rafmacalaba/datause-encoder-data huggingface dataset:
**TABLE A5: Holding duration regressions are robust when using IRR as a measure of performance.** Specifications are identical to those in Table 4 in the main text, but use IRR as a dependent variable. The IRR is undefined for 138 investments that were written off and have zero or trivial positive cash flows. For the purposes of this table the IRR is coded as -100 in these cases. Constant, R-squared, and number of observations not reported for brevity. Standard errors are clustered at the country-year. *** p<0.01, ** * *** ** p<0.05, p<0.1 Standard errors are robust to heteroskedasticity. p<0.01, p<0.05, * p<0.1

||(1)|(2)|(3)<br>Quant|(4)<br>ile Regre|(5)<br>ssion|(6)|
|---|---|---|---|---|---|---|
||OLS|0.1|0.25|0.5|0.75|0.9|
||IRR|IRR|IRR|IRR|IRR|IRR|
|Panel A of Table 4: Direct investments|, vintage p|re-2010|||||
|Duration X 1(Duration < 10)|-1,440.7**|8.0***|10.5***|2.0***|0.9**|-1.7|
||(644.1)|(0.7)|(0.3)|(0.6)|(0.4)|(1.3)|
|Duration X 1(10 Duration < 20)|-714.2**|5.5***|6.2***|1.3***|0.5**|-1.2**|
||(351.2)|(0.2)|(0.2)|(0.3)|(0.2)|(0.6)|
|Duration X 1(20 Duration)|-366.2<br>(247.3)|2.4***<br>(0.1)|3.2***<br>(0.2)|0.6***<br>(0.2)|0.3**<br>(0.1)|-0.8***<br>(0.3)|
|Panel B of Table 4: Fund investments,|vintage pre|-2010|||||
|Duration X 1(Duration < 10)|1.1|2.2|0.4|-0.1|-0.3|-1.9*|
||(1.5)|(1.7)|(1.0)|(0.6)|(0.5)|(1.0)|
|Duration X 1(10 Duration < 20)|1.2|2.2**|0.5|0.1|-0.2|-1.5***|
||(0.8)|(1.0)|(0.5)|(0.3)|(0.3)|(0.4)|
|Duration X 1(20 Duration)|-0.1|-1.0|0.4|-0.2|-0.5**|-1.6***|
||(1.0)|(0.9)|(0.4)|(0.2)|(0.2)|(0.3)|
|Country fxed efects|No|No|No|No|No|No|

50
triggering and aggravating East Asia's financial crisis, were thus already in existence in the early 1990s.

# **2. Data**

The data come from annual reports of the companies listed on the major stock exchanges in the region and come from Worldscope and Extel databases. The datasets are unbalanced, i.e., the number of observations varies from year to year. We have excluded companies, which report data less than three times over the period 1988-96. We have also excluded financial and banking institutions (SIC6000-6999). Finally, in any given year, we exclude companies which do not include all of the following variables - net sales, net income after taxes, cost of goods sold, total assets, and the value of common equity. The data set consists of 588 companies in Hong Kong, 317 companies in Indonesia, 2526 companies in Japan, 392 companies in Korea, 772 companies in Malaysia, 170 companies in Philippines, 348 companies in Singapore, 265 companies in Taiwan, and 564 companies in Thailand.

Several caveats apply to the data. First, the statistics we report do not attempt to correct for cross-country differences in industrial structure. If a country data set has many utility firms, for example, average leverage might be higher and profitability lower. A forthcoming companion paper breaks down the sample into sectors (based on two-digit SIC codes) to provide a more accurate comparison of company performance across countries. The data also cover mainly large firms-the median size of the _5550_ firms is 4273 employees, with the largest company employing more than 150,000 employees. This selection pattern arises since firms have to be listed on a stock exchange in order to enter the database, and listed companies tend to be large. The bias towards larger companies may be problematic if one were studying the effect of the Asian financial crises on the corporate sector. It does not pose a problem here, since we focus on the years preceding the crisis, when (as critics argue) large companies were at the root of the corporate and financial sector difficulties.

Whenever possible, we have compared the main variables of interest with those reported in other studies, in particular Demirguc-Kunt and Maksimovic (1995), Glen et al. (1998), and Goldman Sachs (1998).2 We also cross-checked the data for Japan with the _Comparative Economic and Financial Statistic for Japan and other Major Countries,_ published by the Bank of Japan and the OECD Financial Statistics Part 3, _Financial Statements of Non-Financial Enterprises._ The similarity in calculations-large companies are also used there-provides some comfort in the robustness of our results.

