rafmacalaba/datause-encoder-data huggingface dataset:
children or population increase. Highest increase in PCA was found among districts in Bihar while the lowest, in Kerala, showing clear move towards EDI groupings. The PCA of 2007-08 of 20 districts (15 of which were from Bihar, and the rest from Jharkhand and UP) that were in the bottom 100 in terms of both EDIs (EDI Quintile 1) and PCAs (PCA 2004-05 quintile 1) had tremendously improved, and in terms of PCA 2007-08 classifications, half of these districts were in the highest quintiles in terms of PCA 200708 quintiles and the rest 10 districts in the next 100 best districts in terms of allocations (except one district in UP). Similarly, The PCA of 2007-08 of the 20 districts that were in the top 100s both in terms of EDI and PCA shows that though there is an increase in the PCA, they are no longer the top districts in terms of allocations. Most of the districts in this category are hill districts in HP and Uttarakhand, and couple of districts from TN and Karnataka.

||**Table 7**|**: Comp**|**arison**|**of PCA**|**betwee**|**n 2004-0**|**5 and 20**|**07-08**||
|---|---|---|---|---|---|---|---|---|---|
|||**EDI in**|**dex**|**Per chil**|**d allocation**<br>**2004-05**|**(PCA):**|**Per child**|**allocation**<br>**2007-08**|**(PCA):**|
||||||**Min in**|**Max in**||**Min in**|**Max in**|
||**EDI**<br>**index**|**Worst**|**Best**|**State**<br>**average**|**.**<br>**a**<br>**district**|**.**<br>**a**<br>**district**|**State**<br>**average**|**.**<br>**a**<br>**district**|**.**<br>**a**<br>**district**|
|AP|0.56|0.34|0.57|265|101|394|998|492|1573|
|Assam|0.45|0.28|0.51|393|157|1091|1632|793|5430|
|Bihar|0.07|0.17|0.32|300|137|457|1781|1026|2471|
|Chhattisgarh|0.3|0.18|0.45|721|525|981|1955|1063|3528|
|Gujarat|0.54|0.37|0.55|223|100|608|526|293|847|
|Haryana|0.47|0.34|0.55|446|250|879|747|423|1119|
|HP|0.82|0.54|0.77|1169|650|4045|1733|870|6076|
|Jharkhand|0.15|0.21|0.39|532|105|1306|2155|978|4990|
|Karnataka|0.66|0.43|0.68|367|159|768|951|453|1750|
|Kerala|0.79|0.58|0.68|318|226|413|338|136|513|
|Maharashtra|0.61|0.32|0.62|379|157|809|664|242|1093|
|MP|0.48|0.32|0.54|590|204|1016|1148|543|1849|
|Orissa|0.44|0.3|0.51|640|434|1195|1626|911|3224|
|Punjab|0.62|0.41|0.63|329|181|554|467|316|662|
|Rajasthan|0.41|0.28|0.51|403|205|978|1586|819|3518|
|TN|0.64|0.5|0.66|394|132|907|707|143|1589|
|UP|0.27|0.22|0.47|417|167|1000|969|334|2147|
|Uttaranchal|0.63|0.42|0.57|733|122|1468|1685|720|3095|
|W. Bengal|0.41|0.2|0.53|446|277|587|951|296|1498|

Overall, now the allocations seem to follow the EDI distributions, with worse off districts receiving more funds compared to better off districts. See graph 3 for a distribution of districts by EDIs and PCA for 2007-08. The negatively sloping trend is an indication of allocations becoming more and more targeted.

We also tried to see whether the pattern is similar across states. A correlation analysis of all the sub-dimensional indices along with PCAs for 2004-05 and 2007-08 was carried out. The results reveal much to what is really happening in planning for SSA.

21
The CPI is available from 1995 when countries were first scored and has been updated yearly, so that in 2016, 176 countries and territories received a score. Over time, both the scope (number of countries) and the accuracy (standard error) of the index have improved. Furthermore, the CPI has been found to be highly correlated with measures of actual corruption (business regulation, public perceptions of corruption; see Treisman, 2007), thus suggesting that the CPI is effectively measuring an aspect of corruption.

