rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 3. Industry Composition**

|Industry|Number<br>Firms|of<br>Percent of T<br>Number|otal<br>Total<br>(million|Sale<br>LE)|s<br>Percent<br>of<br>Total Sales|
|---|---|---|---|---|---|
|Agriculture|15|4.0|480||1.3|
|Construction|43|11.6|3,082||8.1|
|Building Construction|20|5.4|1,240||3.3|
|Manufacturing|152|41.0|22,463||59.3|
|Food and kindred products|28|7.6|6,749||17.8|
|Chemicals and Allied Products|28|7.6|4,523||11.9|
|Stone, Clay, Glass and Concrete|16|4.3|2,166||5.7|
|Utilities|24|6.5|3,268||8.6|
|Whole trade and retail trade|36|9.7|3,068||8.1|
|Finance, Insurance and Real estate|58|15.6|3,517||9.3|
|Real Estate|47|12.7|2,315||6.1|
|Services|43|11.6|2,005||5.3|
|Hotels|21|5.7|1,215||3.2|
|Total|371||37,883|||

Notes: Source CMA (own calculations). Data are for year 2000. Only the main subsectors are reported separately. Number of firms and total sales for main sectors include the numbers reported in subcategories.
control share of A via B and D would be = 25+35 =60% while the total cash flow rights of A would be = 0.25*60+0.50*35 = 32.5%.

Panel A of Table III presents the cash flow and voting rights of the controlling shareholders for the full sample and also on the basis of the identity of controlling shareholder and legal tradition. The data are presented for the year of cross-listing. Full sample characteristics show that the percentage of voting rights held by the controlling shareholder are significantly different from the cash flow rights at the 1% level. This separation of ownership and control is further reflected for each type of controlling owner. The separation is maximum in the case of family controlled enterprises (-5.12 % at 1% significance) and least for firms controlled by a widely held corporation. (-1.48 %). The degree of separation is also seen by looking at the ratio of cash flow rights to voting rights (C/V). The mean C/V for the whole sample is 0.89, which implies that the mean controlling shareholder can control 100% of the voting rights using only 89% of the cash flow rights. The same statistics are reported for different legal traditions. Separation is least significant for firms listing from English common law countries (-1.8%) and maximum for firms from French civil law countries (-6.8%). There does not appear to be much separation of cash flow and voting rights in the firms listing from German and Scandinavian civil law countries or from Socialist law countries.

The ratios are also significantly different from each other across different legal traditions as shown in Panel B of Table III. Panel B of Table III also reports the mean ratio of cash flow to voting rights when countries are classified on the basis of Law and Enforcement, Financial Development and Transparency. To investigate if the degree of law and enforcement in the home country of the cross-listing firm affects the ratio of cash flow to voting rights, I use three alternate measures from LLSV (1998): _Shareholder Rights, Law and Order Score and Judicial Efficiency._ The countries are classified into high and low depending on the median value of each of these measures.

16
# **_The 2009 VAT Reform Impact on Wind Power Industry_**

30. The concept of value added tax (VAT) reform and its impacts on the economy and the competitiveness of Chinese enterprises and consumers are well beyond the scope of this paper, since China is gradually reforming its VAT system to align it with international (European) practice defined below: _“Value added tax or VAT is an indirect tax, which is imposed on goods and services at each stage of production, starting from raw materials to final product. VAT is levied on the value additions at different stages of production. VAT is widely applied in the European countries. However, now a number of countries across the globe have adopted this tax system._ _Value added tax, also known as goods and services tax or GST proves to be beneficial for the government. Through implementation of this tax system, government can raise revenues invisibly, where the tax is not shown on the bill paid by the buyer. VAT is different from sales tax in various aspects. While sales tax is to be paid on the total value of the goods and services, VAT is levied on every exchange of the product, so that consumers do not have to carry the total cost of tax. However, VAT is generally not applied on export goods to avoid double taxation on the final product. However, if VAT is charged on export goods, the tax amount is usually refunded to the tax payer._ _The value added tax serves as the solution for different problems related to the sales tax system. Unlike sales tax, in VAT, there is provision for input tax credit or ITC. Because of the simplicity of the VAT system, the entire taxation system on consumer products and services has become easier. (Economy Watch)”_ 31. In 2009, China implemented a VAT reform, which decreased the tax burden on wind farm developers,<sup>18</sup> but reduced the tax revenue to the local government. Some local governments responded by using their administrative authority to either force the wind farm developers to pay additional local fees, purchase equipment produced by local companies, or request that developers build equipment manufacturing facilities in their counties to mitigate their losses in tax resources. These measures could have negatively influenced the quality of the investment made. (See Figure 5.)

