rafmacalaba/datause-encoder-data huggingface dataset:
- 2 -

for which sufficient data are available (box 1). The effect of reforms (in the incentives is also examined, as are changes ir- economic performance in countries that have received trade adjustment loans and have carried out reforms.

# Bcx 1: Trade Loans an Country Grouping

The analysis in this paper with **a** significant trade reform component that wore approved during 1979-87 -- eighty-one trade loans to forty countries. Among the eighty-one trade-related loans, forty-seven wero structural adjustment loans (SALs), tl;rty-two **were** sectoral adjustment loans (SECALe), and two progra loans. Many of thes operations Included technical assistance components or were accomponied by technical assistance loans in support of trade reform. Detailed implemntation data were available for only twenty-four of the forty countries for which sufficiont time had *lapeed since they had received their first trade adjustment loans. Most of those twenty-four countries received a trade loan before 1986, although a sligttly different group of twenty-six countrels constitutes the pre-1986 recipients. Among the forty countries, ton Ointensive adjusters received three or more trade adjustment loans. Some Indicators (such **as** the real exchange rate and the composition of imports) **are** available for all forty countries, but others (extent of liberalization) **are** available only for the twenty-four, and yet others (offective protection) only for six to ten **cases.** Twenty-throe countries In the **sample wore** middle-incom, countries and seventeen wore low-income countries (GNP per capita below 3460 In 1987).

The full country **sample** comprise eighty-eight developing countries: the ninety-five countries under the World Development Report 1989 definition of developing countries, excluding ton countries with serious data problems (Afghanistan, Bhutan, Iran, Iraq, Kcmpuchea, Lao PDR, Lebanon, Libya, Romania, and Viet Nam); but Including Gambia, Quines-Biesou, and Guyana because thoy received adjustment loans. Thus the sample Includes forty countries that received trade adjustoont. loans and forty-eight countries that did not.

# Extent of Reforms

# Degree of Restrictiveness

In reviewing commercial policy (export and import policy) reform, Halevi (1989) considered the following restrictions: export impediments,1 import impediments on inputs used in export production, quantitative restrictions,2 on both noncompetitive and competitive imports, and tariff rates<sup>3</sup> and rate dispersion. Based on the evidence, the countries were
# **5.1.3 Consumption and Income in Adult-Equivalent Scales**

An important aspect of household structure is its potential role in explaining the hump in household consumption, in particular, nondurable consumption (see Deaton and Paxson (1994) and Attanasio et al. (1999)). For completeness, here we reproduce the results in De Magalh ̃aes et al. (2015) that find the life cycle behavior of consumption in adult-equivalent scales shows practically no hump in SSA. That is, the evolution of the life cycle household structure (see the panels in Figure 4) is the main determinant of the observed hump in the age profile of household consumption (see the right panes in Figure 4). This is particularly the case for rural areas where, while household consumption shows an increase from ages 25 to 45 of a factor of roughly 1.25, adult-equivalent consumption does not grow over the life cycle and, if at all, slightly declines to almost 0.90 at age 50 (see the center panels in Figure 4). In urban areas, while household consumption shows an increase from ages 25 to 45 of a factor of 1.70, this factor drops to 1.15 in terms of adult-equivalent consumption (see bottom panels in Figure 4). Combining rural and urban areas (i.e., nationwide) adult-equivalent consumption shows absolutely no trend over the life cycle (see top panels in Figure 4).

Finally, the behavior of income per adult over the life cycle displays a clear hump that increases from the age of 20 to a peak at 35 years of age by a factor of 1.20/0.77=1.55 in rural areas, while in urban areas the hump in income per capita is larger and increases from the age of 20 to a peak at 45 years of age by a factor of 2.25/0.80=2.81 (see the right panel in Figure 4). Our results echo those in Bils and Klenow (2000) and Lagakos et al. (2012) that focus on the behavior of wages and document flatter age profiles in poor countries. Here we instead focus on household total income to consider the fact that in the poor countries that we investigate, there is a large rural population (approximately 80%), for whom agricultural production is the main source of income. Our findings suggest that the flatter country-wide life cycle profiles of income in developing countries may be the result of a composition effect driven by a large share of rural households, which indeed have flat age profiles, and balance the steeper age profiles of urban households.

