rafmacalaba/data-use-mentions-extended huggingface dataset:
and then normalized to 0. As to whether we might expect to see improvement in empowerment in this context, we were encouraged by Abdallah Ali et al.’s (2021) results showing that women’s access to microfinance results in a sense of increased empowerment among relatively more educated women in Djibouti. In other contexts, outside labor earnings have been shown to give women a sense of greater agency (Anderson & Eswaran, 2009, in Bangladesh, and Majlesi, 2016, in Mexico). Although it is important to also add that, in their review of experimental and quasi-experimental studies, Chang et al. (2020) conclude that little evidence exists that employment leads to greater empowerment or agency for women. This is also true in studies that look specifically at public works. Croke et al. (2024) find a short-run increase in women’s control over household resources from public works in the Arab Republic of Egypt, but a decline in control occurred both after the program ceased and two years later. The World Bank East Asia and Pacific Innovation Gender Lab (2020) find mixed results in the Lao People’s Democratic Republic. Leight and Mvukiyehe (2023) find an improvement in women’s empowerment in

> friend or relative to help provide food? The food insecurity index in Table 1 and Annex A Table 5 documents the number of affirmative responses to questions (2) through (6). The coping strategy index in Annex A Table 5 adjusts the food insecurity index by applying weights to the responses to questions (5) and (6) by a factor of 3 and 2, respectively. These measures are based on the U.S. Agency for International Development and CARE (2008).

14
s exports and economy. Above-average growth in new categories of exports—food and agriculture, non-fuel minerals, and miscellaneous exports—helped to diversify Russia’s exports, as has a pivot to China and the rest of Asia, Latin America, and Africa as new destinations for exports. The regions differ in their ability to take advantage of emerging diversification. For example, the Far East is oriented toward Asia (regional diversification), while regions in the South, the North Caucasus, and the Far East are oriented toward food and agriculture (product diversification).

Russia’s current export basket offers different levels of stability. Some of Russia’s exports can be sold year after year in the same markets and enjoy a solid competitive position. Others have higher rates of experimentation and failure. In an analysis of export survival, Russia’s relationships within the EACU<sup>15</sup> and secondarily in the rest of the CIS are by far the most lasting compared with other geographical regions, while export of primary and resource-based intermediate goods have higher survival rates than other manufactured exports. Russia’s comparatively low survival performance relative to the BRICS is accounted for by low survival of exports outside the former Soviet Union and of exports other than the traditional resource-based exports.

Exporting for technological upgrading can promote greater sophistication of products. While the broader benefits of a strategy of promoting high-technology exports are a subject of some dispute among 13 The source is Comtrade 14 “Mineral products” in this report includes not only mineral fuels (HS 27), but non-fuel mineral products (HS 25 and 26), which account for 1 percent of Russia’s exports. Rosstat data on regional exports group all mineral products together, but in fact 98 percent are mineral fuels. Since exports are assigned the location of company headquarters, high export shares for places like Moscow city, Moscow region, and St. Petersburg city may not reflect where the exports are actually produced.

17
4 Work using macro data are, however, less conclusive on a possible association between poverty and access to finance or job opportunities. Beck et al. (2007), Honohan (2004), Jalilian and Kirkpatrick (2002), and Singh and Huang (2011) find that the degree of financial intermediation has a strong positive impact on the income of the poor. Jalilian and Kirkpatrick (2002) use the ratio of bank assets to GDP to measure financial intermediation in a sample of advanced and developing economies. Beck et al. (2007) and Honohan (2004), looking only at developing countries, capture the role of finance by considering private sector credit. Singh and Huang (2011) also use private sector credit, but with a sample covering only Sub-Saharan African countries.

In contrast, Dollar and Kraay (2002) find that financial development does not affect the poor. They examine the relationship between the average income of the poorest quintile in a sample of advanced and developing economies, and measure financial depth using the ratio of commercial bank assets to total bank assets. Kraay (2004) reaches similar results studying the association between the change in absolute poverty and the ratio of M2 to GDP in a sample of developing countries.

