rafmacalaba/datause-encoder-data huggingface dataset:
**Executive Summary**

A staple feature of cross-country comparisons is that poor countries typically lag behind rich countries on almost any indicators of educational development. What in fact is the relation between country wealth and education? What underlying factors account for rich countries' advantage in education? How does the impact of these factors change in the course of economic growth? What scope is there for policy choice to improve education sector outcomes?

We explore these questions by first documenting the relation between per capita GNP and various indicators of educational development in a systematic fashion. We then exploit a simple accounting identity relating the availability of resources to the expenditure of those resources to clarify the sources of rich countries' advantage in education. Data for our sample of some 125 countries in 1993 confirm the expected favorable relation between per capita GNP and each of the following dimensions of educational development: (a) the sector context as characterized by the demographic burden on the education system, the government's overall fiscal capacity, and so on; (b) the production of education services, including such factors as public spending on education and the composition of spending; (c) education outcomes in terms of coverage and student learning; (d) efficiency of sector operations; and (e) equity in access and distribution of public spending on education.

An appealing explanation of why richer countries achieve better results is that they have more resources to run well-functioning and efficient education systems. Comparing pairs of countries at contiguous levels of per capita GNP between $200 and $10,000 (e.g. countries at $200 and $400 would form one pair, and those at $400 and $800, another pair), we found that indeed the richer country in each pair enjoys an advantage of almost 50 percent in the resources available per school-aged child. Yet a closer look reveals that bigger budget allocations to education make a relatively small contribution to the differences in resources. The commonly-cited role of lighter demographic burdens in the richer countries is also relatively modest, contributing between 17 and 32 percent of the advantage among the pairs of countries compared. By far the most important factor is the decline of teacher salaries relative to the per capita GNP, accounting for at least 50 percent of the advantage at all stages of economic development.

The extra resources per school-aged child that materializes in the process of income growth allow countries to expand enrollments and improve classroom conditions by reducing the pupil-teacher ratio. At the earliest phase of income growth-corresponding to a rise
|**Tool name:**|**SimSIP Poverty**|
|---|---|
|**What is it?**|SimSIP Poverty is a generic Excel-based simulator that allows one to estimate the changes in poverty<br>and inequality over time resulting from changes in output and employment growth.|
|**What can it be used for?**|This tool may be used to simulate the poverty and inequality impact of policies affecting sector-level<br>output and employment growth.|
|**What does it tell you?**|It simulates poverty and inequality measures nationally and within sectors (urban and rural, agriculture,<br>manufacturing, and services). It may simulate the impact that various sectoral growth patterns and<br>population shifts between sectors will have on future poverty and inequality.|
|**Complementary tools:**|Other tools for poverty forecasting include PovStat (see the table on PovStat) and DAD (a software for<br>distributive analysis).|
|**Key elements:**|On the basis of existing information on group-level household survey data (typically by deciles or<br>quintiles), the software translates differential output and employment growth across sectors into<br>differential growth in the per capita income or consumption of households across these sectors. The<br>tool simulates the impact on poverty of policies affecting output by using the fact that poverty changes<br>can be decomposed into two parts: a component related to the uniform growth of income and a<br>component due to changes in relative income. The simulations are made under the assumption that the<br>policy analyzed will be distribution neutral or, conversely, that there is a specific, quantifiable form for<br>the distributional change. Changes in employment distribution are accommodated by reweighing the<br>sample households.|
|**Data/information:**<br>**ents**|SimSIP Poverty uses grouped household data, typically grouped by income; the mean income or<br>consumption by group and the share of these groups are required. In addition, SimSIP Poverty requires<br>macroeconomic data at a nationally aggregated or sectorally disaggregated level. This includes, for<br>example, past or expected growth rates of output, employment, and population by sector. Finally, the<br>population size and growth and a poverty line are necessary for calculating poverty incidence.|
|**Time:**<br>**quirem**|One day to gather the data on population shares and mean income or consumption by group, check the<br>credibility of scenarios, and enter the data into the software.|
|**Skills:**<br>**Re**|Familiarity with Excel.|
|**Supporting**<br>**software:**|Excel.|
|**Financial cost:**|–|
|**Limitations:**|SimSIP does not capture second-round effects. These may be captured by CGE models.|
|**References and**<br>**applications:**|�<br>For an overview of the technique, see Datt and others(2003).<br>�<br>Wodon, Ramadas, and van der Mensbrugghe (2003).<br>�<br>Ramadas, van der Mensbrugghe, and Wodon (2002).<br>�<br>Software available at www.worldbank.org/simsip.|

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The main finding of the paper is that assuming seemingly plausible allowances for migration costs, the market for work permits could support a basic income for the citizens of the destination countries at a similar or higher level to that found in recent policy discussions about a universal basic income. The simulation of the market is confined to the US and Mexico. With low tax rates on the work permits, we expect something around 30 million participants in the market at a single work permit price.

