rafmacalaba/data-use-mentions-extended huggingface dataset:
al. (2007)|
|Grit|Adults between 15-64 years old|2.72|0.6|1|4|3843|STEP Data Kenya|
|Grit|Adults between 15-64 years old|2.98|0.61|1|4|3978|STEP Data Macedonia|
|Openness to experience|Adults between 15-64 years old|3|0.56|1|4|3844|STEP Data Kenya|
|Openness to experience|Adults between 15-64 years old|3.28|0.55|1|4|3979|STEP Data Macedonia|
|Conscientiousness|Adults between 15-64 years old|3.22|0.52|1.33|4|3844|STEP Data Kenya|
|Conscientiousness|Adults between 15-64 years old|3.05|0.5|1|4|3979|STEP Data Macedonia|
|Extraversion|Adults between 15-64 years old|2.85|0.59|1|4|3845|STEP Data Kenya|
|Extraversion|Adults between 15-64 years old|3.02|0.61|1|4|3979|STEP Data Macedonia|
|Agreeableness|Adults between 15-64 years old|2.86|0.57|1|4|3843|STEP Data Kenya|
|Agreeableness|Adults between 15-64 years old|3.28|0.59|1|4|3978|STEP Data Macedonia|
|Emotional stability|Adults between 15-64 years old|2.69|0.5|1|4|3843|STEP Data Kenya|
|Emotional stability|Adults between 15-64years old|2.09|0.66|1|4|3979|STEP Data Macedonia|

_Notes_ . This table shows descriptive statistics for the grit and big-five measures. Panel A reports on the LEAPS sample. Panel B shows results from other samples reported on in the literature. For the STEP data, we computed the results using the STEP surveys conducted
While 37% of listed properties inside blocks have title, 83% of those without a title inside blocks (and 81% overall) are interested in acquiring a title and 96% (95% overall) are willing to pay ZMK 300 per ha (ZMK 226/ha overall), an amount that would be sufficient to cover the costs of efficient provision. Although acquiring title should be much easier within blocks as no conversion to state land is required, demand and willingness to pay for such documents are, if anything, higher inside blocks than the immediate vicinity. This may suggest binding supply side constraints to accessing title beyond land conversion in line <mark>with studies suggesting that farm blocks often fail to offer higher levels of tenure security or marketability of land, thus not meeting the expectations placed on them (Middelberg</mark> _<mark>et al.</mark>_ <mark>2020).</mark>

# **3.3 Evidence from the farm performance survey**

Table 4 provides descriptive evidence for the total of about 3,000 owned (panel A) and 2,700 owned and operated parcels (panel B) used in the regression analysis. Figures are reported for the entire sample (col. 1) and parcels with title (col. 2), chief certificate (col. 3), without a document (col. 4) and for medium scale (≤ 20 ha) and large (> 20 ha) farms (cols. 5 and 6) separately. Parcels with title are larger than those without (28 ha vs. 12 ha) and that more than two thirds of documents (69% of titles and 74% of chief certificates) are issued exclusively in the name of men with 8% joint and the remainder in the name of women only.

While perceived risk of conflict is, with 2% negligible, levels of perceived transferability are also very low; with 30%, the share of parcels
compensated for by higher spreads and margins. We also find that higher market power

> 8 Tajikistan’s estimate of the Regulatory Quality index in 2022 (the latest available data point) was –1.198.

> 9 The economic significance coefficients are obtained by using standardized variables z = (x-μ)/σ where μ is the mean of the population; and σ is the standard deviation of the population.

