rafmacalaba/datause-encoder-data huggingface dataset:
between the average Gini coefficients of market and disposable income across European countries, about two-thirds (10 percentage points) are due to transfers.

**Figure 14. The role of taxes and transfers in Europe and Latin**

# **<u>America</u>**

<!-- Start of picture text --> Difference between Gini coefficients of gross income and market income<br>Panel A. Latin America Panel B. Europe<br>0.00 0.00<br>-0.04 -0.04<br>-0.08 -0.08<br>-0.12 -0.12<br>-0.16 -0.16<br>Difference between Gini coefficients of disposable income and gross income<br>Panel C. Latin America Panel D. Europe<br>0.00 0.00<br>-0.04 -0.04<br>-0.08 -0.08<br>-0.12 -0.12<br>-0.16 -0.16<br>Argentina Brazil Chile Colombia Mexico Peru LAC 6 Austria Belgium Denmark Finland France Germany Greece Ireland Italy Luxembour Netherlands Portugal Spain Sweden UK EURO 15<br>Argentina Brazil Chile Colombia Mexico Peru LAC 6 Austria Belgium Denmark Finland France Germany Greece Ireland Italy Luxembour Netherlands Portugal Spain Sweden UK EURO 15<br><!-- End of picture text --> Source: Goñi, Lopez, and Serven (2008) Such low levels of income redistribution through the State may be a reflection of high levels of State capture, and this in turn a reflection of high inequality levels as discussed below. But the fact that Europe was able to “break” with a history of high inequality during the twentieth century as indicated in Figure 15 below, suggest that such an event is not an impossibility going forward in Latin America.

20
decadal census data on India, we have to limit our analysis of Indian trends, as well as our comparison of trends in China and India, to the decadal census data on these countries. However, we are able to use China’s quinquennial data to further break down the trends in that country, and this also offers some insights into what may be happening in India.

Figure 4 shows the decadal rate of increase in child sex ratios in India and China. Comparing the rate of increase in child sex ratios during the 1980s with that during the 1990s, we find that it changed little in India, while it rose sharply in China. The rate of increase of masculinization of child sex ratios in China was already 1.7 times higher that of India in the 1980s, and rose to 4.7 times that of India in the 1990s. These decadal trends suggest that child sex ratios have continued to rise sharply in China.

However, the quinquennial data for China show that the decadal data hide important trends. They show that the rate of increase in China’s child sex ratios peaked in 1990-95, and then _fell_ sharply (Figure 5).<sup>11</sup> The lack of quinquennial data for India precludes this more nuanced analysis. It is evident that the rate of masculinization of India’s child sex ratios remained relatively low and unchanged during the 1980s and 1990s. However, as in the case of China there may well be a slowing down in the rate of masculinization of child sex ratios in India which is masked by the decadal data. This would be consistent with the survey evidence that reported son preference is falling across India (Retherford and Roy 2003), and with the results of our sub-national analysis below which shows that most states show a decline in the rate of masculinization of child sex ratios.

# **_Sub-national trends_**

The trends from the decadal censuses in the sub-national rates of masculinization of child sex ratios in India and China are shown on the lefthand side of Tables 2 and 3 respectively. Bold italics indicate sub-national units where the rate of masculinization was lower than in the preceding decade. The right hand side of Tables 2 and 3 show the actual child sex ratios in these sub-national units.

# <u>Sub-national trends in India</u>

The rate of masculinization decreased in most Indian states between the 1980s and the 1990s — including states such as Rajasthan and Uttar Pradesh, which had some of the more elevated child sex ratios in the country. Many of the other states have historically shown lower son preference, and had registered a more modest masculinization during the 1980s, perhaps partly in response to better access to technology of sex-selection. However, the trend was towards greater masculinization in the two states of Western India (Maharashtra and Gujarat), which comprise around 14% of the total population of the country.<sup>12</sup> The upward trend is especially notable in Gujarat state.

The sharpest rise was in the two Northwestern states of Punjab and Haryana — where 5
# **Table A.5: Factors related to provincial dissimilarity index**

