rafmacalaba/datause-encoder-data huggingface dataset:
**Other Data.** Data on real GDP per capita, investment, government consumption, and the relative

price of investment are from the Penn World Table (Heston et al., 2012). Data on the share of population ages 15 years and above with tertiary education, the share of population ages 15 years and above with secondary education, and the average years of schooling are from Barro and Lee (2010). Descriptive statistics for the above variables are provided in Appendix Table 1.

# **4. Estimation Framework**

# _4.1 Identification of Simultaneous Equation Model with a Zero-Covariance Restriction_

In this section we discuss identification of a simultaneous equation model using a zero-covariance restriction. The discussion corresponds to the case discussed in Hausman et al. (1987) on page 854.

Consider an econometric model with two equations that shows a two-way causal relationship between GDP per capita and inequality:

- _(1) Y=bX+rR+e_

- (2) _X=aY+u_

where the error terms _e_ und _u_ are uncorrelated; _R_ is an exogenous variable that is uncorrelated with _u_ and _e_ . It follows from substituting (2) into (1) that

- _→ Y =_ (1- _ab_ )<sup>-1</sup> ( _bu+rR+e_ )

- → X = (1- _ab_ )<sup>-1</sup> ( _arR+ae_ + _u_ )

_R_ can be used as an instrument for _Y_ in equation (2). The instrumental variables estimator for _a_ in equation (2) is:

# _a_<sup>_IV_</sup> _=cov(R,X)/cov(R,Y)=cov(R, aY+u)/cov(R,X)=a+cov(R,u)/cov(R,X)=a_

where the last line follows from cov(u,R)=0.

With a consistent estimate of _a_ in hand, one can then generate a variable _Z_ = _X-a_<sup>_IV_</sup> _Y=u._ And

> filtered the data to drop low-quality observations. The data were supplemented with data from the World Bank’s POVCALNET database for developing countries. To ensure comparability between the two data sources, Brueckner et al. made adjustments to the data sets for individual countries so that the income shares consistently correspond to those of a consumption (or income) survey. The authors then identified and dropped duplicates; eliminated duplicate survey-years with inferior quality data from the WIID; eliminated survey-years for which no extra information (consumption/income; etc.) is available as well as survey-years for which the income shares add up to less than 99 or more than 101 percent. The authors then aggregated the inequality data to the 5-year level by taking a simple average of the observed annual observations over five years. In the regression analysis countries are only included for which inequality data are available for at least two or more consecutive 5-year intervals.