**Coding.** As noted above, the CPI is an interval measure that ranges continuously from 0 (most corrupt) to 10 (least corrupt). The CPI is a composite index meaning that it utilizes a variety of different sources to arrive at each country’s score (see Table 1). One of the essential criteria for a source to be used as part of the CPI is that it “...must provide a ranking of nations” (Transparency International, 2011). This criterion has the effect of precluding the use of sources that may provide scores for different countries, but do not use the same methodology (sampling frame etc.) across these countries. In short, all the sources used by CPI provide a consistent and comparative measure of perceived corruption<sup>7</sup> at any given time. The way the composite index is developed is that each individual indicator of corruption is standardized (such that each source has the same weight) and then the average (mean) standardized score is calculated. Thus, the CPI score of a country in any one year is the (standardized) average score of all the sources available for that country.<sup>8</sup> In order to reduce abrupt variations in scoring, the CPI actually tries to include sources from the last three years. This ensures that reliable scores for any given country are only developed if there are at least three sources available for that country in any given year.

**Over Time Variation.** Because the number of sources that can be used to construct the CPI can fluctuate over time (as sources must be current from the last three years and provide consistent comparative information), comparisons over long time periods are not advised, as the marginal change in the CPI over a given time period may reflect measurement error. However, because of the inclusion of sources from the last three

> 7 Transparency International also ensures that the definition of corruption used does not vary significantly by source (TI, 2000, p.6).

> 8 The standardization occurs in stages with each source.

10
8

# _Voucher Beneficiaries in Kilimanjaro_

The data used here are from the Vulnerability Household Panel conducted in the Kilimanjaro region of Tanzania in 2003, 2004 and 2009.<sup>10</sup> Kilimanjaro is a well-connected and dynamic coffee growing region located in the Northern Highlands, where maize is an important staple (in addition to bananas). It consisted of 5 districts in 2003 (one district was split up later on) and the sample was designed to be representative for all agricultural households in rural Kilimanjaro. In the first round, conducted in November-December 2003, 954 households were surveyed in 45 villages selected using the probability proportional to size procedure, or about 21 households per village. Households were revisited in NovemberDecember 2004 and 2009, with little attrition in 2004 (915 households surveyed), though a significant loss of households in 2009 (772 households interviewed).<sup>11</sup> To correct for underrepresentation due to attrition of households with certain characteristics, the sampling weights of the remaining households were adjusted, as outlined in more detail in Appendix A1.

Each round the survey comprises a comprehensive community and household survey with most of the modules identical across rounds.<sup>12</sup> The data in the third round capture the results of the 2008/9 agricultural season, which coincides with the input voucher pilot. A special module about the input voucher was added to the household questionnaire, including questions about whether households were determined as eligible, their actual uptake as well as the kinds of vouchers received. The total number of vouchers received by the village was recorded in the community survey.

> 10 See Christiaensen and Sarris (2007) for a detailed description of the survey and sampling design. 11 Some households with only one elderly were lost due to the death of the person. Some households moved out of the village, and some households were not surveyed because they were working far away on their farms. 12 The community survey collects information about the village governance structure and the demographic, geographical, agro-ecological and socio-economic conditions in the village. The household survey provides information on household demographic characteristics, including educational status and health, agricultural production and sales, non-farm activities and income, consumption, shocks, assets, access to credit and perceptions on trust and empowerment.
taxes held constant) is to reduce hydrous ethanol prices by 21 percent (= – 0.20/0.96 [x100%]). Our other research (Drabik, 2011) finds a comparable effect of eliminating U.S. ethanol policies for the same time period (a 24 percent reduction in U.S. ethanol prices), although ethanol prices are higher in Brazil and U.S. ethanol consumption is twice that of Brazil.

Unlike the United States, where the mandate always acts as a lower bound on the ethanol price (i.e., it determines the minimum price), the mandate in Brazil may be either a lower or an upper bound. The latter may occur, for example, if sugar demand is low and sugarcane supplies plentiful. In that case, the removal of the mandate would increase the ethanol price.

