rafmacalaba/data-use-mentions-extended huggingface dataset:
24 Moreover, singlehood is uncommon **Table 3: Household Characteristics in Malaysia, 1995 Citizens Non-citizens** among Malaysians, less than 10 percent Marital Status (% of sample): of those surveyed were single, Single 9.1 27.5 compared to 27.5 percent of nonMarried 79.8 68.1 Other 11.1 4.4 citizens. Average household size also Household size (# of members) 4.6 3.8 varies according to citizenship. For Age _(years):_ Male 44 34 Malaysians, households have a mean of Female 49 33 4.6 members, with a fairly normal Total sample of 36,700 observations, of which 95.5 % are Malaysian. distribution. Non-citizens, however, typically reside in smaller households, averaging 3.8 members; the observed log-normal distribution also indicates a higher concentration among smaller households. The age composition of foreign workers compared to Malaysian nationals lends further support to the assertion that immigration policy targets foreign labor for only temporary contracts, discouraging permanent settlement among the low-skilled. The mean age for males interviewed was 34 for non-citizens and 44 for citizens. The discrepancy aLmong women is even greater, with female non-citizens averaging 33 years of age, compared to 49 yeairs for citizens.

# **V. Determinants of earnings in Malaysia**

The results comparing the group profiles of Malaysians and non-Malaysians on the basis of age, marital status, household size, type of employment and sources of income, are consistent with the predicted effects of foreign labor management policy. In an effort to assess the actual policy impact, the analysis in this section investigates the determinants of earnings using regression analysis on household income data and controlling for individual characteristics. Malaysia's immigration policy suggests that foreigners will be paid less than Malaysian citizens with similar characteristics, due to the added unit labor cost incurred by employers for work permit fees. In theory, the wage gap between citizens and non-citizens at the margin should be equal to the
2~~ 018 ~~G~~ HSP-W4 ~~v~~ 03 ~~M~~ . Downloaded from `https:// microdata.worldbank.org/index.php/catalog/3557` on 9 February 2024.

- National Statistical Office (NSO) (2012). Malawi Third Integrated Household Survey 2010-2011. Public Use Dataset. Ref: MWI ~~2~~ 010 ~~I~~ HS-III ~~v~~ 01 ~~M~~ . Downloaded from `https://microdata. worldbank.org/index.php/catalog/1003` on 6 September 2019.

- National Statistical Office (NSO) (2015). Malawi Integrated Household Panel Survey 2010-2013 (short-term panel, 204 EAs). Public Use Dataset. Ref: MWI ~~2~~ 010-2013 ~~I~~ HPS ~~v~~ 01 ~~M~~ . Downloaded from `https://microdata.worldbank.org/index.php/catalog/2248` on 6 September 2019.

27
. Regressions are estimated by Ordinary Least Squares (OLS) using data from the 2019 edition of the HIES. Land inequality in each district is defined as the variance of the log of landholding size across its landholders. Standard Errors are shown in parentheses. Column (2) uses an adjusted version of the dependent variable that maps more closely to the theoretical constructs inˆ equation (14), namely ln( _Xn_<sup>_A−φE_ˆ</sup><sup>_n_),where</sup><sup>_X_</sup> _n_<sup>_A_isaggregatefoodexpendituresand</sup><sup>_En_isaggregatetotalexpendituresindistrict</sup><sup>_n_,and</sup> _φ_ = 0 _._ 0105.

> 111Specifically, in Column (2) the dependent variable is not ln( _XnA_<sup>) but ln(</sup><sup>_X_</sup> _n_<sup>_A−φE_ˆ</sup><sup>_n_), with</sup><sup>_φ_ˆ = 0</sup><sup>_._0105 being our</sup> estimate of _φ_ as displayed on Table II and explained in Section VI.A.1.
We combine data on built-up area with information on agro-ecological zones to capture the wide variety of landscapes and agricultural potential that characterize Ghana.<sup>5</sup> We divide this information into two variables, which do not vary over time. The first of these variables characterizes the humidity of a particular region, i.e., whether moist/dry, semi-humid, or humid. In Ghana as in the rest of West Africa, the rains follows a monsoon pattern and rainfall generally decreases along a North-South gradient, with the North being drier and the South being more humid. There are exceptions to this general pattern, however – the immediate coastal area, for instance, tends to be drier than inland. In the data, humid soils are associated with the entire valley of the Volta river and with the forested region North of Takoradi.

