rafmacalaba/datause-encoder-data huggingface dataset:
33 Table 4. Impact of Full Liberalization of OECD Agricultural Tariffs and Subsidies on Sub-Saharan Africa

||Changein reg|ionalwelfare ($|billion) due to:|
|---|---|---|---|
||Change in<br>export<br>prices|Change in<br>import<br>prices<sup>b</sup>|Sum of export<br>and import<br>price effects|
|Agric and food products – all SSA|0.94|-0.38|0.56|
|South Africa|0.05|-0.09|-0.04|
|Other Southern Africa<sup>a</sup>|0.36|-0.03|0.33|
|Rest of Sub-Saharan Africa|0.53|-0.25|0.28|
|Non-agricultural products – all SSA|1.45|-0.53|0.92|
|South Africa|0.35|-0.15|0.20|
|Other Southern Africa<sup>a</sup>|0.37|-0.10|0.27|
|Rest of Sub-Saharan Africa|0.72|-0.29|0.43|

a Botswana, Madagascar, Malawi, Mozambique, Tanzania, Uganda, Zambia, Zimbabwe.

b The numbers in this column have the opposite sign to the import price indexes in part (a) of this Table because an import price rise reduces real income (whereas numbers in the export columns have the same sign).

Source: Anderson, Martin and van der Mensbrugghe (2006c, Tables 5 and 6)
have grown to include female migrants’ working conditions: how little they are paid and how exploited they are. They believe that textile workers are insulted and oppressed by their Chinese employers; they work in very cold conditions and inhale dangerous chemicals; witchcraft sometimes happens among factory workers trying to vie for better positions; and “thugs” in urban areas steal from them. They also believe domestic workers are sexually harassed by their female bosses’ husbands; they are treated “like slaves”, working from dawn until dusk; and paid very poorly. Overall, rural communities understand the work that migrant women do to be in extremely harsh conditions and for women to be vulnerable and cheated by men.

Aside from working conditions, rumors and thoughts about migrants themselves abound. Both men and women in rural communities believe migrant men engage in extramarital relations: they spend their money on other women and remit a smaller percentage of their money. Multiple stories emerged of miners moving away, starting new families and never returning, leaving their families to fend for themselves. Female migrants are not held in entirely favorable light either, although perceptions varied by gender: rural men see migrant women as succeeding professionally because of sexual relations: “It is women because they have a lot of challenges. Some of them get jobs as a result of sexual favors. Men do not usually face those challenges. Women do not even go home often, so it is highly probable they will engage in other extra affairs”. In contrast, some rural women see migrant women as engaging in extramarital relations out of spite (“Men will start and women will revenge on them by also doing the same”; “Women do engage in extra relationships if their husbands do not give them attention any more”) but also due to a newly enjoyed freedom. Perceptions were also sector‐specific: textile workers are often seen as “promiscuous, enjoying cohabiting with other men”, while domestic workers are rumored to “bring their boyfriends over while their employers are gone” or steal their employers’ husbands. People also dismissed women working in South Africa as “acting as a man’s wife”, living with him and making his home as a way to live more comfortably and supplement their income.

# _Prevalence and necessity of migration_

A brief look at the national data shows that despite half (43%) of Basotho households reporting at least one member living away, migration tends to be a survival strategy rather than a strategy for creating wealth. Remarkably, rural households with only internal migrants are as poor than households without any migrants: more than a third (32%) of households with members elsewhere in Lesotho are among the poorest, compared to 31% of non‐sending ones (see Figure 3). Having migrants in South Africa, however, 19
By the early 1990s, total contribution rates to the PAYG amounted to around 34.5 percent of gross wages, including 30.5 percent for old age and survivors and around 4 percent for underage disability pensions.

The Hungarian PAYG scheme arrived in the 1990s with difficulties to balance expenditures and revenues, despite charging one of the highest contribution rates in the world (Tables I and 2)3 During the 1990s the PAYG system was subject to further pressures, caused by a significant loss of revenues and a sharp increase in the system dependency ratio. As shown in Tables 1 and 2, the loss of revenues amounted to almost 3 percent of GDP before the reform, due primarily to an erosion of the tax base (the covered wage bill). Such base erosion was due not only to problems of ceilings and exemptions, but also to increasing evasion of the heavy payroll tax. The increase in the system dependency ratio amounted to 66 percent during the 1990s, most of which occurring in the first half of the decade (Figure 1). This dramatic increase in the dependency ratio was due to a reduction in labor force participation, increases in unemployment, and the maintenance of generous early retirement and disability schemes as a buffer against unemployment.