8
# **Table 4**

||**Gove**<br>**Individual**|**rnance regression**<br>**Pradhan**|**s: police o**|**fficers**<br>**Basti age**||**Village**||
|---|---|---|---|---|---|---|---|
||<br>**regression**|<br>**characteristics**|**2SLS**|<br>**x**|**2SLS**|<br>**x**|**2SLS**|
|Assets (Rs. 10000s)|0.061|0.061|0.200|0.060|0.188|0.059|0.222|
||[0.014]**|[0.014]**|[0.130]|[0.014]**|[0.123]|[0.014]**|[0.124]+|
|Init. contact: close relative|0.032|0.032|0.006|0.033|0.014|0.029|-0.004|
||[0.043]|[0.041]|[0.046]|[0.042]|[0.047]|[0.041]|[0.041]|
|Init. contact: villager|0.009|0.013|-0.037|0.013|-0.032|-0.137|-0.250|
||[0.035]|[0.035]|[0.065]|[0.035]|[0.063]|[0.109]|[0.167]|
|Init. contact: other|-0.053|-0.045|-0.063|-0.041|-0.053|-0.040|-0.060|
||[0.031]+|[0.030]|[0.050]|[0.030]|[0.048]|[0.031]|[0.053]|
|Pradhan-shared identity||0.010|0.024|0.049|0.232|-0.016|0.000|
|||[0.030]|[0.037]|[0.091]|[0.139]+|[0.044]|[0.079]|
|Pradhan's education||-0.008|-0.009|-0.004|0.018|-0.008|-0.004|
|||[0.004]+|[0.005]+|[0.011]|[0.012]|[0.003]*|[0.005]|
|Pradhan- political affil.||-0.008|0.060|0.048|0.089|-0.078|-0.133|
|||[0.040]|[0.053]|[0.095]|[0.138]|[0.035]*|[0.117]|
|Pradhan's community yrs||-0.004|-0.006|0.006|0.002|-0.004|-0.004|
|||[0.002]|[0.004]|[0.008]|[0.009]|[0.002]|[0.003]|
|Basti age x prdn shared||||||||
|id.||||-0.001|-0.009|||
|||||[0.004]|[0.006]|||
|Basti age x prdn's educ.||||0.000|-0.001|||
|||||[0.001]|[0.001]*|||
|Basti age x pol. affil.||||<br>-0.003|<br>0.000|||
|||||[0.003]|[0.005]|||
|Basti age x prdn's||||||||
|<br>res.(yrs)||||0.000|0.000|||
|||||[0.000]|[0.000]|||
|Villager x prdn shared id.||||||0.054|0.042|
|||||||[0.063]|[0.100]|
|Villager x prdn's educ.||||||-0.001|-0.009|
|||||||[0.007]|[0.006]+|
|Villager x pol. affil.||||||0.153|0.315|
|||||||[0.072]*|[0.153]*|
|Villager x prdn's res.(yrs)||||||0.000|-0.003|
|||||||[0.004]|[0.005]|
|Household / basti controls|yes|yes|yes|yes|yes|yes|yes|
|Observations|780|780|474|780|474|780|474|
|Adjusted R-squared|0.07|0.07||0.07||0.07||
|<br>F|13.26|44.48|0.091|64.93|0.212|44.08|0.171|
|Hansen's over-id J|||0.618||0.684||0.476|
|Prob > chi2|||0.432||0.408||0.49|
|Robust standard errors in br|ackets|||||||
|+ significant at 10%; * signif|icant at 5%; **|significant at 1%||||||
|robust standard errors, clust|ered at the slu|m level||||||

30
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The export restriction on cotton has resulted in a number of negative consequences. The yield growth of cotton has stagnated as the price of cotton is suppressed, thus it is not an attractive crop for farmers. Inefficient public traders have lost export opportunities and waste cotton while lobbying the governrnent to allocate them higher export quotas. The spinning industry and weaving industry continually ask the government to restrict the level of cotton exports. In addition, the nationwide cotton export quota is decided in an unscientific way based upon crop estimates made by interest groups, who tend to pad the estimations up in order to influence the level of nationwide cotton export quotas. The Indian spinning industry, which was one of the most competitive in the world in the eighteenth century, has now become one of the world's most inefficient. In fact, India has become one of the few countries where the production of cotton yarn per spindle has declined since the 1960s. Moreover, the export restriction of cotton did not contribute to an increase in cotton yarn and textile exports. Despite the abundant supply of cotton for the domestic cotton textile industry, India's share in the cotton yarn and cloth markets in the world is also limited **(Table 2).**

**Table 2:** **_India's Share and Ranking in the World Cotton and Textile Markets_**

||Area, Capacity|Production|Exports|
|---|---|---|---|
|Cotton|24 % (1)|13 %O<br>(3)|1 %|
|Cotton Yarn|17 %(2)|9 9/o (3)|2**%**|
|Cotton Cloth|19 % (3)|14 % (2)|3**%**|

I: ndia's ranking in the world Sources: Office of the Textile Cormnissioner (1995), ICAC (1994) In the past, there have been a nuimber of attempts to incorporate political issues into economic theory.' More recently, several important empirical studies were conducted on the political economy of agricultural trade policy in developing countries.<sup>2</sup> In addition, a theoretical attempt was made to fill the gap between international economics and domestic politics by modeling the mechanism whereby interest groups 1 Buchanan and Tullock (1962) and Olson (1971) are classic literature which incorporated politics into economic theory. Krueger (1974)'s model of "rent-seeking activities" and Bhagwati (1982)'s work on "directly unproductive profit-seeking (DUP) activities" illustrate how the regulation induces socially undesirable activities.