> 18 It was noted that the impact of VAT reform on wind farm developers (in Type I area, it is about RMB 0.03/kWh) was taken into consideration when the FIT was determined. But the FIT is still very attractive to the developers, and the developers welcome the VAT reform.

13
considerably. These fluctuations are mainly explained by the large swings in reference prices. While there were some years in which there were large changes in domestic prices (e.g. with partial decontrol of prices in the 1967-68 crop year, and as a result of severe floods in the 197172 crop year) , domestic prices have been much more stable than reference prices, both on average over the period as measured by the coefficient of variation, and in terms of year to year variations, as measured by the Z statistic (Table 7). The big changes in reference prices were mainly due to even larger swings in world free market prices, which were offset-but only to a small extent- by offsetting changes in the Rupee /dollar exchange rate (cf. Tables 6 and 7).

Figure 10: **Nominal protection coefficients for mill sugar, 1965-94** <!-- Start of picture text --> Z~ ~ ~ ~ ~ ~ ~ ~ i-s ~ ~~~.:.- - .<br>- -\IMPORTABLE<br>.-. \ ; ~< ~ -4EXORTABLE<br>4?<br><!-- End of picture text --> Source and notes: Weighted averages of NPCs in UP. Maharashtra, and TN See Annex 7 for details.

Figure 11: **Estimated world prices of plantation white sugar in 1981 USD, 1965-94** <!-- Start of picture text --> .:.tjti 'a !jj9j..'.h:'._^'-,'-'. :S --7 M '.,>'. ?_..--wgj<br>7_ ;,<br>jtA<br>Soreadnts Fob, Erp.SeAnx7fodtal<br>/\ ', :33<br><!-- End of picture text -->
65

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negative associations, but the influence of political participation on labor participation is relatively small with wide confidence intervals (Figure 12).

**Figure 12: Effect of female political participation (chamber of representatives) on the female labor force participation rate**

<!-- Start of picture text --> 0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>-0.4<br>1960 1970 1980 1990 2000 2010 2020<br>Range of variation Female political participation<br>Percentage points<br><!-- End of picture text --> Source: Own calculations.

In the 1960s and 1970s, the average coefficient was -0.12pp indicating a negative relationship between women's labor and political participation. Women's political involvement during this time primarily consisted of voluntary and community work, lacking meaningful representation and power. Their participation rates remained lower than men's, and their voting rates consistently lagged, possibly influenced by their traditional homemaker roles (Luna and Villarreal, 1994; Pinzón de Lewin and Rothlisberger, 1977). The unstable and fluctuating growth of women's political participation during these years may have contributed to the observed small or negative correlation, as their labor participation was gradually increasing but still relatively low.

From 1980-1989, the coefficient of 0.15pp suggests that women's labor participation also exhibited an upward trend as political participation increased. This positive association gains further strength with the coefficient of 0.34pp from 1990-1999, indicating a stronger relationship between the two variables, which coincides with significant developments in legislation and policies promoting gender equality and women's participation in politics. For instance, the Political Constitution of 1991 recognized the equality of men and women and guaranteed the participation of women in politics (Iregui-Bohórquez et al., 2021).<sup>12</sup> Notably, the participation of women in the chamber of representatives saw a significant jump from 4% to 10% between 1989 and 1991. The positive trend 12 For a more detailed explanation of the effects of the Colombian constitution of 1991 on female labor participation, see Galindo-Silva and Herrera-Idárraga (2023).

21
Despite the economic case for access to vaccines, as of March 2021, more than a year into the pandemic, the international community had not yet committed to purchase enough vaccines for the world’s population, even though developers then reported available capacity to produce at least 10 vaccine candidates with Phase 3 trial results suggesting efficacy above the WHO minimum threshold (Agarwal and Reed 2021). High-income countries made advance commitments to purchase more vaccine than needed for their populations, including to hedge against the risk that some candidates would fail to secure approval. Low- and middle-income countries and institutions operating on their behalf did not take a comparable risk, committing to purchase far less than needed for their populations.

