rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Legal knowledge and economic development:**

# **The case of land rights in Uganda**

**Abstract:** Mixed evidence on the impact of formal title in much of Africa is often used to question the relevance of dealing with land policy issues in this continent. We use data from Uganda to assess the impact of a disaggregated set of rights on investment, productivity, and land values and to test the hypothesis that individuals’ lack of knowledge of the new law reduces their tenure security. Results point towards strong and positive effects of greater tenure security and transferability. Use of exogenous knowledge of its provisions as a proxy for the value of the land law suggests that this piece of legislation had major economic benefits that remain to be fully realized.

# **1. Introduction**

There are few issues on which neoclassical development economists seem to be able to agree as easily as the importance of secure property rights. Giving secure land rights to households has been shown to increase land-related investment, augment land values, reduce the level and likelihood of conflict, and spur economic participation. It will also provide a basis for reallocating an important factor of production to more efficient users and, if accompanied by a low-cost and formal means of verifying land ownership status, improve credit access (de Soto 2000). Based on this view of formalizing property rights as a key element for expanding the rule of law and reducing poverty, policy-makers all over the world have spent significant resources on programs to formalize land tenure, award title to land, and establish the administrative infrastructure to maintain the records created in this process.

However, empirical evidence concerning the impact of traditional methods to increase land tenure security in Africa is rather mixed (Bruce and Migot-Adholla 1994, Besley 1998). In fact, a large number of rather ineffective interventions (Atwood 1990) led some to characterize efforts to increase tenure security as “unimportant” or even dangerous (Pinckney and Kimuyu 1994) while prompting others to warn against
1997) which used similar surveys to analyze the effects of privatization in Central and Eastern European transition economies. The questionnaire was offered to general managers in Georgian (Modlovan respectively) and Russian, as a number of directors in both countries were still ethnic Russians and did not use the local language.

The survey in Georgia covered only enterprises located in Tbilisi (the capital city). This was because the Georgian Statistical Office (our counterpart in the survey) did not have regional branches and could not conduct the survey without large travel-related expenses. Since 72% of all manufacturing activity in Georgia in 1997 was concentrated in Tbilisi, the representativeness of the sample was not discredited significantly. The selection of enterprises in the main survey was based on the 1996 census of manufacturing firms in Tbilisi which covered 5,543 business entities. We chose only enterprises which reported more than 25 employees in 1991, i.e., we do not cover small enterprises or new private firms that started operations following 1991. This category comprised of 1,326 firms. We then calculated the number of firms in each manufacturing sector as a share of the sub-sample. We next multiplied this share by 100 (the desired sample size for the survey) to come to the target number of firms in each manufacturing sector. This process was designed to ensure a distribution of the enterprises in the survey sample that was representative of the industry mix in Tbilisi.

One hundred questionnaires were hand-delivered to General Directors in September, 1997. The survey team re-visited the enterprises in the first week of November, 1997 and collected the responses. In four cases the General Manager was either on sick leave or a business trip. Four other questionnaires were left unanswered since the enterprises were closed down and we excluded them from the subsequent analysis. The total number of surveys included in the analysis was 92. The survey asked managers to identify the main sector of activity of their enterprise. More than a third of all enterprises were in the food and beverage sector (35),
The conclusion of this breakdown is that at federal level only about 10% of the budget is subject to system internal ex-ante budget controls. The remaining 90%, including the majority of salary and pension payments, is handled outside the system. The system can thus not credibly affect aggregate expenditure control or expenditure targets. As the payment and reporting is only integrated for those payments transacted through FABS, the integrity of reporting can only be guaranteed for those 10%.

At the provincial level FMIS coverage is significantly larger ranging from 44% in Punjab to over 65% for other provinces. Payroll and pension expenditures constitute a large share of the budget and are well captured by the system. This supports greater overall expenditure control at this level.

Agencies whose payroll and pensions are processed outside the system should be brought in. This is true mainly for the federal level where the percentages of the payroll and pensions processed thru the system are very low. The low percentages for payroll and pensions at the federal level are due to the fact that defense does not use the FMIS. The pension figures for Sindh and Khyber Pakhtunkhwa are low because not all pension payments in Sindh and Khyber Pakhtunkhwa are done through the FMIS and still follow the legacy PPO process.

# Analysis of the Pakistan Expenditure Transaction Profile

Assessing the transaction profile in more detail reveals that most non-salary transactions processed by the system are of low value and don’t make up a large share of the budget. At the federal level, non-HR related transactions smaller than PKR 10,000 make up 40% of the budget and the percentage of transactions below PKR 20,000 is about 50% for the center and all provinces. However, all of these transactions together only make up between 0.4% and 1.5% of non-HR related disbursement through the system. Conversely, high value transactions (>PKR 500,000) are few across all systems but make up a large share of the budget as detailed in table 5. The entire transaction profiles for
POLICY RESEARCH WORKING PAPER **2323**

# **Summary findings**

Schmukler and Vesperoni investigate whether integration with global markets affects the financing choices of firms from East Asia and Latin America. Using firm-level data for the 1980s and 1990s, they study how leverage ratios, the structure of debt maturity, and sources of financing change when economies are liberalized and when firms gain access to international equity and bond markets.