> **2** Pomerleano (1998) also analyzes East Asian corporations. He uses alternative measures of performance and leverage that are not easily comparable with the statistics in this study.

3
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the case of flooding or in the Tsunami, populations living close to the river bank/sea were likely very different from those further away.<sup>8</sup>

# **Data**

Our data are based on a survey conducted in 2009 of 126 randomly selected villages in four earthquake-affected districts. Two of the districts—Bagh and Muzaffarabad are in the Azad Jammu Kashmir area of Pakistan and two—Abbottabad and Mansehra are in the North Western Frontier Province, or NWFP. The data-collection was done in two parts. The first part, conducted in spring 2009, was a complete census listing of more than 28,000 households living in these villages including their GPS coordinates. As part of this data-collection exercise, we also collected information on socioeconomic characteristics of all individuals within the household and mortality from the earthquake. In addition, the census also asked households about organizations that came to help them in the 3 months after the earthquake. Following the detailed census, we randomly sampled 10 percent of the households for detailed follow-up on the impact of the earthquake and the post-quake recovery. In addition to standard modules on living conditions and socioeconomic status, we also implemented a complete module on attitudes towards foreigners. This module included questions on trust, the willingness and ability of different communities to work together and the kindness of strangers (the questions are detailed further below). We emphasize that the data-collection was conducted by surveyors local to each district surveyed, led by supervisors who have been trained by the authors over a 4-year period in the context of other survey-based work in Pakistan on education (www.leapsproject.org).

> 8 A similar argument could be made in the context of earthquakes a well—people can _choose_ to live far from a faultline. The key observation for our results is that the households we compare are never those living far from _any_ faultline to those living close to the activated fault-line. By including district fixed-effects, we compare households who vary in distance from the fault-line by 20-40Km and given the preponderance of fault-lines in the region (54 in all), each one of these households lives close to _some_ fault-line that may be activated in the future.

10
Survey, the adjusted version shows good targeting performance on the entire wealth distribution ∂f<sup>∗</sup> <u>(x)</u> with < 0, ∀x∈[1,100].<sup>18</sup> However, testing for robustness with respect to α�, a flat area ∂x appears among the 10% poorest. Using NSS Bihar data we also find high targeting performance ∂f<sup>∗</sup> <u>(x)</u> for FCi∗(x) ∈[20,40], but we also see a deterioration among the poorest 20% ~~�~~ ∂x ≥0, ∀x∈ . [1,20]� The definition of Li is not exactly the same in the NSS and in the World Bank’s Bihar surveys. This implies some discrepancies in the impact of the proposed adjustment on targeting performance assessments. Nevertheless, we see consistently better targeting performance among the 50% poorest when using Ci∗ instead of Ci, with less impact of the adjustment among the richest 30%.

We test if involuntary unemployment matters by estimating f<sup>∗</sup> and f for un-rationed households only using the information available in the World Bank’s Bihar survey. Redoing our analysis on this subsample, we get an estimated f<sup>∗</sup> that is clearly steeper around the bottom of the distribution. The participation rate among the poorest is more than double using adjusted consumption. This pattern is robust when changing α�, as observed in Figure 6.

It will be recalled that the above calculations only adjust for the disutility of casual manual wage labor. As we note in the introduction, this is a type of work in this setting that is very likely to yield disutility—far more so than self-employment on one’s own farm or regular salaried work. But that is an assumption on our part. If in fact these other forms of work also yield disutility then we expect that the corrected targeting performance will not be as pro-poor as we report above, using only our adjustment for the disutility of casual manual wage work. Given that we will now show that (for Bihar at least) the improvement in measured targeting performance is not great enough to outweigh the reduced benefits of participation in NREGS when we allow for the disutility of the work provided, our main qualitative conclusion concerning the poverty impacts will remain valid.

Using the World Bank’s Bihar survey we can also study the distribution of the net gains from the scheme along the distribution of Ci∗ and Ci . Figure 7 shows how the mean net gains

> 18 This result is qualified when checking for robustness with respect to the choice of α� as reported in Figure 6.