|||Dissimila<br>|rity Index||
|---|---|---|---|---|
|Explanatory variables|Model 1|Model 2|Model 3|Model 4|
||(1)|(2)|(3)|(4)|
|L f it dit|0.005***|-0.018***|-0.014**|-0.012**|
|og o per capa expenure|(0.001)|(0.006)|(0.006)|(0.006)|
|Gini index|0.029|0.066***|0.052***||
||(0.022)|(0.022)|(0.018)||
|Log of state spending|||-0.000|0.000|
||||(0.000)|(0.000)|
||||-0.000|-0.000|
|Log of investment spending|||(0.001)|(0.001)|
||||0.030***|0.030***|
|Log of population density|||(0.007)|(0.007)|
||||-0.000***|-0.000***|
|Share of urban population|||(0.000)|(0.000)|
||||-0.017|-0.019|
|Share of population with high-school diploma|||(0.020)|(0.021)|
||||0.000|0.000|
|Share of ethnic minority population|||(0.000)|(0.000)|
|Share of wage income|||0.016**|0.021***|
||||(0.007)|(0.007)|
|Share of non-farm income|||0.015**|0.021**|
||||(0.007)|(0.008)|
|Constant|-0.040***|0.129***|0.146***|0.147***|
||(0.096)|(0.419)|(0.350)|(0.024)|
|Year FE|No|Yes|Yes|No|
|Province FE|Yes|Yes|Yes|Yes|
|Observations|630|630|630|630|
|Number of provinces|63|63|63|63|
|R2|0.112|0.0189|0.0339|0.0226|
|sigma_u|0.0118|0.0137|0.0264|0.0286|
|sigma_e|0.00573|0.00508|0.00461|0.00471|
|rho|0.810|0.880|0.970|0.974|

43
# Table 3: Static Estimates Patents on R&D

|**Observations**|636|||||||||
|---|---|---|---|---|---|---|---|---|---|
|**Countries**|49|||||||||
|**Period**<br>|1976-99|||||||||
|||**Linear**||||**Expon**|**ential**|||
||||||**Negbin**|||**Poisson**||
||**Levels**|**WG**|**PSM**|**Levels**|**WG**|**PSM**|**Levels**|**WG**|**PSM**|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|**ln R&D**|1.01<br>***|0.36<br>***|0.44 ***|1.14<br>***|0.56<br>***|0.78<br>***|1.00<br>***|1.65<br>***|0.86<br>***|
||(0.05)|(0.04)|(0.05)|(0.03)|(0.04)|(0.06)|(0.03)|(0.01)|(0.04)|
|**ln US trade**|0.17<br>***|0.25<br>***|0.14 ***|0.11<br>***|-0.29<br>***|0.14<br>***|0.19<br>***|0.04<br>***|0.22<br>***|
||(0.05)|(0.07)|(0.03)|(0.03)|(0.05)|(0.03)|(0.03)|(0.01)|(0.02)|
|**Natural Resou**|0.05<br>***|-0.10<br>***|-0.02|0.06<br>***|0.00|0.00|0.05<br>***|-0.01<br>***|0.01|
||(0.02)|(0.02)|(0.03)|(0.02)|(0.02)|(0.01)|(0.01)|(0.00)|(0.01)|
|**ln (Mean Pate**|**nts)**||0.61 ***|||0.33<br>***|||0.11<br>***|
||||(0.04)|||(0.04)|||(0.04)|
|**Over-Dispersio**|**n**|||0.65 ***|0.28 ***|0.50 ***||||
|||||(0.04)|(0.01)|(0.06)|||(0.04)|

**Note: The dependent variable in the linear specifications is log Patents. We also include an dummy for those countries with no Patents. The dependent variable in the exponential specification is the count of Patents. Robust standard errors were estimated in all the specifications. All specifications include time dummies. WG and PMS refer to the within-group and the pre-sample mean estimations respectively.**

***Significant at 10%, ** Significant at 5%, *** Significant at 1%**

27
**_j=1_**

the weighted average of the concentration indices of the components, G(v)<sup>_=_</sup> _AjCj(v)._ If we multiply each concentration index in this expression by a<sup>neutralelement</sup> [Gj(v)/Gj(v)], we obtain the following alternative decomposition of the<sup>extendedGini</sup> coefficient: Box 2.4: Plotting Concentration Curves in EViews INCLUDELYCDF LOAD %O SMPL @ALL CALL LYCDF({%1}(1), {%1}(2), {%1}(3)) SERIES P=F_HAT SERIES L45=P GROUP LCC P L45 !M={%2}.@COUNT FOR !K=1 TO !M %sv={%2).@SERIESNAME(!K) SERIES x{!K}={%1}(3)*{%2}(!K) SERIES SHARES{!K}=X{!K}I@SUM(X{!K}) SMPL @FIRST @FIRST SERIES LP{%sv} =SHARES{!K} SMPL @FIRST+1 @LAST LP{%Sv}=LP{%Sv}(-1) + SHARES{!K} LCC.ADD LP{%Sv} SMPL @ALL NEXT FREEZE(LCCS) LCC.XY LCCS.SCALE RANGE(O, _1)_ 'End of Program The coefficient Rj(v) is known as the _"Gini correlation"_<sup>betweencomponentj</sup> and overall x. This expression thus reveals that the contribution of a<sup>componentto</sup> overall inequality is determined by three factors: (1) the proportion of the<sup>componentin</sup> the total value of x, (2) the correlation between the component<sup>and thetotal, and(3)the</sup> extent of inequality in the distribution of the component.

