rafmacalaba/datause-encoder-data huggingface dataset:
for rural land alone cannot be sustained,<sup>15</sup> establishing property registries is a long-term agenda that has to start in urban areas. Given the adverse and potentially irreversible equity and productivity impacts from gradual erosion of individuals’ customary land rights, more agile means to measure such rights and protect them if needed may be required.

> 15 Analysis shows that, with costs that exceed any imaginable benefits as well as available resources by orders of magnitude, provisions regarding customary rights in Uganda’s 1998 Land Act are unlikely to be ever implementable (Hunt 2004). This has neither prevented donors from trying nor neighboring countries from passing similarly ambitious pieces of legislation. Ali _et al._ (2019) show that even in Rwanda, the only African country with a functioning national registry, sustaining rural registries will require efficient operation with a key role for IT; active engagement with the private sector (e.g. by using mobile phone providers as a first point of contact, and cross-subsidization from urban registry operations.

15
Figure 11: Correlation between R&D expenses and IO Coefficient of ICT‐services (2015), China <!-- Start of picture text --> R&D per empl. and IO Coefficient<br>Computer & Electr.<br>Other transport<br>Rubber & Plastics Chemicals & Pharm.<br>Electrical equipment<br>Metal productsOther manuf. & RepairNon-metallic mineralMachinery and equip.<br>Basic metals<br>Paper & Printing<br>Textiles & Wearing<br>Food & Beverages<br>Wood products<br>.0005 .001 .0015 .002 .0025<br>IO Coefficients (Domestic)<br>2<br>1.5<br>R&D per employee 1<br>.5<br><!-- End of picture text --> Source: World Bank. Note: the IO Coefficients are computed as the fraction of ICT‐services usage in total input use for each industry in China using IO tables. Innovation variable in this figure represents R&D expenditures over employee for each firm. Coke & Petroleum is omitted because of lack of credible data.

Figure 12: China’s level of data policy restrictiveness (2009‐2019) <!-- Start of picture text --> Level of data innovation restrictiveness<br>2009 2011 2013 2015 2017 2019<br>1<br>.8<br>.6<br>.4<br>Data innovation restrictiveness .2<br>0<br><!-- End of picture text --> Source: ECIPE 29
Table 1. Annualized growth changes in skill premium, 2003-08, 2008-10 and 2010-12.

|Country|Tertia<br>sec|ry / Incom<br>ondaryor l|plete<br>ess|Terti|ary / Comp<br>secondary|leted|
|---|---|---|---|---|---|---|
||2003-08|2008-10|2010-12|2003-08|2008-10|2010-12|
|Countries where<br>|inequality redu<br>|ced<br>|||||
|Guatemala|-0.6|-4.0||8.9|-3.1||
|Nicaragua|-6.6|||-6.2|||
|Uruguay urban|-3.7|9.6|-15.4|-3.9|8.0|-8.7|
|El Salvador|7.7|-10.5|-3.7|6.7|-11.9|0.7|
|Paraguay|-10.2|5.2|-5.7|-10.2|12.0|-6.9|
|Argentina urban|-4.0|-1.3|-8.7|-4.8|1.0|-6.3|
|Ecuador|-7.2|0.8|-6.1|-3.7|6.2|-7.5|
|Dominican<br>Republic|-3.3|0.8|-3.6|-0.5|-0.1|5.1|
|Colombia|-3.3|4.6|-1.7|0.1|3.6|-0.1|
|Bolivia|-11.7|-10.3|-6.1|-8.4|-4.9|-6.6|
|Chile|-3.5|-1.4|-2.3|-1.2|-0.5|2.8|
|Brazil|-5.4|-5.5|-4.5|-2.4|-5.2|-1.2|
|Countries where|inequality stag|nated or in|creased||||
|Panama|-1.4|-2.3|2.0|0.3|-8.2|11.9|
|Peru|-2.3|-10.0|1.8|-3.6|-12.5|6.0|
|Costa Rica|0.5|3.2|6.4|3.1|-3.5|5.8|
|Mexico|-2.5|-0.7|2.0|1.1|-0.9|3.1|
|Honduras|-1.8|-3.9|2.0|-0.4|-2.4|4.3|

Source: Authors’ calculation from SEDLAC (CEDLAS and the World Bank). Note: The figure reports the average annual change in the relative wage gap between educational groups in percentage points between 2003-2010 and 20102012, or the nearest years when data for the cut-off years are not available. Nicaragua and Guatemala only have information for the first period 2003-2010. Countries are listed by magnitude of inequality reduction between 2010 and 2012, from highest to lowest. The incomplete secondary or less group includes those workers without education, primary education or incomplete secondary education. Completed secondary includes those who completed secondary schooling and those with some tertiary education. Tertiary includes only those with completed tertiary education.

