rafmacalaba/datause-encoder-data huggingface dataset:
Figure 1: Percentage of People Living on Less Than US$1.25, US$2.50, and US$4 per Day, Late 2000s Weighted Average for Bolivia, Brazil, Ecuador, Guatemala, Mexico, and Peru <!-- Start of picture text --> 50<br>43<br>40<br>30<br>24<br>21<br>20<br>9 9<br>10<br>3<br>0<br><$1.25 <$2.50 <$4.00 <$1.25 <$2.50 <$4.00<br>Indigenous Nonindigenous<br>Percent<br><!-- End of picture text --> _Source_ : SEDLAC data (CEDLAC and the World Bank) in World Bank 2015a.

Even in the more developed economies of Europe, minorities often are among the most disadvantaged. The European Union (EU) Special Eurobarometer 437—a public opinion survey conducted regularly on behalf of the European Commission, which looks into attitudes and perceptions of Europeans toward discrimination based on different grounds—reports that “discrimination on the grounds of ethnic origin continues to be regarded as the most widespread form of discrimination in the EU (64 percent), and it is more likely to be seen as widespread than was the case in 2012 (+8 percentage points). More than half of respondents in the EU say that discrimination is widespread in their country on the basis of sexual orientation (58 percent; +12) and gender identity (56 percent; +11), both showing substantial increases from the 2012 survey” (European Commission 2015). As in Africa, Asia, and Latin America, discrimination in Europe seems to be associated with poverty and economic marginalization. In a recent study on poverty and unemployment rates among the Roma population in 11 EU member states, the European Union Agency for Fundamental Rights concluded that, despite being the largest ethnic minority in the EU, 90 percent of the Roma people surveyed “live in households with an equivalised income below national poverty lines” and that “on average, around 40 percent of Roma live in households where somebody had to go to bed hungry at least once in the last month since they could not afford to buy food” (FRA 2014, 11).

# IV. Indicators and Methodology

This paper proposes six new indicators for the collection of comparable cross-country data on discriminatory legal frameworks and tests the data collection methodology in six pilot economies (Bulgaria, Mexico, Morocco, the Netherlands, Tanzania, and Vietnam).<sup>17</sup> 17 The pilot countries were selected to represent the various regions and legal traditions of the world. They range from low- to highincome economies, and all of them contain segments of the population that are considered minorities and that face discrimination.

Anti-Discrimination Law and Shared Prosperity 9
Based on data for 2019 or the latest year available lending [) Blend J) erp [wa I) Notciassifiea <!-- Start of picture text --> IDA<br>Blend<br>D<br>£&<br>no}<br>c<br>2<br>IBRD<br>Not classified<br>0 25 50 75 100<br>SPI Score<br>Source: World Bank. Statistical Performance Indicators.<br><!-- End of picture text -->
**Figure 13.  Average Number of Siblings of Brazilian Youth Aged 12-14, Brazil 1960-2000 Census Data**

<!-- Start of picture text --> 8.0<br>Siblings ever born<br>7.0<br>Siblings surving<br>6.0<br>5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>1960 1970 1980 1990 2000<br>Number of siblings<br><!-- End of picture text --> 39
world merchandise exports increased at a compound annual rate of 7.6 per cent while exports of developing countries increased at an annual rate of 9.5 per cent. Developing countries' merchandise imports increased even faster, at an annual rate of 10.4 per cent (see Table 1). The 42 countries in the study experienced a slightly greater growth in trade than developing countries as a whole: their merchandise exports grew in value at an average annual rate of 10.2 per cent and their imports at 12.2 per cent. This performance contrasts starkly with the perfomance of the 48 least-developed countries, many of which are not WTO members: In the 1990's exports of the least-developed countries as a group grew at _5.5_ per cent per annum in value, resulting in a further marginalization of these small economies, whose exports at present account for no more than 0.6 per cent of world exports (WTO, 1998).