Earlier vaccine purchases by low- and middle-income countries could lead to earlier deliveries and equitable access It is an open question whether earlier purchases of health products would have accelerated deliveries to low- and middle-income countries. It might not have made a difference, if highincome countries paid extra for priority delivery, or if export restrictions favored deliveries to high-income countries as a group. We use a novel data set of vaccine advance purchase agreements between developers and governments (“bilateral purchase agreements”) to quantify the role of late purchase orders in delivery delays to low- and middle-income countries, relative to other factors causing delay that are independent of the purchase date.<sup>5</sup> Available data suggest that middle-income counties and some low-income countries did have capacity to enter into advance purchase agreements, but that they did so later than high-income countries (Table 1). High-income countries signed contracts as early as May 2020, and on average in December 2020. Middle-income countries signed contracts on average three months later. The low-income countries that did sign contracts signed them on average five months later. The earliest purchase by a low-income country was in February 2021, nine months later than the earliest purchase by a high-income country. ACT-A did make an advance commitment to procure vaccine doses in June 2020 but did not manage to raise resources to scale up purchases until after December 2020 (see Agarwal and Gopinath, forthcoming). The Africa Vaccine

> 5 Data on contract dates are from the IMF-WHO COVID-19 Vaccine Supply Tracker, and data on delivery dates for those contracts are from the UNICEF COVID-19 Vaccine Market Dashboard.

7
**Table 7.  Dynamic firm growth equations: GMM-sys estimates, all businesses**

Dependent variable: annual sales growth= Δ _y it_

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||All states|All states|High-income<br>high- growth<br>states|Low-income<br>high-growth<br>states|Low-income<br>low-growth<br>states|High-income<br>low-growth<br>states|
||0.007|-0.207|-0.060|0.009|-0.027|-0.056|
|1<br>−<br>_it_<br>_y_|(0.40)|(1.42)|(1.39)|(0.15)|(1.07)|(2.25)*|
|1<br>−<br>Δ<br>_it_<br>_y_|-0.035|-0.013|-0.027|-0.041|0.084|0.009|
||(3.04)**|(0.90)|(1.91)|(1.30)|(6.89)**|(0.80)|
|(<br>)<br>1<br>/<br>−<br>Π<br>_it_<br>_K_|-0.122|-0.113|-0.195|-0.110|-0.149|-0.129|
||(2.10)*|(0.85)|(2.18)*|(2.41)*|(2.61)**|(2.81)**|
|(<br>)<br>1<br>/<br>−<br>_it_<br>_K_<br>_D_|0.017|0.079|0.042|0.248|0.044|-0.434|
||(0.63)|(1.70)|(1.21)|(6.99)**|(0.83)|(7.43)**|
|Outage loss|-0.009||-0.011|-0.018|-0.006|0.004|
||(1.61)||(0.91)|(1.10)|(0.86)|(1.56)|
|Constrained by labor regulation|-0.162||-0.359|-0.369|-0.045|-0.073|
||(1.85)||(2.45)*|(1.37)|(0.49)|(1.84)|
|Constant|0.217|2.101|1.340|-0.441|0.499|0.796|
||(1.03)|(2.03)*|(3.09)**|(0.60)|(1.74)|(3.57)**|
|Observations|2087|2089|1034|437|360|256|
|Number of businesses|1301|1303|609|335|244|113|
|Overidentification test:|||||||
|Chi-square|69.45|21.02|53|31.7|42.7|42.8|
|P-value|0.4|0.86|0.40|0.85|0.48|0.20|
|AR in first dif. error (z-stat):|||||||
|m1|-5.29|-3.66|-4.04|-2.46|-2.65|-3.36|
|m2|-0.58|-0.03|0.35|1.14|2.09|-1.04|

Absolute value of z-statistics in parentheses All columns excpet col.2 include state, industry and year dummies * significant at 5% level; ** significant at 1% level Instruments: lags of growth, sales, profitabilability and indebtedness variables t-2 onwards, one step lagged first differences of the same, state of location, year of observation lagged business envirnoment variables, and city averages of business environment variables 24
inflation from domestic food price inflation is still weak but stronger than when the IMF food index is used.

**Table 7: Response of Domestic Inflation to International Food Price Inflation Shocks (using the FAO food index)**

||Model 1(hea|dline inflation)|Model 2(f|ood inflation)|
|---|---|---|---|---|
|Horizon|percentage<br>points|alternative<br>measure|percentage<br>points|alternative<br>measure|
|1|1.3|4.9|0.0|0.0|
|3|2.8|6.0|9.3|20.6|
|6|5.0|8.2|12.6|22.0|
|12|6.3|9.5|9.7|16.1|
|18|6.4|9.7|8.6|14.5|
|24|6.3|9.7|8.6|14.5|

**Table 8: Response of Core inflation to Domestic Food Price Inflation Shocks (using the FAO food index)**

||Core inf<br>|lation<br>li|
|---|---|---|
|Horizon|percentage<br>points|aternatve<br>measure|
|1|-2.9|-18.2|
|3|0.8|3.6|
|6|2.1|9.0|
|12|2.5|10.6|
|18|2.4|10.2|
|24|2.4|10.2|

# 5.2.2 Sources of inflation: Variance decomposition analysis

Although IRFs provide information on the impact of the fluctuations of world food prices on the CPI, they do not indicate the importance of these shocks in CPI fluctuations. By contrast, variance decomposition indicates the contribution of the different shocks for the variance of the k-step ahead forecast errors of the variables.

