rafmacalaba/datause-encoder-data huggingface dataset:
some aggregation method to convert these assets into a (single-valued) index. Filmer and Pritchett (2001), who popularized the use of such indexes in the economic literature, employ the following variables from the India National Sample Survey (NSS): household ownership of consumer durables (including clock/watch, bicycle, radio, television, sewing machine, refrigerator, and car), characteristics of the household’s dwelling (including indicators about toilet facilities, the source of drinking water, the rooms in the dwelling, the building materials used, and the main source of lighting and cooking), and household land ownership. They then utilize the principal component analysis (PCA) technique to create a wealth index, which offers different combinations of these assets that aim to capture as much variation in the data as possible.<sup>6</sup> Variations exist on both of these steps. Since household questionnaires vary in different contexts, the list of assets to be employed depends on data availability. For example, Filmer and Scott (2012) examine all available asset variables in the DHS from four different countries and find these variables to vary across countries. In particular, their data sets contain between 12 (Uganda) and 29 (Nicaragua) indicators of asset ownership, and between 4 (Ghana) and 12 (Albania) and even as many as 37 (Zambia) indicators of housing characteristics. Besides the PCA technique, the aggregation method can range from simply counting (or adding up) all the indicators of asset ownership to using other techniques such as factor analysis (Sahn and Stifel, 2000).<sup>7</sup> _Examples and Remaining Challenges_

> 6 See, for example, Jolliffe (2002) for a comprehensive treatment of PCA methods.

> 7 Moser and Felton (2009) propose a variant of the PCA technique, where analysis is done on the components of each of several types of capital including physical capital, productive capital, human capital, and social capital. Another approach is to produce households’ ranks in the population with the number of consumption items they own, if we make the additional assumption that households place an order of importance on their consumption items when having to reduce their consumption expenditure (Deutsch, Silber, and Wang, 2017). Notably, a common mistake when constructing wealth indexes is to convert ordinal asset variables to dummy variables and then aggregate; see Kolenikov and Angeles (2009) for a careful analysis of this issue. An alternative approach is to collect data on a reduced set of consumption items that may offer strong correlation with the total consumption aggregate (Morris _et al._ , 2000).

9
Test score data are available for the most recent cohort (those born between 1973 and 1980). For this cohort, we examine how test scores relate to school choice, and whether including test scores alters the estimated effect of the other independent variables, especially parental education. Table 3 provides the estimation results for males, while Table 4 shows the results for females.

For both sexes, students with test scores in the top tercile are far more likely to attend public schools. Private vocational schools attract the lowest scoring students. Including test scores does not alter the finding above that highly educated parents choose general schools over vocational schools.

In sum, the probability that students enroll in public vocational schools declined substantially for the middle and youngest cohort. However, this does not seem to be caused by a decline in the quality of public schools, as high scoring students are still more likely to attend public schools. Most likely this is caused by an increase in the number of private schools, which have responded to the continued high demand for highly educated workers (World Bank, forthcoming).

Choice of school type is driven by two main factors: scholastic ability and parental education. With regards to the former, higher test scores are associated with the largest increase in the probability of attending public schools, followed by private general school. With regards to parental education, private general schools attract the sons of better-educated fathers, followed by public general and public vocational schools. Private vocational schools therefore act as a last resort; students who enroll in these schools are disproportionately likely to have scored in the bottom tercile and to have poorly-educated parents.

12
the developed economies, this share is slightly lower, and is closely followed by mechanical equipment and metals productions."3 In Asia, on the other hand, the chemicals industry hosts more than 10 percent of the total FDI flowing into the region, although its share still lags behind that of the electrical equipment industry.

