rafmacalaba/datause-encoder-data huggingface dataset:
4 the groups are defined by specific household attributes, such as regional location, urban-rural status, or age, gender, race or education of the head.

However, the personal distribution of income does not only reflect differences in these household characteristics, but also differences in the extent to which households have access to formal employment, vis-à-vis a reliance on self-employment, and indeed variation in their access to capital or transfer incomes. Therefore, this section also examines the income sources of each household and their relationship with inequality in total household income per capita.

The next two sections turn to the dynamics of inequality and poverty. Section 4 discusses a dynamic decomposition methodology due to Mookherjee and Shorrocks (1982), which separates _changes_ in inequality into components due to changes in the mean incomes of different groups, changes in the composition of these groups, and unexplained changes. It also presents the Datt and Ravallion (1992) decomposition of changes in poverty into a growth and a redistribution component. Section 5 briefly reports on the correlations between poverty, inequality and a couple of macroeconomic variables, focusing on the rate of inflation. Section 6 concludes and presents the candidate hypotheses that our analysis suggests might explain the recent changes in Brazil’s income distribution. Some of the questions raised for future research are briefly discussed.

# **2) The Data and What it Says.**

The data sets we use are the household-level micro-data from the _Pesquisa Nacional por Amostra de Domicílios_ (PNAD) for 1981-2004, produced by the _Instituto Brasileiro de Geografia e Estatística_ (IBGE)<sup>5</sup> . Data were collected each year from a representative national sample of households, with a sample size ranging from 291,000 to 525,000 individuals.<sup>6</sup> The

> 5 Three years are missing from the time series presented below: 1991 and 2000 were Census years, during which PNADs are not fielded. Income data from the Censuses are based on very different questionnaires, and are not comparable with PNAD data. The survey was not fielded in 1994 either, for cost-related reasons. 1982 is included but the reader is cautioned that income questions had a different reference period on that year. They were asked with respect to a quarter, rather than a month, giving rise to different recall periods. The answers are therefore not comparable with those from other surveys.

> 6 Sample sizes rose gradually from 482,611 individuals in 1981 to 525,023 individuals in 1985. The sample size was then scaled back to 290,518 in 1986, within the same sampling frame and with care to maintain representativeness. It then rose gradually to 389,073 in 2004. The PNAD survey is not carried out in the rural areas of the old North Region of Brazil, which roughly corresponds to the Amazon rainforest. The current North Region includes the state of Tocantins, which was previously part of Goiás state. The rural areas of this state are included in the PNAD throughout the time series.
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composite indexes with BEEPS and WEF, reported in the final two columns of Table 8, must be interpreted differently. Many of the firm survey variables are correlated by construction with the composite indexes; these correlations are shown in bold in Table 8.

The WEF administrative corruption variables – in the bottom five in Table 8 - are part of the TI index. Not surprisingly, they are more strongly correlated with TI than are the other WEF variables, and the BEEPS variables, which are not components of TI.

Because the WEF administrative corruption variables enter the TI index as three separate sources – for the three most recent annual surveys – the overall TI index is likely to emphasize administrative corruption more than state capture. Therefore, we can expect the administrative corruption measures in BEEPS to be more strongly correlated than the BEEPS state capture measures with TI, even though BEEPS is not a component of the TI index. That is in fact what we find in Table 8. Correlations of TI with the three state capture variables in BEEPS range from .11 to .28. Correlations with TI exceed .40 for 7 of the 9 BEEPS administrative corruption measures, with the highest for tax collection (.66) and business licenses (.70).

For WBI - in marked contrast to TI - variables included in the index are no more highly correlated with it than are variables excluded from the index. The two BEEPS variables most highly correlated with WBI (and with TI) are bribe frequency in the areas of business licenses and permits, and in tax collection. Neither of these variables is a component of the WBI index, however. State capture measures in BEEPS are even more weakly correlated with WBI than with TI, despite the fact that some of them are components of WBI (unlike the case for TI). Although they are components, their weight is extraordinarily small, only 1/24<sup>th</sup> of the weight given to NIT in the 2004 WBI index.