Figures 7a and 7b summarize the results on the variance decomposition of both domestic food inflation and core inflation respectively. Variance decomposition of domestic food price inflation indicates that it is explained by inertia or its own innovation (65%). International food price inflation accounts for 10% of the variance of domestic food inflation. As for core inflation, again innovations in the variable explain a bit more than half of its variance, but together domestic and international food inflation account for 25% of its variability.

18
**Table 8: Investment in Islamic mutual fund Y with decreasing returns – Alternate fund is traditional (control) or socially responsible** **<u>(treatment)</u>**

|Depen|dent Variable:<br>|Investment i<br>|n Islamic fund<br>|- Alternate: Soci<br>|al vs Traditional<br>|)<br>||
|---|---|---|---|---|---|---|---|
||I<br>Baseline|II<br>Controls|III<br>Decision<br>interaction|IV<br>Risk<br>preference<br>interaction|V<br>Religious<br>preference<br>interaction|VI<br>All<br>interactions|VII<br>Triple<br>interaction|
|Treatment (Alternate is Social fund)|-128.90|-172.90|19.69|-157.50|11.01|146.50|36.68|
||(100.1)|(105.2)|(134.9)|(371.5)|(146.0)|(382.4)|(392.5)|
|Decision number (1 - 12)|-182.9***|-182.9***|-168.0***|-182.9***|-182.9***|-168.0***|-187.9***|
||(6.5)|(6.5)|(9.2)|(6.5)|(6.5)|(9.2)|(12.6)|
|Risk preferences||-32.35|-32.35|-29.86|-16.52|-25.87|-25.87|
|7 = Risk-seeking||(58.74)|(58.74)|(82.31)|(58.82)|(81.52)|(81.52)|
|<br>Religious preferences||<br>1.392**|<br>1.392**|<br>1.395**|<br>2.502***|<br>2.501***|<br>0.918|
|Donation to mosque||(0.55)|(0.55)|(0.56)|(0.82)|(0.83)|(1.08)|
|Treatment X|||-29.63**|||-29.63**|-12.730|
|Decision number|||(12.99)|||(12.99)|(18.81)|
|Treatment X||||-5.19||19.9|19.9|
|Risk preference||||(119.9)||(119.5)|(119.5)|
|Treatment X|||||-1.967*|-1.988*|-0.570|
|Religiouspreference|||||(1.10)|(1.11)|(1.43)|
|Religious preference X|||||||0.244**|
|<br>Decision number|||||||(0.11)|
|Treatment X|||||||-0.218|
|Religiouspreference X Decision|number||||||(0.14)|
|Constant|3145.4***|2626.2***|2529.9***|2619.7***|2606.0***|2534.5***|2663.5***|
||(82.44)|(948.50)|(949.40)|(964.70)|(939.20)|(956.00)|(957.70)|
|Additional Controls|NO|YES|YES|YES|YES|YES|YES|
|Observations|1464|1464|1464|1464|1464|1464|1464|

Notes: Random effects panel (GLS) regressions. Dependent variable is amount (in tokens) invested in mutual fund Y. The baseline is where the fund with fixed returns and risk is the traditional fund, the treatment is where the fund with fixed returns and risk is the socially responsible fund. * 10%, ** 5%, *** 1% significance level. Additional controls include gender, age, financial literacy, investing experience, confidence that the charity was paid in line with instructions, and clarity of instructions (see table A.5 in the appendix B). Appendix A defines the main variables.
_Remember that you are an Englishman, and have consequently won first prize in the lottery of life_ . Cecil Rhodes _Where in the world are you?_ Assessing the importance of circumstance and effort in a world of different mean country incomes and (almost) no migration Branko Milanovic<sup>1</sup> Development Research Group, World Bank JEL classification: D31; I3; 057 Key words: Global inequality, income distribution, migration Number of words: about 8,700

> 1 I am grateful to Patrick Conway, Jim Davies, Francisco Jose Goerlich, Francisco Ferreira, Misha Lokshin, Jaime Pozuelo Monfort, John Roemer, Xiaobo Zhang and the participants of the World Bank conference on Global distributive justice held on May 1-2, 2007  in Washington, International Association for Research in Income and Wealth held in Beijing on September 19-21, 2007, and students and faculty at Free University Amsterdam  for helpful comments and suggestions. I am also thankful to Gouthami Padam for excellent help in collecting and processing the data.

1
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