**Table 1**

# **Trends in World Merchandise Trade**

(in US$ million and %)

||**Exp**|**orts**|**Imp**|**orts**|**Exports**<br>**Growth Rate**|**Imports**<br>**Growth Rate**|
|---|---|---|---|---|---|---|
||**1989**|**1997**|**1989**|**1997**|**1989-1997**|**1989-1997**|
|42 Developing Countries|399368|865921|396712|994633|10.2%|12.2%|
|Developing countries Members of WTO<br>Least developed Countries|466320<br>14044|962419<br>21507'|456939<br>21698|1091432<br>32751'|9.5%<br>5.5%'|11.5%<br>5.3%'|
|All Developing Countries|674924|1395585|658899|1451235|9.5%|10.4%|
|World Trade"|2237081|4023348|2341482|4185652|7.6%|7.5%|
|Explanation:<br>*<br>1996|||||||

** excluding significant double counting and EU intra-imports.

All developing countries- based on the WTO statistical "definition" with the following changes: South **Africa** is **included** in developing countries and Israel is excluded.

_Source: WTO, 1998._ At the beginning of the period, the 42 developing countries had economies which were slightly less dependent on international trade than developing countries as a whole: The ratio of their total trade (merchandise exports plus imports) to GDP in 1989 was 36.2 per cent compared to 38.3 percent for all 5
# **Box 1**

**The Relative Autonomy Index (RAI)**

The RAI, a measure of motivational autonomy developed in psychology studies, is based on selfdetermination theory, and measures an individual’s ability to act on what he or she values.

RAI is constructed from answers to the following: (1) “My actions in [activity area] are partly because I will get in trouble with someone if I act differently,” (2) “Regarding [activity area] I do what I do so others don’t think poorly of me,“ and (3) “Regarding [activity area] I do what I do because I personally think it is the right thing to do.” Each of the three questions mentioned above is aimed at capturing a different kind of motivation: **external** (coerced), **introjected** (trying to please), and **autonomous** (own values), respectively. The standard weighting structure for a RAI incorporating these three types of motivation is -2 for external motivation, -1 for introjected motivation and +3 for autonomous motivation.

A small-scale pilot of this tool conducted in Uganda showed relatively high RAI scores for both men and women, which was likely driven by poor respondent understanding of the questions (Sproule and Kovarik 2014).<sup>7</sup> A larger-scale validation was recently conducted by Vaz et al. (2016) in Chad with men and women in over 4,000 households.<sup>8</sup> While the WEAI uses only one statement for each of the three motivational states described in Box 1 above, Vaz et al. (2016) used multiple (e.g., “because I will get in trouble if I don’t” and “because that is what other people tell me to do” to measure external motivation). As a result, they were able to conduct several validation tests of the scale, including factor and cluster analysis, and evaluate how well different parts of the scale measured the same concept or idea.

Vaz et al.’s conceptual validation of the RAI rested on two main hypotheses: (1) there are three main “dimensions” in the autonomy data, with each reflecting one of the following motivations (from least to most autonomous): external (undertaking a goal because of external pressure or potential reward), introjected (undertaking an activity because you ‘ought’ to do it), and identified (autonomous); (2) since the subscales correspond to a _continuum_ of autonomy, the adjacent subscales (e.g., external and introjected motivations) are expected to correlate more strongly than subscales at opposite ends of the continuum (e.g., external and autonomous motivations). While Vaz et al. (2016) were able to corroborate (2), as well as the separation between controlled and autonomous motivations, they were _not_ able to distinguish between external and introjected motivations. Internal consistency was measured by the Cronbach alpha, which captures how closely related a set of items are as a group (specifically, the inter-correlation among scale

> 7 In Uganda, between 7-14 percent said they found the questions difficult, and 29-60 percent said they thought others would find the questions difficult.

> 8 Among selected households, both the head of household and any women aged 15 or older were interviewed.

