rafmacalaba/datause-encoder-data huggingface dataset:
# **B Additional Tables**

## Table B1: Informality and Commuting Patterns

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||_Panel A: _|_Probability of working in t_|_he same municipality of re_|_sidence_||
|Outcome:|Workplace municipality|Workplace municipality|Workplace municipality|Workplace municipality|Workplace municipality|
|Informal|-0.265***|-0.231***|-0.231***|-0.132***|-0.079***|
||(0.009)|(0.008)|(0.008)|(0.006)|(0.008)|
|Observations|577,041|577,039|577,039|517,354|516,931|
|R-squared|0.069|0.098|0.123|0.215|0.465|
||_Pane_|_l B: Probability of working_|_in the CBD of Mexico Ci_|_ty_||
|Outcome:|Workplace-CBD|Workplace-CBD|Workplace-CBD|Workplace-CBD|Workplace-CBD|
|Informal|-0.086***|-0.056**|-0.059***|-0.037***|-|
||(0.026)|(0.024)|(0.018)|(0.011)|-|
|Observations|577,041|577,039|577,039|517,354|-|
|R-squared|0.007|0.042|0.468|0.444|-|
|Individual Characteristics||X|X|X|X|
|Origin FE|||X|X|X|
|Transportation Mode FE||||X|X|
|Destination FE|||||X|

_Notes:_ This table reports the results of a linear probability model relating the probability of working in the same municipality as the one in which the worker resides, and the probability of working in the CBD with a dummy variable that takes the value of 1 if the worker is informal. Panel A reports the results for working in the same municipality, and panel B whether the individual works in the CBD. Standard errors are clustered at the residence municipality level and reported in parentheses. _∗p <_ 0 _._ 1, _∗∗ p <_ 0 _._ 05, _∗∗∗p <_ 0 _._ 01.
these questions, we look at how poverty shapes the culture of deliberation, and how public discourse shapes the understanding of poverty by contesting its meaning.

Our goal in this paper is twofold. Theoretically, we wish to contribute to the scholarly literature on culture and poverty (Lamont and Small 2008) by proposing a view of culture that amalgamates sociological perspectives on culture with the human capabilities approach in the interdisciplinary scholarship on development (Sen, 1985). Empirically, we wish to contribute to the field of scholarship concerning deliberative democracy (Fung 2004; Gutmann and Thompson 2004; Elster 1998; Bohman 1996; Mansbridge 1980), by bringing in rich qualitative data to a field which has been largely restricted to theoretical discussions about the ideal nature and purported virtues of public deliberation. The data are drawn from qualitative analysis of the recorded proceedings of a large representative sample of 290 randomly selected _gram sabha_ s held in four states in South India.

The rest of the paper is divided into six sections. In the second section we outline our understanding of culture and contextualize it within past and present usages of culture in the literatures on poverty and development. In the third section, we outline relevant parts of the deliberative democracy literature and highlight the implicit assumptions about culture embedded within it. The fourth and fifth sections include details about the context and data and method. In the sixth section we present our substantive discussion. Finally, in the seventh section we conclude by highlighting our important findings and elaborate on their implications.

6
Some Policy Implications.” OECD, Paris.

http://www.oecd.org/mena/investment/35402110.pdf.

Okulicz-Kozaryn, A. 2011. “City Life: Rankings (Livability) versus Perceptions (Satisfaction).” _Social Indicators Research_ 110: 433–51.

Ott, J. C. 2010. “Good Governance and Happiness in Nations: Technical Quality Precedes Democracy and Quality Beats Size.” _Journal of Happiness Studies_ 11: 353–68.

Rijkers, B., C. Freund, and A. Nucifora. 2014. “All in the Family: State Capture in Tunisia.” Policy Research Working Paper 6810, World Bank, Washington, DC.

Stevenson, B., and J. Wolfers. 2008. “Economic Growth and Happiness.” _Brookings Papers on Economic Activity_ 35.

Veenhoven, R. 1984 _Conditions of Happiness_ . Dordrecht/Boston: Reidel.

- ———. 1989. _National Wealth and Individual Happiness_ . Springer Netherlands

- ———. 2002. “Why Social Policy Needs Subjective Indicators.” _Social Indicators Research_ 58: 33– 46.

- ———. 2012. “Happiness: Also Known As ‘Life Satisfaction’ and ‘Subjective Well-Being.’” In _Handbook of Social Indicators and Quality of Life Research_ , edited by K. C. Land, a. C. Michalos, and M. J. Sirgy, 63–77. Berlin: Springer.

