rafmacalaba/datause-encoder-data huggingface dataset:
(g) Changes in synthetic benefits per population and LPG usage per population, Farm Bill (h) Changes in synthetic benefits per population and LPG prices, Farm Bill <!-- Start of picture text --> 20 ME NH VT 20 ME NH VT<br>CT OH OH CT<br>MA OR OR MA<br>15 TX MO NE CO WIILSDOKCAAZSCKSUTNCNDINWVGAIAMINJARRINMNYMNALMSKYMTMD PAIDWA DCVA DE NV TNFL LA 15 UTND NE OKSD IL MOARCOIDCAMNWAMTGAALINNMKSWIIAKYMSTXNVLA WVVAFLSCNCDCMDTN AZ PA DE NJ NY MIRI<br>10 10<br>WY WY<br>5 5<br>-20 -10 0 10 20 30 -25 -20 -15 -10 -5<br>(1-mo % change) LPG residential, Btu per pop, SNAP FY (1-mo % change) LPG residential, price per MBtu, SNAP FY<br>Fitted values % change in synthetic benefits per population Fitted values % change in synthetic benefits per population<br><!-- End of picture text --> (i) Changes in synthetic benefits per population and natural gas usage per population, Farm Bill (j) Changes in synthetic benefits per population and natural gas prices, Farm Bill <!-- Start of picture text --> 20 VTNH ME 20 NH VT ME<br>OH CT CT OH<br>OR MA MA OR<br>15 AZ INID IAMOAROK KYTNIL NE NMWICOALUTWVMNMILAFLCAMSTXWAGAKSNVSCPASDMTNDDC DE NCMDRINJNY VA 15 CAIL NMMN SDNDMSTX IAMTDC NE WI INMDLAVANCTNKYKS SCGANYCOPA OK MOMIARIDFLNJ ALNV WVUTRIAZ WA D E<br>10 10<br>WY WY<br>5 5<br>-10 -5 0 5 10 15 -30 -20 -10 0 10<br>(1-mo % change) Natural gas residential, Btu per pop, SNAP FY (1-mo % change) Natural gas residential, price per MBtu, SNAP FY<br>Fitted values % change in synthetic benefits per population Fitted values % change in synthetic benefits per population<br><!-- End of picture text --> Notes: This figure plots the changes in synthetic SNAP benefits per population, SUA per population, energy usage per population and prices for the residential sector and different fuel types, and temperature by state during the Farm Bill.

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# **Making Secure Land Tenure Count for Global Development Goals and National Policy: Evidence from Zambia**<sup>*****</sup>

Daniel Ayalew Ali<sup>1</sup> , Klaus Deininger<sup>1</sup> , Thea Hilhorst<sup>1</sup> , Frank Kakungu<sup>2</sup> , Yuanyuan Yi<sup>1</sup> 1The World Bank, 1818 H Street NW, Washington, DC 20433 2Central Statistical Office of Zambia, Lusaka JEL Classification: Q15; O13; O17; O55 Keywords: Sustainable Development Goals; land tenure security; transferable rights; land title; customary land rights; demand for title; Zambia; Africa

> *We thank the Zambia Central Statistical Office team led by Victor Bwalya for support to data collection, Ndashe Yumba, Joseph Minango, Kelvin Chibangula and Emmanuel Tembo from the Ministry of Lands and Natural Resources as well as Ademola Braimoh, Douglas Graham, Neeta Hooda and Tasila Banda from the World Bank supported ZIFLP project for support to survey implementation. We duly acknowledge funding support from the German Ministry for Economic Cooperation and Development. We are also grateful to Malcolm Childress, Ina Ruthenberg, Manohar Sharma, Matt Sommerville and seminar participants at the 2019 World Bank Land and Poverty Conference and the World Bank’s Zambia country office for comments and suggestions.
2

# **I. Introduction**

Crime is among the most difficult of the many challenges facing South Africa in the post-apartheid era. The country's crime rates are among the highest in the world and no South African is insulated from its effects. Beyond the pain and loss suffered by crime victims, crime also has less direct costs. The threat of crime diverts resources to protection efforts, exacts health costs through increased stress, and generally creates an environment unconducive to productive activity. Additionally, the widespread emigration of South African professionals in recent years is attributable in part to their desire to escape a high crime environment.1 All of these effects are likely to discourage investment and stifle long-term growth in South Africa. Consequently, it is important to understand the factors that contribute to crime.