Figure 2.2 shows the configuration of concentration curves of<sup>two itemsfrom the</sup> 1994 Integrated Household Survey in Guinea. The concentration<sup>curveof cerealslies</sup> between the Lorenz curve of total expenditure and the 45 degree<sup>line.Wetherefore</sup> expect its Gini elasticity to be positive but less than one. Expenditure on<sup>beveragesis</sup> more unequally distributed than total expenditure. The corresponding concentration curve lies below the Lorenz curve. Thus we expect the Gini<sup>elasticity of beveragesto be</sup> grater than one. The values of Gini elasticities implied by this configuration<sup>are</sup> presented in table 2.4. This table also reveals that the distribution of<sup>food expenditureis</sup> 15
of reforms would require longer time series of our dependent variables. Thus, more future research in this direction would be welcome.

Our conclusions should thus remain tentative. But they do suggest that reforms do not result in a lower level of activity abroad and a concentration of stock market activity in the domestic market, as some arguments predict. Our findings also suggest that financial globalization could pose a significant challenge to policymakers, as their efforts to foster domestic stock market development seem to translate into more activity abroad. The migration of trading to international markets may adversely affect the liquidity of those firms that remain in the local market and their ability to raise new equity capital. This could have a significant impact on medium sized firms, which are not able to directly access international markets. The unexpected impact of reforms on internationalization calls for a revision of the reform agenda and related expectations. Further research is necessary to understand whether the impact of reforms differs across countries and regions and if differences in the timing of specific reforms affect their impact on stock market development and internationalization.<sup>45</sup>

> 45 Some of the cross-regional differences are already studied in de la Torre and Schmukler (2006) and de la Torre, Gozzi, and Schmukler (2006).

20
Diagram 1: Grounding, facilitating and validating the PPA research

|Month|Village & Commune Level<br>District and province level|National / program level|
|---|---|---|
|1/1999||**Stage 1:**PPA study Ppsa|
||**GROUNDING**|prepared and inuldisciplnay<br>PPAacitaton temformed|
||StWge 2: .Meetigwith Province Pe6ples<br>Committeto' prpsent<br>theieaofjo1r1*<br>conducting the PPA. Consul aonion<br>objectves and selection of study locations<br>etc.||
|**2**/1999|Stage2aP: Prpa tory vistitsto 2 District<br>Popl<br>**commiee6**<br>todiscuss<br>scope and<br>pupo of tthe PPA and practic details.<br>.-State hP-atpy<br>visits to 4<br>d4isuss**sope**n ppose of the<br>PPA_and pitl_<br>_tis_||
|3 /1999||Stage 3: Preparation of training|
||**FACILITATING**|<br>guidelines|
||**Stage 4:**2preparatory visits 2<br>districts to gterbackgound data and<br>co. d<br>poverty assssment with district<br>staff from MrngvrpAswu<br>s r<br>*<br>n~~diffeetgoenmetectios||
|**4**/1999|.<br>**Stae**5*PPA**field**workt rig<br>workshops<br>2 ititwit mixed gropps0<br>o istrict,||
|**_4/_**|||
|**_-_**<br>1999|Stage a:<br>nduct PPA exercises in6 6<br>villgsi in 4 conmunes||
|6/1999|Stage**6:**Preparation of fieldipo<br>**VALIDATING**<br>+a---<br>parpr<br>ono|its, follow-up on rmissing data<br>fdrarrt|
|7 /1999|**Stage 6a.**Distribution ofDraft<br>PPA report to ALL provimncand<br>disuict stffinvolved in the study||
|8 1999|||
||**Stage 6b-**2 daYverification<br>woshopwith province distri and commune staff<br>-o discuss th result- Adjustments made to report||
|9 /1999|I**F**|. Stage**k;**Presentation and<br>discussion of the PPA results at<br>a number of national<br>..<br>workshops.-|
|10 /1999|**Stage**112: Preentation**of Final, Report**||
|(|source: Shanks and Toai, 2000, p96)||

13
48. Most of these sectors meet the pre-screening criteria, i.e., they have some upside potential for growth and meet feasibility criteria. A notable exception is sportswear—unavailability of PVC in the domestic market creates a comparative disadvantage and has already resulted in the closure of domestic production. Its competitiveness may depend on the establishment of a domestic petrochemical industry. Also, in the comparator countries, fertilizer and petrochemical production as well as TV production may not take place in small or medium-size enterprises; however, targeted foreign direct investment may be able to attract investments to establish larger firms, including through joint ventures, provided enabling conditions have been put in place.