Our results point to the potentially important role of the tax rate on work permits in managing the market and targeting beneficiaries. Increasing the tax rate would reduce the size of the beneficiaries of the work permit scheme and, at the same time, increase fiscal revenues. These revenues, in turn, can be spent as social protection transfers, increasing the size of the basic income guarantee scheme. A regulated market for work permits can, therefore, provide a substantial social safety net for the native population, while allowing immigrant workers to access economic opportunities to not only increase the productivity of the destination country, and contribute taxes, but also generate income for them and their origin communities.

# **References**

- Albert, C. and J. Monras (2022, November). Immigration and spatial equilibrium: The role of expenditures in the country of origin. _American Economic Review 112_ (11), 3763–3802.

- Argente, D., C.-T. Hsieh, and M. Lee (2023, July). Measuring the cost of living in mexico and the united states. _American Economic Journal: Macroeconomics 15_ (3), 43–63.

- Auriol, E. and A. Mesnard (2016). Sale of visas: a smuggler’s final song? _Economica 83_ (332), 646–678.

- Beaudry, P., D. A. Green, and B. M. Sand (2018, September). In search of labor demand. _American Economic Review 108_ (9), 2714–57.

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# Climate Shocks, Vulnerability, Resilience and Livelihoods in Rural Zambia

_Hambulo Ngoma_<sup>_1,3_</sup> _, Arden Finn_<sup>_2_</sup> _, Mulako Kabisa_<sup>_1_</sup> 1Indaba Agricultural Policy Research Institute, Lusaka, Zambia 2The World Bank, Poverty and Equity Global Practice, Washington, DC 3International Maize and Wheat Improvement Center (CIMMYT), Harare, Zimbabwe

**Keywords:** Resilience, vulnerability, climate shocks, smallholder farmers, Zambia

**JEL codes: I32, Q54**
5 TRADE, TRANSPORT, AND TERRITORIAL DEVELOPMENT India, we shut down this channel in our model (Munshi and Rosenzweig, 2016; Kone et al., 2018). Instead, internal and external trade costs shape economic activity in our model through movement of labor across sectors _within a region_ .

The rest of the paper is organized as follows. In Section 2, we describe the data and present some stylized facts about the spatial distribution of organized manufacturing in India. Motivated by these facts, Section 3 develops a model of production and trade that combines both internal and external trade costs. In Sections 4 and 5, we provide evidence that is supportive of the model. Section 6 concludes.

# **2. Spatial Distribution of Organized Manufacturing in India: Stylized Facts**

## **2.1 Data**

We use the same plant level information as much of the prior research on the Indian economy, including Duranton et al. (2015) and Ghani et al. (2016). Our main data source is the Annual Survey of Industries (ASI), the annual survey of organized sector manufacturing plants undertaken annually by the Central Statistical Organization, a department in the Ministry of Statistics and Program Implementation, Government of India. We use information on real output, employment and the number of plants for the following years: 1989, 1994, 2000, 2005 and 2009. These plant-level variables are aggregated to the level of a district (details in Appendix B). Following a routine data cleaning exercise, we have an unbalanced panel of an average of 344 districts per year.

We complement the manufacturing data from ASI with population data from the 1991 Census. This data is used to create district-level weights when running the regressions. External trade cost is measured using data on output tariff and effective rate of protection (ERP) from Topalova and Khandelwal (2011), while internal trade cost is measured as distance from the GQ highways.

## **2.2 Stylized Facts**

We classify Indian districts into three regions based on their distance from the nearest seaports and airports: (a) _primary_ , the group of districts located within 200 kilometers from ports; (b) _secondary_ , the districts located between 200 and 400 kilometers from ports; and (c) _interior_ ,
first, does the sampling bias for the early years of the survey (and its correction through the application of the H-S weights) significantly affect the _level_ of variable means? Second, are the _patterns_ of expenditure per capita, poverty and basic needs fulfillment (both regionally and across socio-economic groups) changed as a result of the weighting?