The second agro-ecological variable we construct captures soil quality – going from poor to moderately good to good. There are two additional categories: hydromorphic soil, which is characterized by excess moisture which tends to suppress aerobic factors in soil-building; and soils unsuitable for buildings or agriculture, such as steep soils or soils covered by standing water at least part of the year – these soils require special investments (e.g., terassing, drainage) in order to become usable. Given that Ghana is relatively flat with no mountains and a relatively homogenous physical landscape, we abstract from altitude: whatever variation in altitude there may be, it translates into variation in rainfall and soil quality and is already captured. Information on agro-ecological zones comes from Global Agro-Ecological Zones (GAEZ) 2012 data from FAO. The GAEZ2012 dataset has a resolution of 10km which was overlaid on our square pattern in order to derive relevant values for each cell.

To this data we add longitude and latitude information expressed in decimal degrees, which we normalize to have mean zero and unit variance. A higher value of longitude means more to the West; a higher value of latitude means more to the North.
_Ei_ is national employment level in industry _i, e_ is total regional employment, E is total national employment.

While there can be many explanations for high and low concentrations of industries in a given region, high LQs suggest regional comparative advantage for the industry, and therefore they point to industries that might be important exporters and sources of regional product, and low LQs suggest that these industries might be underrepresented in a region and hence their products might need to be imported, hence accounting for some negative entries in regional balance of payments. These speculations depend on a range of assumptions about industry mix, consumer tastes and preferences, and the like, so LQs are clearly only broad-brush indicators, but can be useful in pointing the analyst to industries that warrant further attention. Other methods discussed below will relax some of the assumptions that underpin LQ interpretations.

# **_Related Measures of Concentration_**

A related measure that focuses on a single industry’s concentration across all regions is the Coefficient of Localization (CL). The CL relies not on the ratios of ratios, but instead is computed as one-half the sum of the absolute differences of between numerators and denominators in the LQ calculations for the industry in all regions. The closer this value is to unity, the more concentrated the industry is in the nation. An example for a fictitious industry and nation can be seen in the table below, where the LQ indicates that the industry is relatively concentrated in regions 4 and 5, and the value of the CL is 0.21. Industries with low CL might be less tied to specific regional attributes, and therefore might be good candidates for regional diversification in regions where the LQ is less than one. Diversification strategies will be discussed further, below.

||All Regi<br>|ons<br>||||||
|---|---|---|---|---|---|---|---|
64 based on the “nominal rate of protection at farm gate”, which is defined as the difference between actual and expected farm-gate fertilizer prices, with expected prices computed based on international prices and the cost of transportation from port to farm. Since there is no available subsidy data for cinnamon, cloves, and groundnuts, we impute their fertilizer subsidy rates with the average rate across potato, maize, tea, and onions.

# **A.F Potential Crop Yields**

To estimate the elasticity of substitution across crops of our model’s demand system, we use an IV approach that leverages variation in agricultural productivity across space due to exogenous geographic factors (such as climate and geology). To do so, we use data on district-level potential attainable yields for each of seven crops, which we obtain from the FAO-GAEZ project. The crops are: coconut, groundnut, maize, onion, rice, tea, and white potato. Unfortunately, cinnamon potential yields are not available in GAEZ. We extract potential yield data (in kg/ha) at the grid cell level and aggregate up to the district level. GAEZ offers different yield estimates depending on the irrigation system (rainfed or irrigated) and climate conditions (e.g. historical conditions, various climate change scenarios). To reflect the actual technologies commonly used in Sri Lankan agriculture, we choose the rainfed option for all crops except rice, which is irrigated. We choose the climate conditions of 1981-2010 without climate change. Section VI.A.1 describes how this data is used in estimation.

