rafmacalaba/datause-encoder-data huggingface dataset:
Shown in Figure 1, the questions test understanding of three basic financial concepts: interest rate compounding, inflation, and risk diversification. The first two questions also require basic numeracy skills, while the third question requires familiarity with the definition of stocks and mutual funds.

Although the usefulness of these questions, at least when employed on their own, has been the subject of some debate, they form a useful basis of comparison across countries.<sup>2</sup> A summary of results from nationally representative surveys is shown in Table 1. These include those used in a series of studies focusing on financial literacy and retirement, including Lusardi and Mitchell (2011b) in the U.S., Fornero and Monticone (2011) in Italy, Bucher-Koenen and Lusardi (2011) in Germany, Sekita (2011) in Japan, Crossan et al. (2011) in New Zealand, Almenberg and Säve-Söderbergh (2011) in Sweden, Alessie et al. (2011) in the Netherlands, and Klapper and Panos (2011) in Russia. Additional surveys using these questions were conducted or analyzed by Cole et al. (2011) in Indonesia and the state of Gujurat in India, Behrman et al. (2010) in Chile, and a series of recent World Bank studies in Romania, Azerbaijan, Bulgaria, Bosnia and the West Bank and Gaza.

Even so, careful attention should be paid to differences in the wording of the questions and answer options (as noted in the footnotes to Table 1). In other cases, the spirit and wording of the questions may be very similar, but the numbers used can affect the mathematical difficulty of the required calculations. Insofar as it is possible to generalize, it appears that higher-income countries perform better than lower-income countries on average.

> 2 Cole and Fernando (2008) note that the ability to answer these questions is highly predictive of financial decisions, while Lusardi and Mitchell (2007) find that the first question on compounding interest happens to be the most predictive of financial planning. However, Huston (2010) criticizes their relatively limited scope as ―deficient to capture the breadth of human capital specifically related to personal finance.‖ Carpena et. al. (2011) test and propose a broader measure of financial literacy, one that encompasses both basic financial awareness and financial attitudes, in addition to numeracy skills. They find that the financial education program they evaluate only improves financial awareness and attitudes, and not numeracy skills.

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using weighted least squares regression (WLS) with robust standard errors and weighting observations using the sampling weights provided by the Gallup World Poll.<sup>11</sup> The annual Gallup World Poll includes at least 1,000 randomly selected respondents (adult population of 15 years and older) per country and is representative at the national level. In the Gallup World Poll, individuals report on several aspects of their life, including how satisfied they are with their life as a whole and how satisfied they are with different domains of their life. The common sample we use in this paper comprises in total 25,244 respondents from 10 developing MENA countries, including Algeria, Egypt, Iraq, Jordan, Lebanon, Morocco, Palestine, Syria, Tunisia, and the Republic of Yemen.

**Figure 3: Distribution of Life Evaluation Scores in Developing MENA (percent by decile)**

<!-- Start of picture text --> 30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>0 1 2 3 4 5 6 7 8 9 10<br><!-- End of picture text --> _Source:_ Gallup World Poll 2013.

Life satisfaction was measured using a single question, known as the ‘Cantril Ladder’ or ‘SelfAnchoring Striving Scale’ (Cantril 1965). This question asks on which step of the ladder, with steps from 0 to 10, a person feels he or she stands at present. The higher the score on the ladder, the closer one’s life is seen to his or her ideal life. Figure 3 shows the distribution of happiness scores in the developing MENA region in the 2009-10 period. The unhappiness in the region is evidenced by the fact that 61 percent of the developing MENA population scores 5 or lower on the Cantril Ladder, while only 10 percent gives his or her life a score of 8 or higher. Within developing MENA, the degree of life satisfaction ranges by country from 4.66 in the Republic of Yemen to 6.23 in Jordan (table 1). It is worth noting that a person with high expectations is more likely to be

> 11 Following Ferrer-i-Carbonell and Frijters (2004), we treat the dependent variable as cardinal and not as ordinal.

