rafmacalaba/datause-encoder-data huggingface dataset:
In addition, the Tobacco Clubs Survey reveals that some non-members may actually benefit from some of the club’s services if non-club smallholders send their tobacco to the auction floor through club members at a fee. Also, some club members may sell secretly a fraction of their fertilizer obtained through the club to non-members and sometimes some non-members find temporary work from the club members. This reinforces the notion that the binary indicator of club membership is only an imperfect proxy for the services of the club. Being thus measured with error, it may lead to downward bias of the OLS estimates.

# **Empowering Women**

A different concern arises if there is heterogeneity in the impacts of club membership across different types of farmers. In particular, if this heterogeneity is caused by unobservable factors that determine club membership as well, the IV strategy pursued here will not work well. For example, it could be that more able unmarried (young) women have a higher probability of becoming club members. If, further, the benefits of the club depend on unobserved farming ability, then the IV estimate will be biased. In these instances, we can only hope to recover an estimate of the LATE, the local average treatment effect (Angrist, Imbens and Rubin, 1996). Hence, _β_ in (1) is applicable only to the portion of the population affected by the instrument, i.e., unmarried women under 45 years old that would belong to a tobacco club if they were allowed to.<sup>18</sup> Even under heterogeneity, our estimates are valuable. Women are often poorer and more vulnerable and membership in tobacco clubs can empower them. In the end, while the interpretation of our IV results varies under heterogeneous impacts, the key finding of our work is that club membership has the potential to truly enhance member’s productivity.

> 18It is difficult, a priori, to indicate how the average impact of clubs would compare with our IV estimate. The fact that our IV is actually larger than the OLS would suggest that unmarried women benefit more than the average farmer. However, this argument would hold if measurement error were not present. Without further information on the magnitude of the attenuation bias, nothing conclusive can be said about the biases in the estimation.

31
care very little about relative deprivation, with absolute consumption being the dominant concern, although the same study finds that relative deprivation does appear to be a concern for relatively well-off urban residents.

Since its 1990 _World Development Report_ , the World Bank’s “global” poverty measures have mainly been based on an international poverty line of about $1 a day; more precisely, the line is $32.74 per month, at 1993 international purchasing power parity. This is a deliberately conservative definition, being anchored to the poverty lines typical of low-income countries (World bank, 1990; Ravallion et al., 1991). To gauge sensitivity, we also use a line set at twice this value, $65.48 per person per month. Following common practice we refer to these as the “$1 a day” and “$2 a day” lines ($1.08 and $2.15 would be more precise). The higher line is more representative of what “poverty” means in middle-income developing countries.

The international rural line is converted to local currencies using the Bank’s 1993 Purchasing Power Parity (PPP) exchange rates for consumption. PPP exchange rates adjust for the fact that non-traded goods tend to be cheaper in poorer countries. There is more than one way to measure PPP exchange rates. The Geary-Khamis (GK) method used by the Penn World Tables (PWT) uses quantity weights to compute the international price indices; for our purposes, this method give a too high a weight to consumption patterns in richer countries when measuring poverty globally. The EKS method — a multilateral extension of the usual bilateral Fisher index — attempts to correct for this bias. Since 2000 the World Bank’s global poverty and inequality measures have been based on the Bank’s PPPs, which use the EKS method.

In decomposing our estimates by urban and rural areas, a key issue is how to deal with the fact that the cost-of-living (COL) is generally higher in urban areas. Casual observations suggest that relatively weak internal market integration and the existence of geographically nontraded goods can yield substantial cost-of-living differences between urban and rural areas. Any assessment of the urbanization of poverty that ignored these COL differences would simply not be credible. Yet existing PPP exchange rates used to convert the international line into local currencies do not distinguish rural from urban areas.

6
5 Equalizing Incomes, or redistributing purchasing power, is another justification for Ul that has been cited. For Ui to accompilsh this it must be the case that the Incidence and duration of unempioyment are negatively correlated with household Incomes and that the taxes that finance benefits are not too regressive. There are no fundamental Inconsistencles In this goal, though here too one wondes whether U! Is the bust way to redistribute Income. Ignoring this, the question becomes whether a particular Ul program is redistributive.