Both economic and sociological theory has linked the distribution of welfare to criminal activity. Economists have suggested that inequality may capture the differential returns to criminal activity and thereby have an association with crime rates. If criminals travel, not only the welfare distribution in the local area, but that of neighboring areas as well, may be linked to local crime levels. Sociologists have hypothesized that inequality and social welfare in general may have effects on crime through other channels. Inequality may be associated with lack of social capital, lack of upward mobility, or social disorganization, all of which may cause higher levels of crime. Furthermore, iAccording to a survey conducted by the South African Migration Project, blacks and whites both "...rated security and safety as the most significant push factor, reinforcing the national importance of addressing the crime problem as a deterrent to the brain drain" Dodson (2002).
# Appendix C. Additional Results

## **Table C.1. Mean comparison across household conflict exposure, first round**

||No conflict|Conflict|Difference|
|---|---|---|---|
|_a. Household characteristics_||||
|Coping Strategies Index (CSI score)|2.48|2.05|0.43|
|Poverty status|0.24|0.24|-0.01|
|Consumption (ln)|11.32|11.32|0.01|
|Food consumption (ln)|10.93|10.92|0.00|
|Nonfood consumption (ln)|9.78|9.77|0.01|
|Health expenditures (ln)|2.95|3.29|-0.33|
|Education expenditures (ln)|5.91|6.10|-0.19<br>|
|HH size|6.04|7.71|-1.67<sup>***</sup>|
|HH head male|0.87|0.89|-0.02|
|HH head age|47.99|47.29|0.70<br>|
|HH head years of education|7.63|6.64|0.99<sup>*</sup>|
|Household lives in an urban area|0.34|0.31|0.03|
|HH head monogamous|0.63|0.56|0.06<br>|
|HH head polygamous|0.19|0.29|-0.10<sup>**</sup>|
|HH head divorced, separated, or widowed|0.14|0.11|0.03<br>|
|HH head employed|0.91|0.85|0.06<sup>*</sup>|
|Asset index|0.00|0.02|-0.01|
|HH owns livestock|0.42|0.53|-0.12<sup>**</sup>|
|_b. Geographical characteristics_||||
|Annual mean temperature (°C * 10)|263.76|257.69|6.07<sup>***</sup><br>|
|Annual precipitation (mm)|1696.27|1260.34|435.94<sup>***</sup>|
|Slope (percent)|3.23|2.95|0.27|
|Elevation (m)|235.57|398.19|-162.62<sup>***</sup>|
|Potential wetness index|14.55|15.24|-0.69|
|Terrain roughness|2.93|3.15|-0.21<br>|
|Avg. 12-month total rainfall (mm) for Jan.–Dec.|1452.58|1174.47|278.12<sup>***</sup>|

_Note_ : Consumption, poverty, and the other variables include 553 households with no conflict events and 137 households with at least one conflict event. _First round_ here indicates the second round for the CSI (the first round of analysis), the first wave of the three-wave panel used for the consumption and poverty analysis, variables on household characteristics as well as geographical variables. The asset index is constructed using factor analysis that includes dummies for the ownership of the following assets: radio, television, refrigerator, sewing machine, computer, stove, bicycle, motorcycle, car, generator, iron, fan, and bed or mattress. Summary statistics and a description of geographical variables are given in appendix A, tables A.1 and A.2, respectively.

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disproportionately come from firms with high shares of temporary workers. It would therefore seem likely these gains are not really gains in employment but rather changes in status from temporary to permanent.

I use the firm-level data to investigate this possibility. I calculate all of the job- and worker-flows variables described in the data and methodology section at the firm level. Within each country, each firm’s weight in the regressions is proportional to the inverse of its ex-ante probability of being in the sample multiplied by the average of its current number of permanent employees and the number of permanent employees it would have if labor regulations were made more flexible. Across countries I set the sum of the weights equal to each other, that is, I give each country equal weight in the regressions. This weighting scheme is chosen to make these firm-level regressions exactly analogous to the country-level regressions from table 4. I present these results in table 5.

The first column of table 5 presents the regressions of the five measures of job and worker flows used in table 4 against the standardized value (multiplied by minus one) of the labor-market flexibility indicator from the EFW report. The construction of the weights guarantees that the estimated coefficients be identical to those in table 4. The standard errors tend to be somewhat smaller than those reported in table 4, which is caused by the fact that I am using a country-level indicator in a firm-level data set. Although the standard errors are clustered at the country level, Bertrand et. al. (2004) warn that standard errors tend to be too small when the number of clusters is small. For this reason the results using the indicator of labor-market flexibility from table 5 need to be interpreted with some caution, although the main results will later be confirmed in models with one observation per country.

The second column of table 5 reports the results of regressing the job- and worker-flows variables against the percent of the firm’s workforce that is temporary or seasonal. The results show that, for each percentage point increase in temporary workers as a percent of employment, the net gain in permanent employment from making labor regulations more flexible rises by 0.13 percentage points.<sup>5</sup> Not surprisingly, the variable “temporary workers as a percentage of employment” has no effect in the job destruction model and has a statistically significant but small effect in the fire equation. The fact that the creation of permanent jobs and the hiring of permanent workers in response to labor reform disproportionately

> 5 Since this independent variable is calculated at the firm level, there is no clear reason to cluster the standard errors in this regression. Indeed the standard errors are nearly identical when the clustering procedure is dropped.