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based on linear systematic sampling. The final step in the sampling plan was to make the choice of sample households within each PSU. The number of households to be surveyed in each of the selected PSU was fixed at 20; these were chosen from the total of 200 households in each PSU on the principle of systematic sampling. Weights based on population were developed for each of the three PSU categories i.e. rural, other urban and SMA.

Insert Table 1 The questionnaire for the household survey was formulated around five modules. Module 1 focused on household consumption expenditure, with a detailed list of both food and non-food items. Module 2 focused on household income, including changes relative to the previous year. The other variables covered in module 2 include access to safety net programs, participation in micro-credit programs, current levels of debt and credit-worthiness. Module 3 focused on shocks and coping. Specific variables were exposure to a listed range of shocks and coping strategies to deal with each category. Module 4 focused on assets and investment. Variables included approximate value for each type of listed asset, amount invested in last year in listed range of investment area, and source of investment funds. Module 5 explored the level of optimism of individual household on a range of issues, expectations from the government, and assessment of priority problems in 2009 and in 2008. We do not use this last module for the purposes of this paper.

Table 2 provides descriptive statistics summarizing key household characteristics. A little over one fourth of the sampled households were from urban areas, consistent with other information on the extent of urbanization. Households had multiple sources of income with a mean of 2.6. The major sources of household income included farm and non-farm manual labor (36.8% of respondents) and agriculture (22.9%), followed by small business activities (11.9%). Over forty percent of the household heads are illiterate or can only sign their name.

Almost half of the households are members of a microcredit organization which is consistent with other data on micro-finance membership (e.g. World Bank 2008). Consistent with this high level of micro-credit borrowing, access to credit is not perceived by households to be an important constraint: 72.4% of households had a loan at the time of the interview and 86.4% report that they could get a loan if needed during an emergency. A significantly lower 4
of this 85% lived in cities with more than 100,000 residents (UN 2011). Furthermore, the large, urban municipalities we study in Brazil are increasingly representative of cities in other developing countries due to continued increases in global urbanization rates (UN 2011). We acknowledge the distinct possibility the 253 most populous, urbanized municipalities are not representative of the remaining 5,317 Brazilian municipalities. However, these omitted municipalities are physically large and sparsely populated in most cases. They do not represent the average Brazilian’s experience and are therefore of less interest for our purposes.

_Administrative Capacity: Permanent administrative employees per capita, decentralized management score (IGD)_ The Bolsa Família program is administered at the municipal level and management quality varies considerably. We use operational data from the Ministry of Social Development (MDS), called the Index of Decentralized Management (IGD), to capture this variation. The MDS rates each municipality on how well it administers program elements, such as updates to the Unified Registry and tracking of beneficiaries’ compliance with conditionality requirements. The MDS offers greater funding to cities that perform better on the IGD. Quality of local management should reflect a combination of local political commitment as well as existing municipal state capacity. Quality of local management is likely to influence local outcomes independently from the broad Bolsa Família coverage, which is often high in municipalities where management is poor, such as settings with dense poverty. This variable is continuous from 0 to 1 and each municipality receives an annual score; better management results in scores closer to 1 and worse management closer to zero. The mean score is .76 and the standard deviation is .15.

_Other Sources of Revenue: Capital Revenue, Federal Budget Transfers, Gross Local Product per Capita_ We collect data on sources of revenue beyond local tax collection as well as federal transfers, the primary source of municipal budgets in Brazil. Revenue collected from non-tax sources and federal transfers could influence local tax collection in different directions. On the one hand, access to revenue and resources beyond the local tax base may limit municipal governments’ incentives to collect additional revenue from local sources, especially if collecting that revenue comes at a political cost. On the other hand, external resources may promote municipal capacity to enforce tax regulations and facilitate payments. Either way, we include data on these external resources to account for their influence on tax collection. other sources evaluate the extent to which public spending contributes to well-being. Finally, we include an indicator for local GDP per capita to account for local economic activity that we expect to be associated with tax revenue. Including local GDP per capita in models of tax collection also captures economic and resource based unobservables that could influence both the adoption of participatory institutions and tax outcomes. The 18
**Figure 1. Agriculture’s share of total GDP (1970 – 2008)**