49. In a second step, Nigeria’s basic wage competitiveness in these sectors needs to be reviewed to determine whether that would allow Nigeria to reap the advantage of backwardness.

50. The table below summarizes wage data for China, Vietnam and Nigeria on a sectoral basis.

**Table  11: Average wage (incl. benefits) by industry (USD)**

||**skilled**|**labor**|||**unskille**|**d labor**||||
|---|---|---|---|---|---|---|---|---|---|
|**Sector**|**Nigeria**|**Ethiopia**<br>**(ICA)**<br>**Ethiopia**|**Vietnam**|**China**|**Nigeria**|**Ethiopia**<br>**(ICA)**|**Ethiopia**|**Vietnam**|**China**|
|food|135|82<br>89-<br>141|181-<br>363|398-<br>442|87|45|26-<br>52|78-<br>207|192-<br>236|
|Garments|85|82<br>37-<br>185|119-<br>181|331-<br>370|54|48|26-<br>48|78-<br>130|237-<br>296|
|textiles|120|71|||71|20||||
|machinery<br>&<br>equipment|163||||125|||||
|chemicals|212||||127|||||
|electronics|119||||79|||||
|non-metallic<br>minerals|106||||66|||||
|wood,<br>wood<br>products & furn|102|151<br>81-<br>119|181-<br>259|393-<br>442|67|35|37-<br>52|85-<br>135|206-<br>251|
|metal & metal<br>products|107|<br>181|168-<br>233|265-<br>369|82||89|117-<br>142|192-<br>265|
|other<br>manufacturing|130|154|||87|67||||

_Source: Nigeria - Productivity and Investment Climate Survey, 2009 and Ethiopia Investment Climate Survey, Manufacturing 2006 (values reported are mean over sample); Others - Light Manufacturing in Africa, Vol II (values reported are the range reported by sample firms)_ 25
23 become significant. Also in the public sector the coefficient of the iriport penetration variable is relatively small, although higher than in the private sector. The negative sign for the variable capital-output ratio in the public sector conforms to the earlier finding that public enterprises are more concentrated in capital intensive, non-profitable industries. Finally, the significance of industry dummy variables and time dummy variables is a clear indication that studies that do not include these variables suffer from a serious shortcoming.23

# III.c Analysis of Productivity

The relation between total factor productivity growth in Turkish industry and its trade policy during the 1976-85 period is examined in this section. Total factor productivity growth is a measure of industry performance that proxies the efficiency with which factors are used. TFP growth is computed as the rate of growth in real gross output minus a weighted average of the rates of growth of individual inputs where the weights are the average share of each input in total value of output over the period t-1 to t, namely: Q is the the real gross output, SL, SK, and SM refer to the 23.See for example the references cited in the previous note.
These results are interesting but very preliminary. Regulatory institutions per se are not considered explicitly in the modelling. The simple cross-section framework militates against any real attempt at analysing causation and (as BHS recognize) is bedevilled by problems of multi-collinearity. Nevertheless, the significance of _judicial tenure_ (as a % of US judicial tenure) corresponds interestingly to the ICB literature where, for developing countries, the turnover rate of central bank governors dominates the formal legal attributes of the ICBs – unlike developed countries where the opposite is the case. Hence, for developing countries, the BHS regression results provide some supporting evidence for the conjecture that, for good performance on output measures, the reliability of the legal system _in practice_ is more important than the formal properties as written on paper.

# **5.2.2 Zhang, Kirkpatrick and Parker (2002)**

This paper (henceforth ZKP) explores the roles of competition, privatization and regulation in 51 countries in Africa, Latin America, the Caribbean and Asia on:

- Net electricity generation per capita

- Installed generation per capita (ie the BHS proxy for investment)

- Net electricity generation per employee (ie labour productivity)

- Generation relative to average capacity (ie capacity utilization)

- Final prices to consumers.

The empirical work uses data for 1985-2000 and applies a fixed-effects panel data estimation technique as in the similar studies that have been carried out for telecoms.

The objectives of the paper and its methodology are all good practice, particularly over specifying the hypotheses to be tested. Besides the institutional variables, the regressions include standard control variables e.g. per capita GDP, share of industry output in GDP and an urbanization indicator. The problem is that the data for the key institutional independent variables are, as the authors recognize, low powered and poor quality proxy variables for the underlying effects that they wish to test.