There are both theoretical and practical advantages in using total household expenditure as a measure of household welfare, and this is reflected in the popularity of this variable (expressed in per capita terms) in many of the poverty studies conducted with CILSS data (Kakwani, 1990, Kanbur, 1990, Glewwe, 1991). It seems sensible therefore to begin with an assessment of how the H-S weights affect mean per capita total household expenditure.

Table 9 reports two estimates of the variable for each year. Column A gives mean expenditures without any adjustment for household size, and column B reports mean values after the application of H-S weights. For Cote d'Ivoire as a whole, the effect is to _increase_ mean expenditure by 7.5 percent in 1985, 9.7 percent in 1986 and 3.18 percent in 1987. The lower figure for 1987 is to be expected, given that only a half of the sample of that year was subjected to the H-S weights. Clearly, the per capita expenditures of larger households are _lower_ than average, so that reducing their weight has the effect of increasing the overall mean. The order of magnitude of the error introduced by the sample bias is not insignificant: in 1986, for example, the original estimates of mean per capita expenditure for CMte d'Ivoire as a whole were about 10 percent too low.

Of equal significance is the uneven effect that the H-S weights have across the regions. Predictably, given the fact that mean household size corrections were least in Abidjan and Savannah, per capita expenditure in these regions are also the least affected by the H-S weights. And again, the most serious bias appears in Other Urban areas and West Forest, at least in 1985 and 1986. Whilst the correction does not change the rank ordering of the regions, it does change some differentials noticeably. For example, originally mean per capita expenditure in Abidjan was estimated to be 32.7 percent higher than other urban 17
varioas age groups" (Mills 1985). For example, "healthy days<sup>of life</sup> lost" (Ghana Health Assessment Project Team 1981) - a form of a "quality adjusted life years" based on incidence rates, case fatality rates and the extent/duration of disability produced by disease - has been sugsested as an index of effectiveness. This measure has its<sup>own</sup> bias - in favour of rhildren - and judgements of "quality" are inevitably subjective.

In order to measure effects on physical health, better information<sup>is</sup> required about the mortality, morbidity and fertility of the community than is currently routinely available in developing countries. Specific measures which embody preferences for health now rather<sup>than</sup> .tater, fer preventing mortality over disability and for improving the health of different groups (children vs. productive adults vs. elderly) also require information about the~ relative value that individuals and coounities place on these health effects. Without such information the measures are likely to be both inappropriately value-laden and inaccurate.

All the measures so far considered focus on health outcomes - but<sup>the</sup> .'xplicit objectives of many PHC programmes are wider, encompassing self-reliance, increasing the dignity of community members, changing attitudes to health authorities etc. It is not clear how these could be measured or, if that were possible, whether the result could be added to the changes in health outcome to produce aggregate variables which would be comparable across programmes with differing objectives. Ignoring these effects inevitably undermines evaluation of programmes for which they are objectives.

Even if the focus on health impact is appropriate for evaluation of the programme under examination, other problems associated with measuring health outcomes further undermine use of the results<sup>to</sup> guide investment decisions. Many of the problems involved in investigating the determinants of health arise even more strongly in attempts to measure health impact. In the case of a few interventions, clearly defined measures of programme coverage can be obtained and differentials in health associated with these investisated using statistical controls for confounding factors. More 17
are introduced to control for country differences. Similarly, time dummies are included in the regression analyses to control for time effects on tax corruption.

After dropping the insignificant control variables, which do not affect the robustness of the estimated coefficients, the final benchmark regression specification becomes: Tax corruptionit = β0 + β1. log (tax simplificationit) + β2. bureaucracy qualityit + β3. democratic accountabilityit + β4. government effectivenessit + β5. burden of governmentit + β6. rule of lawit + country fixed effects+ time fixed effects+ εit The tax corruption ratio and the two tax simplification variables are defined in the previous section. TAXTIME and TAXPAY are included one by one as well as together in the regression analyses. In the regression specification regional dummies are also included in some regression analyses.

**_Bureaucracy quality_** (BUREAUC) is taken from the International Country Risk Guide Database and it is defined as: “Institutional strength and quality of the bureaucracy is a shock absorber that tends to minimize revisions of policy when governments change.” It is an index number between 1 and 6, where 6 corresponds to the highest quality. Thus the expected sign of the estimated coefficient is negative.