Table 7 presents results for simulations where the Brazilian market experiences a shock in supply of sugarcane; demands for fuel and hydrous ethanol; and demand for sugar. The magnitudes of the shocks correspond to the medium scenario values presented in Table 2. Both “increase in demand for sugar” and “reduction in supply of sugar” scenarios yield the same qualitative conclusions: the price of sugarcane increases, making production of anhydrous and hydrous ethanol and sugar more expensive; thus the market prices of these commodities rise. A higher market price of anhydrous ethanol increases the marginal cost for fuel blenders; hence the increase in the consumer price of fuel blend.

|**Table 7. Market Shocks Sim**|**ulations Re**|**sults***<br>|D<br>|ifference relati<br>Increase in|ve to baselin<br>demand for|<br>e||
|---|---|---|---|---|---|---|---|
|||||||||
||Baseline|eucton<br>sugarc|suppy o<br>ane**|fuel & red<br>demand for|uction in<br>E100 ***|ncrease n<br>suga|eman or<br>r ****|
|||B = 1.2|B = 5|B = 1.2|B = 5|B = 1.2|B = 5|
|Fuel price ($/liter)|2.47|0.04|0.03|-0.01|0.00|0.02|0.01|
|Market price of anhydrous<br>ethanol ($/liter)|1.18|0.15|0.10|-0.02|-0.02|0.09|0.06|
|Market price of E100 ($/liter)|0.96|0.15|0.10|-0.02|-0.01|0.08|0.06|
|Consumer price of E100<br>($/liter)|1.54|0.15|0.10|-0.02|-0.01|0.08|0.06|
|Price of sugarcane ($/tonne)|56.11|11.07|7.47|-1.58|-1.11|6.29|4.29|
|Price of sugar ($/tonne)|700.93|72.88|49.14|-10.42|-7.31|41.38|28.23|

* Mandate binding in all simulations.

- ** Reduction of 18.3%.

- *** Increase of 22.8%; reduction of 20.6%.

- **** Increase in domestic demand of 12.6% and increase in export demand of 33.8%.

B = 1.2 means that flex cars owners are less sensitive, and B = 5 that they are very sensitive to a change in the fuel price gap.

Source: own calculations 32
**Benefits in Russia**

7 ilggest that firms have widely tried to economize on providing benefits, but the extent of 'ivestiture to date is perha-s less than might have been expected. 2; Costa of Benefits Ltttle ha%rd evidence exists that allows computEtion of the share of benefit costs in firms' labour payments. The survey, however, allows us to get a reasonably detailed picture on this score. First, wages as reflected in the **firms' wage** bills (inclusive of the wage tax) account for roughly 53% of total labour costs. Including bonuses raises this siiare to just over 60% for the entire _sanpls._ The remaining costs are distributed over Pensions, Social Insurance **taxes,** Employment Fund payments and, most significantly, Soci.ai Fund allocations s. The latter amount to over 20% of total labour-related c.sts and around 40% of benefit charges (ie, excluding the wage bill). Wh4.e we observe considarable variance over branches and firm size classes for the respective shares, we do in genetal find a surprising degrece of stability. This is also true for the leve2s. Comparing the first three quarters of 1991 with 1992, real aggregate labour costs fell slightly. Small declines were registered in 70% of branches with, significant ircreases in the remaining 30' Figures 1 to 3 show the path of aggregate labour-related costs and their - components for the period 1991.1 1992.4. The main story is the fair constancy of real labour-related costs. As such, we find considerable corroborative support for the conclusions derived from more aggregated wage data ;. Further, we can pin down reasonable stability in the cost structure allowing us to conclude that, at least for our samvle, benefits have not been usci as a mechanism for significantly raising aggregate labour costs. The path of both the levels and the shares indicates that including benefits costs in total labour costs has no marked effect on the distribution. This holds whether classifying over branch or firm size, as measured by employment or value of fixed assets.

Taken from the income side, we likewise find considerable stability in the The bulk of benefits costs >ave normally been subsumed in Social Fund expenditures. Qimiao Fan has pointed cut to us that firms could also write cenerits into production costs part.y ror tax reasons, as aiso because of restrictions placed on the use of the Soc,al Fund.

See Commander et al (1993b)
tions per country and not eliminating too many countries from the sample. At any rate, as a robustness check we shall vary the threshold from a minimum of two to a maximum of eight consecutive pairs of observations.