The evidence shows that integration with world financial markets has uneven effects.

On the one hand, debt maturity for the average firm shortens when countries undertake financial liberalization.

On the other hand, domestic firms that actually participate in international markets get better financing opportunities and extend their debt maturity.

Moreover, firms in economies with deeper domestic financial systems are affected less by financial liberalization.

Finally, they show that leverage ratios increase during times of crisis.

In an appendix, they analyze the previously unstudied case of Argentina, which experienced sharp financial liberalization and was hit hard **by** all recent global crises.

This paper product of Macroeconomics and Growth, Development Fa Reseach Group **-** is part of a larger effort in the group to understand financial development and financial integration. Copies of the paper are available free from tie World Bank, **18 18** H Street NW, Washington, **DC** 20433. Please contact Emily Khine, room MC3 -341, telephone 202-473-7471, fax **202-522-3518,** email address kkhineCIworldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbank.org/research/workingpapers. The authors may be contacted at sschmuklercsworldbank.org or verperolbaziwam.umd.edu. April 2000. **(67** pages) _The Policy Research WVorking Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about det,elop,nent issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished
0.000402|||||
||(0.000917)||(0.000914)|(0.000928)|(0.000312)||(0.000311)|(0.000310)|||||
|Share of hhlds with computer||0.139|0.138|0.16||0.0706|0.0691|0.0788|||||
|||(0.127)|(0.128)|(0.131)||(0.0801)|(0.0802)|(0.0809)|||||
|IDN exports per worker *||||-0.00426||||-0.00191|||||
|Share of hhlds with computer||||(0.00252)*||||(0.000792)**|||||
|Observations|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|||||
|R-squared|0.915|0.915|0.915|0.915|0.759|0.759|0.759|0.759|||||

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010 and 2011-2015, 2015-2016 Philippines STEP survey, and UN Comtrade database 2005-2010 and 2011-2015.

Notes: IV regressions. IV: IV: Global demand for foreign products excluding Indonesian products. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province level controls include the age structure of the population, the share of urban population, real GDP per capita, and the minimum wage level
households we are comparing have very different characteristics. We therefore follow the recommendations of Crump et al. (2009), who recommend estimating a propensity score, and then dropping observations with estimated propensity scores outside the range [0.1,0.9]. This ensures the regression is estimated only for the sample where the covariate distribution overlaps for the RSE and non-RSE households.

Our study includes many of the features identified as desirable for propensity-score matching (Dehejia, 2005). Our surveys of RSE and non-RSE households were conducted at the same time in the same villages (and hence local labor markets) using the same questionnaire. We have good knowledge of the characteristics villages and employers were looking for in selecting workers and can include these in the matching specification. In addition we have more than one period of 10
Third, the imputed values for observations above the consumption thresholds are combined with observed expenditures for those lying below to produce partially synthetic datasets for each country and year to which complete-data methods can be applied for estimating inequality statistics, such as the Gini coefficient, the MLD, and the Theil index.

is the average of the sampling variances and

# `-` 4.3 Estimates of SSA Inequality, 2004 17

This section is divided in two parts. In the first, we look at whether the proposed parametric distributions fit the original survey data for both income and consumption. In the second, we calculate a set of distributional indicators on the original consumption data and compare them to the corrected ones.

> 18 Hence, the square root of (7) is an estimate for the standard error of . 16 RR qq�
(where numbers in parentheses refer to activity classification codes of the database): Food and Beverages (4); Textiles and Wearing Apparel (5); Wood and Paper (6); Petroleum, Chemical and Non-Metallic Mineral Products (7); Metal Products (8); Electrical and Machinery (9); and Transport Equipment (11).

The most recent analyses of GVC linkages in Africa include Allard et al. (2016) and AfDB, OECD, UNDP (2014). Allard et al. (2016) make use of the EORA database to estimate upstream linkages in GVC participation for countries in the region as the outcome of a range of control variables that are also of interest to this study. The report by AfDB, OECD, UNDP (2014), looks specifically at the role of GVC participation in the context of a strategy for the industrialization of an economy. It is also based on estimates of upstream linkages (the share of FVA in country exports) as well as downstream linkages (the share of DVX in county exports) for a wider range of two-digit or three- digit ISIC industries than those reported in Allard et al (2016).<sup>3</sup> Allard et al (2016) conclude that many countries in the region have comparative advantage in tasks that might have high shares in the value added of final goods in manufacturing industries. This is consistent with the even stronger conclusion of AfDB, OECD, and UNDP (2014), also based on the EORA database, that in Africa as a whole –including North Africa –local manufacturers are more integrated into GVCs than domestic firms in agriculture, mining or services.