**_Democratic Accountability_** (DEMOC) is also from the International Country Risk Guide Database. The database defines the series as: “A measure of, not just whether there are free and fair elections, but how responsive government is to its people. The less responsive it is, the more likely it will fall. Even democratically elected governments can delude themselves into thinking they know what is best for the people, regardless of clear indications to the contrary from the people.” The series consists of index numbers taking a value between 1 and 6. 6 represents the highest democratic accountability. Its sign is expected to be negative.

22
ALRI. In the baseline, this variable was not balanced, as households in the control group were performing significantly worse than those in the treatment group. In the follow-up survey, the situation reverted but there are no grounds to think that it was the effect of the program. This suggests that in this specific health dimension the two groups might not be comparable and that respiratory diseases are influenced by regional temporary variability.

For the district-level intervention (community and school component samples), the improvement in knowledge and behavior change do not seem to have resulted in better health for the children. There are no statistically significant differences between the prevalence of diarrhea or ALRI among children in treated households and those not treated. Nor do we observe any important improvement in the anthropometric measurement<sup>50</sup> or anemia indicators. The prevalence of parasites and the parasite counts obtained from the stool samples (of children under 5 years old) are not significantly different in the control and treatment groups. These results are consistent with the fact that the treatments had no effect on diarrhea, micronutrient malnutrition, or anemia, as these health problems are usually related to parasitic infestations.<sup>51</sup>

# **8     Conclusion**

Some simple technologies, such as vaccines and mosquito bed nets, are cheap and effective preventative measures that can save lives at minimal costs.<sup>52</sup> Handwashing with soap at critical times is another of these technologies: soap is generally easy to use, is relatively cheap, and has the potential of substantially improving the living standards of households. Despite these potential benefits, however, only 3 to 34 percent of people in developing countries routinely wash their hands with soap at critical junctures during the day.<sup>53</sup>

> 50The z-scores for the anthropometric measures were calculated discarding the lower and upper 1 percent of the distribution.

> 51 Unfortunately, a disaggregation of the prevalence of different parasites shows extremely large variability, suggesting that making inference at that level of disaggregation could be unreliable. Nevertheless, we report finding large drops in the treatment group in the prevalence of any _Ascaris Lumbricoides_ infections and any _Trichuris Trichuria_ infections, no differences in Hookworm infections, but a significant rise in the treatment group in any other parasites infection.

> 52 Banerjee and Duflo 2011

> 53 World Bank 2005

29
loss is 6%, then provisions should be set at 6% and the appropriate capital requirement would be 12%.

However, this may be felt by some not to comply with Basel II’s IRB as the rating scale is centralized. Moreover some developing country jurisdictions may find it difficult to obtain the necessary authority from Congress or other relevant authority to implement such a complex system or give the supervisor the necessary autonomy to set capital in that way. An alternative approach, as outlined in the previous section, is for the country to adopt the Basel II’s SA or even SSA for Pillar 1. For most loans this will give a capital requirement of 8%, except in the unlikely event that the loan has a rating. Then, the above technique can be used to set forward looking provisions. In the example cited if the Basel SA gave a requirement of 8%, then the forward looking provisions would have to equal 10%. Although it is often difficult to reconcile logically, while many developing country supervisors do not have the autonomy to set capital, they tend to have more autonomy to set provisions. This would allow a country to be compliant with Basel II by the letter and yet the total bank reserving policy would still be that equal to a Basel II (CP3), IRB type rule of Expected plus Unexpected Loss.

This process may sound simple. However, the data and other requirements are large and there is also a tremendous amount of work that is required to ensure that the data is of reasonable quality and that each step in the process is carefully checked. In the case of Argentina, to develop a public credit registry with a reasonable standard in terms of data quality and of a useful size to cover most of the banks’ loan portfolios took a process of some 5-6 to years. And the data that was then available to truly test the Basel calibration was only for about a two year period before the crisis began - when the database suffered once more from serious problems of non-reporting from some institutions, changing definitions of variables and generally a poorer quality consistency. A two year period is clearly an insufficient one to truly test a capital rule and the results were only thought of as indicative by the Argentine authorities.

However, independently of Basel II, the learning process on the part of bank supervisors and bank regulators to understand the loss probability distributions of different banks, which in turn depend on the distribution of the default probabilities and the concentration of each bank’s loan portfolios and how they changed over time, and how that started to feed into thinking regarding 43