8
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Policy Research Working Paper 6958

# **Abstract**

Programs that induce citizen participation to improve the quality of government at the local level are the subjects of large amounts of funding and intense debate. This paper combines a randomized control trial of a citizenship training and facilitation program in rural India, with an in-depth, four-year ethnography of the intervention to understand the underlying mechanisms of change. The quantitative data show no impact from the intervention. Household and village survey data from 100 treatment and 100 control villages show considerable improvement across a wide variety of governance and participation indicators over time, but the differences in the changes between treatment and control villages are not statistically significant. The detailed qualitative data from a 10 percent subsample allow us to unpack the reasons why the intervention “failed,” highlighting the role of variations in the quality of facilitation, lack of top-down support, and difficulties with confronting the stubborn challenge of persistent inequality. However, the qualitative investigation also uncovered subtle treatment effects that are difficult to observe in structured surveys. The paper thus demonstrates that a concerted effort to use “thick description” to uncover the process of change using careful and detailed qualitative work can add value to standard impact evaluations.

This paper is a product of the Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at vrao@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
14 Figure 4a. Per capita Income and Vehicle-to-Road Ratio <!-- Start of picture text --> 1000 ooo r _ ___<br>Manila Bangkok H-<br>. Bandung A\ A A San§ago London A Osaka<br>Jakarta 2 _4<br>UV 100 Gunzou Singapore<br>urabaa A 00 Malaysia reece<br>o . ............ ABeijing * Korea * * Japan<br>. .. or.. * A * + Switzerland<br>r4'; ~~~~~~Thailand * +<br>> 10 Indonesia + Ireland * Australia<br>China Brazil * National total road<br>A Urban road<br>- * *+ Philippines Gabon Nationat total road eq.<br>Malawi<br>India --- National paved road eq.<br>Rwanda + Cameroon \ Urban road eq.<br>1<br>100 1,000 10,000 100,000<br>Per capita GNP (in 198 7 US$)<br><!-- End of picture text --> Figure 4b. Per capita Income and Vehicle-to-Road Ratio: Alternative Specifications **for** National Equations <!-- Start of picture text --> 1000 - - . --<br>Seoul<br>Manila Bangkok .<br>Bandung,, A A San ago on A Osak<br>Jakartaz,<br>2 100 Suaaa Guangzoou Singapore<br>a~~~~~~~~~~~~Nl<br>0 E ~~~~AB4o..- . oea*-Jaa<br>2 E y t+ K-witzerland+ A<br>. --- ~ Thailand* *<br>¢ 1 0 Indonesia 4Ireland<br>China 4 Brazil * National total road<br>A Urban road<br>Malaw - Philippines + ~~~~~GabonG National total road eq.<br>/ India * .... ---- National paved road eq .<br>Rwanda + Cameroon Urban road eq.<br>- .,. . +. . 4 . _ __ __ _I __ , j___<br>100 1,000 10,000 100,000<br>Per capita GNP (in 1987 US$)<br><!-- End of picture text -->
in the United Sates there has been a great deal of discussion regarding the revised St. Louis adjusted monetary base'<sup>6</sup> . Consequently, more research on this important issue is warranted particularly for less developed countries since most empirical evidence has been developed for the United States and some European countries.

**Primary fiscal balance: should we increase taxation or reduce expenditures?** There are three channels through which a reduction in government expenditures affect debt sustainability".

- (1) Increment in the primary fiscal surplus (or reduction in the deficit)

(2) Increment in the rate of growth of the economy

- (3) Reduction in the real rate of interest

The primary surplus plays a critical role in the debt sustainability analysis. The only circumstance in which the primary balance would not be important, regarding this issue, would be if the economy's rate of growth were higher than the real rate of interest'<sup>8</sup> . This is not the usual case.

Most countries in search of fiscal solvency have stressed increased taxation but have done little to reduce expenditures. Nevertheless, higher tax revenues have frequently led to higher expenditures. In fact, the general trend in the Western World has been to increase expenditures (Lindauer, 1988) which according to McDermott and Wescott (1996) is responsible for the fiscal gap in industrial countries. The increase has indeed been striking: from 1960 through 1994 the ratio of govermnent expenditures to GDP in these countries rose to 50 percent from 28 percent while tax revenues increased from 28 percent to 44 percent.

This is an important issue considering that changes in tax revenues or in government expenditures have different effects on the real economy. Even equal increments in tax revenues and expenditure would have a non-neutral effect according to conventional (text-book) analysis of the balance budget multiplier'<sup>9</sup> . For example, in a full employment situation a balance budget multiplier would lead to increments in interest rates.