# **A.G Labor Share in Value Added**

To estimate labor and land coefficients in our model’s Cobb-Douglas agricultural production functions, we use data from Sri Lanka’s IO table for 2000, produced by the Institute of Policy Studies (Amarasinghe and Bandara, 2005). We extract information on employee compensation and value added (at factor cost prices) for a variety of agricultural sectors: “paddy”, “
POLICY **RESEARCH WORKING PAPER** 2528

# Summary findings

Rao examines the paradox of very poor households spending large sums on celebrations. Using qualitative and quantitative data from South India, Rao demonstrates that spending on weddings and festivals can be explained by integrating an anthropological understanding of how identity is shaped in Indian society with an economic analysis of decisionmaking under conditions of extreme poverty and risk.

Rao argues that publicly observable celebrations have two functions: they provide a space for maintaining social reputations and webs of obligation, and they serve as arenas for status-making competitions.

The first role is central to maintaining the networks essential for social relationships and coping with poverty. The second is a correlate of mobility that may become more prevalent as incomes rise.

Development policies that favor individual over collective action reduce the incentives for the networking function and increase the incentives for status-enhancing functions-thus reducing social cohesion and increasing conspicuous consumption.

Market-driven improvements in urban employment, for example, could reduce a family's dependence on its traditional networks, could reduce incentives to maintain these networks, and could reduce social cohesion within a village and thus its capacity for collective action. In contrast, microfinance programs and social funds try to retain and even build a community's capacity for collective action.

This paper-a product of Poverty and Human Resources, Development Research Group-is part of a larger effort in the group to study the relationship between poverty and collective action. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Patricia Sader, room MC3-556, telephone 202-4733902, fax 202-522-1153, email address psader@worldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbank.org/research/workingpapers. The author may be contacted at vrao@worldbank.org. January 2001. (25 pages) _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings
# **_Acknowledgements_**

This diagnostic builds on the experience and resources of the Digital Business Indicator (DBI) project, a joint initiative started among the Indicators Group of the Development Economics Vice Presidency (DECIG), Macroeconomics, Trade, and Investment (MTI), and Digital Development Global Practices. In 2018, the DBI project collected information on the regulatory environment for the digital economy, covering topics of broadband connectivity, digital payment, data privacy and security, as well as logistics, in 21 pilot countries. Information about data privacy and security, regulatory practices regarding the rights of data subjects, cross-border data flow, and cybersecurity is collected. This study refines the methodology of the data privacy and security indicator (now named as “data governance” indicator) of the DBI project to ensure its consistency with the new framework proposed in WDR21.

The diagnostic project team appreciates funding support from WDR21 and USAID. Vivien Foster provided guidance throughout the diagnostic. Rong Chen was the Project Lead managing the diagnostic. David Satola and Adele Barzelay helped design the questionnaire used in the diagnostic. Yasmin Zand and Olga Kuzmina helped to disseminate questionnaires. Aliaksandra Tyhrytskaya, Federico Cardenas Chacon, Lillyana Sophia Daza Jaller, New Doe Kaledzi, Nicolas Conserva, and Paris Gkartzonikasm contributed to data collection. Nicolas Conserva also provided research support for the paper. David Medine, Malarvizhi Veerappan, Martín Molinuevo, and Sara Nyman provided valuable comments on the methodology of the diagnostic.

30
12 Database includes total assets of NBFIs to GDP, which includes pension fund assets to GDP, mutual fund assets to GDP, insurance company assets to GDP, insurance premiums (life) to GDP, and insurance premiums (non-life) to GDP _._ For financial markets, it is possible to obtain cross-country data on stock and bond markets (both sovereign and corporate). To approximate the size of stock markets, the most common choice in the literature is stock market capitalization to GDP. For bond markets, the mostly commonly used proxy for size is the outstanding volume of debt securities (private and public) to GDP.