Finally, Guillaumont-Jeanneney and Kpodar (2011) suggest that the poor benefit primarily from the ability of the banking system to facilitate transactions and provide savings opportunities rather than reaping the benefit of greater access to credit. Looking at a sample of developing countries, they find a positive relationship between financial development and poverty if financial development is measured by the ratio of M3 to GDP. If private credit is used instead, the association turns out to be statistically insignificant.

# **B.   Banks, markets and the institutional environment**

Financial institutions operate in settings where complete information is often not available. Entrepreneurs seeking financing normally have more information about their projects than their banks do. In this setting, from the viewpoint of a financial institution projects that may have different probabilities of success are
women face much higher levels of discriminatory behavior. Because the legislation covering the restrictions we consider are set at a national level, we posit that this higher level of discrimination outside the capital is, at least in part, due to more deeply entrenched social norms that serve to disempower women. Differential enforcement of laws may partly explain this, but enforcement is also likely to be endogenous to social norms. This suggests that, in order to change the discriminatory behavior of firms outside Kinshasa, changes in laws alone will not suffice and that policy makers may also need to engage in outreach campaigns to change social norms that hold women back. A recent study by Bursztyn et al (2018) found that married men in Saudi Arabia privately support female labor force participation; however, they underestimate the level of support by other men. They show how social norms constraints might be lifted by the simple provision of information. Such outreach programs might be particularly important in more traditional environments where the social control is stronger as it could be in areas outside the capital city.

An interesting question to explore in future research is to what extent the legislative changes that have been enacted in the DRC since the time of the survey have resulted in changes in discriminatory behavior by firms. At the time of the survey, the World Bank’s WBL index assigned a score of 42.5 to the DRC – suggesting that women in the DRC had less than half the rights of men in the areas covered by the index. By 2018, the score had increased to 70 (World Bank, 2019). It would also be interesting to consider whether the relative change in behavior has differed between firms inside and outside the capital city. We leave these questions to future work.

22
Literature has also used panel data across the world to discuss sophisticated methodologies of verifying determinants of the property taxes and estimating their impacts on the property tax revenue. Davoodi and Grigorian (2007) attribute urbanization's importance to high demand for public services.

Using data from Office of Real Property Tax Service and Office of State Comptroller’s Office of New York State in 1992, Tae Ho Eom (2008) examines a model for the determinants of property tax assessment quality. The author uses two stage least square (2SLS) regression to solve the endogeneity of the property tax administration variables and concludes that “the median tax share, the median property value as a share of median income, the ratio of state aid to total expenditures, and the share of adults with college or higher education have significant impacts on the quality of property tax assessment”.

Sepulveda and Martinez−Vazquez (2012) estimate the determinants for property tax collection for nine Latin American countries during 1990-2007. They use fiscal decentralization, dependency of transfers, government size, income per capita, municipal cadaster, competition for public positions, and index of democracy as main independent variables. They find that fixed effect results yield statistically significant effects for fiscal decentralization, dependency on transfers, GDP per capita, competition for public positions and index of democracy. And for the random effects model, the rest of the variables are statistically significant (urbanization, municipal cadaster, government size).

Hultquist and Petras (2012) employ a panel of 635 year-location observations covering the largest city in each U.S. state from 1997-2010 to measure the impact of the various political, fiscal, administrative, demographic, and economic factors on the use of fractional assessment of local property taxation. They use panel data techniques incorporating both year and location-specific fixed effects and conclude that fractional assessment is associated with political factors, such as higher levels of Republican legislative representation and divided government, suggesting that the fractional assessment of local property taxation may be the path of least resistance for enacting effective property
# **3 Data and spatial methods**

In order to test this model, attention is restricted to a set of 15 _coastal primate_ countries in which the main city is also the main port, so transportation to the primate city is important for trade with both the largest domestic market and the rest of the world (Figure 1).<sup>13</sup> Counterclockwise from the northwest, these countries are Mauritania, Senegal, Guinea, Sierra Leone, Liberia, Cˆote d’Ivoire, Ghana, Togo, Benin, Nigeria, Cameroon, Gabon, Angola, Mozambique, and Tanzania. Further details about all data used are in the Appendix.