Many developing countries, in an effort to achieve macroeconomic stability and to sharpen their competitive edge, many developing countries have privatized most of their

- 16 Federal Reserve Bank of Saint Louis, Proceedings of a Symposium held in March 29, 1996. November/December 1996, volume 78, number 6.

- **17** The first is direct and straightforward; the other two indirect and depend on other assumptions.

- **18** The NPV of addition to income higher than the NPV of addition to debt.

- **19** The balance budget multiplier would be smaller (instead of the text-book case of one) if the public substitutes the incremental public expenditures in good and services with a reduction in its expenditure. If substitution is perfect the balance budget multiplier would be zero (Bailey 1971).

16
_Table 6 – The effect of shocks on social cohesion measures. The sample includes households in the HEIS sample. Shock is measured as the year-onyear difference in log(USD to IRR rate) in each month._

|||||**Regre**|**ssion**|||||**Mat**|**ching**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**A**|**id**|**Tra**|**nsfer**|**Free**|**items**|**don**|**ation**|**Aid**|**Transfer**|**Free items**|**donation**|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|**Afghan dummy**|1.485***|1.471***|1.380***|1.386***|1.449***|1.425***|-0.94***|-0.90***|1.194***|0.443*|1.255***|-0.102|
||(0.162)|(0.160)|(0.212)|(0.203)|(0.155)|(0.151)|(0.066)|(0.065)|(0.260)|(0.231)|(0.165)|(0.076)|
|**Shock**|0.155***|0.154***|0.074***|0.072***|0.061***|0.053***|0.050***|0.048***|0.048|0.668**|0.541***|0.282**|
||(0.017)|(0.016)|(0.028)|(0.027)|(0.017)|(0.017)|(0.014)|(0.014)|(0.295)|(0.291)|(0.165)|(0.111)|
|**Afghan * Shock**|0.612*|0.601*|-0.193|-0.058|-0.361|-0.286|0.047|0.058|0.962*|-0.851**|-0.772**|-0.131|
||(0.329)|(0.324)|(0.404)|(0.386)|(0.285)|(0.278)|(0.138)|(0.136)|(0.542)|(0.421)|(0.315)|(0.161)|
|**Male head**||-0.077***||-0.915***||-0.065***||0.252***|||||
|||(0.015)||(0.020)||(0.018)||(0.016)|||||
|**Literate head**||-0.089***||-0.073***||0.751***||0.370***|||||
|||(0.010)||(0.016)||(0.010)||(0.008)|||||
|**Married head**||-0.015||0.499***||0.095***||0.233***|||||
|||(0.015)||(0.022)||(0.017)||(0.016)|||||
|**Size**||0.087***||-0.099***||0.043***||0.067***|||||
|||(0.003)||(0.004)||(0.002)||(0.002)|||||
|**Age of head**||0.005***||0.011***||-0.002***||0.010***|||||
|||(0.000)||(0.000)||(0.000)||(0.000)|||||
|**Constant**|15.515***|14.988***|15.261***|15.305***|14.279***|13.717***|13.32***|11.85***|15.180***|15.967***|14.243***|12.101***|
||(0.018)|(0.029)|(0.030)|(0.044)|(0.017)|(0.028)|(0.014)|(0.023)|(0.133)|(0.166)|(0.076)|(0.051)|
|**Observations**|56601|56601|64727|64727|223640|223640|273388|273388|187|170|927|1994|
|**R-squared**|0.175|0.196|0.006|0.094|0.008|0.057|0.012|0.041|0.221|0.037|0.074|0.007|
|**Time FE**|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|No|No|No|No|
|**Occupation FE**|No|Yes|No|Yes|No|Yes|No|Yes|No|No|No|No|

28
<!-- Start of picture text --> Daughters' Primary Completion Estimates by Birth Year<br><!-- End of picture text --> <!-- Start of picture text --> Intercept Linear Coefficient<br>6 .02<br>A oye +E tet a<br>+ 7<br>| 3<br>><br>2 -.02 t ><br>><br>><br>¢ ¢ ¢ 4<br>a a a Oe Oe ee 04<br>><br>-.2<br>-.06<br>59 60 61 62 63 64 65 66 67 68 69 70 71 72 59 60 61 62 63 64 65 66 67 68 69 70 71 72<br><!-- End of picture text -->