18
"The fourth wave of the Indonesian Family Life Survey (IFLS4): overview and field report."</mark> _<mark>RAND Corporation</mark>_ <mark>(2009).</mark>

- <mark>Strauss, John, Kathleen Beegle, Bondan Sikoki, Agus Dwiyanto, Yulia Herawati, and Firman Witoelar. "The third wave of the Indonesia Family Life Survey (IFLS3): Overview and field report."</mark> _<mark>NIA/NICHD</mark>_ <mark>(2004).</mark>

- <mark>Thomas, Duncan, Kathleen Beegle, and E. Frankenberg.</mark> _<mark>Labor Market Transitions of Men and Women During an Economic Crisis: Evidence from Indonesia</mark>_ <mark>. RAND-Labor and Population Program, 2000.</mark>

- <mark>World Bank. 2018. World Development Indicators. Washington, D.C.: The World Bank.</mark> <u><mark>http://databank.worldbank.org/data/source/world-development-indicators#</mark></u> <mark>(accessed December 15, 2018)</mark>

- <mark>World Bank.</mark> _<mark>Live Long and Prosper: Aging in East Asia and Pacific</mark>_ <mark>. World Bank Publications, 2016.</mark>

33
assumptions, the total sample size needs to be 1,277 or higher: 405 units statutorily urban; 557 units urban in terms of the census; and 315 villages. For the full sample we achieve a relative standard error of 8 percent, which is below the 10 percent threshold required for a survey design to be considered good (UN 2005).

In the second stage, each of the eight strata is divided into three substrata defined by average consumption per capita. Consumption data are from the household consumer expenditure module of the 68th round of National Sample Survey of India (NSSO 2012). The data quality is considerably better for the 21 largest states and union territories than for the smaller ones. The monthly household expenditure is divided by household size to estimate consumption per capita. To account for the spatial variation in prices, the result is deflated by a year‐specific price index that differs across states and union territories and between rural and urban areas. The 21 states and union territories are ranked on the basis of their average consumption per capita, and they are divided into three groups of equal size: richer, intermediate, and poorer.

The sample for each administrative category is split evenly across these three groups of states and union territories. For example, of the 264 units retained in the outgrowth stratum of the sample, 88 would be from richer states and union territories, another 88 units from the intermediate group, and the remaining 88 from the poorer group.

The sampling process is completed by conducting random draws without replacement for each of the 24 substrata. Our final sample with 1,277 administrative units is relatively evenly distributed across India’s 7
<!-- Start of picture text --> Figure 6. Coefficient of lagged presence of roads<br>2.5%<br>2.0%<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>In cell In N1 In N2 In cell In N1 In N2 In cell In N1 In N2 In cell In N1 In N2<br>-0.5%<br>Class 1 road Class 2 road Class 3 road Track road<br>-1.0%<br>-1.5%<br>1990 regression 2000 regression<br>Impact on buildup proportion<br><!-- End of picture text --> Notes: Figure 6 summarizes the coefficients from two separate regressions. The dependent variable in the two regressions is the proportion of a cell that is built up in 1990 and 2000, respectively. The Figure shows the coefficients of the presence of a road of a certain class (i.e., category) in the cell, the neighborhood N1 and the neighborhood N2. The road quality ordering is: class 1 (top category), class 2, class 3, and track (bottom category). In the 1990 regression, the road data refers to 1976; in the 2000 regression, the road data refers to 1986. Each of the regressions also includes all the regressors that were present in those for Figure 5. More precisely, each regression includes lagged values of buildup in the cell and in the neighboring cells n1 and n2. The lagged values are for 1975 and 1990, respectively. Each of the three lagged buildup variables appears in the regression as a series of 10 dummies, one per buildup decile. Each regression also includes the following time invariant regressors: longitude, latitude, and their product [three variables]; soil humidity and soil quality dummies [six variables]; and
a respondent was likely a refugee – rather than to ask explicitly about citizenship status. Because many Afghan nationals living in Pakistan lack documentation and are not registered with UNCHR as either asylum seekers or confirmed refugees (UNCHR, 2024b), the study did not differentiate between verified refugees, asylum seekers and unregistered Afghan nationals, the group of which are collectively referred to as “refugees” within this paper. Enumerators determined refugee status of the household by confirmation of at least one of the following factors: the primary caregiver or her parents were born in Afghanistan; the use of Afghani Pashto, Persian and/or Dari as the primary language in the home; respondent declined to share identification, or the target child’s date of birth card or other documentation was valid for Afghanistan but not Pakistan; community leaders had identified the household as Afghani; or enumerator-reported knowledge of the household’s refugee/migrant status based on previous survey engagement. A “yes” response to any one of these indicators placed the respondent and household in the refugee category for analytical purposes.