The main working hypothesis of this paper is that natural resource endowment and economic geography are major influences on countries’ linkages and role in manufacturing GVCs. These influences can be estimated and identified in the framework of an econometric factorproportions- based gravity model of “supply-side differences” between countries as partners in trade in goods and services or
both normal and monsoon seasons at walking speed. The latter analysis is included in the annex for use by researchers but not described in this report.

For each combination of service type, season and travel modality we calculated two summary statistics:

**1.** The cumulative person‐hours to reach that service, aggregated across that administrative area.

**2.** The average travel time for the average resident

We first calculated the cumulative person‐hours of travel by downsampling the cost time rasters from 30 meters resolution to WorldPop’s 100m resolution, then multiplying the WorldPop gridded population density data set by the cost time raster for the service and season in question. This yielded the total travel hours to reach the given service for each grid cell. The results were then summarized for each administrative level using a Zonal Statistics function. In producing this metric, we closely followed the methodology of the Department of Roads’ 2007 Priority Investment Plan (PIP) (DHV _et al._ 2007).

Cumulative hours of travel strike an effective, easily interpreted balance between inaccessibility indicators and population density. For example, multiplying 10 hours times 1 person will produce the same results as 1 hour times 10 persons. However, an area with a population density of 0.001 times 100 hours will only yield 0. 1 cumulative hours. The formula thus mitigates the impact of largely unpopulated areas, common to hill and
Brazil and remain below 10 percent all along the distribution. Labor market regulations and high employer costs attached to formal employment in Brazil may simultaneously explain the large extent of informal work and the relatively modest informal

> 15As a caveat, we point out that the World Bank Enterprise Survey covers only rms of the formal private sector with ve or more employees. Hence informal and micro rms are excluded from the sample.

> 16This assumption allows us to reproduce a wage premium equal to 30 percent as in Charlot et al. (2015). 17 We use vulnerable employment as a proxy for informal employment.

15
Rep.|0.656|0.025|2934|Senegal|0.792|0.069|354|
|Jordan|0.543|0.023|5047|Sierra Leone|0.597|0.039|1139|
|||||South Africa|0.592|0.072|578|
|South Asia||||Tanzania|0.711|0.052|1213|
|Afghanistan|0.561|0.017|4857|Togo|0.837|0.073|410|
|Bangladesh|0.718|0.025|1712|Uganda|0.704|0.037|1710|
|India|0.686|0.004|72192|Zambia|0.741|0.027|2011|
|Nepal|0.640|0.032|1317|Zimbabwe|0.721|0.054|518|
|Pakistan|0.762|0.013|5583|||||

_Notes_ : This table presents the estimated share of the intergenerational component using the Bingley and Cappellari (2019) method for each of the 53 developing countries in the Demographic and Health Surveys (DHS) using the 1990s birth cohort sample.

45
_ANDR EE_<sup>_ ́_</sup> _AND PAPE_ _SEPTEMBER 5, 2023_ _8_ The model (2) is estimated multiple times as the imputation process continuous to iterate over the data. Andr ́ee (2021) noted that the statistical properties of the data change throughout the imputation process, and that the ideal prediction model grows in flexibility as the process iterates. Specifically, in the sequence of models _{f_ 1<sup>_A, f_</sup> 2<sup>_A, ..., f_</sup> _I_<sup>_A}_for</sup><sup>_I_iterations,themodel</sup><sup>_f_</sup> _i_<sup>_A_</sup> may train on better data than model _fi_<sup>_A_</sup> _−_ 1<sup>andthusthecomplexityofthelearnermaygrowwiththedata.</sup> The application here performs the first half of iterations (set at 4) using an elastic net model (implemented by Hastie et al. (2021)). The penalties in the elastic net model help reduce the impact of uninformative predictors (Friedman et al., 2010). At iterations 5 through 8, a cubist regression is used following Quinlan (1992); Witten et al. (2016).

Cubist is a piece-wise linear model that combines decision trees, boosting, and neighborhood smoothing, that together allow the model to capture several features typical to spatial time series data, including smooth nonlinearities (Andr ́ee et al., 2019), spatial regime switches (Andr ́ee et al., 2017; Andr ́ee, 2020), as well as threshold nonlinearities (Tong, 2015). This makes the cubist nonlinearities model more suitable for numerical data than Random Forest-type nonlinearities (Kuhn et al., 2012).<sup>3</sup> The hyper-parameters of both models are tuned at each step. The modeltuning focuses on a Normalized Mean-Absolute-Error criterion
.1|3.2|
|Uganda|2.6|2.2|2.5|—|4.8|4.8|2.8|2.4|
|Zambia|5.7|7.0|5.8|8.8|7.9|8.7|6.1|5.7|
|Median|3.3|3.2|3.3|3.7|4.2|3.8|3.6|3.2|

_Source_ : World Bank staff calculations using household survey data.