To measure ―stock market‖ development, this paper primarily uses Stock value traded, which equals the value of stock market transactions as a share of GDP. This market development indicator incorporates information on the size and activity of the stock market, not simply on the value of listed shares. Earlier work by Levine and Zervos (1998) indicates that the trading of ownership claims on firms in an economy is closely tied to the rate of economic development. There is substantial variation across counties. While the mean value of Stock value traded is about 29 percent of GDP the standard deviation is about double this value. In Armenia, Tanzania, and Uruguay, Stock value traded annually averaged less than 0.23 percent over 19802008 (10th percentile). In contrast, Stock value traded averaged over 75 percent in China (both Mainland and Hong Kong SAR), Saudi Arabia; Switzerland; and Unites States (90th percentile).

The relative size of financial institutions and financial markets is an important topic in a part of the financial development literature, in particular the literature on ̳financial structures‘ (Demirgüç-Kunt and Levine 2001; Demirgüç-Kunt, Feyen, and Levine 2012). The ratio of the size indicators for banks and financial markets, called financial structure ratio, can be used to gauge the degree to which the financial system is relatively bank-based or market-based. Financial structure differs markedly across economies.
, even though data for most countries show a gap between rural and urban income. Kummu, Taka, and Guillaume (2018) use a similar methodology, and Yetman, Gaffin, and Xing (2004) use it to generate a future gridded GDP based on a future gridded population. The method to grid GDP by UNEP and the World Bank distinguishes between rural and urban population assuming the latter to have a higher GDP per capita and allocates GDP using an estimated ratio of urban to rural GDP per capita by country (UNISDR 2011).<sup>6</sup> Ghosh, Powell, Elvidge, et al. (2010) use LandScan population grids (e.g. Dobson et al. 2000) to distribute national level agricultural GDP into the rural area, then use night lights to distribute the remaining GDP to urban areas. Rafa, Moyer, and Wang et al. (2017) also use a hybrid method for distributing rural and urban GDP in Uganda. Likewise, Gunasekera et al. (2015) use separate models for the non‐agricultural and agricultural sectors with refinements of the non‐agricultural model in Blanchard et al. (mimeo).

<mark>Turning to the first of two new methodologies for generating gridded agricultural GDP, we note that previous work of spatial allocation models includes GDP models (Ghosh et al. 2010; Nordhaus 2006; UNISDR 2011; and Gunasekera et al. 2015), population models (Tobler et al. 1997) and agricultural models such as MapSPAM (You et al. 2006) and Global Agro‐Ecological Zones (GAEZ) (FAO/IIASA 2012). GAEZ has a total crop production data set for the year 2000 that combines actual yield and production and FAO crop statistics (international) prices for 23 major commodities.</mark>

> 6  Available at Global Risk Data Platform: http://preview.grid.unep.ch.

3
|
|Kotayk|Rural|10|108,771|94,876|13,895|
|Lori|Urban|11|124,050|134,962|-10,912|
|Lori|Rural|12|87,532|76,157|11,375|
|Shirak|Urban|13|133,620|128,691|4,929|
|Shirak|Rural|14|96,856|77,832|19,024|
|Syunik|Urban|15|90,205|65,044|25,161|
|Syunik|Rural|16|44,350|33,042|11,308|
|Tavush|Urban|17|49,859|39,214|10,645|
|Tavush|Rural|18|69,943|58,907|11,036|
|Vayots Dzor|Urban|19|16,160|16,811|-651|
|Vayots Dzor|Rural|20|31,501|25,678|5,823|
|Yerevan|Urban|21|1,098,866|824,317|274,549|
|Armenia|||2,917,151|2,405,869|511,282|

_Notes_ : The 2023 election data on the number of voters or adult population and other spatial information are obtained from the CRRC-Armenia. Column (3) presents the difference between the 2022 population updated based on WorldPop data and the 2023 number of voters or adult population from the election data.