Birth Years Omitted Birth Year 1957-58 © Comparison Cohort © Partially Exposed Cohort @ Fullly Exposed Cohort
Policy Research Working Paper 6670

# **Abstract**

Using a unique firm level data set—the Enterprise Surveys—this paper develops a new measure of creditconstrained status for firms using hard data instead of perceptions data. The paper classifies firms into four ordinal categories: Not Credit Constrained, Maybe Credit Constrained, Partially Credit Constrained, and Fully Credit Constrained to understand the characteristics of the firms that fall into each group. Comparable data from the Enterprise Surveys for 116 countries are used to look at the relationship between firm size and credit-constrained status. First, the analysis finds that small and medium enterprises are more likely to be credit constrained (either partially or fully) than large firms. Furthermore, small and medium enterprises finance their working capital and investments mainly through trade credit and informal sources of finance. These two results hold to a large extent in all the regions of the developing world. Second, although size is a significant predictor of the probability of being credit constrained, firm age is not. Third, high-performing firms, as measured by labor productivity, are less likely to be credit constrained. This result applies to all firms but is not as strong for small firms as it is for large and medium firms. Finally, in countries with high private credit-togross domestic product ratios, firms are less likely to be credit constrained. Given the importance of access to credit for firm growth and efficiency, this paper confirms that throughout the developing world access to credit is inversely related to firm size but positively related to productivity and financial deepening in the country.

This paper is a product of the Enterprise Analysis Unit, Financial and Private Sector Development. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. We would like to thank all the participants in the European Central Bank Workshop “Access to finance of SMEs: What can we learn from survey data?” for their comments. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The corresponding authors may be contacted at jrodrigurezmeza@worldbank.org and rramalho@ifc.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Table 3: Correlation between GNI per capita and selected indicators**<sup>**10**</sup>

||Secondary<br>school<br>enrollment (%<br>net)|Births attended<br>by skilled health<br>staff (% of total)|Malnutrition<br>prevalence, weight<br>for age (% of<br>children under 5)|Poverty<br>headcount ratio<br>at US$2 a day<br>(2005 PPP)<br>(% of population)|
|---|---|---|---|---|
|**Spearman’s rank correlation coefficient**|||||
|GNI per capita (current US$, Atlas)|0.78|0.80|−0.70|−0.88|
|GNI per capita (current US$, PPP)|0.79|0.78|−0.68|−0.87|
|**Number of observations (economies)**|||||
|GNI per capita (current US$, Atlas)|132|147|98|120|
|GNI per capita (current US$, PPP)|95|95|78|100|

_Source_ : _World Development Indicators_ database, accessed May 30, 2014 (indicator codes NY.GNP.PCAP.CD, NY.GNP.PP.CD, SE.SEC.NENR, SH.STA.BRTC.ZS, SH.STA.MALN.ZS, SI.POV.2DAY).

While it is recognized that GNI per capita does not measure welfare or success in the fight against poverty, GNI per capita is found to correlate closely, in terms of both values and rankings, with a number of accepted indicators of development outcomes such as secondary school enrollment, stunting (malnutrition), births attended by skilled staff, and the poverty headcount ratio (see Table 3). Heckelman, Knack and Rogers (2011) also find that it is highly correlated with broad-based measures of institutional development and creditworthiness.

There is an important practical consideration for the regular production of a country classification: data availability. Both GNI and population estimates are readily available on an annual basis for most countries, with GNI compiled by countries using the international standard System of National Accounts (SNA), and population data available from the United Nations Population Division and national sources. While some users have suggested a new classification more closely aligned with poverty incidence rates and with new measures of shared prosperity, current data availability for most countries of interest is far less than the annual frequency desired for classification purposes.

There are, however, issues of data quality related to the GNI estimates that may result in systematic bias. First, infrequent adjustment of the national accounting framework in countries

> 10 In order to increase sample size we take five‐year averages between 2006 and 2010 for the selected indicators.

16