<!-- Start of picture text --> Figure 1: Agriculture Value Added, % of GDP<br>40.0<br>35.0<br>30.0<br>25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>0.0<br>Agriculture Value Added, % of GDP<br>Source: Economic Reports, various years.<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008<br><!-- End of picture text --> 7. **Agricultural sector in Malawi is highly dominated by a few food and cash crops** . In spite of past attempts to diversify the food and export basket, there is still high concentration on maize and tobacco as key food and cash crops, respectively. For example, in 2007, the main products with positive volume growth were tobacco (53 percent), sugar (9 percent), tea (9 percent), cotton (3 percent) and edible nuts (4 percent). Although there is a slight shift away from tobacco, Malawi’s agricultural export base is highly concentrated in tobacco (see Figure 2). This high dependence on a narrow range of commodities makes Malawi agriculture highly vulnerable to effects of climate change (such as drought), in the case of maize and to the international lobbies against tobacco. The estate sector is relatively small and is confined to specific cash crops such as tobacco, tea and sugar. Its growth has been stagnant since the 1990s<sup>2</sup> .

**Figure 2** : Composition of Malawi’s agricultural export commodities by value (‘000 USD)

<!-- Start of picture text --> 1200<br>1000<br>800<br>Other<br>600 Tobacco<br>Sugar<br>400<br>Tea<br>200 Apparel<br>Cotton<br>0<br><!-- End of picture text --> 8. **Food crops such as maize, cereals and roots and tubers form the basis of agricultural production in Malawi** , and are grown by the majority of smallholder farmers on

- 2 The estate sector includes about 30,000 estates with about 1.1 million hectares under leasehold tenure. Comparatively, the smallholders occupy over 6 million hectares under the customary tenure.

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Figure 6: Supply Chain Complexity and Depth <!-- Start of picture text --> 8 4<br>6 3<br>4 2<br>2 1<br>0 0<br>customer supplier customer supplier<br>Number of partner firms per industry Number of partner firms per country<br>Number of sourcing / supplying industries Number of countries covered<br>2.5 2.5<br>2.2 2.2<br>1.9 1.9<br>1.6 1.6<br>1.3 1.3<br>1 1<br>2014 2015 2016 2017 2018 2019 2020 2021 2022 2014 2015 2016 2017 2018 2019 2020 2021 2022<br>Firms per customer industry Distinct customer industries Customer firms per country Distinct customer countries<br>Firms per supplier industry Distinct supplier industries Supplier firms per country Distinct supplier countries<br>2014 2015 2016 2017 2018 2019 2020 2021 2022 2014 2015 2016 2017 2018 2019 2020 2021 2022 2014 2015 2016 2017 2018 2019 2020 2021 2022 2014 2015 2016 2017 2018 2019 2020 2021 2022<br>Average change relative to Average change relative to<br>firm's 2014 baseline (2014=1) firm's 2014 baseline (2014=1)<br><!-- End of picture text -->

**Note:** The top left graph shows the average number of suppliers (customers) per NAICS 6-digit industry (light blue bars) for the sample of actively covered agribusiness companies for 2014 to 2022, as well as the average number of industries to which the firm maintains direct customer or supplier links in a given year (dark blue bars). The top right graph shows the average number of suppliers (customers) per country (light blue bars), as well as the average number of countries to which the firm maintains direct customer or supplier links in a given year (dark blue bars). Partner firms without industry affiliation and missing headquarter countries are excluded. The bottom left graph focuses on a balanced sample of actively covered agribusiness companies, thus excluding entrants and exiters after 2014. It shows the average over firm’s change in their number of partner firms (supplier/customer) per industry, and the number of industries that its partner firms are affiliated to, relative to 2014. The bottom right graph depicts the relative change in the number of countries in which the firm is active as a supplier or customer, as well as the number of customer or supplier firms per country.