The changes in the wage gap between high-skilled and low-skilled labor are partially attributable to a shift away from agriculture, the sector of employment of 43 percent of Latin America’s poor but only 9 percent of its top 60 percent, towards higher paying sectors, particularly construction and retail. These shifts away from agricultural employment represent increased earnings opportunities for the lowest skilled. Agriculture, the third lowest wage sector in Brazil and the lowest wage sector in Mexico in 2003, saw declines in the share of employment between 2003 and 2008 in both countries. While these declines reversed after 2008 in Mexico, they continued in Brazil. Some of this movement back to agriculture in Mexico may have been the result of the decline in employment in construction and manufacturing – while in Brazil, construction and hospitality services continued to grow. In fact, this shift back to agriculture in Mexico was accompanied by a shift away from wage employment to selfemployment on the order of 1.6 percent per year, indicating a reduction in the number of jobs available in the economy after the crisis. This suggests that low-skilled workers were pushed into lower productivity jobs as a result of the crisis, and these jobs had not recovered as of 2012.

16
In summary, the most important food product for tax reform is maize, which figures relatively heavily in the budgets of low-income households. A reduction of its price is desirable for both equity and efficiency reasons. Legumes, alcoholic beverages and vegetables are also candidates for price decrease on the basis of efficiency and equity. On the other hand, taxes could be increased in the case of fruits, oils and fats, meat and nonfood products without substantially decreasing welfare of poor households.

# **6. Conclusions**

This paper adapts the Deaton methodology to derive income, quality, own-price and cross-price elasticities for 10 aggregated goods and for five levels of income of Mexican households using six household surveys collected between 1989 and 2000. Those estimates are then fed into a micro simulation model to estimate the effect of a marginal tax reform.

The results on the elasticity estimates indicate that there is a substantial difference between estimates obtained when households are differentiated by level of income. Poorer households tend to have larger income elasticities, suggesting a level of income barely able to support the basic necessities of the household. Similarly, price elasticities are usually larger for poorer households, likely because of more stringent budget constraints. For richer households, response to change in prices tends to fade. Results also found that differences between urban and rural areas are small and likely to be caused by differences in income more than differences in behavioral parameters between rural and urban households.

The results from the micro simulation suggest that since poorer households rely mostly on maize, legumes and vegetables to fulfill their caloric intake, any price reform that reduces the price of those products will have a larger effect on the welfare of poor households. Furthermore, a reduction in the taxation on maize, alcoholic beverages and vegetables would be both more equitable and more efficient in terms of social welfare.

19
Even though the 41 articles reported in Table 3 surely do not constitute the entire literature, it is notable that only four focus on the experiences least developed countries. Few studies attempt cross-country comparisons of reform experiences or procurement practices—exceptions being Trepte (1998) for Bhutan and Laos and Wittig (1999) for South Africa and Uganda. Moreover, only a few of the articles<sup>11</sup> attempt any form of quantification of the costs, benefits, or effects of existing procurement practices or reform proposals.

Another source of information are the Country Procurement Assessment Reports prepared by the World Bank and other development agencies. These reports provide a diagnostic assessment of the strengths and weaknesses of the procurement systems in individual developing countries.<sup>12</sup> The CPARs that are publicly available are listed in Appendix 2 of this document. The CPAR process has led to recommendations and financing of action plans to modernize or reform existing procurement systems in a number of countries. As is true of the more academic literature, the impact of this work on the performance of national public procurement systems has not been well documented, in the sense that the CPARs tend to focus primarily on legal compliance – whether laws and required procedures have been implemented. There is little focus on quantifying performance, the actual return to having applied processes aimed at enhancing competition, transparency and combating the scope for corruption, or the expected return to adopting new procedures that have such outcomes as objectives.

The only cross-country data that is available on the procurement ‘performance’ of countries are international surveys. An example is the survey of perceptions of

> the WTO Working Group on Transparency in Government Procurement Practices, at <u>http://www.wto.org/english/tratop_e/gproc_e/gproc_e.htm; the World Bank’s Country Procurement</u> Assessment Reports (not all of which are publicly available); and Individual Action Plans of APEC members, at <u>http://www.apec-iap.org/.</u>

> 11 See, for example, Mardas and Varsakelis (2000), Srivastava (1999), Khorana (2002), and Choi (1999). Interestingly, most of these refer to the trade policy-related aspects of procurement reforms—suggesting that empirical analyses of domestic procurement practices are rather thin on the ground.