As shown in Column 2 of **Table 1** , the total number of voters or adult population is 2.4 million, which is quite close to the total population. It indicates approximately 18% of the population is children below 18 years old. However, children below 18 years of age account for around 23%-24% of Armenia’s population in other datasets.
changes in response to individual characteristics. In the ordered logit regressions we also estimate savings and credit behavior, but here the coefficient estimates indicate the expected change in the ordered log-odds scale. The coefficients therefore indicate the significance and direction of the effects with positive coefficient estimates corresponding to an increased probability of higher valued responses; exponentiating the coefficients yields the proportional odds-ratios.

In the multinomial estimates we observe that the log-odds ratios of saving formally, compared to not saving at all, is significantly decreased for women, whereas saving only informally, compared to not saving at all, is not affected by gender. This is despite the fact that we also observe significant differences in informal savings by gender in the univariate setting. The absence of a significant relationship may be explained by gender differences in income, education and employment status which we documented earlier in Table 1. Our results suggest, first, that log-odds of saving using an account at a bank or other financial institution, as compared to not saving, are lower for women. This may be driven, in part, by the lower percentage of woman that have a bank account (a precondition to using an account to save), as well as the possibilities that women might have less ownership of assets, less money left over at the end of the month, less access to formal institutions (or greater difficulty traveling to access a financial institution), or less interest in formally saving (that could also be linked to lower financial capability).<sup>19</sup> Second, our results show that the log-odds of saving informally, compared to not saving at all, are not affected by gender, suggesting that the gender gap in formal savings may be related to issues other than women’s lower savings rates overall. Importantly, the Findex database does not collect the amount of personal savings. For instance, if

> 19 Previous literature studying adults in the United States have documented lower financial literacy among women (see Lusardi and Mitchell, 2008).

20
the treatment group were more likely not to receive the ‘GYM package’, thereby potentially generating sample selectivity. To deal with this, we report results excluding remote schools from the control group. We do this by excluding schools from the control group located in areas the courier was not able to reach. These results largely resemble those obtained from our main sample (see **Table A3** in the Web Appendix). Also, our core analysis results remain robust to controlling for school characteristics that are different between schools assigned to treatment and control.<sup>20</sup> 20 Though the stratified randomization strategy was designed to minimize systematic imbalances between the treated and control samples, this possibility could not be ruled out. A key balancing test would have been to assess whether school attainment between treatment and control schools differed before treatment. Unfortunately, no school assessment data were available before 2015. Instead, we carried out a balancing test using administrative schoollevel data obtained from the 2014 Annual School Census. Treatment and control schools do 16
by coefficient of variability of education for population aged over 15, and several other dispersion measures. The calculation took several steps. First, we obtain data on educational attainment at various levels for the population over the age 15 from Barro and Lee 1997. Second, school cycling data was obtained from Psacharopoulos & Arriagada 1986, Table B-I. Third, we then calculated the mean year of education, variance of education, standard deviation of education, coefficient of variability of education, and GINI coefficient all for the population aged over 15. Table 2 presents the calculated coefficient of variability of education by five-year intervals. In most countries, this variable shows a decline over 25 year period.

The dummy variables for economic reforrns (DREF) and for political upheavals (DPOL) were constructed based on the chronological description of development and reform for each country (Appendix 11), which summarize both the economic and the political processes occurred in the 12 countries during the historical period from 1970 to 1995. On the terms of trade (TOT), we used the country specific terms of trade for each country whenever it is available in the IMF International Finance Statistics Yearbook (IFS). Some proxy measures were used when the TOT data is not available for a particular country.

Twelve countries were selected into the sample based on the following rationales: First, determined by the objectives ofthis paper, we must select countries which conducted some policy reforms. Second, the effects of policy reforms must have been felt within a reasonable period. This rules out most countries in East Europe and Formal Soviet Union because their market reforms were too recent. This also rules out some low-income countries in Africa where the implementation of reform measures were too uncertain due to weak institutions or lack of legal infrastructure. Third, in order to do a good in depth analysis, one has to be reasonably familiar with the economy and political history in these countries. Data availability, our knowledge regarding the countries and resource constraint put a limit on the number of We are grateful