The sharp increase in the number of pensioners implied strong pressures on expenditures and, combined with the revenue loss, would have resulted in very large PAYG deficits, in the absence of other correcting measures. The main correction that took place involved the manipulation of indexation parameters in the benefit formula, and which resulted in a significant drop of the average replacement ratio. A sharp real wage compression that took place in the mid-1990s (around 15 percent in 1995 and 1996) also contributed to the decline in real pensions, given the wage indexation of pensions. The final result of these measures was a drop in pension expenditures relative to GDP, and only modest PAYG deficits in the early and mid-1990s!4 Although these corrections prevented the emergence of large deficits in the PAYG system, they were perceived as arbitrary and unfair, diminishing the credibility of the PAYG in the eyes of the population. Furthermore, the scope for additional _ad hoc_ corrections narrowed severely, making the system even more vulnerable to the demographic shocks projected for.the 215' century. As the consequences of a "do nothing" scenario were more widely understood, it became increasingly apparent that that the public pension system needed more fundamental reform. Before examining the long-run projections of the system, it must be noted that the PAYG deficits have increased somewhat after 1997 (the year when the reform was passed), but these deficits raise less concem, because they

> **2** This section draws on Palacios and Rocha (1998).

> 3The PAYG is defined so as to include contribution revenues, the pension expenditures of the Pension Insurance Fund, and the underage disability pensions of the Health Insurance Fund. It is arbitrarily assumed that the underage disability expenditures are covered by an equivalent amount of revenues.

3
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Rajpal, H. and Guerrero, O.A. (2023) ‘Synergistic Small Worlds that Drive Technological Sophistication’. arXiv. Available at: http://arxiv.org/abs/2301.04579 (Accessed: 2 July 2024).

Romero, J.P. and Gramkow, C. (2021) ‘Economic complexity and greenhouse gas emissions’, _World Development_ , 139, p. 105317.

Santos, M.E. (2019) ‘Challenges in designing national multidimensional poverty measures’.

Sciarra, C. _et al._ (2020) ‘Reconciling contrasting views on economic complexity’, _Nature communications_ , 11(1), p. 3352.

Sciarra, C. _et al._ (2021) ‘A network approach to rank countries chasing sustainable development’, _Scientific Reports_ , 11(1), pp. 1–12.

Sen, A. (1979) _Equality of what?_ na. Available at: https://books.google.com/books?hl=en&lr=&id=yvuMDwAAQBAJ&oi=fnd&pg=PA439&dq=Sen,+ A.+(1980).+Equality+of+what%3F+&ots=vBSZOFk2J&sig=W0ickzW1bMBM0YIaWQBAk5VkD0s (Accessed: 7 June 2024).

Sen, A. (1999) ‘Commodities and capabilities’, _OUP Catalogue_ [Preprint]. Available at: https://ideas.repec.org/b/oxp/obooks/9780195650389.html (Accessed: 7 June 2024).

Stiglitz, J.E., Sen, A. and Fitoussi, J.-P. (2009) _Report by the Commission on the Measurement of Economic Performance and Social Progress_ . OECD.

Stojkoski, V., Koch, P. and Hidalgo, C.A. (2023) ‘Multidimensional economic complexity and inclusive green growth’, _Communications Earth & Environment_ , 4(1), p. 130.

Suppa, N., Alkire, S. and Nogales, R. (2022) ‘The many forms of poverty: analyses of deprivation interlinkages in the developing world’.

Tacchella, A. _et al._ (2012) ‘A new metrics for countries’ fitness and products’ complexity’, _Scientific reports_ , 2(1), pp. 1–7.

35
measurement to these dimensions by asking about food coping strategies and anxiety over insufficient food access (e.g, Maxwell et al. 2014). However, there are significant additional challenges to incorporating anxiety regarding poor food access and other subjective measures. In particular, answers to subjective welfare questions depend on respondentspecific scales that: (1) may not be comparable across individuals or stable over time; (2) are potentially subject to frame-of-reference effects; and (3) suffer from measurement errors, over and above those affecting traditional welfare metrics (e.g., Ravallion 2013; Tandon 2021; etc.).