## **3.1 City lights**

To date, very little economic data, especially for income and especially as a panel, have been available for individual African cities.<sup>14</sup> In most national household surveys, if any city is individually identifiable, it is only the largest city in a country. The largest program of firm surveys, the World Bank Enterprise Surveys, rarely collects data in more than four cities per African country, and it is typically not clear how the surveyed cities are selected. Censuses often report populations for many cities, but they are almost always carried out at intervals of at least ten years, which limits their usefulness. In order to fill this gap, I propose a novel data source as a proxy for city-level income: satellite data on light emitted into space at night.

Satellites from the United States Air Force Defense Meteorological Satellite Program (DMSP) have been recording data on lights at night using their Operational Linescan System sensor since the mid-1960s, with a global digital archive beginning in 1992.<sup>15</sup> Since two satellites are recording in most years, 30 satellite-years worth of data are available for the 17-year period 1992–2008. Each 30-arcsecond pixel in each satellite-year contains a digital number (DN), an integer between 0 and 63
_CHAPTER 6. UNIT LEVEL MODELS: GENERAL INDICATORS_ 42 between observations in different clusters within the same area. These two problems were eventually solved by the Empirical Best (EB) method and the bootstrap MSE estimator proposed by Molina and Rao (2010). The EB method is similar to ELL in that it combines survey data with auxiliary data, obtained typically (but not exclusively) from a census or administrative records, uses a unit-level model and is designed for general (and perhaps several) indicators that depend on a welfare measure, based on a single model. This original EB method of Molina and Rao (2010) assumes that a one-to-one transformation of the welfare variable, _ydi_ = _T_ ( _zdi_ ), follows the nested error model (5.1) with normality for the area effects _ud_ and the errors _edi_ . Under this model, the area vectors **y** _d_ = ( _yd_ 1 _, . . . , ydNd_ )<sup>_′_</sup> , _d_ = 1 _, . . . , D_ , are independent and satisfy _ind_ **y** _d ∼ N_ ( **_μ_** _d,_ **V** _d_ ), with mean vectors **_μ_** _d_ = **X** _d_ **_β_** , and covariance matrices **V** _d_ = _σu_<sup>2</sup><sup>**1**</sup><sup>_N_</sup> _d_<sup>**1**</sup> _N_<sup>_′_</sup> _d_<sup>+</sup><sup>_σ_</sup> _e_<sup>2</sup><sup>**A**</sup><sup>_d_,where</sup><sup>**A**</sup><sup>_d_=
of picture text --> Source: Barbier and Hochard (2018), Rozenberg and Fay (2019), United Nations Development Program. Note: EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, SSA = Sub-Saharan Africa, and LMICs = Low- and middle-income countries.

A. Education index from the Human Development Index. 2007 and 2017 are unweighted means in EMDEs. Inter-quartile range is for 2017 observations.

B. Life expectancy index from the Human Development Index. 2007 and 2017 are unweighted means in EMDEs. Interquartile range is for 2017 observations.

C. Investment needs based on goals as set out in Rozenberg and Fay (2019), including both new investment and maintenance of existing capital stock. Infrastructure investment includes investment in electricity, transport, water supply and sanitation, flood protection, and irrigation. “Preferred” is defined as the infrastructure “pathway [that] limits stranded assets, has a relatively high per capita consumption due to electric mobility, and invests mostly in renewable energy and storage.” D. Less-favored agricultural areas are agricultural lands constrained by difficult terrain, poor soil quality, limited rainfall, or with limited access to markets. “Sea level" identifies areas where elevation is below 5 meters. Data are from 2010.
the actual financial losses that would be experienced by households under different scenarios, conditional on households adopting all other risk management strategies at their disposal (which may have already reduced their welfare in advance of the shock).