# **Table 10: Global FDI Stock Distribution by Sector, 1993; China, 1984, 1988, 1993 Value (Number of Observations)**

||Overal|l|Devel<br>Econo|oped<br>mies|Asia||Latin<br>Ameri|ca|1984|Chin<br>1988|a<br>1993|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Primary|22.09|(44)|8.32|(14)|33.37|(14)|24.26|(16)|40.88|12.30|<br>3.1|
|Agriculture|5.03|(44)|1.05|(14)|11.81|(14)|2.58|(16)|1.69|2.69|1.50|
|Mining|8.64|(44)|5.58|(14)|7.90|(14)|11.98|(16)||1.37||
|Petroleum|8.39|(44)|1.77|(14)|13.53|(14)|9.69|(16)|31.18|<br>6.41|<br>1.60|
|Secondary|42.42|(44)|43.04|(20)|46.06|(11)|38.40|(13)|26.97|47.60|51.20|
|Food|6.30|(41)|5.29|(17)|5.40|(11)|8.38|(13)|2.90|||
|Textiles|2.93|(36)|1.43|(13)|3.07|(11)|4.45|(12)|1.69|||
|Paper|2.02|(33)|2.76|(14)|0.57|(9)|2.30|(10)|5.79|||
|Chemicals|9.16|(43)|8.46|(19)|10.57|(11)|9.01|(13)|1.31|||
|Coal products|4.06|(18)|9.10|(5)|1.90|(7)|2.39|(6)||||
|Rubber|1.43|(21)|0.98|(7)|1.65|(7)|1.67|(7)||||
|Minerals|2.73|(31)|3.36|(11)|3.21|(9)|1.70|(11)|0.11|||
|Metals|5.16|(39)|7.09|(18)|4.66|(10)|2.47|(11)|0.30|||
|Mech equipment|6.18|(36)|7.94|(15)|4.09|(10)|5.69|(11)|7.62|||
|Elec equipment|6.81|(23)|4.87|(9)|12.58|(7)|3.54|(7)||||
|Motor vehicle|3.61|(20)|2.14|(9)|5.95|(6)|3.88|(6)||||
|Other transport|0.87|(18)|1.66|_(5)_|2.32|(11)|0.13|(7)||||
|Tertiary|38.20|(53)|47.51|(20)|28.58|(15)|35.89|(18)|32.14|40.10|47.30|
|Construction|3.38|(40)|0.71|(14)|2.49|(13)|7.15|(13)||1.56|3.00|
|Distr trade|11.13|(50)|17.03|(19)|6.10|(14)|8.67|(17)|2.79|3.31|3.60|
|Transport|3.66|(41)|3.21|(17)|1.51|(11)|6.06|(13)|3.04|1.68'|<br>1.60|
|Communication|0.71|(11)|0.39|(5)|||0.99|(6)||||
|Finance|14.52|(46)|17.68|(20)|9.59|(11)|13.92|(15)||0.29|0.07|
|Real estate|4.79|(18)|10.09|<br>(7)|1.99|(3)|1.21|(8)|15.36|28.34|32.60|

_Sources: UN World Investment Directory; China Economic News, No 28, 25th July, 1994. Data on Chinese transportation and communication are combined._ The variations in FDI shares in both the primary and the tertiary sectors are more noticeable. While the world average for FDI stocks in the primary sector (which comprises agriculture, mining and quarrying, and petroleum) is 22 percent, only a little more than 8 percent of the FDI in developed countries goes to this sector; yet more than a third of Asian FDI accumulation is here. On the average, the mining industries attract the most FDI in the primary sector, while agricultural production the least. This trend is especially reflected in the developed economies and the Latin American and the Caribbean countries, where the share of FDI in mining and quarrying is 5.3 and 4.6 times, respectively, that of agriculture, fishery, and forestry.

13 The 9.1 percent FDI share estimated for coal and petroleum products is based on the figures of 5 countries, too small a sample size to be the basis of general conclusions.

14
Hall, D., E. Lobina, and V. Corral. 2010. _Replacing Failed Private Water Contracts_ . PSIRU (Public Services International Research Unit), University of Greenwich, London.

Kikeri, S. 1999. “Privatization and Labor: What Happens to Workers when Governments Divest?” World Bank Technical Paper No. 396, World Bank, Washington, DC.

Lee, L., M. R. Rosenzweig, and M. M. Pitt 1997. “The Effects of Improved Nutrition, Sanitation, and Water Quality on Child Health in High-Mortality Populations.” _Journal of Econometrics_ 77: 209-235.

McKenzie, D. 2012. “Beyond Baseline and Follow-up: The Case for More T in Experiments", _Journal of Development Economics_ 99 (2), 210-221.

Nauges, C. and D. Whittington. 2009. “Estimation of Water Demand in Developing Countries: An Overview.” _World Bank Research Observer_ 25 (2), 263-294.