The variable _Flex_ measures the regulatory environment, and consists of the Fraser Institute index of credit, labor, and regulatory flexibility (http://www.fraserinstitute.ca). _Flex_ measures how much the regulation of credit and labor markets, and of the business environment, “restricts entry into markets and interferes with the freedom to engage in voluntary exchange” (Economic Freedom of the World, 2005). The index considers factors such as the presence of foreign Banks, interest rates controls, minimum wages, and firms’ entry costs. It varies from zero to ten, where ten represents the highest degree of flexibility.

The variable _Law_ measures the degree to which contracts are enforced and the efficiency of the police and judicial systems. It consists of the International Country Risk Guide index of law and order (http://www.icrgonline.com), which measures the strength and impartiality of the legal system, and the popular observance of the law. The index varies from one to six, where six represents the highest degree of law and order.<sup>4</sup> Finally, the variables _Y_ and _Govex_ represent, respectively, the log of GDP per capita in constant 2000 US Dollars, and general government expenditure as a percentage of GDP. Both variables are from the World Development Indicators (2005). Appendix 2 shows univariate summary statistics and bivariate correlations for all variables included in the empirical analysis.

> We also consider two different samples of countries: all countries, and only low and middle income countries with a per capita GDP below 9000 US dollars. We work with

> 4A main caveat of the _Flex_ and _Law_ indexes is that they are based to a large extent on subjective assessments, such as firms’ surveys (for instance, 10 out of 15 components used to create the _Flex_ index are based on survey data). Thus, the indexes can change from one year to the next if people’s perceptions changes (because, for instance, of a corruption scandal), even if structurally little has changed in the country. It is also to avoid these noisy fluctuations that we take countries’ averages of _Flex_ and _Law_ .

16
and an observational equivalence. Section 3 describes the decomposition methodology. Section 4 applies the methodology using data from Mexico and Canada. Section 5 discusses the implications of the results for understanding emerging market uctuations and concludes.

# 2 A Prototype Small Open Economy

This section describes a standard small open economy model with incomplete asset markets augmented with ve stationary reduced form shocks, referred to as wedges. In the model, these reduced form shocks are interpreted literally as productivity shocks (the eciency wedge), as labor income and investment taxes (the labor wedge and investment wedge), as uctuations in interest rates (the risk premium wedge), and as government consumption (the government consumption wedge).

Time is denoted by _t_ = 0 _,_ 1 _,_ 2 _..._ and the state of the economy at period _t_ by _st_ . Let _s_<sup>_t_</sup> = _{s_ 0 _, s_ 1 _, ..., st}_ denote the history of the economy until time _t_ and _π_ ( _s_<sup>_t_</sup> ) the probability of observing the history _s_<sup>_t_</sup> as of time zero. Wedges in period _t_ depend on the history _s_<sup>_t_</sup> . The eciency wedge is denoted by _A_ ( _s_<sup>_t_</sup> ) ; the labor wedge, by (1 _− τl_ ( _s_<sup>_t_</sup> )) ; the investment wedge, by 1 _/_ (1 + _τx_ ( _s_<sup>_t_</sup> )) ; the risk premium wedge, by 1 _/Z_ ( _s_<sup>_t_</sup> ) , and the government consumption wedge, by _g_ ( _s_<sup>_t_</sup> ) . Note that a drop in any of these wedges is interpreted as an increase in the corresponding distortion.<sup>1</sup> A representative household has preferences over contingent sequences of consumption, _c_ ( _s_<sup>_t_</sup> ) , and hours worked, _l_ ( _s_<sup>_t_</sup> ) , represented by the expected utility function where 0 _< β <_ 1 is a subjective the discount factor and _η_ is the growth rate of the population. Here and throughout this section, all variables are expressed in per capita terms.

> 1While a drop in government expenditures may not mean an increase in any distortion, it does induce a drop in output.

4
Fig 4 15

# Real Vclue of Housing Expenditures

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with positive and large assigned profits in the final sample.

# **C National Accounts Aggregates from WID dataset**

Here we discuss the national accounts aggregate we use in more detail and provide additional information on the adjustment of the microdata to macroeconomic aggregates.