# **5.2 Migration**

The process of economic growth and structural transformation (Gollin et al., 2002, 2004) is inevitably related to migration. We find clear evidence of this structural shift as 60% of the current urban residents consist of migrants from rural areas (see panel B1 in Table 11), where rural-to-urban migrants are defined as those who resided in rural areas prior to their current resources (i.e., land) by the chief/community.

30
53

> government with the sales. An international accounting firm was contracted to generate and organize the information necessary to support due diligence. An independent international

> consultant was hired to provide general advice and support to the government, especially in

> clearly defining its privatization objectives, managing the process of contracting with the investment banks, and agreeing on the overall structure of transactions that would be acceptable to government.

Although the originally stated intention was to privatize the banks through a transparent international auction, that process broke down. Apparently, the investment bank's estimated value of the banks to be sold did no t meet government expectations. When the banks were first nationalized and partially recapitalized, the authorities publicly declared that the banks were fully recapitalized, which may have unrealistically raised expectations. There also were concerns about the structure of the deal propcosed by the investment bank, especially about the contingent liabilities that government likely would have to accept. In the end, the one privatization transaction that was achieved, involving the sale of 51 percent of Korea First Bank, was concluded via direct negotiations between the purchasers (a private investment group) and the government.

> In Thailand, the sale of some nationalized local banks to foreign banks, which for the first

> time were granted the right to operate full branch networks and to raise local funding, was a significant milestone for the financial system. Sales were conducted by sealed bids followed by direct negotiation with the winner.

Although foreign banks are not expanding aggressively due to the lack of sound lending opportunities, competition aniong banks has intensified. The entry of global banking networks with local distribution capacity, the rising costs of information technology and multiple distribution channels (branch, telephone, Internet), scarce management talent, competition from nonbanks, and declining margins throughout the region have increased competition. These forces may favor further consolidation.

> In Indonesia, the government nationalized 13 banks in all. Four large, but severely insolvent, banks were nationalized early in the crisis. Another nine were nationalized as a consequence of the failure of the private bank recapitalization scheme (see chapter 5). IBRA has
the rest of the distribution, finding that the estimated income Gini coefficient is unchanged by this replacement of top incomes.

# _Using survey data and administrative data_

As noted above, Hlasny and Verme (2017, 2022) described solutions to under-capturing of the top tail of the income distribution in household surveys as methods that used either replacement or reweighting. Lustig (2020) used this distinction to also classify many of the methods that use both household survey and administrative data as methods using either replacing and/or reweighting. In the context of combining administrative and household survey data, replacement involves replacing some part of the top of the income distribution in the household surveys with data from another source. Reweighting involves increasing the weights of those at the top of the distribution, to match the distribution of income in the administrative data source. We now discuss each of these methods and provide examples.

# Reweighting

Survey calibration means the adjustment of survey weights, usually after unit non-response adjustment of the weights, so that the weighted population estimates for basic demographics (such as age, sex or regions) from the household surveys matches information from demographic models or other external sources of population information. These “reweighting” methods have also since been used to improve estimates of the top tail of the income distribution when administrative data on earnings has been available. For example, Campos-Vazquez and Lustig (2019), obtained Mexican Social security administrative earnings data between 2000 and 2017. The admin data they had access to is the proportion of employed individuals in 25 earnings categories representing multiples of the minimum wage. The authors’ comparison of the survey and admin data clearly shows the undercapturing of the top tail of the earnings distribution. In 2017 the admin data showed that 9% of formal sector workers earned more than 10 times the minimum wage, whereas the proportion was only 2% in the labor force survey. The authors then adjust the survey weights so that the proportion of formal sector workers in each earnings category in the surveys matches the proportion in the administrative data.

