly show that wages are not equal across provinces and industry-provinces in Viet Nam, providing strong support for the existence of segmented labor markets during our study period.

# **4 Identification**

The goal of our empirical strategy is to understand how rising export expansion affects real wages, informality, and female labor force participation, exploiting data on cross-regional exposure to total exports in Viet Nam between 2010 and 2019. To this effect, we consider the following simple linear regression model: where ∆ _Yr,t_ + _h_ is the change in outcomes of interest, may it be employment rate, informality rate, female participation rate, average annual income average annual wage, college premium or gender wage gap, among others, identified at province _r_ over the period from time _t_ to _t_ + _h_ . ∆ _Xr,t_ + _h_ is our main independent variable, which stands for the change at regional level of total export exposure, as defined in the previous section. The key coefficient of interest is β1, which measures the effects of total trade exposure on the outcome after accounting for the I-O structure, _Kr,t_ is the vector of ex-ante control variables including individual demographic background taken from the LFS such as urban dummy, gender, marital status, age group, education level, social security ownership, among others.

A relevant issue that needs to be addressed is potential endogeneity in the export exposure covariate. Since we observe changes in labor outcomes and exports simultaneously, we cannot identify which one is driving the other. To ensure truly exogeneity of our export exposure, we need a variable that predicts exports from Viet Nam based solely on its trading partners internal 11