rafmacalaba/datause-encoder-data huggingface dataset:
# **_Contexts, efforts and performances_**

The first thing is to assess the overall context. Of course, usual data on the business environment, the education level, the information infrastructure is important and generally available. More detailed information on innovation systems, as collected within the WBI KAM, is also interesting, although there is a lack of such data for a number of less developed countries.

_Box 8. Variables for innovation systems(WBI Knowledge Assessment Methodology – 120 countries included in the Data Base)._ Gross Foreign Direct Investment as % of GDP Royalty and license fees payments / mil. pop. Royalty and license fees receipts / mil. pop. Researchers in R&D Total expenditure for R&D as % of GNP University-company research collaboration Scientific and technical journal articles Admin. Burden for Start-Ups Patent applications granted by the USPTO High-Tech exports as % of manuf. Exports Royalty and license fees payments ($ mil) Royalty and license fees receipts ($ mil) Science & engineering enrolment ratio (% of tertiary level students) Researchers in R&D / mil. pop. Manuf. Trade as % of GDP Entrepreneurship among Managers Scientific and technical journal articles / mil. Pop Availability of Venture capital Patent applications granted by the USPTO / mil pop. Private sector spending on R&D With regard to innovation performances, it is important to identify appropriate proxy variables. For medium income countries, data relating to the evolution of the patenting activity (including deposits in national regimes), levels of business RD, the capacity of retention of educated workforces, and the rates of the creation and growth of new firms, are relevant.

For low income countries, some of those data are losing their relevance. In particular, the lower the development of a country, the less interesting and relevant is the data on research and development. It would be more interesting today to capture data on know-how and, notably, on local indigenous knowledge in the developing countries; however, this would require new types of data surveys. The creation and development of firms is also an issue difficult to track, particularly given that a large share of newly created enterprises operate in the informal sector.

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**Figure 8: Crises in emerging market and developing economies**

<!-- Start of picture text --> A. Frequency of crises B. Likelihood of financial crises in response to<br>reaction shock<br>Number Percent<br>35 Banking Currency Sovereign debt 25<br>30 20<br>15<br>25<br>10<br>20<br>5<br>15<br>0<br>10<br>5<br>0<br>Debt crisis Banking Currency<br> crisis crisis<br>0 +0.25% +0.50% 0 +0.25% +0.50% 0 +0.25% +0.50%<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2018<br><!-- End of picture text --> _Source:_ Laeven and Valencia (2020); World Bank. _Notes:_ Based on results from panel logit model with random effects. B. “0” indicates the probability of a crisis in a given year when there is no change in the underlying shock and all other variables included in the regression are at their sample means. “+0.25%” and “+0.50%” indicate the crisis probabilities in the case of 25 and 50 basis point increase in the 2-year US treasury yield driven by the underlying shock. Orange whiskers reflect 95 percent confidence intervals.

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Milanovic, Branko, 1999, "Do more unequal countries redistribute more? Does the median voter hypothesis hold?" World Bank policy Research Working Paper No. 2264, December.

Muller, E.N., 1988, "Democracy, economic development, and income inequality," _American Sociological Review,_ 53, 50-68.

Muller, E.N., 1989, "Democracy and inequality," _American Sociological Review, 54,_ 868-871.

Muller, E.N., 1995a, "The economic determinants of democracy," _American Sociological Review,_ 60, 966-982.

Muller, E.N., 1995b, "The economic determinants of democracy: Reply to Bollen and Jackman," _American Sociological Review,_ 60, 990-996.

Nielsen, F. and A. Anderson, 1995, "Income inequality, development, and dualism," _American Sociological Review,_ 60, 674-701.

Olson, M., 1982, _The Rise and Decline of Nations: Economic Growth, Stagflation, and Social Rigidities,_ New Haven, Conn. Yale University Press.

Pelzman, S., 1976, "Towards a more general theory of regulation," _Journal of Law and Economics,_ 19,211-240.

Pelzman, S., 1980, "The growth of government," _Journal of Law and Economics, 19,_ 211-240.

Perotti, Roberto, 1993, "Political equilibrium, income distribution, and growth", _Review of Economic Studies,_ vol. 60:755-76 October.