We start by using World Inequality Database (WID) aggregates for nominal National Net Income (NNI) in local currency for the period 2003 - 2019. We provide all numbers used in the analysis in Table C1. For 2019, for example, the Net National Income of reference is L 541 billion - in real terms, NNI grew by 80% in the period 2003-2019.<sup>2</sup> We then decompose NNI in four main components – Compensation of Employees (L 260 billion in 2019); Capital Income (L148 billion); Mixed Income (L 87 billion) and Taxes on Production (L 47 billion). In Figure C2, we plot the share of main components in total NNI over time - compensation of employees represent approximately half of total income over the period; total capital income ranges from 20-30%; mixed income approximately 15% and total taxes on production 10%.<sup>3</sup> The main change in aggregates’ shares we observe is a decrease in compensation of employees from a peak of 54% in 2013 to 48% by 2019, accompanied by an equivalent 6 That increase is p.p. increase in the participation of capital income from 21% to 27%. largely explained by a substantial expansion of corporate profits in total income - the level of corporate profits increases nominally by 150% between 2013 and 2019, from L46 billion to L116 billion.

As discussed in the text, we match wages in the microdata to Compensation of Employees; we match Mixed Income in the microdata to its equivalent in national accounts; and distribute taxes on production proportionally to total income, so it has no bearing on inequality measures. For distribution of capital income, we use a more detailed disaggregation. Using WID decomposition, capital income can be separated in property income of households; net operating surplus (NOS) of the households; corporate profits; and property income of the government. Similarly to taxes on production, we distribute the (small) property income of the government proportionally to households. Following Alvaredo _et al._ (2020), we match NOS to total imputed rent

> 2In Figure C1, we show that the growth of NNI closely matches the growth of Gross Domestic Product in the period. The levels of GDP available at WID match those in other sources such as the World Bank’s World Development Indicators.

> 3We note that not all subcomponents are available in the WID data in recent years, so we impute some of the aggregates. Specifically, no information for the four components of NNI are available for 2019, so we use the same shares observed in 2018 to recover levels in 2019, multiplying imputed shares by the NNI value in that year. Similarly, the subcomponents of capital income are not available between 2016-2019, so we impute the shares observed in 2015 and use them to recover levels of each of them in the latter period.

51
# **Table 1b. The Effect of Informality on Economic Growth, controlling for government expenditure/GDP**

|**_Dependent variable: Per c_**|**_apita GD_**<br>|**_P Growth,_**<br><br>OL|**_1985-200_**<br><br>S|**_5, countr_**<br>|**_y average_**<br>|<br><br>I|V||
|---|---|---|---|---|---|---|---|---|
||[1]|[2]|[3]|[4]|[5]|[6]|[7]|[8]|
|Initial Government Expenditure|-0.0340*|-0.0513**|-0.0681***|-0.0588**|-0.0593**|-0.0717***|-0.1008**|-0.0776***|
|(% of GDP, 1985)|1.96|2.60|2.82|2.59|2.14|2.94|2.55|3.34|
|Schneider Shadow Economy|-0.0622***||||-0.0789***||||
|(% of GDP)|4.76||||4.31||||
|Heritage Foundation Informal Mark|et|-0.6724***||||-0.6085***|||
|(ranging 1-5: higher, more informality)||5.52||||4.18|||
|Self Employment|||-0.0557***||||-0.0596***||
|(% of total employment)|||3.84||||2.85||
|Non-contributor to Pension Scheme||||-0.0183***||||-0.0203***|
|(% of labor force)||||3.58||||3.51|
|Constant|4.1214***|4.5441***|4.6023***|3.5267***|5.0933***|4.6934***|5.0909***|4.1156***|
||6.71|7.98|6.69|6.19|6.18|6.72|4.50|6.70|
|No. of observations|112|118|69|85|88|91|59|72|
|R-squared|0.20|0.18|0.18|0.11|-|-|-|-|
|Hansen J Statistic (P-value)|-|-|-|-|0.53|0.89|0.62|0.72|

Notes:

1. Heteroskedasticity-robust t(z)-statistics are presented below the corresponding coefficients.

2. *, ** and *** denote significance at the 10 percent, 5 percent and 1 percent levels, respectively.

3. For IV regressions [5] to [8],

- Endogenous variable: each of four informality measures.

- Instruments: Business regulatory freedom; Average Years of Secondary Schooling; Sociodemographic factors

- Law and order is not included as an instrument because it does not pass the exogeneity test using the C statistic (Difference-in-Sargan statistic).

4. See Appendix 1 for definitions and sources of variables.

Source: Authors’ estimation 18