The challenges noted above are evident in existing research to elaborate quantitatively the relationships between hazard occurrence and household‐level poverty outcomes, with backward looking data. A few examples for further reading are listed below:

- The derivation of quantitative relationships between typhoon wind speed and infant mortality from historical typhoon data in the Philippines (Anttila‐Hughes and Hsiang 2013);

- Quantitative examination of the impact of rainfall variability on household consumption in Ethiopia (Hill and Tsehaye 2014, Porter 2012);

- Quantitative examination of the impact of both climate variability and disaster occurrence on the annual probability of permanent household migration in Indonesia (Bohra‐Mishra et al. 2014);

- Use of panel datasets to examine the impact of climate variability on allocation time among child labour activities as well as participation in education and labour activities  (Colmer 2013).

# **a. Selection of hazard measure**

The measure of drought chosen for this study is an index of crop yield shortfall, taken from the World Food Programme’s LEAP (Livelihoods Early Assessment and Protection) software. The index has been developed for the two main cropping seasons in Ethiopia, the _Meher_ (main) and _Belg_ (minor) seasons.

The yield shortfall calculation uses time‐variable meteorological recordings combined with data tables on soil and crop characteristics to calculate yield reductions relative to the expected production under non‐limiting water conditions. The yield shortfall calculation is a seasonal value and can be calculated for both _Belg_ and _Meher_ cropping seasons. It is available at the Woreda<sup>7</sup> level through creation of a composite index for the relevant crop basket.

The methodology for the calculation is described in Box 2.1 below. If we consider the methodology in the context of a probabilistic catastrophe risk modelling framework, the key
br>liberalized)|Abiad, Detragiache and<br>Tressel 2009 (IMF)|
|GDP per capita growth|GDP per capita growth annual %|World Development<br>Indicator (World Bank)|
|International capital liberalization|Measure of restrictions and regulations on international financial<br>transactions|Abiad, Detragiache and<br>Tressel 2009 (IMF)|
|Liquid Liabilities / GDP|Ratio of liquid liabilities to GDP, calculated using the following deflation<br>method: {(0.5)*[Ft/P_et + Ft-1/P_et-1]}/[GDPt/P_at] where F is liquid<br>liabilities, P_e is end-of period CPI, and P_a is average annual CPI|Financial Structure<br>Database (World Bank)|

34
The individual random effects η� help control for unobserved individual characteristics (e.g., innate ability). We fix the values of these characteristics in the previous year to reduce possible contemporaneous issues between them and the outcomes in the current year.

As discussed earlier, we are particularly interested in individuals’ occupation transitions over time (i.e., from period _t-1_ to period _t_ , or ΔO��). We will consider these transitions for various types of occupations such as public sector versus private sector, formal work versus informal work, fulltime work versus part-time work, and having an increase versus having no increase in work skills. A more detailed definition of these transitions is provided in Table 1.11, Appendix 1. To keep a reasonable estimation sample, we generally define three categories as follows: i) transition to the desirable occupation category (e.g., full-time work), ii) no transition within the desirable category (e.g., remained in full-time work), and iii) either transition to or no transition within the less desirable occupation category (e.g., part-time work). We employ the last transition as the reference category. However, data are only available since 1998 for the formal sector, and 2004 for the public sector.

To offer robustness checks and further analysis, we also employ the standard linear regression models with individual random effects to estimate income growth rate where y�� is defined as individual _i_ ’s income in logarithm at survey year _t_ . The coefficient δ� can then be interpreted as the proportionate (percentage) change in individual _i_ ’s income that is associated with the occupation transitions ΔO��.