Campos-Vazquez and Lustig (2019) note that the item non-response rate for the earnings questions in the Mexican labor force survey rose from 5% in 2002 to 30% by 2017. To solve this issue one of the sets of corrections implemented by the authors is to first undertake hot deck imputation for the item non-response and then the reweighting explained above. So this set of corrections actually involves replacement and then reweighting. As well as mechanically increasing the share of individuals in the 17
increase the indigents’ broader asset base. Moreover, improving the rural dwellers’ connections with towns is key for speeding up rural and semi-rural area relations.

The paper is organized as follows. Section 2 addresses demographic changes. Section 3 presents data and methodologies applied in the following sections. Section 4 addresses poverty, income inequality, and UBN. Section 5 presents a poverty profile. Section 6 addresses access to selected services and assets. Finally, section 7 concludes and gives policy recommendations.

# **2. Population**

Demographic factors have direct and indirect impacts on prices and poverty. As the size and age composition of the population change, the relative size of the labor force and the number of dependents also change. This modifies the dependency ratio of families and therefore their level of poverty. This is the direct effect of demographic changes. It captures the effect that demographic changes have on quantities: number of children, size of the labor force, and the number of elderly people. However, these changes in quantities will generally influence prices in the economy. In particular, changes in the population’s growth rate and in the age structure may have important impacts on labor supplies, savings, household production decisions, and migration. As a consequence, demographic changes may have considerable impact on the level of wages and on interest rates. Since these prices are important determinants of family income, they are bound to have a profound influence on the level of poverty. These are the indirect impacts of demographic changes on poverty, which occur through the effects of demographic changes on savings, wages, production decisions, and interest rates.

Changing demographics can also have important impacts on the demand for public sector investments and public services, incentives for private sector investments, political power, and labor markets. As a result, it is important to look at recent changes in demographic patterns in rural Argentina. The following overview describes demographic changes between rural and urban areas that have taken place from 1960 to 2001 (rural labor markets are not addressed in this paper, but in Verner [2005]).

## **Overview of demographic changes**

Argentina is in the midst of a baby bust. After expanding at 16.4 percent between 1980 and 1991, Argentina’s population increased by only 11.2 percent or 3.6 million people during 1991–2001 and reached 35.9 million in 2001 (Table 2.1).<sup>4</sup> The main explanation is the sharp drop in the birth rate and some emigration.

> 4 The most recent population census was undertaken in 2001.

5
**Costa, Dora L.** “From mill town to board room: The rise of women's paid labor.” _Journal of Economic Perspectives_ 14, no. 4 (2000): 101-122.

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**Gindling, Thomas H.** “Women's wages and economic crisis in Costa Rica.” _Economic Development and Cultural Change_ 41, no. 2 (1993): 277-297.

**Gollin, Douglas.** "Getting income shares right." _Journal of Political Economy_ 110, no. 2 (2002): 458-474.

**Guerriero, Marta.** “The labor share of income around the world: Evidence from a panel dataset.” _ADBI Working Paper 920_ , Asian Development Bank Institute, Tokyo (2019).

**Hsieh, Chang-Tai, Erik Hurst, Charles I. Jones, and Peter J. Klenow.** “The allocation of talent and US economic growth.” _Econometrica_ 87, no. 5 (2019): 1439-1474.

**International Labor Organization.** “International standard classification of occupations: Structure, group definitions and correspondence tables.” _International Labor Office, Geneva_ (2012).

**Juhn, Chinhui, Gergely Ujhelyi, and Carolina Villegas-Sanchez.** “Men, women, and machines: How trade impacts gender inequality.” _Journal of Development Economics_ 106 (2014): 179-193.

**Kalsi, Priti.** “The impact of US deportation of criminals on gang development and education in El Salvador.” _Journal of Development Economics_ 135 (2018): 433-448.

**Kasy, Maximilian, and Alvaro Ramos-Chaves.** “The Impact of Changing Family Structures on the Income Distribution among Costa Rican Women 1993–2009.” _Feminist Economics_ 20, no. 2 (2014): 122-144.