41
# **Exhibit 3.11 – Non-privatization public offering activity**

_Number of IPOs and value of IPOs as % of market capitalization (upper left graph) Number of SPOs and value of SPOs as % of market capitalization (upper right graph)_ <!-- Start of picture text --> 12 2.5% 40 0.9%<br>10 35 0.8%<br>2.0% 30 0.7%<br>8 0.6%<br>1.5% 25<br>0.5%<br>6 20<br>0.4%<br>1.0% 15<br>4 0.3%<br>0.5% 10 0.2%<br>2<br>5 0.1%<br>0 0.0% 0 0.0%<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br>Number of IPOs (left) Value of IPOs, % of market cap. (right) Number of SPOs (left) Value of SPOs, % of market cap. (right)<br><!-- End of picture text --> _Source: EGX, World Federation of Exchanges, World Development Indicators; World Bank staff analysis_ 32
sectors are regulated jointly, bribery increases and social welfare declines. By contrast, if formal and informal sectors are regulated separately (what Echazu and Bose term as “horizontal centralization”), rent extraction cannot be coordinated and maximized, thus increasing social welfare. Both models, however, do not include differential effects of bribery exposure _within_ the informal sector.

Empirical evidence on the informal sector’s exposure to bribery is also lacking. The number of studies using micro-level data to study corruption remains limited, and the share incorporating the informal sector is smaller still. While not using data on the informal sector directly, Safavian _et al_ . (2001) study microenterprises and corruption in the Russian Federation and find that larger firms are more likely to report constraints due to corruption. Svensson (2003) controls for firms in Uganda paying taxes (a proxy for formality), finding that those that do are more likely to encounter a bribe request. Rand and Tarp (2012) use data for Vietnam and include a sub-set of un-registered businesses in their analysis of bribery incidence, which is found to be lower among informal businesses: they speculate that this lower probability is due to the less visibility. A reading of the literature yields few studies using micro-data for informality and bribery in Sub-Saharan Africa, specifically. Lavallée and Roubaud (2018) are an exception. They use a sample of informal-sector businesses found through household surveys<sup>6</sup> in six West African countries. In line with results from analysis of the formal sector, they find that a business’s ability to pay (proxied by sales per worker) is associated with a higher likelihood of facing a bribe request.

# III. Informality, Corruption, and Harassment in Zimbabwe _<u>Informality and Employment in Zimbabwe</u>_

Zimbabwe’s statistical office (ZimStat) reports some figures on the informal sector in its 2014 labour force survey (LFCLS). According to the LFCLS, about 14% of employment is provided by the informal sector (informal employment, by contrast, nears 90%, implying substantial off-thebooks employment elsewhere). ZimStat places unemployment in Zimbabwe at 11%, a contested figure, as its definition of the labor force excludes informal operators and vendors not actively seeking employment.<sup>7</sup> Some accounts at the time of the LFCLS claimed that once this informal employment was included, nearly 9 out of 10 Zimbabweans were unemployed.

> 6 They use data from surveys using what is known as the 1-2-3 method, which finds informal sector businesses by first interviewing a representative sample of households.

> 7 BBC, “Reality Check: Are 90% of Zimbabweans unemployed?” <u>https://www.bbc.com/news/business-42116932</u>

(7)
while other none. Finally some are considered attractive destinations to FDI and other not. Are these features associated with the quality of the investment climate?

To investigate this relationship we classify the 16 Indian states in our sample in 6 categories: (1) by level of development - according to their per capita income; (2) by growth performance – in proportion to their per capita income growth; (3) by market size - in line with their GDP level; (4) by ease of regulatory environment - according to the ranking of the Doing Business report<sup>15</sup> ; (5) by level of FDI attractiveness - depending on the their level of FDI; and (6) by quality of infrastructure – in accordance with their ranking in the infrastructure index published by the Government of India (GoI) in 2000.<sup>16</sup> For each of these state typologies we estimate and compare the average value of their ICI ranking. Consequently each column in Figure 4 represents the average quality of the investment climate, as measured by the ICI, for each of these six typologies of states.

The first column shows that a better investment climate is indeed associated with the level of development. More developed states present a much better quality of the investment climate than less developed states. Similarly states that grow faster also show a better business environment. Although unavailability of data makes it hard to establish the order of causality, evidence from other countries<sup>17</sup> shows that the quality of the investment climate is significantly associated with subsequent growth rates.

> 15 World Bank (2007)

> 16 Source: Report of the Eleventh Finance Commission (for 2000-2005), Government of India, 2000.

> 17 More specifically, a similar Investment Climate index in 24 ECA (East Europe and Central Asia) countries (see Iarossi, et al. 2007).