2 Krueger, Schiff and Valdes(1995) studied the agricultural pricing policies in eighteen countries, and concluded that discriminating against exporting agriculture causes a number of negative effects. The cross-country study by Sachs and Warner (1995) revealed a negative relationship between resource abundance and economic growth. Bates (1981, 1983) studied several African countries, and concluded that the negative consequence of export restrictions of natural resources in Africa was largely a result of the governments' intention to use the economic rent not for irrigation or infrastructure but for "divisible ilnputs" to some particular politically influential groups.

2
The measure _EComp PSo_ 5 is calculated using data on completion rates for students in Grade 1 through 5 at time t (for more details, see Appendix A). The cutoff point for calculation of the completion rate is Grade _5_ rather than the final grade in primary school, because the latter varies substantially across countries.<sup>10</sup> There is ample anecdotal and empirical evidence that educational outcomes are affected by family factors such as parents' income, occupations and educational background (Psacharopoulos and Woodhull 1985, Hanushek 1995, Barro and Lee 1996). These are reflected in three of our right-hand side variables: per capita income, the Gini coefficient (an indicator of income distribution), and the overall level of adult literacy. One could argue that the adult literacy variable is endogenous as unobserved factors affecting it may also affect primary school attainment. This is not a major issue in our analysis as our primary interest is not in the coefficient of the adult literacy variable. We do check our results by performing each education regression twice: once with adult literacy as a right-hand side variable, and once without it. Primary education spending is one of the key regressors in our analysis, along with the same two governance variables as in the health regressions: the level of corruption and of bureaucratic quality. Other right-hand side variables include ethno-linguistic fractionalization, the proportion of Muslims in the population, the level of urbanization, the proportion of the population aged 6 to 12, and a dummy variable for East Asia. The first three are also included in the child and infant mortality regressions; they reflect racial, cultural and physical circumstances that may arguably affect education provision and demand. The proportion of the population aged between 6 and 12 is a demographic factor that has shown to be strongly related to educational outcomes (Mingat and Tan 1998). A dummy variable for the countries in the East Asia Region is included as there seems to be a strong emphasis on the importance of education in these countries, probably due to cultural reasons (Stevenson 1992, Barro and Lee 1996). Our sample includes three countries from this Region: China, Thailand and South Korea.

> 9 To be consistent with our mortality measures of the health sector, we use education non-attainment as our outcome measure.

> 10En principle, a similar technique could be used to comnpute attainment at the secondary and tertiary levels. In practice, however, the data required for this are not available for a sufficiently large number of countries. We tried using secondary school enrolment as a proxy for secondary level attainment, a not indefensible procedure since secondary school dropout rates tend to be relatively low in many countries. The ensuing regressions did not, however, perform well. In this paper, we report only the results of regressions explaining variations in failure to attain five years of primary education.

_18_
an increase in international integration with e.g., exports growing much faster than income, making it interesting to analyze the link between international activities and the demand for skilled labor.

Our analysis draws on survey data recently collected by the World Bank from manufacturing establishments in Brazil and China under the Investment Climate Assessment initiative.<sup>19</sup> The surveys applied a (mostly) common questionnaire covering a variety of topics (e.g., infrastructure, fi nance, etc.) to random samples of establishments and were implemented in China in 2001 in partnership with a local statistical institute and in Brazil in 2003 in partnership with a local consulting fi rm.<sup>20</sup> The random samples were drawn from a sampling frame of manufacturing establishments in each country. The sample design varied by country: (i) in Brazil, strati fi ed samples based on industry-state-size were drawn for thirteen states and all the regressions estimated in the paper are weighted accordingly and (ii) in China, the sample was randomly chosen for each of ve cities.<sup>21</sup> Both surveys cover different industries and regions and the entire range of plant sizes, but the exact composition of the samples differs across countries. In Table 2, we show the distribution of plants across industries (Panel A) and across size groups (Panel B) for each country.<sup>22</sup> The apparel, auto-parts and electronics industries are covered in both countries. Large plants represent about a half of the sample for Brazil whereas for China the majority of plants in the sample are small.

> 19More information on the initiative can be found at http://www.worldbank.org/privatesector/ic/.