_Food Insecurity._ Food insecurity was assessed by the Food Insecurity Experience Scale (FIES), an 8-item questionnaire comprised of yes/no questions relating to experiences of food insecurity. It has been previously used in Pakistan and found to be reliable (Afridi et al., 2021). The Cronbach’s alpha of the scale was .91, indicating high internal consistency reliability.

_Asset Index._ An asset index was calculated by a principal components analysis of a series of questions related to household materials, access to water, and home ownership, among other resource-related factors. For each household, based on respondent report, an asset quintile was created to categorize by quintile from lowest asset index values (quintile 1) to highest asset index values (quintile 5).

_Flooding Impact._ Respondents were asked a series of questions about the impact of flooding on their day-to-day life in both 2022 and 2023. Households were categorized as being “impacted by flooding
to be poor is decreasing for higher asset deciles in the DHS; and the share of the

> 9Details on the imputation procedure and for the results from the full regression models are available from the authors upon request.

> 10This is only one of many different ways to potentially impute estimates of monetary poverty. Some alternative methods were compared in the construction of these estimates, but the SWIFT methodology still had the highest predictive power. See Yoshida et al. (2022) for more details.

> 11Summary statistics for all the remaining variables that are common between the two surveys are available from the authors upon request.

> 12The imputation module includes both variables that can adjust quickly and ones that adjust over time, and thus can flexibly capture changes in poverty over the time horizon under analysis (e.g., Yoshida et al. 2022).

> 13The model had a relatively high predictive power relative to other countries where the imputation models have proved to be precise estimates of poverty through subsequent household surveys (e.g., Yoshida et al. 2022).

9
start by classifying skilled labor as workers with some secondary education, as in Jones (2014) and Caselli and Ciccone (2017).<sup>14</sup> The second and third column of Table 6 show that the amplification of human capital gaps when incorporating the variation in _hS,c_ { _hU,c_ is dramatic. The cross-country variance in human capital is five times larger than the cross-country variance in GDP per worker. This partially reflect an extremely high value of _hS,c_ { _hU,c_ estimated for the United States, where the share of skilled labor is much higher than in poorer countries, while the skill premium is not dramatically different. However, even when taking France as a benchmark (fourth and fifth columns), human capital accounts for the majority or the entirety of income gaps with respect to other countries.

These results are even larger than those reported in Jones (2014). Given that I am using much more recent data, one valid concern is that considering skilled every worker with at least some secondary education might not be appropriate for my exercise. In 2010, according to this criterion, 97% of the US labor force is high-skilled, and in a few other countries

> 14I also follow Jones (2014)’s approach for the construction of skill premia: I impute the wage for each level of eduational attainment using country-specific Mincerian return, and compute _wS,c_ and _wU,c_ as weighted averages across the educational categories belonging to the skilled and unskilled groups. The share of skilled and unskilled workers in 2010 is computed from Barro and Lee (2013)’s data on educational attainment of the population above 25.

20
**Table 11**

**Indicators of Debt Burdens and Debt Payment Difficulties**

|**Families ranked**<br>**by income**|**Percentage with**<br>**ratio of debt payments**<br>**to family income above**<br>**40 percent**|**Percentage with a**<br>**debt payment late**<br>**60 days or more in**<br>**previous year**|
|---|---|---|
|Lowest 20%|27.0|13.4|
|20-39.9%|16.0|11.7|
|40-59.9%|11.7|7.9|
|60-79.9%|5.6|4.0|
|80-89.9%|3.5|2.6|
|90-100%|2.0|1.3|

Source: 2001 Survey of Consumer Finances (Aizcorbe et al, 2003) The strong link between household income and indicators of credit risk should not be surprising. After all, since many lower-income households have no financial savings, any economic disruption, such as a health crisis in the family, a job termination, etc, can force a family to put off non-essential expenditures, including debt service obligations. In addition, there is a strong correlation between household education and income. If education is linked to money management skills or to an awareness of the benefits of a good credit record, this may also partly explain the correlation between household income and indicators of credit risk.