There have been extensive efforts to validate food access metrics by comparing them to the quantity of food consumed and to nutritional outcomes (e.g., Maxwell 1996; Weisman et al. 2009; Broussard and Tandon 2016; etc.). Yet measuring the quantity of calories consumed and the nutritional content of food consumption is difficult and nutritional outcomes are also strongly impacted by health and non-food factors. Therefore, food access measures have also been validated by comparing them to _other_ household characteristics (e.g., Maxwell et al. 2014). One such household characteristic is monetary poverty and low levels of expenditure, which are strongly correlated with a number of key food access metrics (e.g., Maxwell et al. 1999; Webb et al. 2002).

However, it is important to note that one cannot validate a new food access measure simply by comparing it to another previously-validated food access measure. If different food access metrics are poorly aligned, it might be that each food access metric is capturing different dimensions of food access (e.g., Maxwell et al. 2014), or it might be that the new metric is indeed poorly correlated with the underlying dimensions of food access that it purports to capture. Better understanding the degree to which different metrics complement each other and knowing which metrics might be poorly suited to specific contexts will help to weight metrics properly and, in turn, to characterize overall food access more fully.

The FIES is an experiential food access metric that incorporates information on anxiety over poor food access, food coping strategies, and household behaviors that are consistent with low levels of consumption and poor diet quality through eight yes-no 8
# **IV. Impact of taxes and social spending on poverty and inequality.**

This section presents the general results of the impact of the tax system and social spending in Colombia on poverty and inequality. Likewise, some comparisons are shown with the results obtained for other countries and with the results of the exercise previously carried out in the country. Likewise, the main methodological differences between the present work and the previous exercise that sought to assess the distributional impacts of the tax policy and social spending for Colombia are presented.

## i. Methodological comparison

This section discusses some of the methodological differences between the present and the previous Commitment _to Equity_ (CEQ from now on) made for Colombia. The first and main limitation of this comparison lies in the non-existence of a public document that sets out the details of the assumptions made in the previous exercise. This comparison is based on the work of Lustig, Meléndez, and RodriguezCastelán (2014). To the extent that the assumptions were clear, and the existence of programs allowed, the calculations were made following the previous methodology. However, in many other cases, the lack of available information or the elimination of certain programs made comparison impossible.

### **_a) Direct taxes_**

Lustig, Meléndez, and Rodríguez-Castelán (2014) include direct taxes, those corresponding to income, property, betterment levies, and vehicles and directly take the payments reported by households for these items from the Quality of Life Survey. As far as it can be seen in the methodology of the previous version of the CEQ, social security contributions were not included as part of direct taxes. This new version of the CEQ for Colombia takes the sum of personal income tax, property tax and betterment levies, vehicle tax and social security contributions paid by both employers and employees as direct taxes. The property and betterment levies tax and the vehicle tax had the same treatment as in the previous CEQ, as the values reported in the survey were used. The main difference lies in the treatment of personal income tax and in the inclusion of social security contributions that were calculated with the rates in law and the income of the people who report they are contributing.

Concerning income tax, for this version of the CEQ, there was the advantage of having aggregate information from the DIAN tax returns. This information allowed for a better simulation of what households pay. Information from DIAN was used to calculate, for each of the income types, the proportion of revenue that does not constitute taxable income, that is exemptions and deductions, to finally deduce the net taxable income. It is also important to recall the significant changes in the tax code between 2010 and 2017, where the form of a statement of personal income and rates have changed at least twice. The tax law applicable to 2017 classifies income according to their origin and imposed upper bounds on the values that could be deducted in each income type and tax dividends.

24
Policy ReseaRch WoRking PaPeR 4323

# **Abstract**

Although opinions on impacts of land market transfers are sharply divided, few studies explore the welfare and productivity effects of land markets on a larger scale. This paper uses a large Indian panel spanning almost 20 years, together with a climatic shock (rainfall) indicator, to assess the productivity and equity effects of marketmediated land transfers (sale and purchase) compared with non-market ones (inheritance). The analysis shows that frequent shocks increase land market activity, an effect that is mitigated by the presence of safety nets and banks. Land sales markets improved productivity and helped purchasers, many of whom were formerly landless, to accumulate non-land assets and significantly enhance their welfare.