34
CF models we see some statistically significant effects of vintage on forest quality for the quadratic specification, but due to multicollinearity between the moments of vintage not when we add the third moment. For CFs, we therefore use a quadratic specification. In the interest of brevity, because covariate variable results are similar to those in Tables 8 and 9, they are not presented. These results are, of course, available from the authors.

# Table 10

Effect of Forest User Group Formation Vintage (years since 1991) on Forest Quality in all Forests that Can Identify Formation Year

||Carbonper ha. (kg)|Trees per ha.|CrownCover(%)|Seedlings per ha.|
|---|---|---|---|---|
|Years after 1991<br>that FUG was<br>formed|-0.115|-0.266|-0.088|-0.377|
||(0.56)|(2.64)***|(0.91)|(2.16)**|
|Years after 1991<br>that FUG was<br>formed squared|0.010|0.023|0.004|0.035|
||(0.48)|(2.16)**|(0.39)|(2.10)**|
|Years after 1991<br>that FUG was<br>formed cubed|-0.000|-0.001|-0.000|-0.001|
||(0.37)|(1.89)*|(0.08)|(1.91)*|
|_R_<sup>2</sup>|0.24|0.34|0.29|0.41|
|_N_|102|101|101|95|

* _p_ <0.1; ** _p_ <0.05; *** _p_ <0.01; Dependent Variables in Logs, Robust t Statistics in Parentheses

# Table 11

Effect <u>of Forest User Group Formation Vintage (years since 1991) on Forest Quality</u> **<u>in CFs</u>**

||Carbonper ha. (kg)|Trees per ha.|CrownCover(%)|<br>Seedlings per ha.|
|---|---|---|---|---|
|Years after 1991<br>that FUG was<br>formed|-0.034|-0.199|-0.093|-0.243|
||(0.28)|(2.70)***|(2.45)**|(1.95)*|
|Years after 1991<br>that FUG was<br>formed squared|0.002|0.010|0.004|0.011|
||(0.32)|(3.32)***|(2.55)**|(2.32)**|
|_R_<sup>2</sup>|0.33|0.34|0.31|0.55|
|_N_|65|64|64|61|

* _p_ <0.1; ** _p_ <0.05; *** _p_ <0.01; Dependent Variables in Logs, Robust t Statistics in Parentheses

**28 |** P a g e
urban areas, whereas in Terai and the Hills and Mountains, own production is the most important source of income. Even within broad geographical regions there are substantial differences: in rural eastem and central Terai almost a fifth of income comes from own enterprises, whereas in mid- and far-west Terai only 4% of income comes from this source. Similarly remittances are key to those living in the rural western hills, but mostly irrelevant to those in the eastem hills.

**Table 3: Household income sources by time to market in Nepal**

||||D|ecilesofD|istanceto|Market||||
|---|---|---|---|---|---|---|---|---|---|
|ShareofIncome|Closest<br>Decile|2|3|5|6|7|8|9D<br>D|istant<br>ecile|
|cashwagesfrom<br>workinginothersfarm|2.1|3.9|8.8|8.1|6.6|7.0|8.9|6.4|5.4|
|<br>Otherwages|18.0|13.8|13.7|15.9|11.2|8.5|8.2|11.6|16.1|
|Landrent|1.8|0.6|0.7|1.1|0.6|0.1|0.1|0.2|0.1|
|netagriculturalincome|1.8|3.7|7.7|10.7|10.2|3.7|6.8|4.4|1.5|
|fromsalesofcrops<br>netlivestockincome|1.5|2.0|3.6|3.8|3.9|3.3|3.6|4.1|5.8|
|netincomefromown<br>enterprises|48.2|29.7|13.1|7.0|8.2|15.4|5.6|10.1|5.1|
|totalnetremittances|7.1|20.3|9.4|5.8|12.7|8.4|14.3|8.6|8.3|
|other income|8.1|6.0|3.2|1.4|2.9|4.6|2.2|2.6|2.6|
|consumption ofown<br>production<br>total income|11.4<br>100.0|20.0<br>100.0|39.7<br>100.0|46.2<br>100.0|43.7<br>100.0|49.1<br>100.0|50.4<br>100.0|51.9<br>100.0|55.0<br>100.0|

Source: Baris Sivri - personal communication.