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Despite an extensive quasi-promotional literature, detailed microeconometric analyses are, for these technical reasons, surprisingly non-committal about whether direct financial access has a major effect in reducing poverty (Honohan, 2004b).

What can be said at the macro level? Addressing this question evidently requires a crosscountry data series on access. There are many dimensions to financial access, but to simplify matters, given the shortage of data, it is preferable to concentrate on one summary indicator. The most widely accepted indicator is the percentage of adults that have any type of account (including all types of transactions, savings or loan accounts) at a bank or other formal financial intermediary (World Bank, 2005). How many people in developing countries have access to a bank or other formal financial intermediary account? This empirical question is the subject of vigorous current investigation. Piecing together elements from several other studies (especially Beck et al. 2005; Christen et al. 2004; Peachey and Roe, 2006), Appendix 2 details the methodology of a new composite indicator available for over 150 countries and which has the merit of drawing on available data on the number of accounts in commercial banks, savings banks and various forms of microfinance institutions, and the average size of these accounts, as well as on the results of household surveys for the much more limited number of countries for which those are available.

While recognizing the shortcomings of the imputation procedures used to construct the composite indicator, as well as the limitations (outlined in the appendix) of the underlying data sources, the resulting dataset is nevertheless interesting. Appropriate econometric techniques can limit the consequences of random error.

Returning then to the question of whether financial access reduces poverty, we may use the new access data to obtain a macro cross-country perspective on this issue. Earlier work (using the CGAP data also described in Appendix 2) failed to find any significant cross-country correlation between the density of microfinance accounts and poverty rates (Honohan, 2004b). But given the extensive differences between the old data and the new composite series, it is worth repeating the experiment.

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privatization) than those privatized later or not all. Likewise, late privatizers appeared to have been performing less well in 1992 than those not privatized at all.

**Table 2: Median 1992 Performance Indicators for Early, Late and Non-Privatized Provincial Banks**

|**Group**|No.**of**<br>**Banks**|Net**Worth**<br>**divided by Net**<br>**Liabilities****|**"Normal"`**<br>**Loans as Share**<br>**of**Total**Loans****|**Loans to Public**<br>**Sector as Share**<br>**of Total Loans****|
|---|---|---|---|---|
|_Privatized Prior to Dec.1994***_|4|0.082|0.474|0.205|
|_Prnatized After Dec. 1994_|13|0.191|0.625|0.174|
|_Not Privatized By Dec. 1996._|7|0.217|0.702|0.135|

* "Normal" Loans are Loans that are not overdue. (See Table 3)

7 ** All data is for June 1992 (prior to any privatizations).'

*** Omits Corrientes since this bank was privatized in 1991, and we want pre-privatization data. Including Corrientes makes all performance measures even worse for early privatizers.

# 4. ECONOMETRIC RESULTS.

Using Cox's proportional hazard model, the timing of privatization is estimated as a function of the quality of the provincial bank, the political affiliation of the key decision-makers, and the fiscal situation of the province.I" The hazard rate in this model - - which, loosely, is the rate at which banks are privatized after time t given that they were not privatized prior to time t -- is:'<sup>9</sup> _A(t)_ = _AO(t)_ **_e8x1+ +flkxk_** _Ao(t)_ is the "baseline" hazard function, a non-parametric function of time; , is the parameter vector; and x is the vector of covariates (e.g. the bank quality variables). In the results, a positive coefficient indicates that an increase in that variable increases the 17 The privatization process for Corrientes started in October 1991, however this province is omitted from all analysis due to missing data in 1990 and 1991.