13
**Table 14: Number of the permanent residents by nationality** **<u>(as of the end of the year)</u>**

|**Nationality**<br>**(place of birth)**|<br>**2000**|**2001**|**2002**|**2003**|**2004**|**Composition ratio**<br>**(%)**|**Percentage change**<br>**from the end of the**<br>**previous year (%)**|
|---|---|---|---|---|---|---|---|
|**Permanent resident**|657,605|684,853|713,775|742,963|778,583|100.0|4.8|
|**General permanent resident**|145,336|184,071|223,875|267,011|312,964|40.2|17.2|
|**China**|48,809|58,778|70,599|83,321|96,647|12.4|16.0|
|**Brazil**|9,062|20,277|31,203|41,771|52,581|6.8|25.9|
|**Philippines**|20,933|26,967|32,796|39,733|47,407|6.1|19.3|
|**South Korea / Korea**|31,955|34,624|37,121|39,807|42,960|5.5|7.9|
|**Peru**|7,496|11,059|13,975|17,213|20,401|2.6|18.5|
|**Others**|27,081|32,366|38,181|45,166|52,968|6.8|17.3|
|**Special permanent resident**|512,269|500,782|489,900|475,952|465,619|59.8|-2.2|
|**South Korea / Korea**|507,429|495,986|485,180|471,756|461,460|59.3|-2.2|
|**China**|4,151|4,060|3,924|3,406|3,306|0.4|-2.9|
|**Others**|689|736|796|790|853|0.1|8.0|

Source: Japanese Ministry of Justice.

36
**Table 11: 2007 as non-reform year—By firm size—Fixed effects—regression on firm profitability as dependent variable**

||All firms|Small|Medium|Large|
|---|---|---|---|---|
|**Treatment (ITT)**|0.00619|0.00808|-0.152|-0.0514|
||(0.59)|(0.80)|(-0.45)|(-0.56)|
|Reform Period|-0.0328***|-0.0333***|-0.00801|0.000533|
||(-3.44)|(-3.40)|(-0.28)|(0.02)|
|Constant|-0.0184|-0.0151|-1.292|-0.0409|
||(-0.23)|(-0.18)|(-1.15)|(-0.32)|
|Year Dum.|Yes|Yes|Yes|Yes|
|Region Dum.|Yes|Yes|Yes|Yes|
|Sector Dum.|Yes|Yes|Yes|Yes|
|Observations|1 363 022|1 321 871|34 695|7 160|

**t-statistics shown in parentheses, treatment variable bolded**

***p<0.1          **p<0.05          ***p<0.01**

**Table 12: Only firms with calendar year reporting—By model design size—Random effects—regression on firm profitability as dependent variable**

||No Dummies|Year Dum.|+Reg. Dum.|+Sect. Dum.|Frm. Skill|Frm. Size|Full Model|
|---|---|---|---|---|---|---|---|
|**Treatment (ITT)**|0.00969|0.0103|0.0108|0.0114|0.0115|0.0114|0.0115|
||(0.69)|(0.75)|(0.78)|(0.82)|(0.83)|(0.82)|(0.83)|
|Pre-reform<br>youth share|0.0753***|0.0745***|0.0661***|0.0401***|0.0560***|0.0403***|0.0562***|
||(6.86)|(6.93)|(6.22)|(4.20)|(5.45)|(4.21)|(5.46)|
|Reform Period|-0.0145**|-0.0459***|-0.0461***|-0.0467***|-0.0472***|-0.0467***|-0.0472***|
||(-2.27)|(-3.09)|(-3.10)|(-3.15)|(-3.18)|(-3.15)|(-3.17)|
|Pre-reform<br>Education level|||||-0.0858***||-0.0858***|
||||||(-6.39)||(-6.39)|
|Pre-reform firm<br>size||||||-0.0000190**|-0.0000176**|
|||||||(-2.16)|(-1.98)|
|Constant|0.0426***|0.0505***|0.0897***|0.134***|0.275***|0.134***|0.275***|
||(8.03)|(10.30)|(11.58)|(10.33)|(9.71)|(10.33)|(9.70)|
|Year Dum.|No|Yes|Yes|Yes|Yes|Yes|Yes|
|Region Dum.|No|No|Yes|Yes|Yes|Yes|Yes|
|Sector Dum.|No|No|No|Yes|Yes|Yes|Yes|
|Observations|840 610|840 610|840 610|840 610|840 610|840 610|840 610|

**t-statistics shown in parentheses, treatment variable bolded**

- ***p<0.1          **p<0.05          ***p<0.01**

24
- Knight, J., L. Song (1993), “The spatial contribution to income inequality in rural China”, _Cambridge Journal of Economics_ , 17(2), 195-213.

- Lachaud, J-P. (1999), “Envois de fonds, inégalité et pauvreté au Burkina Faso”, _Revue Tiers Monde_ , 40(160), 793-827.

- Lall, S., H. Selod and Z. Shalizi (2006), “Rural-urban migration in developing countries: a survey of theoretical predictions and empirical findings”, Policy Research Working Paper No. 3915, The World Bank.