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Figure 8. Total assets and leverage (leverage and asset growth move together for securities brokers and dealers) <!-- Start of picture text --> A. Households B. Non-financial non-farm corporations<br>8 6<br>5<br>6<br>4<br>4<br>3<br>2 2<br>1<br>0<br>0<br>-2<br>-1<br>-4 -2<br>-1 -0.5 0 0.5 1 1.5 -2.5 -1.5 -0.5 0.5 1.5 2.5<br>Leverage Growth (Percent Quarterly) Leverage Growth (Percent Quarterly)<br>C. Commercial banks D. Securities brokers and dealers<br>6 40<br>5 30<br>4<br>20<br>3<br>10<br>2<br>0<br>1<br>-10<br>0<br>-20<br>-1<br>-2 -30<br>-50 -30 -10 10 30 50 -60 -40 -20 0 20 40<br>Leverage Growth (Percent Quarterly) Leverage Growth (Percent Quarterly)<br>Total Asset Growth (Percent Quarterly)<br>Total Asset Growth (Percent Quarterly)<br>Total Assets Growth (Percent Quarterly)<br>Total Asset Growth (Percent Quarterly)<br><!-- End of picture text --> Source: Adrian and Shin (2008).

Note: Panel A plots the quarterly changes in total household assets to quarterly changes in household leverage as extracted from the US Financial Accounts (formerly called Flow of Funds). Panel B plots the change in leverage and change in total assets of non-financial, non-farm corporations drawn from the US Flow of Funds Accounts. Panel C plots the changes in leverage against the changes in the total assets of US commercial banks. Panel D plots the same for US securities brokers and dealers. The data are from 1963 to 2006.

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Table 2: Summary of the variables, data sources, and sample period used for the growth models.

|Indicator Name|Description|Coverage|
|---|---|---|
|Initial GDP per capita<br>Governance|The logarithm of the initial value of GDP per<br>capita for the growth period in question<br>(2000‐2016). Source: World Bank World<br>Development Indicators (WDI).<br>Index of the quality of governance which takes<br>into account corruption, the rule of law and<br>the quality of institutions. Source: WDI.|2000‐<br>2016<br>2000‐<br>2016|
|Inflation|Measure of CPI change. Source: WDI.|2000‐<br>2016|
|Government Size|Total government expenditure as a share of<br>GDP. Source: WDI.|2000‐<br>2016|
|Years of Schooling|The average number of years of schooling.<br>Source: Barro‐Lee (www.barrolee.com).||

Finally, we develop a comprehensive measure of overall network centrality, referred to as multidimensional connectivity, that combines all six types of connectivity into a single network measure. This indicator takes into consideration the complementarity of the various forms of connectivity, as described in the introduction. Multidimensional connectivity is found to be significantly related to long‐ run growth, and provides a better explanation of long‐run growth than the individual connectivity channels. In other words, the sum is greater than the parts. As a robustness check, we develop an alternative index of network centrality, multiplex connectivity, which describes the complete network but does not impose the restriction that each layer of the network is a complement to other layers. This indicator has a similar, albeit less strong, relationship with growth than the multidimensional connectivity indicator.

# _3.1 Network centrality_

This section introduces a measure of centrality, or influence, based on the well‐known Google PageRank algorithm which was used to rank websites based on their links in the network. The algorithm was initially developed to rank websites in terms of their “importance” and “relevance” to a search query. Network analysis was a natural starting point for this problem because websites with more hyperlinks pointing at them were thought of as being of higher quality. In addition to the number of the incoming links, having more links from higher quality websites is yet another indicator of website quality. The innovation by Page (1999) consisted in modifying the popular network Eigenvector Centrality measure so that the centrality value of a website was proportional to the probability that a person clicking randomly on hyperlinks would 15
In Eq(5), individual logarithmic wage is related to individual characteristics **x** _hi i_ .<sup>5</sup> In Eq(6), logarithmic household non-wage income is related to household characteristics **z** _h_ and the number of self-employed individuals in the household **S** _h_ . Labor supply is modeled by Eq(7) and Eq(8), where individual characteristics **v** _hi_ are related to the ‘utility’ from being a wage-earner and self-employed. _uhin_ can be considered the _w s_ random reservation utility for working. We assume that the error terms μ _hi ,_ η _h , uhi , uhi_ and _uhin_<sup>are independent. Furthermore, we assume that</sup> μ _hi_ follows a normal distribution with mean zero and variance σμ 2 , and η _h_ a normal distribution with mean zero and variance ση 2 . We also assume that _uhiw_ , _uhsi_ and _uhin_ follow an identical Gumbel distribution.