18 The partial likelihood model was first presented in Cox, D. (1972) "Regression Models and Life Tables." Journal of the Royal Statistical Society B, 34, pp. 187-220. Other sources which discuss this class of models include Kalbfleisch, J and Ross L. Prentice (1980) _The Statistical Analysis of Failure Time Data._ New York: John Wiley and Sons; and Greene, W. (1997) _Econometric Analysis 3rd ed_ New York: Macmillan.

**19** The time of privatization is when the law enabling privatization was passed. This point, which is the start of a process which often lasted several years, is relevant because this is when the political decision to privatize is being made.

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inequities remain as the current formula depends almost totally on past year’s allocation. States’ accountability and incentives for performance and local contribution to education finance remain weak, if not absent.

This paper shows that three formulae appear promising for the purpose of establishing an improved formula-driven interstate and intrastate allocation: i) uniform per student formula: simplest and most transparent; rewards enrollment, but does not explicitly address differential needs of states. II) Productivity adjusted per student formula: maximizes enrollment impact by giving more to states with high enrollment elasticity to subsidy; no equity assurance but at this stage of Mexico’s development, it can be favorable to disadvantaged states since they now tend to have higher enrollment elasticities. And III) multi-component formula: divides total transfers into (a) core fund to be distributed on a per student basis; and (b) supplementary fund for extra cost of improving education of selected groups of disadvantaged children (poor, indigenous, rural). This formula requires more information and is more complex; but it has the flexibility for ensuring a balance between efficiency and equity.

Simulations show that adoption of any of the above formulae would tend to improve distribution in favor of low income and educationally lagging states.

This paper is organized as follow: Section II introduces the three competing formulas. Section III discusses the simulations. Section IV has the conclusions.

# **II.-On the Allocation of Federal Education Transfers Among States**

1 Uniform per student allocation This is the simplest, most transparent method. The criticism of it is that it takes into account neither the differential resource needs of states nor their relative productivity. A simulated application of this formula reveals, however, that its use can in fact improve the current situation in regard to equity (see below). Moreover, the formula is promising as far as long-run productivity improvement is concerned, because it rewards and, hence, encourages greater enrollment.

The formula applies a simple distribution of federal resources in direct proportion to the number of eligible students in a state, i.e., the population of children of school-going age. The application of the formula involves following a simple sequence.

1. Step 1 :Take the estimate of school-going age children for each state. For illustrative purposes, it is used the 12 to 14 Age-group estimates provided by CONAPO.

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# **<u>Table A2. Impact of Land Tenure on Productivity (Fixed Effect Estimation)</u>**

||Gross|revenue<br>**Subsample with 5**|Net re<br>**0/50 sharing rule**|venue|
|---|---|---|---|---|
|Barga land|-0.168***|-0.162***|-0.173***|-0.170***|
||(3.67)|(3.59)|(2.99)|(2.98)|
|Land size (acre)|-0.042|-0.039|0.038|0.045|
||(1.40)|(1.48)|(0.80)|(0.97)|
|Distance to homestead (km)|-0.109***|-0.099***|-0.098***|-0.094***|
||(3.80)|(3.62)|(2.80)|(2.87)|
|Observations (plots)|2521|2507|2441|2427|
|R-squared|0.65|0.66|0.60|0.61|
|||**Subsample with 7**|**5/25 sharing rule**||
|Barga land|-0.173***|-0.178***|-0.173***|-0.178***|
||(4.05)|(4.15)|(3.63)|(3.66)|
|Land size (acre)|-0.008|-0.009|0.065**|0.061*|
||(0.29)|(0.34)|(2.10)|(1.91)|
|Distance to homestead (km)|-0.049|-0.039|-0.075*|-0.072*|
||(1.51)|(1.41)|(1.71)|(1.87)|
|Soil/plot characteristics included|No|Yes|No|Yes|
|Observations|2284|2268|2216|2202|
|R-squared|0.63|0.65|0.58|0.59|

_Notes:_ Standard errors corrected for village level clustering. Robust t statistics in parentheses. *, **, and *** denote significance at 10%, 5%, and 1%, respectively.

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