An impaired credit record does not necessarily shut one off from credit. Mainstream lenders will extend loans to people with somewhat impaired credit histories or with fairly heavy debt burdens, but they commonly increase the interest rate on the loans to reflect the increased risk. If an individual
logarithmic points rather than percent to allow decomposition. Professional higher education is not separated from academic. Presented results refer to full-time employees. _Source:_ Calculation based on LFS data.

# **Experience and Earnings**

**Age–earnings profiles.** Cross-sectional age-earnings profiles describe how earnings of individuals differ across cohorts at a given point in time. Age is often taken as a proxy for the length of work experience in studies of returns to human capital. Thus age-earnings profiles give information about how experience is rewarded in the labour market. The typical industrialized country age-earnings profile for men in most education groups is rising up to about age 50-52, after which it decreases. For women it often peaks somewhat earlier.<sup>10</sup>

Compared to the socialist era, a significant decline in returns to experience in most transition countries has been well-documented. The decline is easily attributable to the fact that skills acquired under central

> 10  See e.g. Ehrenberg and Smith (2003), pp. 277 – 278.

32
# **7. TABLES AND CHARTS**

**Table 1. Characteristics of the hypothetical individuals:**

||(1)|(2)|
|---|---|---|
|Year of arrival|1975|1985|
|Age at arrival|25|25|
|Level of education|At least college degree|At least college degree|
|Age as of Census 1980|30|-|
|Age as of Census 1990|40|30|
|Age as of Census 2000|50|40|

**Table 2. Summary Statistics of Datasets Extracted from the US Census Samples**

||**DE**|**CADE 1970-19**|**79**|**DECADE**|**1980-1989**|
|---|---|---|---|---|---|
||**Census 1980 **|**Census 1990 **|**Census 2000 **|**Census 1990 **|**Census 2000**|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|**Individuals in the sample**||||||
|**(unweighted)**|41,234|39,445|35,880|75,851|78,745|
|**Total number of people**<br>**(after weighting)**|824,680|847,573|750,289|1,668,342|1,704,575|
|**Age interval**|16-40|26-50|36-60|16-50|26-60|
|**Median age**|30|40
# FISCAL SIABILIZATION AND EXCHANGE RATE INSTABILITY: THEORETICAL APPROACH AND SOME POLICY SIMULATIONS USING MEXICAN DATA

# I. INTRODUCTION

A striking feature in the reLent 'conomic performance of a number of countries, particularly in Latin America, has been the rapid rise in the siFe of the public sectors. In Mexico, perhaps the most striking example of such a phenomenon, total spending of the public sector, which represented only 25.6 percent of GDP in 1973, had risen to 46.5 percent of GDP by 1982. At the same time that this increase in the size of the government was taking place, there were also equally dramatic increases in inflation, the government budget deficit, and the external debt. A conclusion that has been drawn by planners in Mexico and elsewhere is that stabilization depends in some, perhaps vague, way on a successful reduction in this spending of the government.

A cursory examination of the composition of government spending in most of these countries reveals that the proportion of expenditures going to service debt, both domestic and foreign, has also risen dramatically. Accordingly, attempts to stabilize the government's fiscal situation have often focussed upon reduction of the burden of interest obligations. It is, of course, rather difficult to do so with foreign currency denominated debt. It is also difficult to reduce the real value of domestic debt service by inflating the economy since most domestic public debt is short term and thus pays essentially inflation-indexed interest. Governments have thus attempted to carry out financial policies designed to lower nominal interest rates, while at the same time maintaining a fixed, or at
agricultural land, and other real estate as this “core” set of assets has been found to comprise the majority of personal wealth. Additionally, the guidelines advised countries to determine additional assets (including non-agricultural enterprise assets, livestock, large and small agricultural equipment, financial assets and liabilities, valuables, and consumer durables such as vehicles) to collect data based on the policy needs and prevalence of assets within the country.