> 20The survey questionnaires also included some country speci fi c questions. For our purposes, the relevant survey questions relate to the workforce, technology and innovation, production and costs, ownership structure, trade and manager characteristics.

> 21In Brazil, the sampling frame consisted of the listings of manufacturing establishments provided by IBGE (the national statistical office). The states covered in Brazil are: Amazonas, Bahia, Ceará, Goiás, Maranhão, Mato Grosso, Minas Gerais, Paraíba, Paraná, Rio de Janeiro, Rio Grande do Sul, Santa Catarina and São Paulo. In China, the sampling frame consisted of an electronic list of fi rms in each of the cities covered: Beijing, Chengdu, Guangzhou, Shanghai and Tianjin.

> 22Size groups are de fi ned according to a plant’s total number of permanent employees.

10
- 29 -

# 5.2 2he fia in worker controlled

If the firm la worker controlled the unlua' utlilty functLon is as follows; U?'* -L,IWI 4 B9 LAB, (5. 1) U?'* -L,IWI 4 B9 LAB, (5. 1) The production functian Le similua in both periods and includes capital (K,) as well as labour. We also include an exogenous productLvity shock (O1 > 9,(L.,,K,), i * 1,2 We assume posLtLve but decreasing marginal returns. Fo, simplicity we assue no depreciation of capital. The firm can sell (and buy) capital at the start of each period at the prLce p, i a 1,2 and the VCF has to dcide at the start of each period how much capital to sell (or buy) and how much to allocate to productLon. The WCF pays taxes to the government amounting to T,, . - 1,2. The WCF has inLtlally at period 1 a capital stock R. It decLdes to use K, for production and sells th rest, worth pl(K-K,)o The zero profLt constraint for period 1 can now be expressed ass 9 _j, ,R,)_ - _;L,W1 4_ **p,(K-K,I)** - T, - 0 (5.2) The zero profit constraLnt for period 2 when the firm is still worker controlled ios 62 F(L21,2) - Lr 2 2<sup>+4 P</sup> 2<sup>(K,-)</sup> - T' - 0 (5.3) From expressions (5.2) and (5.3) it can be seen that the WCF cannot lend to, or borrow from, sources outside the fiLrm. Flnally, we specify the tax rule as a wage per worker tax (see SectLon 4).

T, - t;jq{"lj@f), i - 1,2 (5.4) 5.3 The fire is privatised If the fLrm is prLvatLeOd new owners take over ln period 2 and there is a regime change. We **assume** that the decLiLons taken by the workers ln period 1 do not influence the utilLty of the union in period 2 if the firm is privatLzed. Further, if the firm is privatized it is not taxed. In the event of the fLrm being privatized in period 2 the exogenous utLilty level obtained is U2 *-
with an average inter-item correlation of .46. Most of our tests employ this index, but we also show how results are affected by instead using each of the three surveys in isolation.

Year dummies are also included in the regressions where appropriate. The Paris Declaration along with its associated monitoring indicators and follow-up conferences is designed in part to create “peer pressure” among donor agencies toward meeting its numerical targets.<sup>13</sup> If pressure from peers, recipients, or civil society is increased as intended, coefficients for year dummies included in the analysis should exhibit a significant positive trend.

Donors choose whether or not to use country systems. Their choices, however, reflect some combination of donor and recipient country characteristics. We estimate regressions of the general form: where _UCSijt_ is the share of donor _i_ ‟s aid to recipient _j_ in year _t_ that is recipient-managed, _Z ijt_ are regressors that vary by donor, recipient, and year; _X it_ are regressors that vary by donor and year but are recipient-invariant; _M jt_ are regressors that vary by recipient and year but are donorinvariant; and<sup>_Y_</sup> _t_<sup>is a vector of year dummy variables. Recipient variables are lagged by one</sup> year. Donor variables are mostly time-invariant, including donor public opinion, which is constructed from surveys conducted between 1 and 15 years prior to the years for which the PDMS measures use of country PFM systems.

The dataset can be treated as an unbalanced panel, with anywhere between 1 and 80 observations per donor. We can exploit this structure of the data to conduct stronger tests by controlling for donor-year, recipient-year, or donor-recipient fixed effects. For example, in

> 13 International donor conferences on harmonization bring senior managers of aid agencies “in close contact with colleagues from other agencies, pushing them to align with recognized international best practice and not be seen as laggards” (de Renzio, 2005: 11).