The precise definitions of the variables are as follow:

- (i) _Privatization_

Countries are given a score of 1 if there is <u>any</u> private sector generation and zero otherwise. This is much more a measure of private participation than of privatization. On the basis used, India would presumably count as a “privatized” country just as much as Chile.

- (ii) _Competition_

Countries are given a score of 1 <u>either</u> if there is a wholesale generation market and/or if large users negotiate contracts directly with generators. This is more directly relevant than (i) but again, as the authors recognize, it would classify a system with a heavily dominant incumbent and one or two IPPs selling to industrial firms (e.g. Jamaica) as a competitive market.

- (iii) _Regulation_

34
approach in their analysis of the impact of “knowledge capital” on GDP growth across US states. As a first step, they define quality-adjusted human capital as: where _S_ is years of schooling and _T_ is a measure of test scores.<sup>26</sup> They draw their estimates of _r_ and _w_ from the micro-economic literature that estimates hourly earnings as a function of _S_ and _T_ in samples of the working-age population (taking care to use estimates of _r_ only from models that have also controlled for _T_ , and estimates of _w_ only from models that have controlled for _S_ ). Their preferred estimates are _r =_ 0.08 and _w =_ 0.17.<sup>27</sup> They then use a development accounting framework to estimate the share of cross-state GDP growth that is attributable to knowledge capital (and within that share, how much is due to years of schooling and how much to test scores).

It is not straightforward to compare their measures to ours, since Hanushek, Ruhose, and Woessmann (2017) calculate their estimates for US states, not for countries. Nevertheless, to get a rough sense of how the measures compare, we implement their approach to characterizing _h_ on the same Barro-Lee years of schooling estimates and TIMSS test scores used to derive Figure 2.1, and compare it to our measure of _LAYS_ derived from that sample.<sup>28</sup> Figure 5.9 illustrates how the measures derived from these two approaches compare across this set of countries. Clearly there is a high degree of correspondence between the two, with a correlation (and rank correlation) coefficient exceeding 0.9.

> 26 While, of course, _h_ here is a nonlinear function of _S_ and _T_ , the model actually uses ln( _h_ ), which is a linear combination of the two.  In the calculation of _h_ for each State, the authors are very careful about accounting for the effects of migration (both domestic and international) on the estimates of _T_ .

> 27 Prior to applying these weights, they normalize test scores to have a standard deviation of 1.

> 28 Their estimate of test scores has a mean of 5 and a standard deviation of 1 across the US sample, so is comparable to that in TIMSS (if we divide scores by 100).  We do not carry out the careful adjustments for migration that Hanushek, Ruhose, and Woessmann (2015), which is, in part, why we refer to this as a rough comparison.

39
97 Figure A3.4: LPG prices in July 2012, US$ per kilogram <!-- Start of picture text --> Angola 0.39<br>Argentina 0.35<br>Bangladesh 1.37<br>Bolivia 0.33<br>Brazil 1.49<br>Cambodia 1.47<br>Cameroon 0.90<br>Chile 1.75<br>China 1.07<br>Costa Rica 1.40<br>Côte d'Ivoire 0.56<br>Dominican Republic 1.15<br>Egypt, Arab Rep. 0.04<br>El Salvador 0.96<br>Ethiopia 2.22<br>Gabon 0.82<br>Ghana 0.69<br>Guatemala 1.11<br>Honduras 1.02<br>India 0.51<br>Indonesia 0.45<br>Iraq 0.32<br>Jordan 0.73<br>Kenya 2.21<br>Lao PDR 1.25<br>Madagascar 2.74<br>Malawi 4.74<br>Malaysia 0.60<br>Mexico 0.86<br>Morocco 0.37<br>Mozambique 1.96<br>Nepal 1.11<br>Nicaragua 0.85<br>Niger 0.56<br>Nigeria 2.38<br>Pakistan 0.88<br>Panama 0.39<br>Peru 1.35<br>Philippines 1.29<br>Senegal 1.07<br>South Africa 2.54<br>Sri Lanka 1.35<br>Syrian Arab Republic 0.68<br>Thailand 0.57<br>Togo 0.58<br>Tunisia 0.35<br>Turkey 2.81<br>Uganda 2.95<br>Uruguay 1.34<br>Venezuela, RB 0.09<br>Vietnam 1.24<br>Yemen, Rep. 0.56<br><!-- End of picture text --> $0.00 $0.50 $1.00 $1.50 $2.00 $2.50 $3.00 $3.50 $4.00 $4.50 $5.00 _Sources:_ See Table A2.1 in Kojima (2012).

_Note_ : The FOB prices in July 2012 varied between US$0.45 and 0.62 per kg depending on location and LPG composition.