Perotti, Roberto, 1996, "'Growth, income distribution, and democracy: What the data say," _Journal of Economic Growth,_ vol. 1, June, pp. 149-187.

Persson, T. and G. Tabellini, 1994, "Is inequality harmiful for growth?" _American Economic Review,_ 84, 600-621.

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To estimate the impact of the treatments we estimate the following regression analogous to equation 1 above:

- (2) _Yij = δ + α_ 1 _Ordinaryj + α_ 2 _Ord_PrivRafj + α_ 3 _Ord_PubRafj_

_+ α_ 4 _Commitmentj + α_ 5 _Com_PrivRafj + α_ 6 _Com_PubRafj + β_ ’ _X_ ij _+ εij_ _Yij_ is the dependent variable of interest for farmer _i_ in club _j_ . The savings treatment indicators _Ordinaryi_ and _Commitmenti_ and the respective interactions with the raffle treatment indicators _Ord_PrivRafj_ , _Ord_PubRafj\_ , _Com_PrivRafj_ and _Com_PubRafj_ are defined as in equation 1. **_X_** ij is a vector that includes stratification cell dummies and control variables measured in the baseline survey, prior to treatment (the 17 baseline variables in Table 3). Following closely the interpretation of equation 1 above, the coefficients on the treatment indicators ( _α_ 1 and _α_ 4) reflect the impact on the dependent variable of the ordinary treatment and the commitment treatments, respectively, without additional raffle treatments vis-à-vis the control group, as well as the differential impacts of the savings treatments when combined with the private raffle ( _α_ 2 and _α_ 3) or public raffle treatments ( _α_ 5 and _α_ 6).

We focus on intent-to-treat (ITT) estimates because not every club member offered account opening assistance decided to open the account. We do not report average treatment on the treated (TOT) estimates. It is plausible that members without accounts are influenced by the training script itself or by members who do open accounts in the same club, either of which would violate SUTVA (Rubin, 1974).

# **4.** **<u>Empirical results: impact of treatments</u>**

To understand the impacts of access to formal savings, we first study the extent to which funds flowed into and out of the savings accounts in the pre-planting and planting periods. Then we examine impacts on agricultural inputs, farm output, household expenditures and other household outcomes.

# **A. Savings transactions (deposits and withdrawals)**

Table 4 presents regression results from estimation of equation 1. The first column presents results in which the dependent variable is an indicator variable for whether any transfers were made from the club account to the farmer’s individual account after the group loan had been repaid. Columns 2 to 8 present results for three types of savings behaviors: total deposits (separately for ordinary, commitment and other accounts, as well as the sum across all accounts), 12
# Figure 3.  IDA and Rule of Law, 1990s

<!-- Start of picture text --> coef = .27416332, se = .1578533, t = 1.74<br>1.15314 GIN CIV<br>TZA<br>UGA GHA<br>CHN<br>ZMB<br>KEN<br>LKA HND MWINIC<br>MOZ CMR<br>BOL<br>IND<br>SEN<br>BGD TGO GUY DMA YEMETHZWE<br>PAK<br>MDG<br>BFA<br>GNB SLE EGY MLI VNM<br>AGO GMB<br>ZAR HTI NERCOG<br>NGA<br>-2.14241 PHL<br>-2.07101 1.59334<br>e( rule9099 | X )<br>e( lnida9099 | X)<br><!-- End of picture text -->

# **_2. Aid and Growth_**

The basic hypothesis that we want to explore is that the effect of aid depends on the same institutions and policies that affect growth directly. For this analysis we need a good summary measure of institutions and policies. In our original paper we constructed our own index. But in light of the recent literature on institutions and growth, it now seems to us to make more sense to use one of the overall indices used in the literature.<sup>4</sup> The one that provides the best country coverage in the 1990s is the overall institutional quality measure from KKZ, which enables us to include 124 countries in our regressions. To link back to Section 1, the KKZ measure standardizes and averages other institutional

> 4 In retrospect creating our own policy index resulted in limiting the country coverage to countries for which we had all the component data.  The limited number of countries in turn was one reason why we organized the data into a panel with four-year periods.  One thing that we have learned from the dialogue with our critics is that most of the variation in the variable of interest, aid interacted with institutions/policies, is cross sectional.  Therefore, organizing the data into a panel is not particularly useful. Here, we focus on a single cross section for the decade of the 1990s – the era after the end of the Cold War.