- Verme, P. 2009. “Happiness, Freedom and Control.” _Journal of Economic Behavior & Organization_ 71: 146–61.

Verme, P., B. Milanovic, S. Al-Shawarby, S. El Tawila, M. Gadallah, and E. A. A. El-Majeed.

2014. _Inside Inequality in the Arab Republic of Egypt: Facts and Perceptions across People, Time, and Space_ . Washington, DC: World Bank.

World Bank. 2014a. PovcalNet. World Bank, Washington, DC.

http://iresearch.worldbank.org/PovcalNet/.

World Bank. 2014b. _World Development Indicators_ . Washington, DC: World Bank.

Zogby, J. 2005. _Attitudes of Arabs: An In-Depth Look at Social and Political Concerns of Arabs_ . Arab American Institute.

28
and answers. At the same time it was designed to be a mixture of an opinion and evaluation survey (since it collected substantial amounts of qualitative information) and a quantitative data collection instrument.

Duration and costs of the survey were well within expectations. Total time for sampling (including the update of an existing sampling frame), data collection, digitations and cleaning amounted to ten weeks (from beginning August 1999 to October 15, 1999). After several weeks of analyzing the data and drafting a report on poverty and social conditions, we were able to present the major findings in a large seminar in Cali at the beginning of December so that total turnover time was relatively short. Total cost of the survey was moderate. Not including the analysis, the total cost of the survey (including questionnaire design, sampling, data collection, digitations) was well below US$40,000.<sup>10</sup>

# _3.3. Household Welfare Definition_

While the survey was not intended to provide new estimates of absolute poverty levels in the city, we nevertheless needed to distinguish different welfare groups in the population to answer the questions posed. Inclusion of a very detailed income or expenditure module would have increased length of the questionnaire and survey costs considerably. Further, there was no demand in the city for re-estimating absolute poverty levels as Urrea and Ortiz (1999) provided such measurement using detailed income information and a poverty line approach based on the pricing of a minimum nutritional basket. But to answer questions about expenditure distribution and priorities in the city, the survey needed to allow for welfare distinctions between households.

Based on the income questions included in the labor market module, we used household per capita income as the welfare indicator for the purpose of the analysis. While not as comprehensive as in Living Standard Measurement Surveys, the module did distinguish several income sources for all household members: wages and salaries of primary and secondary occupation (by unit and frequency of payment), additional income from work, interest income, rental income, pensions, transfers, and other income sources.<sup>11</sup> Aggregating

> 9 See the homepage of the Core Welfare Questionnaire (http://www.worldbank.org/afr/stats/cwiq.cfm).

> 10 The survey was financed by the Bank-Netherlands partnership program whose support is gratefully acknowledged.

> 11 We encountered, however, that the data base did not include wage and salary income for about one third of informants who worked during the time period of the survey.  For these, we constructed a simple estimation model of hourly wages

10
Figure 12. Concentration of taxes and social spending (by decile of market income) <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Personal GISL Rural GIS NPPF Provident Excise Sales Tax Customs Green taxEducation Health<br>Income Insurance fund<br>Tax<br>Decile1 Decile2 Decile3 Decile4 Decile5 Decile6 Decile7 Decile8 Decile9 Decile10<br><!-- End of picture text --> _Source_ : World Bank staff estimation using BLSS 2017.

# 5. Simulating the Impact of COVID-19 and Changes in Tax Policies

The Bhutanese government has implemented important fiscal reforms in recent years, aimed at improving revenue collection and promoting equity. In this section, we will first describe the changes in tax policies applicable to households and apply the CEQ methodology supplemented with simulations to assess the potential impact on poverty and inequality.

The first reform was a reduction in personal income taxes across the board. With the aim to provide tax relief to lower-income households and to reduce the burden from the introduction of the GST, the government implemented a revision to the personal income tax schedule in 2020. The exemption threshold was raised from Nu 200,000 to Nu 300,0000, which is expected to result in about 31,000 fewer taxpayers.<sup>26</sup> A new maximum marginal tax rate of 30% was introduced and the tax brackets were adjusted. Table 6 and table 7 show the old and revised tax schedules side-by-side.