Using Ul to encourage industrial restructuring is a recently popular socially-based goal. The argument here is politico-economic, essentially based on a compensation principle: Offer Ul to workers In Industries that had previously been protected or subsidized and that now face retrenchment. Ul benefits become a way of spreading the otherwise narrowiy-bome costs of the restructuring that will benefi **t** he entire economy. Presumably this sharing reduces oppositon to the structural change. This goal I intemally consistent, though whether Ul Is the best form of compensation to achieve it Is again an empirical question.

# C. The Strucwre of Ul Programs

Viewing the programs as evolutionary processes, this section illustrates the various paths that the poicy can take, and gives examples from Ul programs in a variety of OECD countries. (The sources for the information are OECD, 1991; Blaustehn and Craig, 1977, and Reubens, 1989.) There are three basic areas In which decisions must be made: Coverage - independent of the characteristics of the workers spell of unemployment, should that worker be covered by a Ul program?

Benefits - how much will be paid to the worker during each week of unemployment, and for how many weeks?

- FinancIng who should pay for the benefits, and how? Before even these choices are made, though, the national govemment must determine whether the program Is to be a national one, or one In which the basic determination about benefits and financing
extensive research has shown that fish consumption conveys more health benefits than consumption of other animal sources. Nevertheless, our results for Bangladesh clearly indicate a “status bias” favoring non-fish animal-source food consumption as mothers’ education and household economic status increase. The education result seems particularly unfortunate, with two possible interpretations: Either current school instruction pays little attention to the health benefits of fish consumption, or the effect of this instruction is overwhelmed by the higher status assigned to non-fish protein sources. In any case, our results for both education and economic status suggest a powerful and perverse “status goods” effect on fish consumption. We believe that these findings have potentially-important implications for health education policy in Bangladesh.

# **5.  Child Health Impacts**

Using DHS 2011 and 2014, we investigate the incremental health impacts of consuming fish and other animal-source foods (eggs, meat) using an estimation model that includes the following elements: (i) Secular declines in mortality and morbidity risk from improved public health services.

(ii) The socioeconomic, temporal and spatial determinants of consumption of animal-source foods incorporated in models (1) and (2): mother’s education; household economic status, seasonal variations in fish supply, and spatial variations in fish supply and consumption norms. After incorporating these variables (and gender) into the estimation exercise, we also include the animal-food sources themselves. Ceteris paribus, the direct estimates for these sources can be interpreted as indicators of individual preferences. Our approach assumes that these preferences are constant through time.

(iii) Maternal fish consumption in the immediate pre-partum and post-partum periods. Although we do not have direct evidence<sup>9</sup> , basic economics suggests that mothers’ fish consumption should also

> 9 We are unable to include direct survey information on maternal fish consumption because the relevant questions were excluded from DHS 2011 and 2014.

19
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# Financial Reform: Lessons and Strategies

_by_ _Gerard Caprio, Jr., Izak Atfyas and James A. Hanson_

## The World Bank

This paper is adapted from Chapter 7 of the draft manuscript, Financial Reform: Theory and ExRerience. The authors wish to thank John Chant, Yoon Je Cho, Hasan Ersel, Patrick Honohan, Ross Levine, Dimitri Margari'is, Sang-Woo Nam, Fabio Schiantarelli, Andrew Sheng, Salvador Valdes-Prieto, and Zainal Amam Yusof for comments.
measure the expected rate of inflation (p0) using the adaptative and perfect expectations hypotheses21/. only the results obtained with perfect expectations are preseented in this paper. Perfect foresight expectations seeiA more approprlate than adaptative because the cost of Ignoring the future effects of current pollcy actlone can be qulte hlgh for economies that have a hiLstory of rapid lnflatLon. Assuming that economic agents refer to domestic condltions rather than to the foreign real interest rate, we defined the expected rate of return on foreign assets only wlth the rate of inflatlon, Data limltation enforced us to approximte the fimascash flow with the difference between potential and effective levels of productlon as measured by the Wharton index (IINDW). We assume that the enterprises could respond more easily to changes in desired investment when demand conditions are buoyant. Finally, in order to improve the specification of the model, we introduced into equations (4) an indicator of uncertainty about inflation (VE) measured as the variation of the rate of inflation22/ and we incorporated public investment (l3) into the private investment function (13) ao suggested by various authors231.