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- Lanjouw, P. (1999b), “Rural Non-Agricultural Employment and Poverty in Ecuador”, _Economic Development and Cultural Change_ , 48(1), 91-122.

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- Mexico”, in A.R. Morrison, M. Schiff, M. Sjöblom (eds), _The International Migration of Women_ (Washington D. C., The World Bank).

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28
- 41 -

over-employment and low labor quality, are among the most serious constraints on firm efficiency in transition economies.

Finally, energy-intensive firms look less efficient in economic terms. This may be able to be interpreted to mean that some fraction of the predicted technical inefficiency would be relevant to firm spending on energy. This may be because of the existing energy use inefficiency embedded in equipment and production systems or because of some extra costs caused by the existing poor quality of public infrastructure services.

Table 14. Average predicted technical inefficiency by firm characteristics (% of total costs)

||Obs|MeanSt|d.Dev.|Min|Max|
|---|---|---|---|---|---|
|By firm size:||||||
|_Y_< 199|246|1.66|1.34|0.506|10.01|
|199 < Y < 612|246|0.35|0.13|0.164|0.86|
|612 <_Y_< 2,451|248|0.11|0.05|0.038|0.25|
|_Y >_2,451|250|0.02|0.01|0.000|0.05|
|By labor intensity:||||||
|_S L _< 0.11|248|0.28|0.75|0.000|10.01|
|0.11 <_S L _< 0.196|246|0.44|0.70|0.001|4.55|
|0.196 <_S L _< 0.287|248|0.59|0.96|0.001|6.67|
|_S L >_0.287|248|0.82|1.19|0.004|6.67|
|By enregy intensity:||||||
|_S E _< 0.029|247|0.23|0.34|0.000|2.38|
|0.029 <_S E _< 0.054|248|0.54|0.89|0.001|5.27|
|0.054 <_S E _< 0.099|247|0.60|1.03|0.002|6.67|
|_S E >_0.099|248|0.76|1.20|0.001|10.01|

# **VI. POLICY IMPLICATIONS**

The following discusses some policy implications of the above findings under simplified circumstances. This is only for illustration purposes; the exact situation of each country may be characterized differently, depending on the specific focus, and it is also changing over time (e.g., IEA, 2008; IPA, 2009). Of particular note, the discussion mainly characterizes the situation around the sample year of the used BEEP data (i.e., in 2004) and may not reflect the latest developments in each country, for instance, prices (Figure 8). It also ignores other energy sources than electricity. Accordingly, the gap between our imputed energy price and the actual non-residential electricity price per kWh could be large, as observed in Bulgaria and Romania. This is a clear limitation to the interpretation of the results in the electricity
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One possible explanation behind this finding is that if volatility is viewed as a measure of risk, then other things being equal, countries with higher volatility will have a tendency to under-invest or undertake inefficient investment projects (see Bertola and Caballero, 1994) and therefore grow less. A similar argument is made by Krebs, Krishna and Maloney (2005) focusing on the impact of risk on education (rather than on physical capital) and of education on growth. Another potential explanation is that there may be asymmetries in the process of capital or knowledge accumulation. If the negative effects of recessions on learning by doing are larger than the positive effects of expansions, then we would also expect that high volatility leads to lower growth (Martin and Rogers, 1997). Similarly, if firm entry and exit rates differ dramatically in good and bad times along the business cycle, then volatility would also lead to lower investment and growth. This can be the case if for example there are important fixed costs (such as establishing an important sales network) associated to market entry.

Thus a natural question that arises is whether inequality contributes to income volatility. If so, this would be uncovering another potential channel by which inequality may have a negative impact on economic growth. Moreover, recent work by Calderon and LeviYeyati (2007) suggests that periods of economic turmoil tend to be associated with deteriorations in the income distribution. In other words, it could that higher volatility leads to higher inequality so that there is a potential for a vicious circle in which high inequality and high volatility reinforce each other.

**Figure 17. Volatility and inequality**

<!-- Start of picture text --> 20<br>15<br>10<br>5<br>0<br>0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8<br>Gini<br>s.d. of growth<br><!-- End of picture text --> Figure 17 presents the scatter plot of output volatility and inequality based on panel data for 118 countries spanning 1960-2000. Each pair represents the volatility of growth over half a decade and the gini coefficient at the beginning of the period. The figure shows that there is significant dispersion around the regression line, but also a positive slope which is significant at standard levels (4.1 with a robust s.e. of 1.6). The existence of a positive and significant correlation between the standard deviation of output and income 26