In line with these recommendations, LSMS+ supported national surveys have focused on the following asset classes on a cross-country basis: residential and non-residential (which, in these countries, was primarily agricultural) land; mobile phones; and financial accounts. A module on livestock ownership was also included in Ethiopia and Cambodia, and the Cambodia LSMS+ Survey additionally included a module on individual-level ownership of durables, such as computers as well as motorized and non-motorized vehicles, including bicycles, motorcycles, cars, tractors, boats and _tuk tuks_ (rickshaws).

Table 1. LSMS+ supported surveys used in the analysis

||Malawi|Tanzania|Ethiopia|Cambodia|
|---|---|---|---|---|
|Survey|2016 Integrated<br>Household Panel<br>Survey|2019/20 Tanzania<br>National Panel<br>Survey|2018/19 Ethiopia<br>Socioeconomic Survey|2019/20 Cambodia LSMS+<br>Survey|
|Implementing agency<sup>(1)</sup>|Malawi National<br>Statistical Office|Tanzania National<br>Bureau of Statistics|Ethiopia Central<br>Statistical Agency|National Institute of<br>Statistics of Cambodia|
|Sample size for individual<br>interviews supported by<br>LSMS+<sup>(2)</sup>|2,508 households|1,184 households|6,770 households|1,512 households|
|Fieldworkperiod|4/2016-1/2017|1/2019-1/
Policy Research Working Paper 6078

# **Abstract**

This paper analyzes the impact of large-scale expansion of biofuels on the global income distribution and poverty. A global computable general equilibrium model is used to simulate the effects of the expansion of biofuels on resource allocation, commodity prices, factor prices and household income. A second model based on worldwide household surveys uses these results to calculate the impacts on poverty and global income inequality. The study finds that the large-scale expansion of biofuels leads to an increase in production and prices of agricultural commodities. The increased prices would cause higher food prices, especially in developing countries. Moreover, wages of unskilled rural labor would also increase, which slows down the rural to urban migration in many developing countries. The study also shows that the effects on poverty vary across regions; it increases in South Asia and Sub-Saharan Africa, whereas it decreases in Latin America. At the global level, the expansion of biofuels increases poverty slightly.

This paper is a product of the Environment and Energy Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at gtimilsina@worldbank.org.

_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 out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support
## **Figure 6 Average MSE and Trend of Gini, for LAC Countries**

- A) Average Gini MSE B) Trend of Average Gini MSE

<!-- Start of picture text --> 3.50 3.50<br>3.00<br>3.00<br>2.50<br>2.50 2.00<br>2.00 1.50<br>1.00<br>1.50 0.50<br>0.00<br>1.00<br>0.50<br>0.00<br>2017 2018 2019 2020<br>Old Model New - Rescaling<br>New - No Rescaling 2017 2018 2019 2020<br>Source: Own estimations based on SEDLAC.<br>Distributional effects<br>This section presents the performance of the considered models' variations along the whole income<br>distribution. The Growth Incidence Curves (GICs) for each country/year are calculated using the SEDLAC<br>actual income data and the simulated income vector for each variation. The squared errors correspond<br>to the difference in the annualized growth rate at each percentile of the income distribution. The MSEs<br>correspond to the average squared error (MSE) across countries over time. Figure 7 presents the results.<br>It is worth noting that these results do not include information on the 1-5 and 96-100 percentiles due to<br>the high dispersion in the tails of the actual distribution.<br>Figure 7 Average MSE of GICs for LAC Countries<br>90.0<br>80.0<br>70.0<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>2017 2018 2019 2020<