_Note_ : Only households with positive expenditures on electricity are analyzed. Total household expenditures exclude imputed values of freely acquired items, largely food.

— = no households with positive cash expenditures on electricity.

When the shares in Table 2 and Table 3 are compared, they increase more in rural areas than in urban areas when imputed values are excluded from household expenditures. The differences also predictably decrease with increasing quintile. Both demonstrate greater reliance on home‐grown food by the poor and rural households. Nationally, the largest increase was found in Nigeria (53 percent increase), followed by Ethiopia (21 percent), and Burkina Faso (18 percent). There was little difference between female‐ and male‐headed households, indicating comparable dependence on home‐grown food.

# Charges for Grid Electricity Use and Connection

How expensive is it to buy electricity? Are tariffs designed to make electricity affordable to the poor? Do connection charges take the ability to pay into account, or is every residential customer charged the same?

This study collected information on residential tariffs for grid electricity in effect in July 2014 in 39 countries, including the 22 countries with household survey data. Table 4 summarizes tariff concessions available to those consuming little electricity in the 39 countries. The column labeled kWh shows the size of the lifeline block (the first block in a tariff schedule with a low price per kWh,
# **1 Introduction**

According to the Food and Agriculture Organization of the United Nations, over 60 percent of the world’s population depends on the agricultural sector for their livelihoods and it provides a key source of income and employment for the poor and vulnerable people (FAO, 2013; FAO, 2019). Yet, economic statistics of the agricultural sector are frequently produced at a national or lower administrative level and may not adequately capture the local variation. Furthermore, a spatial mismatch may exist between the geographic unit of interest like the natural area of a river and the administrative area.

Detailed agricultural data are critical to examining a wide range of agricultural issues including technology and land use (e.g. Bella and Irwin, 2002; Luijten, 2003; Samberg et al., 2016; Staal et al., 2002), exposure to natural hazards (e.g. Murthy, Laxman, and Sai, 2015) and patterns of and productivity of economic development (e.g. Elhorst and Strijker, 2003; Gollin, Lagakos, and Waugh, 2014; G. C. Nelson, 2002; Reddy and Dutta, 2018). Carr ̃ao, Naumann, and Barbosa (2016) examine the exposure of people and economic activity to drought using measures of physical elements (e.g. cropland and livestock). Rentschler and Salhab (2020) find that low and middle-income countries have 89% of global flood exposed population and poor people account for almost 600 million, who are directly exposed to the risk of intense flooding. Vesco et al. (2021) examine linkages between climate variability and agricultural production as well as conflict. They find that climate variability contributes to an increase in the spatial concentration of agricultural production within countries. Furthermore, in countries with a high share of agricultural employment in the national workforce, they find this combined effect increases the likelihood of conflict onset. To better target rural development strategies for economic growth and poverty reduction, as well as conserve the
1996) found a correlation of 0.8 between their index of economic liberalization and the Freedom House index of political freedom. De Melo et al. (1997) use panel data to estimate the determinants of economic liberalization in transition countries. Political freedom appears with a

> 1 This includes all of the countries of Central and Eastern Europe (CEE) and the Former Soviet Union (FSU), except for Bosnia and Yugoslavia (Serbia and Montenegro) for which necessary data are not available.

2
Herfindahl-Hirschman concentration index, calculated using the “CR5” sample. “log(# MNEs)” represents the natural logarithm of the number of foreign investors, and “log(K)” denotes the natural logarithm of the average capital expenditure. “Uncertainty” is based on the “T3” variable from the World Uncertainty database (Ahir et al. 2022), which counts the occurrence of the word “uncertainty” from Economist Intelligence Unit (EIU) reports for 143 countries since the 1950s. A higher value indicates greater uncertainty. Uncertainty in year _t_ is approximated as the logarithm of the average number of uncertainty-related words in EIU reports over the three years preceding the investment (standardized to have a mean of zero and a standard deviation of 1). The analysis includes country fixed effects and (world) region by year fixed effects. Countries are classified into 7 regions based on World Bank classifications. Destination-specific control variables include logarithms of population, GDP, the average exchange rate, and a measure of concentration in domestic sales of 4 FDI sources (i.e., the United States, Germany, the United Kingdom, and France, weighted by their respective destination shares in 2003). Investor country concentration is measured as the share of the top 3 companies in each country’s domestic sales, firm-level sales from Worldscope, aggregate sales from EU-KLEMS. Standard errors are clustered at the destination country. Standard errors are clustered at the destination country level. Significance levels: *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

36