**Table 4: Household income sources and poverty by region in Nepal**

|ShareofIncome<br>cashwagesfromworkingin|Urban<br>Kathman<br>du<br>Valley|Other<br>Urban|Rural<br>Eastern<br>Teral|Rural<br>Central<br>Terai|Rural<br>Western <br>Terai|Rural<br>Midand<br>Far-West<br>Terai|Rural<br>Eastern<br>Hills,<br>Mtns|Rural<br>Cental<br>Hills,<br>Mtns|Rural <br>WesternF<br>Hills,<br>Mtns|Ru<br>Mida<br>ar-Wt<br>Hil<br>Mt|
|---|---|---|---|---|---|---|---|---|---|---|
|othersfarm|2.4|1.8|10.6|10.1|8.0|10.2|3.6|4.5|6.3||
|Otherwages|12.6|20.1|12.3|12.3|8.3|9.2|12.4|12.6|9.0|_1_|
|Landrent<br>|0.0|1.7|0.9|1.5|0.1|0.6|0.2|0.0|0.1||
|net agriculturalincomefromsales|||||||||||
|ofcrops (sales-expenditures)|5.6|1.2|17.4|7.9|<br>6.0|15.8|5.1|1.3|2.0||
|netlivestockIncome|10.3|2.0|2.4|3.9|<br>3.7|0.5|6.1|3.8|1.9||
|netincomefromownenterprises<br>total netremittances(received-|40.4|44.5|19.3|17.2|10.9|4.0|5.9|31.2|10.7||
|sent)|4.3|10.0|6.1|9.8|7.8|4.4|1.1|9.5|22.0||
|otherincome|7.4|7.9|2.0|0.9|2.6|0.6|3.6|1.4|8.3||
|consumptionofownproduction|17.0|10.7|29.0|36.4|52.6|54.7|62.0|35.7|39.7|5|
|totalIncome|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|10'|
|Poverty incidence(%)*|4.0|34.0|42.0|38.0|40.0|53.0|28.0|67.0|40.0|7:|
|Povertygap|0.00|0.11|0.10|0.08|0.09|0.13|0.07|0.11|0.13|0.|
|Illiteracy(%)|24.0|45.0|62.0|77.0|69.0|72.0|59.0|66.0|54.0|7:|
|ShareinNepal Population(%)<br>Source:BarisSivri- personal|1.4<br>communica|6.8<br>tion.|12.6|16.2|7.2|6.7|9.7|13.1|13.0|1|

10
# 6) The adaptation cost is not considered in the analysis.

The standard Ricardian model relies on a quadratic formulation of climate: NR/ha = β0 + β1 F + β2 F<sup>2</sup> + β3 Z + β4 G + u (7) Where: NR/ha = net revenue per hectare F = vector of climate variables Z = set of soil variables G = set of socioeconomic variables u = error term The earlier Ricardian studies did not include irrigation in the analysis. However, initial research on United States data by Mendelsohn and Dinar (1999) suggests that water supply from runoff has an important effect on farms. Farms that can draw from runoff are more likely to use irrigation and earn higher net revenues. Under Egyptian conditions, irrigation is the main method of water application and it is also used as for relief from heat stress. Therefore, modeling runoff across Egypt could reveal the extent to which runoff affects existing farms. Furthermore, it can explicitly capture how runoff changes would interact with direct climate changes and affect farms in the future. The following model is proposed: Revenue/ha = β0 + β1 F + β2 F<sup>2</sup> + β3 Z + β4 G + β5 H (8) where H = relevant hydrology variables.

# **3. Data for analysis**

The data for the analysis were based on a cross-sectional farm household survey at district level for several governorates. The survey used a structured questionnaire and was designed to collect data which would reflect the substantial variation in Egypt’s agro-ecological zones, in particular the major and minor crops and livestock production. Small-scale and large-scale production, traditional agriculture and agricultural production based on improved technology were all reflected in the survey.

9