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To address this problem we turn to the World Bank’s country-specific _Poverty Assessments_ (PA’s), which have now been done for most developing countries. These are core reports within the Bank’s program of analytic work at country level; each report describes the extent of poverty and its causes in that country. The PA’s are clearly the best available source of information on urban-rural differentials for setting international poverty lines. In almost all cases, the PA poverty lines were constructed using some version of the Cost-of-Basic-Needs method.<sup>8</sup> This aims to approximate a COL index that reflects the differences in prices faced between urban and rural areas, weighted by the consumption patterns of people living in a neighborhood of the country-specific poverty line. This is consistent with the use of an absolute poverty standard across countries.

However, while our method appears to be the best option that is currently feasible, internal consistency is questionable if the urban-rural COL differential varies by income, for then the differential from the PA may not be right for the international poverty lines. If the COL differential tends to rise with income then we will tend to overestimate urban poverty by the $1 a day line in middle-income countries relative to low-income countries, given that the PA poverty line will tend to be above the international line for most middle-income countries.

A data constraint that can also create internal inconsistencies is that in setting poverty lines, location-specific prices are typically only available for food goods. Also, while nutritional requirements for good health provide a defensible anchor in setting a reference food bundle, it is less obvious in practice what normative criteria should be applied in defining “non-food basic needs.” In addressing these concerns, the non-food component of the poverty line is typically set according to food demand behavior in each sub-group of the population for which a poverty line is to be determined. Different methods are found in practice, but they share the common feature that the non-food component of the poverty line is found by looking at the non-food spending of people in a neighborhood of the food poverty line, which is the cost for that sub-group of a

> 8 The precise method used varies from country-to-country, depending on the data available.  For an overview of the alternative methods found in practice see Ravallion (1998).

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capturing almost 60 percent of the trading in LAC7 countries (figure 13b). Again, within the region equity markets are most liquid in LAC7 countries, while other countries have generally much smaller and more illiquid markets—with fewer than 50 listed firms on average and turnover rates below 5 percent. These patterns suggest that if there were any deepening of equity markets, it did not bring about a greater breadth of access for firms. Equity markets seem to remain small, illiquid, and highly concentrated in a few firms across the region.

# **4. Which Firms Access Capital Markets?**

While the description above shows that few firms access bond and equity markets, it provides little information about which firms do so. It is well known that larger firms have greater access to capital markets, due at least in part to cost and liquidity considerations. In practice, these considerations render the minimum issue size rather large for smaller firms (see Beck et al. 2006). Furthermore, firm-level data on publicly listed companies (generally the largest firms in an economy) across emerging markets show that not all public firms actually raise capital in bond and equity markets regularly, suggesting that an even more restricted set of firms uses financing from capital markets. Typically, firms that raise capital through either bonds or equity are larger (in assets), are growing faster (as represented by sales growth), are more profitable (greater return on assets), and are more liquid (that is, they have a higher cash-tocurrent-asset ratios) than publicly listed firms that do not issue bonds or equities over a given period. There are, however, some differences across emerging regions: firms raising capital in some LAC7 countries (Brazil and Chile, for example) tend to be more leveraged than firms that do not use capital markets, while the opposite is true on average in a number of Asian countries, like China, Indonesia, and Malaysia. The fact that only a restricted set of firms uses capital markets can be partly explained by supply factors. For instance, the restricted investment practice of institutional investors is one possible explanation. As documented in a number of papers, institutional investors tend to invest in larger and more liquid firms, thereby limiting the supply of funds to smaller and less liquid firms (see, for example, Didier 2011; Didier, Rigobon, and Schmukler 2010; Edison and Warnock 2004; Dahlquist and Robertsson 2001; Kang and Stulz 1997).