13
# **Table 4:  Benchmark Estimates of the Government Spending Multiplier**

||(1)|(2)|(3)|
|---|---|---|---|
|Sample of Countries|**Full**|**IDA**|**Disb/G>10%**|
|**_Panel A: OLS Estimates_**||||
|_(Dependent variable is Change in Real GDP)_||||
|Change in Total Government Spending|0.306***|0.259***|0.277***|
||(0.0377)|(0.0501)|(0.0431)|
|**_Panel B: 2SLS Estimates_**||||
|_(Dependent variable is Change in Real GDP)_||||
|Change in Total Government Spending|0.375|0.408**|0.417**|
||(0.248)|(0.197)|(0.204)|
|Weak Instrument Consistent 95% Confidence Interval|[-0.058,0.827]|[0.071,0.774]|[0.082,0.776]|
|**_Panel C: First-Stage Regressions_**||||
|_(Dependent variable is Change in Total Government Spending)_||||
|Change in Predicted Disbursements|0.531***|0.796***|0.699***|
||(0.150)|(0.150)|(0.149)|
|First-Stage F-Statistic on Excluded Instrument|12.62|28.08|22.18|
|_Number of Observations_|2804|1508|1950|
|_Number of Countries_|102|60|70|
|Note: *** (**) (*) denotes significance at the 1 (5) (10) percent<br>errors are clustered at the country level. All regressions are es<br>a full set of country and year fixed effects. Changes in GDP, go<br>are all scaled by lagged GDP. Weak instrument consistent confi<br>conditional likelihood ratio statistic. Panels A and B report OLS<br>OLS estimates of the corresponding first-stage regression. The<br>countries, the sample of IDA-eligible countries, and the sampl<br>official creditors average at least 10 percent of government spe|level. Heteroske<br>timated using po<br>vernment spendi<br>dence intervals c<br>and 2SLS estimat<br>three columns co<br>e of countries wh<br>nding over the s|dasticity-consiste<br>oled country-year<br>ng, and predicted<br>omputed using th<br>es of Equation (1)<br>rrespond to the f<br>ere disbursement<br>ample period.|nt standard<br>data and include<br>disbursements<br>e Moreira (2003)<br>. Panel C reports<br>ull sample of<br>s on loans from|

38
The present study relates to a large body of empirical literature on mining and development in Africa. Addison et al. (2015) found that, overall, mining activities increase district-level inequality and show differential effects on inequality based on such characteristics as minerals values, scale of mining operations, and the nature of mining activities. Moreover, our study echoes the recent debate on natural resources and management in developing countries (see Venables, 2016; Van der Ploeg 2011; Beny and Cook, 2009). We also add to the recent growing literature on the local economic effects of natural resource wealth (e.g. Aragón and Rud, 2013; Caselli and Michaels, 2013; Fafchamps, et al., 2015; and Aragón, et al., 2015; and Chuhan-Pole, et al., 2015); in contrast to earlier cross-country studies on the resource curse (e.g. Sachs and Warner, 1995, 1997, 2001; Gleason, Herbert son and Sega, 1999; Mehlum et al., 2006; Brunnschweiler, 2008; Chambers D, and Guo J, 2009; and Arezki and Van der Ploeg 2011).

Our paper is closest to that of Andersen et al. (2014), who estimated that the increase in average annual real economic growth in sub-Saharan Africa induced by China’s accession to WTO was approximately 14.6 percent of total (actual) growth. In contrast to our findings, Bhattacharyya et al. (2015) found that mineral production significantly improved district-level economic development in sub-Saharan Africa between 1992 and 2012. Similarly, Tolonen (2015) combined panel data on industrial mines with DHS household survey data for women aged 15-49 and found that the opening of a new gold mine increases women’s income earning opportunities by 41 percent within the service sector and halves infant mortality rate, despite risks of environmental pollution from gold mining. Kotsadam and Tolonen (2013) showed that gold mines increase local female employment in service sectors when a mine opens up, but that the effect dissipates upon the mine’s closure.

The remainder of this paper is organized as follows. Section 2 summarizes the key features on the economic implications of China’s WTO accession for developing nations. That section also provides an account of China–Africa bilateral and regional trade relations in recent decades. Section 3 describes the data and section 4 presents the empirical model. Section 5 presents the main results, robustness checks, and the results on the heterogeneous effects, before Section 6 calls for further studies of the local economic impact of China on Africa.