# **III. Data Description**

The Russian Longitudinal Monitoring Survey (RLMS) was initially created with funding from various sources including the G-7 countries, USAID, and the World Bank. The survey is currently 13
Table A2. FDI inflows <u>and local school enrollment: 2SLS results</u>

||(1)|(2)|
|---|---|---|
||Panel A: School enrollment second stage results<br>||
|Dependent var|∆Highschool|∆College|
|∆FDI_empshare|0.567***<br>(0.169)|0.826***<br>(0.246)|
|L.FDI_empshare|-0.0255<br>(0.0437)|0.127**<br>(0.0544)|
||Panel B: First stage results||
|FDI_shock|2.047***<br>(0.4978)|1.775***<br>(0.4651)|
|First stage F statistics|1022.45|523.26|
|Observations|660|660|
|Prov-Year FE|Y|Y|
|Lagged city characteristics|Y|Y|

Note: This table reports the effect of changes in FDI employment share on city level school enrollment using 2SLS method. Lagged city characteristics include population, average age, share of Han ethnicity. Standard errors are clustered at the city level. *** p<0.01, ** p<0.05, * p<0.1.

The previous regression specification also controls for lagged dependent variable. While we found initial industry structure/school enrollment rate to be a very significant predictor of changes in industry structure/school enrollment, controlling Ycpt−1 creates further endogeneity in the regression. Below we report the results from an alternative regression specification that does not control Ycpt−1 to see whether and how much the coefficients deviate from the main results in Table 8. The coefficients γ and θ are similar to those in Table 8, the positive effect of FDI on structural
In addition to age and location, education plays a strong role in influencing women’s labor market participation. Although a larger share of men than women at every level of education participate in the labor market, the “labor force participation–education nexus” is stronger for women than for men (figure 2). In other words, as their level of education rises, female participation in the labor market increases substantially (from 24 percent for those with intermediate education to 58 percent for those with university degrees). By contrast, there is no discernible trend for men.

**Figure 2 Labor force participation rate by gender, location, age, and education: Tunisia, 2014**

<!-- Start of picture text --> . Overall<br>Urban<br>Location<br>Rural<br>15 – 24<br>25 – 34<br>Age group 35 – 44<br>45 – 54<br>55 – 65<br>Illiterate<br>Read and write<br>Under intermediate<br>Education level<br>Intermediate<br>Above intermediate<br>Univeristy and above<br>0 20 40 60 80 100<br>Labor force participation rate (%)<br>Men Women<br><!-- End of picture text --> _Source_ : Tunisian Labor Market Panel Survey (TLMPS), 2014, http://www.erfdataportal.com/index.php/catalog/105.

The labor force participation–education nexus for women is important because in recent decades Tunisia has achieved major gains in educational attainment for women. For example, between 2003 and 2017 the gross tertiary enrollment for women increased from 30 percent to 42 percent, whereas for men the gross tertiary enrollment remained constant at 23–24 percent during the same 7
01|35.21|36.13|36.17|
|Age at first marriage|19.52|19.39|20.1|20.56|20|
|Number of children|2.61|3.31|2.14|1.39|2.53|
|Number of children under 5|0.85|0.87|0.72|0|0.93|
|Number of household members|<br>4.79|5.36|4.40|2.80|4.80|
|**Educational attainment**||||||
|No Education|0.56|0.65|0.49|0.51|0.48|
|Primary|0.20|0.15|0.24|0.21|0.26|
|Secondary|0.17|0.15|0.18|0.17|0.15|
|Higher|0.05|0.03|0.06|0.07|0.06|
|**D. Outcome variables**||||||
|Currently working|0.26|0.21|0.28|0.28|0.26|
|**N**<br>Source: Authors Calculations – D|19024<br>emographi|7645<br>c and Health Surv|11379<br>ey (DHS): Morocco|1239<br>(1992,2004) and HCP|5790<br>Household survey|

Source: Authors Calculations – Demographic and Health Survey (DHS): Morocco (1992,2004) and HCP Household survey (2007 and 2014).

Note: This table displays full and working sample means for several variables both pre and post Chikak divorce reform in Morocco. For the first three columns, we present full
**Figure 13: Disability rate by gender and age and cause of disability. Cambodia Demographic and Health Survey 2000**

<!-- Start of picture text --> 0.06<br>0.05<br>0.04<br>0.03<br>0.02<br>0.01<br>0<br>15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74<br>Age in 2000<br>Males Females Males/from weapon-landmine<br>Percentage disabled<br><!-- End of picture text --> 33