Eq(9) simply states that the number of self-employed is the sum of **IS** _hi_ over the set of individuals I _h_ within household _h_ . The per capita household income **H** _h_ is, of course, the sum of wage and non-wage income earned by the household members divided by the household size **N** _h_ , as it is defined in Eq(10).

As with the standard SAE, we consider above equations as a predictive model, using a rich set of regressors to explain the variation of left-hand-side variables in Eq(5), Eq(6), Eq(7) and Eq(8). However, regressors can only include the variables shared by the census and the survey.

We first estimate the parameters of the equations above. Only the survey data set is used at this stage. We run OLS to estimate Eq(5) and Eq(6), whereas we use a multinomial logit model to jointly estimate Eq(7) and Eq(8). Therefore, we estimate the regression coefficients α , , , , , β γ δ λ _a w_ , _a s_ , _bw_ and _bs_ and their associated variance-

> 5 **x** , **z** and **v** are expressed in a row vector format.

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partly explains relatively low probability of escaping unemployment. It also points to a possible problem of the skill gap or skill mismatch on the part of the unemployed. Some of them may have troubles finding a job because their skills fall short of those required by employers.

**Table 17 New Hires by Previous Labor Market Status, 2001**

|Laborforcestatusoneyearearlier||
|---|---|
|Employed|45.9|
|Unemployed|45.7|
|Outofthelaborforce|8.4|
|Sample size|136.0|

New hires: persons who hold their present job for 12 months or less.

_Source Lithuanian Labor Force Survey, May 2001, Bank staffcalculations_

# **The Skills Gap**

Indeed, the skills gap on the part of the unemployed seems to partly account for the limited transitions from unemployment into jobs. On average, the unemployed, and especially the long-term unemployed, have lower educational attainment and lower skills than the employed (Table 18). In other words, there is an "excess supply" of poorly educated persons among the unemployed in the sense that, all else being equal, there is not enough low skilled jobs to eliminate unemployment.<sup>19</sup> Consequently, unemployment is disproportionately concentrated among workers with low educational attainment and poor skills. For example, the unemployment rate among workers with less than upper secondary education is around 25 percent compared with 6 percent among workers with university education. Similarly, the unemployment rate among workers in elementary occupations (i.e., the low skilled) is over 20 percent, compared with 4 percent among

> **19** A critical variable that here is assumed to be constant is the structure of wages. A flexible wage structure, entailing the fall in relative wages of low skilled workers, would help to absorb unemployment among poorly educated workers. However, social norms embedded inter alia in the minimum wage, prevent wages from adjustng to supply and demand conditions.

49
to cater to their different needs and overcome barriers related to isolation and social norms (World Bank, forthcoming).

**The vulnerability of the Roma community to multidimensional poverty in Bulgaria is pronounced, with higher probabilities of monetary poverty, low work intensity, and material and social deprivation even after accounting for other socio-economic factors.** Particularly striking is the likelihood of material and social deprivation among the Roma, surpassing that of other vulnerable groups by 12.8 percentage points. These deprivations are more concentrated in material items that cannot be afforded, instead of items associated with social inclusion, exhibiting a lack of economic resources that could be compensated with income measures or educational policies to reduce educational inequalities that enable them to have better job opportunities and financial prospects. Education emerges as a critical factor, with the vast majority of the Roma lacking access to primary education. Additionally, disparities persist in health and housing conditions, with overcrowding prevalent among the Roma population. These findings underscore the urgent need for comprehensive policy interventions tailored to address the multifaceted dimensions of poverty within the Roma community, highlighting the structural nature of their poverty experiences.

**Recommended policies to tackle persistent poverty include a wide spectrum of policies** . These include improving educational outcomes, promoting job opportunities, alleviating the childcare burden of families with many children and working adults through subsidies, increasing social protection and access to health services and as well as improving road infrastructure for those living in rural areas (Addison et al., 2008; Barrientos and Hulme, 2005; Chronic Poverty Research Center, 2011).

**Future lines of exploration could involve an in-depth analysis of material and social deprivation of children, as child poverty is a critical issue in some countries, such as Bulgaria and Romania** . Other authors ( <mark>Guio et al., 2018)</mark> have constructed and proposed a specific suitable and additive deprivation measured focused on the specific situation of children using the EU-SILC data, which could be implemented in future analysis.

**The analysis on Roma in Bulgaria could potentially be extended to other countries, if ethnicity is regularly collected as part of the official household surveys to monitor income and living conditions.** This is important to build consensus about who belongs under the “vulnerable population” rubric and could offer a more comprehensive overview of the risks and vulnerabilities that the Roma community faces.

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