In table 2 we report three-stage Least-Squares (3SLS) estimates for Argentina over the 1961-82 period. On the whole, the results are quite satisfactory. The explanatory power (I') and DV are both acceptable, suggestlng a good specificatlon of the model. The most interestlng aspect of these results concern the real rates of interest. Indeed, the estimated coefficients for r appear to be positive on - financial savings (dAM/dr 3.672) and negatlve on real savings (dOH/dr -1.234) and capital flight (ddJ/dr - -2.438). These results seem to be quite compatible with the Argentine experience, e.g. World Bank (1985). For instance, the deregulation of interest rates in 1977 resulted<sup>in a</sup> further increase in real interest for deposits and thus there was a dramatic increase of savings through the banking system. Our estimates suggest that the buildup of deposits was backed<sup>by repatriation of</sup> capital invested abroad and by decreasing demand for public bonds and capital goods. In short, an increase in the real rates of interest involved a portfolio shift from capital goods and foreign assets into monetary domestic assets. However, these estimates include only the direct effects of a rise in interest rates and, therefore, they cannot be interpreted as the total effect of such increase (see below for further explanations).

Also of relevance is whether the banking system has<sup>played its</sup> intermediary role or not. The estimated impact of a variation in flow - supply of credit (Ld) on private investment (dij/d(OLp/P) 0.258) 21/This reflects the fact that, although rational forecasts could differ from actual price movements in stochastic models, rational expectations are equivalent to perfect foresight in deterministic models.

22/see Blejer (1979) and Gupta (1984).

23/For instance, see Blejer and Khan (1984) and Sundararajan and Thakur (1980).
World Bank’s LSMSs. Carletto et al. (2007) describe how the income data across the surveys were harmonized. It appears that the microdata are available for both rural and urban areas, in which case this is a potentially valuable source of income data to study the top tail of the income distribution. Kerr et al. (2019) undertook harmonization of 26 years of household surveys on earnings income in South Africa and made the harmonized data publicly available. Changes over time in the surveys, the sampling, and the questionnaires meant that obtaining comparable earnings distributions over time even for one country required a substantial amount of work.

We have described the method of Korinek et al. (2006) to adjust survey weights to correct for unit non-ignorable non-response and we discussed several papers that implemented these corrections. Ravallion (2022) notes that implementing this method requires regional non-response rates but that these are seldom available. He argues for the inclusion of PSU-level response rates and notes that this would be simple to include for survey data producers. Hlasny (2020) documents that some of the surveys available through the Luxembourg Income Study (LIS), including Brazil and South Africa, include non-responding households.

## _Administrative data_

The UNU WIDER project “Regional Growth and Development in Southern Africa” resulted in access to South African tax microdata through the South African National Treasury from 2015. This model of partnership between international organizations and national governments, including tax offices, has since been replicated by WIDER in Tanzania, Zambia, Uganda, and Rwanda.<sup>8</sup> Such data can be used to improve the measurement of the top tail of the income distribution in these countries, especially since publicly available household survey data on incomes exists for all these countries. Having a larger number of countries with tax and survey data and undertaking comparisons would allow for more general conclusions about the extent of the differences between survey and tax data for Africa, rather than just two as in Chancel et al. (2023).

# Using New Sources of Data

The most obvious research that could be undertaken using new data sources is obtaining and analyzing administrative tax data for a larger number of LMICs than is currently available. Ideally this would be tax microdata but even tax tabulations would be very useful. The first step would be to compare estimates of top incomes from surveys and administrative data, and then examine the extent to which under-reporting, item non-response and unit non-response and other challenges create any differences between household survey and administrative data.