Table 6. Old Personal Income Tax Schedule Table 7. Revised Personal Income Tax Schedule

|Minimum|Maximum|Rate|Minimum|Maximum|Rate|
|---|---|---|---|---|---|
|-|200,000|0%|-|300,000|0%|
|200,000|250,000|10%|300,000|400,000|10%|
|250,000|500,000|15%|400,000|650,000|15%|
|500,000|1,000,000|20%|650,000|1,000,000|20%|
|1,000,000|-|25%|1,000,000|1,500,000|25%|
||||1,500,000||30%|

_Source_ : Bhutan Income Tax Act 2001. _Source_ : Bhutan Income Tax (Amendment) Act of 2020.

> 26 World Bank, 2020. Program document for a proposed Development Policy Credit to the Kingdom of Bhutan.

22
Table 3: Returns to schooling <u>(Mincer Equations) by sector*: Formal vs Informal, 1992-2002</u>

||All|Formal<br>Men|Women<br>Informal<br>All MenWomen|
|---|---|---|---|
|1992|8.8|8.9|9.7<br>6.5 5.8 8.2|
|1995|7.0|7.4|7.9<br>7.3 7.3 7.7|
|1996|7.1|7.1|8.9<br>6.9 7.2 7.5|
|1997|8.5|8.6|10.0<br>6.8 7.3 7.3|
|1998|9.1|9.3|10.7<br>7.0 7.9 6.8|
|1999|8.8|9.1|10.2<br>6.5 7.0 6.7|
|2000|7.0|7.2|7.9<br>5.7 6.3 5.7|
|2001|8.6|8.4|10.6<br>6.2 6.8 6.4|
|2002|9.7|9.4|11.7<br>6.7 7.46.6|
|Source:_E_<br>* All sect|_ncuesta de Hog_<br>ors of employm|_ares por Muestro_<br>ent included in the|sample|

Figure 3: Rates of return by sector (formal vs informal): 1992-2002 <!-- Start of picture text --> 12<br>10<br>8<br>Formal<br>6<br>Informal<br>4<br>2<br>0<br>1992 1995 1996 1997 1998 1999 2000 2001 2002<br><!-- End of picture text --> To complement the results on the returns to schooling, we look at education premiums by level and sex (see Annex Table 2). The interest here is to document which levels are associated with falling earnings premiums over time and whether the developments are similar for both males and females. Since there is very little evidence of sample selection in Venezuela, rates of return to education by level were derived from OLS regressions.

8
**Figure 4:  Conventional and Weak Instrument‐Robust Confidence Sets for Growth Effect of Inequality Based on the Levels Equation**

<!-- Start of picture text --> Wald AR KJ CLR<br>-2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2<br>Value for Beta Value for Beta Value for Beta Value for Beta<br>50 50 50 50<br>40 40 40 40<br>30 30 30 30<br>Model Model Model Model<br>20 20 20 20<br>10 10 10 10<br>0 0 0 0<br><!-- End of picture text --> Notes: This figure reports confidence sets for the estimated growth effect of inequality from the levels regression of a variant of OBT that excludes redistribution from the growth regression. Each horizontal line represents a different model with a different choice of lags of changes in income and inequality used as internal instruments. In the first 25 models the instruments enter “GMM style” with one variable per time period for each instrument. In the second 25 models the instruments are “collapsed” with one column in the instrument matrix for each instrument.

31
direct ownership. Most common examples are apparel (e.g. Wal-Mart), furniture (e.g. Ikea) and footwear (e.g. Nike); i.e. labor intensive consumer goods. In producer-driven chains, manufacturers of higher technology products like automobiles and computers are the key economic agents that control relations with suppliers of intermediate components, and forward links with distribution and retailing services. The lead firms in producer-driven chains usually belong to large international oligopolies.

Before focusing on these “networks” however, we will first focus on primary commodities (by far Africa’s greatest source of foreign currency revenue through trade). Afterwards, we will consider specific cases of buyer-driven and producerdriven supply chains, ending with one specific case of services network chains: the tourism industry. For each of these value chain analyses, this paper will try to emphasize the public policies that were either lacking or instrumental in the performance of the various industries. After all, in some sectors, certain steps that were or were not taken by the government have been decisive in determining a sector’s relative competitive success or weakness.