> 12 A CPAR it will typically include an assessment of the legal and regulatory framework for procurement, actual procedures and practices; independent auditing and existence and effectiveness of anti-corruption measures, and procurement performance – whether required procedures are known to participants, are applied, the length of time it takes to complete a transaction, etc. They are typically done jointly with the government and the World Bank, and may also involve a regional development bank (such as the Asian Development Bank or the Inter-American Development Bank. See <u>http://worldbank.org/html/opr/procure/cpar.htm.</u>

10
# IV. Resolving Problems of Non-Compatible Data

In this section we outline how our results may be used to deal with the problem of comparing data of differing levels of comprehensiveness. There are two approaches. The first is to make direct use of the robustness result obtained in Section II and restrict attention to the headcount ratio using final poverty lines constructed in the "traditional" manner. This allows one to make comparisons over time or across regions without adjusting the data. The second approach is to calculate predicted values of total expenditure for the periods or countries where actual total expenditure is not available, in order to have consumption measures which are comparable.

We demonstrate the direct use of the robustness result, using data for El Salvador. The _Encuesta de Hogares de Propositos Multiples_ household survey in El Salvador (covering 4220 households during the period July-September, 1994) was the subject of an experiment: two non-overlapping samples were drawn from the same sampling frame and were administered different food consumption modules (both of which include some basic non-food goods and services as well). A short module asking about the consumption of 18 food items and six non-food items was completed for 3,182 households. A long module enquiring into the consumption of 72 food items and 25 non-food items was completed for 1047 households. Only with reference to five basic food items did the two coincide: corn tortilla, bread, sweet bread, beans and rice. Essentially, the long module referred in a more detailed, itemized, way to broad food categories included in the short module. Table 7 shows that average consumption levels by decile in the two subsamples differ markedly, with households covered by the short module spending significantly less than those covered by the long module. If one were to measure poverty by applying a single poverty line across the two sub-samples, one would conclude that poverty among those covered by the short module is much greater than among those covered by the long module. Because both samples are representative of the same underlying population, this is clearly incorrect'.

_Table 7. Per Capita Monthly Expenditure: Long and Short Questionnaires_

|_Percentiles_|_Long_|_Short_|
|---|---|---|
|_10th_|124.97|94.83|
|_20th_|193.60|153.35|
|_30th_|242.20|204.62|
|_40th_|296.78|255.24|
|_50th_|358.54|315.25|
|_60th_|445.25|382.19|
|_70th_|575.17|483.18|
|_80th_|730.99|627.97|
|_90th_|992.79|864.83|
|Top|2090.5|2225.4|

_Source:_ Republic of El Salvador: Encuesta de Hogares de Propositos Multiples, 1994-III.

9 The two sub-samples were drawn from the same frame and were explicitly intended to be identical in all respects except for the consumption modules. Scott and Jolliffe (1995) show, in terms of location, household size, income levels, education, etc., that households in the two sub-samples resemble each other closely.

14
wages in the _factory sector_ and the differences in wages among highly skilled workers and in some geographic areas. But evidence to date that would describe labor market opportunities for the public sector, as a whole, is surprisingly scarce.

In this paper, we use nation-wide, representative survey data to ascertain labor market opportunities for workers in the public sector. We employ a set of econometric techniques to control for the differences in human capital and other characteristics of the workforce in the public and private sectors and control for unobserved characteristics that could be correlated with wages and individuals’ chances to join a particular sector.

Examination of wages in public, formal-private and informal-casual sectors shows that differences in wages between workers in the public and private-formal and informal sectors are positive and high. In 1999-00, the average real wage in the public sector was about 2.1 times that in the organized private sector. The difference in real wages between the public and private-informal sector is even larger at 3.8 times. The public sector tends to employ workers with more human capital. Since the labor market places a premium on more education and experience, adjusting for these differences in characteristics between public and private sector workers reduces the size of the wage differential. To obtain robust estimates of the adjusted wage differential, we employed three methods, each relying on a different set of assumptions about the process of job selection and wage determination. The public sector wage premium remains, ranging from 62% to 102%, over private-formal, and from 164% to 259% over casual-informal sectors, depending on the choice of the methodology.

The wage differentials tend to be higher in rural as compared to urban areas, and are higher among women than among men. The wage differential also tends to be higher for low-skilled workers. Moreover, there is considerable evidence of an increase in the wage differential between 1993-94 and 1999-00, no matter which of the three adjustment methods is used.

This paper is organized as follows. Section 2 presents the salient features of India’s labor market. Section 3 reviews the existing studies on wage differential in India. Section 4 describes data, provides definitions of the main constructed variables and presents results of descriptive analysis. Section 5 discusses the theoretical model and estimation methodology. Main findings are presented in Section 6. Section 7 summarizes and concludes with a discussion of policy implications of these results.