15
8 Figure 4: Government wages, _early 1990's_ <!-- Start of picture text --> Central Government Wage Bill<br>as percent of GDP<br>10.0 - __<br>8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>Afnica Asia ECA LAC MENA OECD<br>IF<br>Average Central Government Wage<br>as multiple of per capita GDP<br>6.0 -<br>5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>Africa Asia ECA LAC MENA OECD<br>Ratio of Public to Private Wages<br>1.4<br>1.2<br>1.0 -<br>0.8<br>0.6 -<br>0.4<br>0.2<br>Africa Asia ECA LAC MENA OECD<br><!-- End of picture text -->
# **Federal Districts of the Russian Federation**

||**FD1-Central**|**FD2-Northwest**|**FD3-South**|**FD4-Volga**|**FD5-Urals**|**FD6-Siberian**|**FD7-FarEast**|
|---|---|---|---|---|---|---|---|
|DK|229 727,99|66 761,96|46 579,30|136 881,46|79 018,28|54 808,76|7 491,64|
|DL|250 300,65|100 797,47|21 441,27|129 133,45|48 030,72|47 957,67|4 610,24|
|DM|205 726,56|111 269,93|49 536,72|529 447,15|58 876,36|53 438,54|17 432,47|
|DN|192 011,46|44 507,97|17 005,14|90 393,42|80 567,66|56 178,98|7 635,71|
|E_distr|432 695,86|143 469,33|80 757,51|205 916,99|173 817,13|127 188,46|58 543,97|
|E_ely|232 554,23|58 182,54|89 847,66|204 195,36|94 444,54|144 957,49|55 949,09|
|F|858 498,82|437 818,94|283 882,31|426 834,45|371 505,35|261 617,21|210 678,51|
|G|4 143 285,55|517 927,98|424 564,12|672 835,63|996 788,33|439 567,72|194 754,17|
|H|160 374,23|38 580,21|46 359,91|52 744,21|61 517,44|36 329,59|16 935,51|
|I_cmn|164 244,23|65 355,58|50 751,75|80 429,06|95 905,87|68 781,08|32 450,74|
|I_trn|1 105 512,87|439 902,43|341 605,38|541 360,64|645 533,61|462 959,17|218 084,60|
|J|1 026 038,75|47 232,14|17 687,30|52 634,57|154 513,13|24 930,87|11 386,42|
|LO|1 140 675,07|378 710,68|315 226,85|436 068,27|362 382,77|389 550,13|229 126,93|
|K|1 697 320,66|308 834,91|188 960,73|419 435,41|588 173,66|259 078,32|122 454,11|
|M|240 250,04|103 744,17|89 169,03|148 128,27|116 202,90|129 762,60|58 811,29|
|N|363 523,35|171 676,78|135 007,83|203 868,61|200 703,92|187 389,36|78,25|

_Source:_ SUST-RUS. 2012(a), 2012 (c).

99
**Figure 5**

Reduced form: effect of road prioritization on indices of major outcomes <!-- Start of picture text --> .2 .2 .2<br>0 0 0<br>−.2 −.2 −.2<br>−100 −50 0 50 100 −100 −50 0 50 100 −100 −50 0 50 100<br>Normalized population Normalized population Normalized population<br>.2 .2<br>0 0<br>−.2 −.2<br>−100 −50 0 50 100 −100 −50 0 50 100<br>Normalized population Normalized population<br>Firms index<br>Transport index<br>Ag occupation index<br>Ag production index Consumption index<br><!-- End of picture text --> Notes: The figure plots the residualized values (after controlling for all variables in the main specification other than population) of the indices of the major outcomes in each of the five families of outcomes (transportation, occupation, firms, agriculture, and welfare) over normalized village population in the 2001 Population Census. The sample consists of villages that did not have a paved road at baseline, with baseline population within an optimal bandwidth (84) of the population thresholds (see text for details). Population is normalized by subtracting the threshold.

43