# **_A. Primary Commodities: Ghana (pineapple), Kenya (flowers) and Nigeria (shrimp)_**

# **a. Ghana: Pineapple**

Pineapple exports are currently leading the horticultural sector in Ghana. Commercial cultivation of pineapple for export is a relatively recent phenomenon in the West-African country; it only really took off with the introduction of seafreighting in the mid-1990s. Prior to 1995, nearly all exported pineapples were transported by airfreight. With the creation of the exporters’ association SPEG – ‘Sea-freight Pineapple Exporters of Ghana’ – sea freighting of pineapples in reefer vessels for exports to the European market steadily grew in importance. In 1995 a mere 2700 tons were shipped, whilst in 2004 a total of 48,000 tons of pineapple were freighted by sea from Ghana to Europe.<sup>23</sup> In the same period the volume of airfreight pineapple was reduced from well over 10,000 tons in 1995 and 1996 to around 6,000 tons in 2002.

The fresh pineapple production sector comprises thousands of small-scale farmers and several large-scale plantation growers. Even though there are several active associations, the pineapple sub-sector is still not sufficiently organized and streamlined to achieve sufficient volume of production, thereby enjoying the benefits of economies of scale and increased negotiation power. The pineapple sub-sector in Ghana will not be able to capitalize on the market opportunities in Europe if the following interrelated issues are not addressed: _low quality, low volume of product, high shipping costs_ . Especially the last factor makes it difficult

> 23 Source: SPEG 2005

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Table 5: Mean Percentage of Total Instructional Time Devoted to Selected Subjects in the Primary School Curricula of Less-Developed Countries by World Region*

|Region:|LAC|Asia|SSA|EMENA|
|---|---|---|---|---|
||(N=21)|(N=18)|(N=29)|(N=21)|
|Comb.Lang.|27.7|37.5|38.2|35.8|
|Nat.Lang.<br>|16.5<br>|25.1<br>|10.9<br>|29.1<br>|
|Loc.Lang.|0.0|0.9|2.5|0.6|
|For.Lang.|_0.5_|2.3|0.5|4.6|
|Off.Lang.|8.4|8.5|24.2|1.0|
|Mathematics|19.1|16.6|17.9|17.1|
|Science|9.9|7.5|7.2|7.4|
|Comb.Social Sci.|13.1|7.6|7.8|6.7|
|Social Studies|8.7|5.3|3.3|3.6|
|History|1.9|_0.5_|1.4|1.3|
|Geography|1.4|0.4|1.6|1.1|
|<br>Civics|0.7|1.4|_1.5_|_0.5_|
|Comb.MoralIRel|.<br>3.2|6.3|4.6|10.0|
|Religion|2.3|2.7|3.8|10.0|
|<br>Moral Ed.|0.8|3.2|0.8|0.2|
|Aesthetic Ed.|7.9|9.5|8.5|8.9|
|Music|3.3|3.9|3.4|3.5|
|Art|4.3|5.7|5.0|5.4|
|Physical Ed.|6.1|6.0|5.9|7.1|
|Hygiene|2.8|1.7|0.9|1.5|
|Comb.Voc.Ed.|7.6|4.2|7.3|2.8|
|Manual|3.6|2.7|2.5|1.8|
|Agriculture|2.0|_0.5_|2.2|0.2|
|Domestic Scienc|e<br>1.8|0.3|2.4|0.4|
|Vocational Ed.|0.0|_0.5_|0.0|0.4|
|Business|0.0|0.0|0.1|0.0|
|Other|1.3|4.0|2.1|2.7|

*Key for regions:

LAC: Latin America and the Caribbean EMENA: Southern Europe, Middle East and North Africa SSA = Sub-Saharan Africa
mechanisms for implementing rules and laws, and access to finance. If informal competition experienced by the formal firms varies systematically with the business environment in which the firms operate, our main results could suffer from the omitted variable bias problem. Thus, we control for the following measures of the business environment all defined at the firm-level: total hours of power outages experienced by the firm in a typical month over the last year ( _Power Outages_ ), a dummy variable equal to 1 if the firm suffered losses due to crime during the last year and 0 otherwise ( _Crime Losses_ ), a dummy variable equal to 1 if the firm reported the functioning of courts as a moderate, major or very severe obstacle for its day to day operations and 0 if it reported it is a minor obstacle or no obstacle ( _Courts Obstacle_ ); and two measures of access to finance which include a dummy variable equal to 1 if the firm has overdraft facility and 0 otherwise ( _Overdraft_ ), and how severe is (lack of) access to finance as an obstacle to the firms’ day to day operations ( _Finance Obstacle_ ) as reported by the firms. The finance obstacle is defined on a 0-4 scale with higher values implying a more severe obstacle.