Papke, L., and J.M. Wooldridge. 2008. “Panel Data Methods for Fractional Response Variables with an Application to Test Pass Rates.” _Journal of Econometrics_ 145 (1-2), 121-133.

Segerfeldt, F. 2005. “Water for Sale: How Business and The Market Can Resolve the World's Water Crisis.” Cato Institute, June.

Shi, A. 2000. “How Access to Urban Potable Water and Sewerage Connection Affects Child Mortality” The World Bank, Policy Research Working Paper 2274, World Bank, Washington, DC.

23
changes and if we gain a better understanding of the factors that affect preferences. In this regard, economics, and the social sciences more generally, are still in their infancy. We still know relatively little about how economic, social, and biological factors, and the interactions between them, shape preferences.

# **2. Psychological Mechanisms Underlying the Susceptibility of Preferences to Social Influences**

# _2.1. Context-dependent visual perception_

People often assume that what they see with their eyes is a correct representation of reality. We see a rabbit and believe it is a rabbit, or we see a duck and believe it is a duck. We see two lines of unequal length and believe they are of unequal length. However, vision research has produced many examples indicating that our subjective perception of objects is not uniquely given. Instead, perception is shaped by _context_ and _memory_ . To illustrate the effect of context, consider Figures 1 – 3.

In the first figure, we perceive square A as darker than square B. However, because A is set against a background of lighter squares, whereas B is set against a background of darker squares, our perception is distorted. It is easy to check by putting the same frame on each square that each is the same colour. In the second figure, the length of the two lines is identical, but the context produces the perception that the left line is shorter than the right one. The third figure shows a duck and a rabbit, but at a given moment in time one can see only a duck or a rabbit. Moreover, the first time one looks at this picture, it is quite difficult to generate a perception shift, _i.e_ . to suppress the first impression and see the ―hidden‖ animal.

# _2.2. Context-dependent preferences – anchoring, framing, and multiple social identities_

A considerable literature in psychology and economics suggests that, like visual perception, preferences are also _context-dependent_ ; they may be influenced by the elicitation method (Lichtenstein and Slovic 1971 and Hsee 1996), the presentation (framing) of the problem (Kahneman and Tversky 1979), the ―anchor‖ (Ariely, Loewenstein, and Prelec 2003), and the 7
11

# **_II. Policy Rigidity and the 2002 Crisis_**

In sharp contrast with the 1998-1999 adjustment, Brazil’s fiscal policy did not react to the shocks in early 2002. This policy rigidity compounded uncertainty arising from other sources and led to asset price changes that complicated the situation even more. The government’s commitment to maintain a constant primary surplus seemed to falter as the primary balance declined during the first semester (Figure 6) amidst a heated political debate on the stance of future fiscal policy.

**Figure 6 Primary Fiscal Balance of the Public Sector 2000-2003 (as a percentage of GDP)**

<!-- Start of picture text --> 4.8<br>4.4<br>4.0<br>3.6<br>3.2<br>2000 2001 2002 2003<br><!-- End of picture text --> Source: Bacen, Boletin Estadistico, several issues The rigidity of fiscal policy may have been at the root of the 2002 crisis. Inflexibility was the result of structural factors and transitory circumstances. The structural inflexibility of the budget exists in expenditures as discussed in the previous section. But fiscal policy rigidity was also due to the short-term effect of the October presidential elections. The government’s coalition had weakened because of internal disputes in anticipation of the presidential election. Additionally, corruption allegations in congress led to the impeachment of its president, a strong supporter of the government’s economic policy. In this context, crucial reforms with fiscal impact, such as the public servants social security and tax reform, were left pending. Other reforms, such as the extension of the financial transactions tax, the CPMF, stalled. With the political campaign heating up in the first quarter, it was practically impossible to get support for any adjustment. Additionally, any change would have been interpreted as transitory given that a new government would take office in the near future.