> 8 <u>https://www.wider.unu.edu/project/building-efficient-and-fair-tax-systems-lessons-based-administrativetax-data</u>

28
In countries with large informal sectors, income protection programs could be the dominant mode of social protection as they can reach a broader share of the population, particularly vulnerable people (Bottan et al. 2021). In contrast, job protection measures may be effective in countries with primarily formal economies. The generosity of unemployment insurance, direct cash transfers, and job protection policies also affect labor market tightness, unemployment, and job participation rates. However, the effects of increases in the generosity of unemployment insurance on the labor market are much stronger than the effects of job protection policies (Giupponi and Landais 2020 Based on these theoretical considerations and empirical evidence, we expect that by mid-2021, the countries that gave higher priority to job protection policies in their pandemic mitigation portfolio would have had lower unemployment and job inactivity levels than countries that expanded and increased the generosity of their unemployment insurance policies and/or implemented large cash or in-kind transfers (e.g., Schwellnus et al. 2020). We also expect that, on average, job protection measures would be more effective in promoting short-term economic recovery, as firms can restart their activities as soon as nonpharmaceutical interventions are lifted. The poverty rate should respond more to generous unemployment insurance and cash transfers, as these measures directly support household consumption. The job protection measures should also reduce poverty, albeit to a lesser degree.

We analyze the impact of different types of social policies on four economic outcomes. To assess the pace of economic recovery, we use the difference in GDP levels between the 2021 World Bank estimate<sup>2</sup> and the pre-pandemic forecast for the same year.<sup>3</sup> This indicator quantifies how close economic activity is to its pre-pandemic level. It can be interpreted as a measure of the strength of the economic recovery. We use the difference in the pre-pandemic (January 2020 forecast) and post-pandemic (2020, actual) poverty headcount rates in a country to evaluate the impact of social policies on poverty.<sup>4</sup> We use differences in the employment and inactivity rates between 2019 and 2020 to assess the impact of the various allocations of social policies on labor market outcomes.

> 2 Values for the 2021 GDP are based on the estimates by the World Bank Global Economic Prospects of January 2022 (World Bank 2022). For countries missing in that publication, the estimates of the IMF World Economic Outlook of October 2021 (IMF 2021) are used.

> 3 The pre-pandemic forecast corresponds to the values published by the World Bank Global Economic Prospects of January 2020 (World Bank 2020a). For countries missing in that publication, the values of IMF World Economic Outlook of October 2019 (IMF 2019) are used.

> 4 We use the poverty headcount rates corresponding to the $5.5 (2017 PPP) per day international poverty line as projected by Lakner et al. (2020).

12
An Assessment Of Reform Options For The Public Service Pension Fund In Uganda In a defined benefit system, one of the key factors determining the required contribution rate is the system dependency rate so the projected time path for the required contribution rate (Figure 14) is consistent with that of the dependency rate (compare with Figure 9).

**Figure 14: Required contribution rate needed to balance the scheme under two alternative mortality assumptions for civil servants**

<!-- Start of picture text --> 50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>variant 1 variant 2<br>2003 2008 2013 2018 2023 2028 2033 2038 2043 2048 2053 2058 2063 2068 2073<br><!-- End of picture text -->

**Source** : WB calculations based on PROST

- 43 -
differences in averages across types of firms, such as small versus large. Here we focus directly on the _variability_ of the firms’ response to the same questions in the same country about how long accomplishing various regulatory tasks take to show that there is indeed enormous variability, which we will then show is related to policy implementation uncertainty. Figure 2 shows the 90<sup>th</sup> , 50<sup>th</sup> and 10<sup>th</sup> percentiles for the most recent Enterprise Survey data for each Africa country for all countries with more than 30 firm respondents for each question. Figure 2a shows the distribution of the number of days to get an operating license and Figure 2b the number of days for imports to clear customs.