Our next set of baseline controls includes macro-level variables. As mentioned above, the ES data we use was collected in different years across countries. Global annual shocks to employment growth could affect our results. Thus, we control for dummy variables indicating the year the ES was conducted in the country (Year fixed effects). Differences in overall economic development and income levels are a cause of concern for our main results. One reason for this is that informality tends to be lower in the richer countries. At the same time, richer countries grow slower than poorer countries (convergence effect). The structure of correlations here implies that failure to control for overall economic development can cause the estimated coefficient value of informal competition to be biased towards zero, weakening our main result (confirmed below). Thus, we control for (log of) GDP per capita, PPP adjusted and at constant 2011 international 14
and prices, in practice data on infrastructure prices are not available in a consistent way for the countries under analysis. In addition, available data on capital do not distinguish between different types of capital, including infrastructure. Instead of having data on _K_<sup>~</sup> , we have data on _K_ .

Because of this, it is clearer to rewrite to model so as to fit the available data, as: which leads to Finally, the trick is to substitute (3) into (6’), so that we get (appending an error term): The left-hand side of (7) is TFP growth as computed (not estimated) in the standard growth accounting approach. An alternative route to a full estimation of (6’) is thus to estimate the _A_<sup>&</sup> reduced form (7) using the (year by year) results of (3) in terms of TFP growth rates ( ), _A_ which is convenient as these are available from standard growth accounting exercises for a number of countries.

Either (7) or (6’) provide an estimation of η , the pure externality effect of infrastructure, as opposed to the full elasticity of output with respect to infrastructure. For example, if an estimation of (7) produces a value for η not significantly different from zero, it suggests that infrastructure has no externality role in that particular economy. However, because _K_ includes _X_ , it does not imply that infrastructure is not productive: it is just not more productive than other types of capital.

~ & _A_ Finally note that (7) or (6’) also provide a basis for estimating ~~~~~ , the “true” TFP growth. _A_ 10
Table 2. Number of physicians per 10,000<sup>inhabitants by WVHO</sup> Regions.

|WHO REGION|1980|1986|CHANGE PERCENTAGE|
|---|---|---|---|
|Africa|1.1|1.0|-9.1|
|Americas|11.6|15.5|+33.6|
|Eastem Mediterranean|3.6|5.1|+41.7|
|Europe|Z2.0|24.4|+10.9|
|Southeast Asia|2.2|3.3|+50.0|
|Westem Pacific|4.6|6.3|+_7.0<br>|||
|AU Regions<br>t|8.1<br>|9.4|+16.1|

ource: Fulop (1986), based on data available<sup>to WHO i 1980 and 1986.</sup> 10. _Managneme_ has received the least attention<sup>of the three phases ofrealth personnel</sup> dfvelopment. Workers hive been trained<sup>largely to fill staff positions. Little attention has been given</sup> to their supervision, motivation, or career development.<sup>The need for better mnanagemtnt, including</sup> better integration of public and private health<sup>workers, has been recognized. This change in perception</sup> recognizes three important facts about health<sup>personnel: they are an expensive resource to train and</sup> employ; they require well managed services<sup>.o be effective; and they are an integral part of the health</sup> sector development process.

# _Conclusion_

11. Several aspects of the development of health<sup>personnel in sub-Saharan Africa emerge from</sup> this discussion:

- The main concem has been with the production<sup>of health workers, and there are few good</sup> examples of comprehensive personnel reviews or plans that<sup>integrate health personnei</sup> development into the overall planning and development<sup>process.</sup>

-

-

-

-

-

- The medical profession has retained a dominant role<sup>in this process and has greatly</sup> influenced the choice of priorities and the<sup>allocation of resources.</sup>

Half or more of health workers are women, but<sup>they have had only a minor role in health</sup> personnel planning and management,<sup>particularly at the national level.</sup> The continuing development of health workers as<sup>a valuable human resource has been</sup> neglected, particularly once they have<sup>undergone their basic training and are employed in</sup> the public sector.

The major concern in health personnel de-elopment<sup>has been the production and training</sup> of health care providers, whereas the training<sup>of administrative personnel and support staff</sup> has been widely neglected.

As a consequence of the recent decline<sup>in economic conditions, ministries of health are</sup> now recognizing the full cost of employing<sup>the large number of health workers envisaged</sup> in most government health plans.

6