Uncertainty regarding the future government’s commitment to fiscal adjustment (irrespective of who won the election) generated concerns about the future value or liquidity of public debt. Given the concentration of public debt holdings in mutual funds (to be discussed in the next section), a large resource outflow affected them in the period April-October. In its peak, the run represented more than 6 percent of the intermediaries’ net worth (Figure 7).
13 A significant portion of the growth in developing countries’ exports of manufactures as well as of commodities is the result of expanding trade among the developing countries themselves. China’s growth has been instrumental in this but trade among developing countries has grown rapidly in other regions as well. Part of this growth may be the result of preferential regional arrangements, but part can be explained by the fact that during most of the 1990s and 2000s growth in incomes in most developing country groupings outpaced that of developed countries. Table 7 shows that South-South trade grew faster than any other trade over the last 30 years. Indeed the share of this trade in the total world quadrupled over this period.

**Table 7: World Market Shares and Trade Growth in Developed and Developing Markets (North-North v.s. South-South Trade)**

|||World Tr<br>|ade Mar<br>|ket Shar<br>|e(in %) <br>|An<br>|nual Trade<br>|Growth(in<br>|%)<br>|
|---|---|---|---|---|---|---|---|---|---|
|Reporter|Partner|1980|1990|2000|2010|1980-90|1990-00|2000-10|1980-10|
|Developed econ (48)|Developed econ (48)|55.8|61.4|53.7|42.9|8.2|5.4|6.4|6.7|
|Developed econ (48)|Developing economies|27.2|18.5|18.5|19.6|3.1|6.9|9.4|6.4|
|Developed econ (48)|World|82.9|79.9|72.2|62.5|6.8|5.8|7.2|6.6|
|Developing econ (ROW)|Developed econ (48)|11.4|11.8|15.5|15.8|7.6|9.7|9.0|8.8|
|Developing econ (ROW)|Developing economies|5.6|8.2|12.3|21.7|11.3|11.2|15.2|12.5|
|Developing econ (ROW)|World|17.1|20.1|27.8|37.5|8.9|10.4|12.1|10.5|
|World|Developed econ (48)|67.2|73.3|69.2|58.7|8.1|6.2|7.0|7.1|
|World|Developing economies|32.8|26.7|30.8|41.3|5.0|8.4|12.0|8.4|
|World|World|100.0|100.0|100.0|100.0|7.2|6.8|8.8|7.6|

Source: Based on UN COMTRADE Statistics.
Our analysis assumes both data sets to be complete and representative of the true distributions of interest and is subsequently conducted using plots for which GPS based-land area measurements are available.<sup>4</sup> Table 2 shows the distribution of plots according to their distance from the dwelling for both datasets. Table 3 presents the summary statistics based on the IHS3 and the ESS2, including the plot-level means for the entire sample; for the sample within 1 kilometer of the dwelling; and for the sample that lie outside of the 1-kilometer radius of the dwelling. Table 4 accomplishes the same objective but for the samples split by the alternative distance threshold of 500 meters. We provide the differences between the sample means and note when a given mean difference is statistically significant.

Several noteworthy findings emerge from Tables 2, 3 and 4. First, the distribution of plots per distance threshold is quite similar across the two countries. Between 54 and 60 percent of the plots are within 500 meters and between 72 to 77 percent are within 1 kilometer. Second, the plots within the distance threshold tend to be of significantly smaller areas than the plots beyond that threshold. Third, several important plot and household level characteristics which are expected to be associated with productivity-related outcomes, display statistically significant differences by distance threshold status. These observations highlight the importance of systematically addressing missingness in GPS-based plot areas, if such GPS data are to be used in a robust fashion.

> 4We cannot work with approximately 50 percent of the ESS2 plots in the public use data since the CSA ancillary dataset with the conversion factors for the non-standard land area measurement units (to express farmer-reported plot areas in hectares) does not include conversion factors for all non-standard measurement units. This limitation further underscores the importance using GPS-based land area measurements. Going forward, the ESS2 can be used to update the referenced ancillary dataset of conversion factors. Prior to the validation exercise, we elected not to update the ancillary dataset using the ESS2 since the imputation model performance would have improved dramatically in a mechanical manner. Further, most of the predictors that we use in the validation exercise based on the ESS2 data do present statistically significant differences across the plots depending on whether land area conversion factor is available. These predictors are included in the imputation model, and to the extent that they are correlated with observed and unobserved attributes that predict the likelihood of a farmer-reported plot area with a missing conversion factors, the ESS2 sample that we end up focusing on should be deemed satisfactory for validation purposes.