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sample, information on both the dependent and explanatory variables for them is missing from the data set. This also implies that, in most of the cases, it is not possible to estimate a sample selection equation to correct for the biases, because it is not possible to identify if a household is missing children from the survey. The maximum likelihood approach developed by Bloom and Killingsworth (1985) can be applied in this case if multivariate normality is a reasonable assumption.

Although non-random sample selection due to coresidency has been a major methodological issue in the research on intergenerational mobility, evidence on the magnitude of coresidency bias has been scarce, with the exception of the analysis of occupational mobility in the UK by Francesconi and Nicoletti (2006). In an interesting paper, they use British Household Panel Survey to estimate the extent of coresidency bias in the estimates of intergenerational persistence in occupational prestige between father and son(s). They use the occupational prestige index due to Goldthorpe and Hope (1974), and estimate intergenerational elasticity as a measure of persistence. The evidence reported in their paper shows that the coresidency bias is substantial, ranging between 20-40 percent.<sup>15</sup> They, however, do not address the question whether intergenerational correlation (IGC) and intergenerational regression coefficient (IGRC) are affected differently by the truncation due to coresidency, which is the focus of our analysis.

We are not aware of any analysis of coresidency bias in the context of educational mobility, either in developed or developing countries. Our analysis can also claim broader applicability as we use data from two developing countries with substantial differences in the coresidency rates, and provide evidence on both father-son and mother-daughter links in educational persistence.

# **3. Data and Variables**

We use two rich data sets particularly suited for the analysis of the extent of coresident

> 15They provide an extensive analysis of alternative econometric approaches for selection correction. Their findings indicate that the inverse probability weighted estimator is the most reliable to tackle coresident sample selection bias among a number of approaches including Heckman selection correction.

7
income or consumption from surveys. Interestingly, we do not see as much narrowing of the gap among the richest countries for GDP, as with HFCE. This results from a widening of the gap between HFCE and GDP over time. Upper-middle-income countries have the largest gap, on average, but it is not much larger than that of the high-income countries in our sample. Additional estimates for gaps in levels with different weighting schemes are available in Appendix 2, Table A2.1.

The larger gaps between national accounts and surveys observed in middle-income countries may reflect the difficulty of measuring consumption in household surveys in the context of rapid growth and associated changes in consumption patterns. The experience of revisions to Vietnam’s consumption survey in 2010 is pertinent to this (see Badiani et al., 2013). From the early 1990s to 2010, a period of rapid economic growth in Vietnam, the national statistics office made few changes to the consumption survey instrument and methodology, resulting in a failure at capturing new durable consumption items, such as cellphone and computers. By 2010, the country’s much greater affluence necessitated the use of a revised consumption questionnaire and survey methodology. Consumption measures from the old survey methodology, which reflected the consumption patterns in Vietnam in 1992, was just 78 percent of what was measured as the average household budget in 2010 using the new survey instrument and the new methodology for measuring household consumption. Household consumption in the surveys leading up to 2010 were falling further and further below the consumption captured in the revised survey instrument. The example of Vietnam gives some context as to how the surveys fail to capture consumption as countries get richer. At the same time, one should recognize the importance of maintaining consistent series of consumption data over time. One approach to combine consistency and revise methodology would be to generate several consumption aggregates chaining new and old survey methods, as commonly done when revising national accounts.

# **_HHS – NAS gaps, growth rates_**

We now turn to assessing how growth rates in national accounts differ from those of household surveys. To assess the relationship between growth rates in the per capita means of surveys and national accounts measures, we follow methods commonly used in evaluating 17
the fault line had no impact on property values when data from 1995 were used for the analysis. Presumably, information on distance from fault lines influenced property values as households became more conscious of hazard risk only after the Kocaeli earthquake in 1999.