# 2. BACKGROUND

# _A. Economic implications of China’s WTO accession to the developing world_

Table 1 shows the patterns of economic growth, income, trade, and resource rents across the income groupings in the developing world, divided into periods before accession (that is, 1991– 1995 and 1996–2001) and after accession (2002–2007 and 2008–2013). Except for the Middle East and North Africa (MENA) region, the growth, income, and trade performance of countries in the remaining groups was relatively low before accession. For 1991–1995, sub-Saharan Africa (SSA) experienced an average income growth of -1.73 percent, with per capita income averaging 4
<!-- Start of picture text --> Chart 3. Prspective Investment in developing countries by region, 1995-2004<br>(Annual average in billions of 1994 US dollars)<br>2,000<br>1,500<br>1,000<br>500 _ _<br>I- _ _ i<br>0 EIII<br>1994 1995-2004<br>Sub-Saharan Africa * MidEast&NAFrica South Asia<br>E uJrope&Ctral Asia EM Latin America E ast Asia<br><!-- End of picture text --> <!-- Start of picture text --> SoDua Wortld bank data; projectIons are from World Bank (I 995b)<br><!-- End of picture text --> <!-- Start of picture text --> Chart 4. Developing Countries' saving, Investment and current account balance, 1970-94<br>(Percent of GDP)<br>30-<br>25<br>1 20 t111Current<br>20 account<br>balance<br>15 -- *-Gross<br>domestic<br>Investment<br>10<br>Gross<br>5 ~~~~~~~~~~~~~~~~~~~~~~~~natIonal<br>saving<br>-5<br>Source. World Bank data.<br>Note:, Excludes transition countrles<br><!-- End of picture text --> 7
|**Table 5.1: Po**|**verty**|**Profil**|**e for B**|**ahia,**|**1981**|**-2000**|||||
|---|---|---|---|---|---|---|---|---|---|---|
|**Bahia Poverty Profile**|**1981**|**1985**|**1988**|**1990**|**1993**|**1995**|**1997**|**1998**|**1999**|**2001**|
|<br>Headcount|40,7|44,7|47,7|54,9|54,6|43,0|43,5|41,5|42,2|41,0|
|_Characteristics of the Head_|||||||||||
|Gender<br>male|40,6|45,4|47,5|54,3|54,8|43,3|43,6|41,8|42,2|40,8|
|female|41,1|41,5|48,3|57,1|53,9|42,3|<br>43,2|<br>40,8|42,3|41,8|
|Race<br>white|||40,4|44,6|42,0|33,6|33,9|34,4|33,0|33,2|
|black|||51,7|63,7|55,7|42,1|44,8|45,2|43,7|42,1|
|mulatto|||49,5|57,3|58,8|46,3|47,2|43,4|45,5|43,7|
|indigenous|||||77,6|70,8|38,5|47,8|38,9|23,2|
|asian|||11,2|0,0|48,0|0,0|20,5|36,8|47,6|43,1|
|Age<br><25|33,9|45,6|47,3|59,0|72,0|58,5|64,2|60,9|60,2|63,5|
|25 to 45|45,1|48,0|50,2|54,7|60,4|49,6|50,0|48,2|49,5|50,2|
|45 to 65|38,0|42,9|46,5|53,4|52,3|40,4|39,5|38,1|39,6|34,5|
|>65|35,7|37,6|42,6|56,7|33,3|20,8|24,5|17,1|17,4|13,1|
|Location<br>urban|32,7|31,4|34,8|43,7|46,7|36,5|37,3|35,3|36,6|35,7|
|rural|49,7|60,4|64,4|70,0|68,3|54,9|54,3|53,0|52,4|53,5|
|Read and<br>Write<br>Yes|31,9|35,5|37,0|42,5|48,8|37,4|38,1|36,4|38,0|37,1|
|No|52,6|56,9|65,1|73,7|65,0|53,5|54,0|52,2|50,9|50,2|
|Years of<br>Schooling<br>none or less<br>than 1|51,3|56,4|63,2|71,9|64,1|52,4|53,1|50,6|49,7|47,9|
|1 to 4|45,8|53,8|54,8|62,9|63,7|48,2|50,9|48,5|49,1|51,3|
|5 to 8|26,3|27,9|34,6|44,0|51,4|39,5|<br>42,7|<br>37,9|41,0|42,4|
|9 to 12|8,2|8,3|14,1|17,1|25,3|18,6|18,9|20,9|23,8|21,8|
|more than 12|0,2|1,0|2,4|4,0|7,0|2,7|1,8|1,2|2,2|2,3|
|NA|25,6|21,2|26,6|67,7|66,7|42,5|0,0|64,3|42,0|56,2|
|Signed<br>workbook<br>yes|25,4|28,2|28,2|42,4|41,7|33,5|29,7|28,9|27,8|25,0|
|no|55,3|58,4|63,7|71,8|77,5|58,1|59,0|56,8|61,2|57,0|
|Economic.<br>Active<br>yes||||53,9|56,4|44,9|45,3|43,0|44,4|43,2|
|no||||59,5|46,8|35,6|37,3|35,7|33,9|33,4|
|Working<br>yes|41,0|45,6|47,6|53,9|55,4|42,9|44,0|41,3|42,5|40,7|
|no|39,4|40,7|48,0|58,6|52,0|43,4|42,3|42,2|41,5|42,1|
|Work Position employee|39,0|42,6|45,7|55,9|56,9|42,8|42,0|40,8|42,6|39,5|
|self-<br>employed|45,1|52,3|53,1|56,5|57,0|45,3|48,7|44,8|44,9|44,6|
|<br>employer|5,4|6,1|12,4|16,0|15,0|7,6|3,5|6,7|8,1|8,7|
|Employment<br> <br><1||||53,5|55,2|43,0|44,0|41,2|42,3|40,6|
|Tenure|||||||||||
|>1||||52,8|52,2|40,9|42,4|39,5|40,6|38,7|
|1 to 3||||52,2|55,9|43,7|44,4|43,3|42,3|40,0|
|3 to 5||||48,2|50,4|42,3|40,4|40,2|39,3|36,4|
|>5||||53,0|51,1|39,8|41,9|38,2|40,0|38,6|
|Work Sector<br>Agriculture|51,6|62,6|66,3|71,3|69,5|60,0|<br>60,8|<br>56,3|58,3|58,5|
|<br>Industry|32,1|35,0|37,7|47,2|52,8|37,9|<br>35,5|<br>36,9|39,3|34,7|
|<br>Service|31,4|29,5|31,6|38,6|43,3|31,3|32,9|31,7|29,9|32,4|
|Social|21,0|18,8|23,9|32,9|30,3|20,7|25,9|22,0|20,1|16,3|
|Public|23,7|26,3|31,7|43,8|44,0|26,9|25,1|23,8|20,1|19,8|
|Other|10,7|17,5|11,2|17,6|33,2|23,2|36,0|38,7|34,4|26,0|