8
virginity and age at first sex should be highly correlated. The fact that we don’t find this correlation might be due to different biases in reporting virginity and age at first sex. Gersovitz (2005) has found that, after their marriage, women tend to “correct” their age at first sex to make it coincide with their age at marriage.

# **4.7 Use of voluntary counselling and testing**

The survey asks whether the individuals have already been tested for HIV and obtained the results prior to the survey. We therefore analyze the probability of using voluntary counselling and testing services before the DHS survey.

# [Insert table 10-10a]

Females living in urban areas are more likely to have received results of an HIV test. Table 10 shows that the probability of using voluntary counselling and testing facilities increases for married individuals. The same is true for formerly married women, while formerly married men are less likely to try to know their HIV status. One hypothesis to explain this difference by gender, that would require further research to be confirmed, is that women who get tested and are HIV positive would be rejected by their husband. Widowers are more likely to seek testing, suggesting that males are more likely to decide to learn their HIV status after their wife's disease or death. As expected, education (especially secondary education) is always positively associated with obtaining information about one's HIV status. The same is true for wealth, but less strongly.

In table 10a we report statistics for the use of voluntary counselling and testing (VCT) at the level of married or cohabiting couples. The results show that in almost 73 percent of couples, neither of the partners had a voluntary HIV test before the survey. In only 5 percent of the couples both partners have used VCT. In 12.9 percent of couples, only the male has had an HIV test before the survey and in 9.3 percent of couples, only the woman visited a VCT centre.

17
# **_Annex 2 Instrumental variables and exogeneity tests_**

All models reported in this paper include one of the variables meant to measure the quality of governance and of public goods in general - _Government Effectiveness_ , _Rule of Law_ or _Satisfaction with the Government_ (see Table A1 for details). Plausibly, same unobserved factors might affect both the (actual and perceived) quality of governance and the level of informal employment in a country. An instrumental-variable approach is used to address this potential endogeneity in each of the equations of the seemingly unrelated models reported in Tables 3, 5, and 7. Each of the tables reports two specifications of equations modeling the levels of informal dependent employment, informal self-employment, and unemployment in three country groups. Thus, we have 18 equations to test.

As instruments we use indicators based on three questions from the same round of ESS survey. These questions ask the respondent to evaluate, at a 0-to-10 scale, (i) Trust in the police ( _POLICE_ ); (ii) Satisfaction with the state of healthcare in the country ( _HEALTH)_ ; (iii) Statement „Most people try to be fair‖ ( _FAIRNESS_ ). For each of these variables, we have tried (on each of the equations) the mean, the median and the 25th percentile, altogether 9 instruments. In theory, the first two variables can influence tax morale, but for 13 cases we could not reject the hypothesis of zero correlation between country-median (9 cases) or country-mean (4 cases) trust in the police and the disturbances in the informality (or unemployment) equations. In remaining cases we have used mean _HEALTH_ (twice), mean _FAIRNESS_ , the 25<sup>th</sup> percentile of _FAIRNESS_ , and, in one case, combination of the _DURATION_ of ESS field work (in months) with the share of local born _MINORITY_ population _._ In most cases, just one of the instruments appeared as convincingly valid and not weak, with one or few more in the grey zone. There have been no cases when different instruments provided conflicting evidence. The rationale for using different distribution parameters is as follows: the causal link (if any) between [dis]trust in the police and informal employment is likely to be rooted in the lower part of the trust distribution, thus affecting the 25<sup>th</sup> percentile and the mean but not the median. On the other hand, the 25<sup>th</sup> percentile of _FAIRNESS_ (and hence it‘s mean as well) likely correlates with perceived government effectiveness, but not necessarily with the unobserved drivers of informality _._ Table A3 reports the details of the tests. Exogeneity of the suspected variables is not rejected, with high enough p-values, in all but four cases (for which instrumented estimates are presented in Tables 5, 7). The whole set of instruments is never weak, as seen from first stage F-tests (see Greene, 2008: 350), but weak identification cannot be ruled out firmly in some cases. However, instrument validity tests by Anderson-Rubin (see Greene, 2008: 378379) and Stock and Wright (2000) are weak-instrument-robust.

56