4
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10

- **SENAI** ( _National Service for Industrial Training), sponsored by_ **National Industry Confederation.** SENAI offers technical training for industry workers interested in further improving or updating their technical/professional skills. Most applicants have a secondary degree; and

- SENAC _(National Service for Commercial Training), sponsored by National Commercial Confederation._ SENAC concentrates on training workers who are interested in the services area. Typical attendees have a secondary diploma.

SENAI and SENAC are financed by a tax called Social Security Contribution Tax, levied on all the core business payroll of all legal entities. SEBRAE is financed by a similar tax levied on the companies' entire payroll. These institutions are typically retained by companies interested in retraining their employees. However, individuals who are not linked to a specific corporation may also sign up for specific training classes by paying the applicable fee.

Upon RFFSA's request, the three institutions agreed to develop new training programs or adapt their already existing programs to the needs of RFFSA's workers. Their average cost per person (including housing, food and transportation) is as follows: SEBRAE: R$ 2.000; SENAI: R$ 2,408 and SENAC: R$ 320. The training courses offered by SEBRAE and SENAI had an average duration of 150 hours. The average duration of SENAC's training courses is only 40 hours, due to a lower degree of specialization of the services (e.g., hotel services, driving, waiting, etc.). SENAI also includes an information and procedure kit to assist trainees in starting their own business. The largest demand was for SENAC's training program, since the type of training offered was well in line with the current demand in the labor market, where the fasted growing needs are in the services sector. The total cost of RFFSA's training program was estimated at about $10.5 million. An additional $2 million was needed to train the company's residual staff.

# **(d)Outplacement** Assistance **Program**

The staff reduction program also provided job search assistance to those employees leaving the company, including resumes preparation and the provision of information on employment opportunities. Employees also were assisted with addressing problems concerning the negotiation of new employment contracts. A similar type of assistance was offered to those trying to start their own business or create cooperatives. Employees leaving the company were not required to attend training classes to benefit from this service. RFFSA created a data bank which indicated all workers who had left the company under the program and those firms potentially interested in their professional skills. This service was made available to participating employees for a period of up to eight months after training. Specialized firms with representation in all Regional Areas were hired to provide this service.
<!-- Start of picture text --> Figure 4.6. Household consumption per capita<br>by year (index 2022=100)<br>130<br>125<br>base<br>120<br>limbo<br>115<br>rec-t<br>110 rec-ta<br>105 rec+t<br>rec+ta<br>100<br>rec+ta+<br>95<br>Figure 4.7. Grant aid for selected simulations (% of<br>GDP)<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>2022 2023 2024 2025 2026 2027 2028 2029 2030<br>rec-t rec-ta rec+t rec+ta<br>Figure 4.8. Taxes for selected simulations (% of<br>GDP)<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>2022 2023 2024 2025 2026 2027 2028 2029 2030<br>rec-t rec-ta rec+t rec+ta<br><!-- End of picture text --> 31
28 measures described above provide perhaps the simplest way to estimate the extent to which agricultural and trade policies distort farmer incentives. And where an economywide CGE model is available, those NRAs can be inserted in its database to estimate the effects of those price distortions on markets, income distribution and the like as well. Without _NRAag_ estimates based on price comparisons, however, such CGE models are likely to rely just on import-weighted tariffs and thereby to generate misleading estimates of the effects of government interventions in goods markets.

One further point about the use of NRA estimates in CGE modeling is worth making by way of conclusion. CGE models are used to estimate the consequences not just of recent policies but also of possible changes in policies. In the case of China, they were used widely to assess the likely consequences of the commitments China made as part of its accession to WTO (one of many examples being by Ianchovichina and Martin (2004)). Such studies compare a projected reform scenario with a base case that typically assumes the current policy regime will continue into the future. In the case of agricultural policies, however, history reveals that countries tend to transform from taxing to assisting farmers relative to producers of other tradables as their economy develops. The extent of that transformation is particularly strong in densely populated countries as their comparative disadvantage in agriculture intensifies, as revealed in the World Bank project’s new estimates of NRAs for key Asian economies (Figure 3(a)). One might therefore anticipate that, in the absence of outside influences such as WTO accession, China might eventually follow the agricultural protectionist path of earlier-industrializing Japan and Korea. In that case, estimating the net present value of its legally bound commitments to reduce farm tariffs and subsidies as part of WTO accession arguably requires comparing that scenario with a base scenario involving a rising counterfactual NRA for agricultural products rather than simply assuming that the low level of recent years would prevail indefinitely. Imagine how much greater would have been the estimated benefits of signing on to the GATT had agricultural reform commitments been required at the time Japan joined in 1955 and Korea in 1967, in the light of the growth as revealed in Figure 3(b) of their agricultural NRAs since then.
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