Similarly in the United States, flood zone disclosure is mandatory in some areas of North Carolina, so buyers are aware of flood risk before buying a property. Using a hedonic property price model, Bin et al. (2008) find that the property market reflects geographic differentials in flood risk, reducing property values on average by 7.3%. The market capitalizes risk as flood insurance premiums equal the discount in property values. Bin and Polasky (2004) also examine the effect on property values in North Carolina of Hurricane Floyd (September 1999), which affected two million people and damaged property worth 6 billion dollars. Most properties did not have flood insurance before the hurricane but the event increased the awareness of flood hazards and houses located on the floodplain faced a price reduction from 4% to 12%. However, price reductions were greater than the capitalized value of insurance premiums suggesting the existence of noninsurable costs associated with flooding.<sup>4</sup> There are two common concerns regarding conventional hedonic approaches discussed earlier. The first concern regards the imposition of a functional form, which can result in specification bias. The fact that theory does not suggest specific functional forms for outcome equations suggests that imposing one may require strong assumptions. The second issue is that conventional regression identification strategies often compare apples to oranges, as there is considerable heterogeneity in the underlying variables used in the analysis. To address these problems, we utilize a micro level database of 800,000 buildings in the city of Bogotá to examine if hazard risk was reflected in land values.

For our analysis we match properties (without using functional form restrictions) on a range of characteristics based on available data. These include quality of construction, use of property (residential, commercial or industrial), and distance from the city center. In principle, the only difference among matched properties is their level of hazard risk.

> 4 The reduction of the property values on average was $7,460 and the increase in premiums for flood insurance was $6,880.

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# **Table 3. Take-up & delegation**

||Contro|l Group|Treatment -|Control|
|---|---|---|---|---|
||Obs|Mean|Coeff.|p-value|
|**Panel A. Midline Survey: Take-up**|||||
|HH took-up|948|0.039|0.920|***<br>_0.000_|
|Woman worked in PW|952|0.021|0.712|***<br>_0.000_|
|Woman delegated PWs|948|0.011|0.213|***<br>_0.000_|
|Husband worked in PW|744|0.005|0.060|***<br>_0.000_|
|**Panel B. Midline Survey: Delegation**|||||
|A female HH member|476|0.042|||
|A male HH member|476|0.053|||
|A female non HH member|476|0.086|||
|A man non HH member|476|0.021|||

Notes: Coefficients are from an OLS regression of the left-hand side variable on a treatment dummy, controlling for strata dummies, survey-time dummies, and baseline traits (age of the head and the beneficiary, number of household members, number of children aged 0-5, number of children aged 6- 15, a dummy equal to 1 if woman is active, share of members who are inactive and a dummy equal to 1 if the household belongs to the top 25 percentile of food per capita distribution). Panel B: Sample: treatment households surveyed at midline. ***, **, * indicate significance at 1, 5 and 10 percent.

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<!-- Start of picture text --> Water access across deciles Sanitation access across deciles<br>LAC general average, worst and best performing country - 2012 LAC general average, worst and best performing country - 2012<br>c d e e ° e e e e e 3 8 +<br>° e e e e ° ° e e e e e<br>2 e e e al e e e ° ° e e<br>ee e<br>&8e°e e°ee bd<br>== e e<br>e ° e<br>Ne a e e<br>fo] o 4? ° ° e e ° e<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>LAC Deciles LAC Deciles<br>Electricity access across deciles Refrigerator ownership across deciles<br>LAC general average, worst and best performing country - 2012 LAC general average, worst and best performing country - 2012<br>c s e ° e e e © ° ° ° . e e e<br>e e e e<br>o2 o Oa e°<br>e<br>S E + e<br>e<br>s y &Y<br>e<br>e<br>°°<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>LAC Deciles LAC Deciles<br>Washmachine ownership across deciles Car ownership across deciles<br>LAC general average, worst and best performing country - 2012 LAC general average, worst and best performing country - 2012<br>e e<br>2e ° e°° e ° ° e ) e<br>e e<br>©2 e ..© > e e<br>e e<br>7 e e < i e ° e<br>at e e ° e ° ° e° eee<br>e e bd e e e ° e ° .<br>o e ° e o s e °° e° e e e<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>LAC Deciles LAC Deciles<br>Motorcycle ownership across deciles<br>LAC general average, worst and best performing country - 2012<br>2<br>&<br>e e e ° ° ° °<br>a ° ° e<br>n e e° ° ee°ee<br>e<br>o l e e ° e e e e e e e<br>1 2 3 4 5 6 7 8 9 10<br>LAC Deciles<br><!-- End of picture text -->