This paper—a product of the Sustainable Rural and Urban Development Team, Development Research Group—is part of a larger effort in the group to assess the impact of land policies on poverty and economic growth. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at kdeininger@ 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 Team
Adjusting for the panel design and for over and above CPI increases in the cost of poverty basket result in less impressive growth. Table 2 shows that moving to the panel constant weights and the welfare index instead of per capita consumption (reflecting real growth rates relative to the cost of the poverty basket) welfare growth was close to 2 percent per year (6.85 percent over 2005-2008 period), and not 4 percent per year as implied by the overall CPI index. Table 5 also shows how standard errors estimated with classical assumptions are different from correct standard errors which take into consideration survey sample design (clustering) and panel properties. It means for example that for rural areas growth rate in average consumption is not statistically different from zero (at 95 percent confidence level the range around the estimate is plus minus 6 percentage points – larger that the mean estimate itself of 5.6 percent).<sup>15</sup>

**Table 2: Growth rate of the welfare index and standard errors**

||**Growth rate of the**||**Standard errors, pe**|**rcent**|
|---|---|---|---|---|
||**welfare index,%**|**Classic**|**Allowing for clustering**|**Panel (boostraped)**|
|Urban|7.83|0.48|3.75|3.59|
|Rural|5.66|0.33|3.13|3.44|
|**Total Egypt**|**6.85**|**0.31**|**2.50**|**2.57**|

_Source: Authors estimates based on HIECPS 2005-2008 and regional CPI indice). Note: Table uses fixed household size to construct constant panel weights._ This 7 percent growth in real average household welfare translated in poverty reduction, but not at the same rate for everyone. Table 3 presents assessment of poverty changes based on poverty line defined in World Bank (2007). The “lower poverty line” used here represents the cost of the minimum subsistence basket comprising food and non-food goods and services. The poverty headcount moved from about 23 percent in 2005 to about 19 percent in 2008. Poverty reduction was especially rapid in urban areas. However, large standard errors mean that only for Egypt as a whole change in poverty was significant at high confidence level (95 percent).

> 15 Notably, allowing for panel design does not change the magnitude of standard error significantly (3rd and 4th columns).  Hence, we can rely on simple survey methods to calculate standard errors (available as standard command in Stata).

10
The remainder of the paper is organized as follows. Section 2 details the specific subjective and objective soil data collected in the Ethiopia Land and Soil Experimental Research (LASER) project and provides descriptive statistics on each. Analytical comparison of the measurement methods is explored in Section 3, with an emphasis on the ability of respondents with various characteristics to more or less accurately assess the quality of their soils against the objective benchmark. Section 4 concludes.

# **2. Data**

# _2.1  LASER Study_

In an effort to collect the highest quality data possible in a large-scale household survey context, the Living Standards Measurement Study has prioritized methodological research in recent years through implementation of the LSMS Methodological Validation Program. With the aim of identifying the magnitude and (potential) systematic nature of measurement error associated with various measurement methods, and with financial support from UK Aid, the LSMS has designed several methodological experiments focused on key aspects of agricultural analysis, including soil fertility. Such methodological experiments strive to find balance between quality and scalability, and ultimately implement the most appropriate methods in future surveys.

Nationally representative LSMS-ISA surveys commonly include basic subjective questions on soil fertility, often asked to the head of household or plot-manager. Additionally, when plots are georeferenced, indicators of soil health such as nutrient availability, toxicity, and salinity are derived from outside sources including the Harmonized World Soil Database and provided as supplementary data along with the full LSMS-ISA data set. However, in order to know how well the subjective assessments of soil quality correlate with true soil fertility measures, and whether there are any systematic measurement biases based on topography or respondent characteristics, the subjective measures must be taken alongside objective, plot-level measures. This was the motivation behind the Land and Soil Experimental Research (LASER) project.

Data collection for the LASER study was conducted in 3 zones of the Oromia region in Ethiopia (refer to Figure 1). Oromia region was selected because it represents a large area of Ethiopia and encompasses areas with great variation in rainfall, elevation, and agroecological zones. In total, 85 enumeration areas (EAs) were randomly selected using the Central Statistical Agency of Ethiopia’s Agricultural Sample Survey (AgSS) as the sampling frame. Within each EA, 12 households were randomly selected from the AgSS household listing completed in September 2013.

5