_Source:_ Author’s calculations based on PNAD 1981-2001.

20
Table 1: Evidence on Gender Effects on Household Expenditures

|Study|Methodology and<br>Data Source|Outcome<br>Variables|Measure of<br>Female<br>Resources|Effect of Increase in Female<br>Resources|
|---|---|---|---|---|
|Attanasio and<br>Lechene<br>(2002)|Randomized field<br>experiment,<br>PROGRESA,<br>Mexico|Expenditure<br>shares|Woman's<br>income share|Share of expenditure on<br>children's clothing<br>increases, alcohol<br>decreases. In some<br>specifications, the share of<br>expenditures on food<br>decreases.|
|Case and<br>Deaton (1998)|Natural experiment,<br>pensions in South<br>Africa|Expenditure<br>shares|Female versus<br>male pension<br>recipients|In female-headed<br>households, smaller<br>expenditures on alcohol,<br>tobacco, and transportation.|
|Doss (2006)|Cross-sectional data,<br>Ghana|Expenditure<br>shares|Woman's asset<br>holdings|Share of expenditure on<br>education and food<br>increases, and alcohol and<br>medical expenditure share<br>decreases.|
|Duflo and<br>Udry (2004)|Natural experiment<br>using variation in<br>rainfall, Cote d'Ivoire|Expenditure<br>shares and<br>education|Women's<br>income share,<br>instrumented<br>by rainfall|Expenditures on food<br>increase. Mixed evidence<br>on education.|
|Gitter and<br>Barham<br>(2008)|Cross-sectional data<br>and field experiment,<br>Nicaragua|Expenditure<br>shares and<br>education|Woman's years<br>of schooling<br>relative to her<br>husband|Higher expenditures on<br>children's education and<br>higher school enrollment.<br>However, treatment effect<br>of conditional cash transfer<br>program does not increase<br>in relative education.|
|Hoddinott and<br>Haddad<br>(1995)|Cross-sectional data,<br>Cote d'Ivoire|Expenditure<br>shares|Woman's<br>income share|Share of expenditure of<br>food increases, while share<br>of alcohol and cigarettes<br>decreases.|

10
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Table 8: Informal Share of Employment

||1991|2000|2010|
|---|---|---|---|
|Overall|0.58|0.64|0.49|
|Agriculture|0.89|0.86|0.83|
|Mining|0.61|0.45|0.21|
|Manufacturing|0.28|0.39|0.29|
|Nontradable|0.55|0.64|0.48|

Decennial Census data.

70
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