What is striking is that the variation across firms within a country is, in many instances, enormously larger than the differences across countries. Of the 24 countries the country with the lowest median time is Rwanda, at just 1 day and many others report low median times—the median in Ghana is only 4 days. The slowest are Zambia and South Africa at 21 days. Clearly the “typical” firm gets a license much faster in Rwanda or Ghana than Zambia or South Africa. However, 25 percent of firms in Zambia or South Africa report they get their license in 7 days or less while 25 percent of firms in Ghana report it took them 14 days or longer. Taking the 90<sup>th</sup> -10<sup>th</sup> or 75<sup>th</sup> -25<sup>th</sup> percentile spread as a measure of dispersion across firms shows that in all but one country (Rwanda) the 90<sup>th</sup> -10<sup>th</sup> spread is as large as or larger than the maximum range (21 days) across countries. The median 90<sup>th</sup> -10<sup>th</sup> spread in these countries is 46 days and the median 75<sup>th</sup> - 25<sup>th</sup> spread is 21 days. The implication is that firms that get licenses quickly get them almost instantaneously while firms that get them slowly get them very slowly. The same is true of spreads across firms of the time reported to get goods through customs. The 22
the same language is spoken in both countries, and a dummy variable valued 1 when the two countries share the same colonial past—these variables come from the CEPII distance database (Mayer and Zignago 2011).

Two additional variables, suggested by Melitz and Toubal (2014), help in controlling for cultural similarities between country pairs: first, an index of language similarity. People whose languages share common roots will likely share similar cultural backgrounds. To compute this index, one single language is assigned to every country, and using information on the classification of languages provided by the Ethnologue Project, a language similarity index based on the distance between branches in this classification is computed.<sup>14</sup> Then the number of branches that coincide between each pair of languages are added together and the result is divided by the sum of branches of each of the two languages (in order to take into account the fact that the granularity of branches may not be the same across languages). As a result, an index between 0 and 1 is obtained, where 0 means complete dissimilarity and 1 means that these two languages are almost the same in linguistic terms.<sup>15</sup> Second, the religious heritage of countries is a critical element of their culture and identity (Guiso et al 2009). Countries with similar religious roots, culturally closer, are likely to interact more (for an application in the trade literature, see, among others, Melitz and Toubal 2014).

14. For example, the linguistic classification of Portuguese, Swedish, and Danish, from the largest, most inclusive grouping to the smallest, is: Indo-European, Italic/Romance, Italo-Western, Western, GalloIberian, Ibero-Romance, West Iberian, Portuguese-Galician (Portuguese); Indo-European, Germanic, North East, Scandinavian, Danish-Swedish, Swedish (Swedish); Indo-European, Germanic, North East, Scandinavian, Danish-Swedish, Danish-Riksmal, Danish (Danish). www.ethnologue.com, accessed May 20, 2014.

15. I arbitrarily set to 0 this variable when the countries share exactly the same language, in order to avoid collinearity with the variable “same language.” 18
70

-

-

> Table A.1 4: Data, hk=" and **Wage** MWtisra for **Rgln** of **Varying AruleWtural** PxroActvKtyi/

||Punjab/<br>Haryana|India<br>Average|Kamataka/<br>Gujarat|Madhya<br>Pradesh/<br>Bihar|
|---|---|---|---|---|
|Data|||||
|Road density|8.3|6.1|4.3|3.2|
|Population density|3,338|2.775|1,645|1,853|
|Dally agricultural wage rate|8.1|5.5|5.4|3.9|
|Wage Multlpilers|||||
|Rural areas|.412|.302|.287|.227|
|Rural towns|.359|.237|.243|.169|
|Rural areas pk. rural towns|.771|.538|.530|.396|
|Income Multipliers|||||
|Rural areas|.337|.248|.235|.186|
|Rural towns|.589|.388|.399|.277|
|Rural areas plus rural towns|.926|.635|.634|.463|

> a.Estimated from 2SLS district regressions. See Table A.1 3 for regression parameters.