rafmacalaba/datause-encoder-data huggingface dataset:
# **Tables**

|**TABLE 1**<br>**Historical Equality of Opportunity and A**|**gricultural E**|**ndowmen**|**ts**|||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Regression||||||Depe|ndent Variable|:Log of Whea|t Sugar Ratio|Sustainability||||||
||OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|
|Index of Knowledge Distribution (1858)|0.00913***<br>(0.00)|||||||||||||||
|Index of Knowledge Distribution (1868)||0.00847***<br>(0.00)||||||||||||||
|Index of Knowledge Distribution (1878)|||0.00889***|||||||||||||
||||(0.00)|||||||||||||
|Index of Knowledge Distribution (1888)||||0.00894***||||||||||||
|||||(0.00)||||||||||||
|Index of Knowledge Distribution (1898)|||||0.00913***|||||||||||
||||||(0.00)|||||||||||
|Index of Knowledge Distribution (1908)||||||0.00887***||||||||||
|||||||(0.00)||||||||||
|Index of Knowledge Distribution (1918)|||||||0.00816***|||||||||
||||||||(0.00)|||||||||
|Index of Knowledge Distribution (1928)||||||||0.00776***||||||||
|||||||||(0.00)||||||||
|Index of Knowledge Distribution (1938)|||||||||0.00681***|||||||
||||||||||(0.00)|||||||
|Index of Knowledge Distribution (1948)||||||||||0.00602***||||||
|||||||||||(0.00)||||||
|Index of Knowledge Distribution (1958)|||||||||||0.00610***|||||
||||||||||||(0.00)|||||
|Index of Knowledge Distribution (1968)||||||||||||0.00438***||||
|||||||||||||(0.00)||||
|Index of Knowledge Distribution (1978)|||||||||||||0.00538***<br>(0.00)|||
|Index of Knowledge Distribution (1988)||||||||||||||0.00405***<br>(0.00)||
|Index of Knowledge Distribution (1998)|||||||||||||||0.00435***|
||||||||||||||||(0.00)|
|Number of Observations|35|37|39|40|40|43|46|53|53|62|71|92|100|101|116|
|R-squared|0.341|0.323|0.379|0.409|0.443|0.425|0.398|0.409|0.355|0.321|0.357|0.288|0.14|0.197|0.202|
|* p<0.05, ** p<0.01, *** p<0.001||||||||||||||||

19
# **Appendix Table 1: Hedonic Price Regression Results, Urban Kigali, OLS**

||**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
|Log of parcel area in sqm|0.781<sup>***</sup>|0.717<sup>***</sup>|0.575<sup>***</sup>|0.552<sup>***</sup>|
||(0.010)|(0.010)|(0.010)|(0.010)|
|Log distance from the village to the CBD in km|-0.280<sup>***</sup>|-0.016|-0.240<sup>***</sup>|-0.098<sup>***</sup>|
||(0.030)|(0.029)|(0.026)|(0.026)|
|Log number of workers at the cell level in 2014|0.239<sup>***</sup>|0.152<sup>***</sup>|0.061<sup>***</sup>|0.044<sup>***</sup>|
||(0.010)|(0.010)|(0.010)|(0.010)|
|Log of residential land at the cell level|-0.192<sup>***</sup>|-0.070<sup>***</sup>|0.004|0.040<sup>***</sup>|
||(0.014)|(0.014)|(0.014)|(0.013)|
|Log distance to tarmac road in meters||-0.241<sup>***</sup>||-0.145<sup>***</sup>|
|||(0.006)||(0.006)|
|Log distance to a primary school in meters||-0.117<sup>***</sup>||-0.059<sup>***</sup>|
|||(0.011)||(0.010)|
|Log volume of buildings in m3|||0.195<sup>***</sup>|0.186<sup>***</sup>|
||||(0.005)|(0.005)|
|Density of built up area of the village|||2.571<sup>***</sup>|1.947<sup>***</sup>|
||||(0.093)|(0.095)|
|Log average height of buildings at the village in<br>meters|||2.783<sup>***</sup>|2.580<sup>***</sup>|
||||(0.098)|(0.096)|
|Log SD of building height at the village in<br>meters|||<br>-0.506<sup>***</sup>|<br>-0.538<sup>***</sup>|
||||(0.039)|(0.038)|
|Longitude, decimal degree|7.062<sup>***</sup>|3.936<sup>***</sup>|3.320<sup>***</sup>|2.032<sup>***</sup>|
||(0.419)|(0.404)|(0.366)|(0.362)|
|Latitude, decimal degree|<br>-0.327|<br>0.079|<br>1.918<sup>***</sup>|<br>1.827<sup>***</sup>|
||(0.399)|(0.379)|(0.346)|(0.341)|
|Constant|-206.820<sup>***</sup>|-111.092<sup>***</sup>|-95.100<sup>***</sup>|-55.417<sup>***</sup>|
||(12.673)|(12.211)|(11.055)|(10.938)|
|Number of observations|12,592|12,592|12,592|12,592|
|R<sup>2</sup>|0.359|0.432|0.529|0.551|
|Adjusted R<sup>2</sup>|0.358|0.432|0.528|0.551|
|Residual Std. Error|0.910|0.856|0.780|0.762|
|F Statistic|1,172.842<sup>***</sup>|1,197.294<sup>***</sup>|1,411.107<sup>***</sup>|1,287.790<sup>***</sup>|
|Spatial autocorrelation tests on the OLS<br>|||||
|residual<sup>a</sup>|||||
|Moran’s_I_||||0.104|
|p-value||||2.2e-16|
|<br>LMerr||||3961.5|
|p-vlaue||||2.2e-16|
|<br>LMlag||||1821.5|
|<br>p-vlaue||||2.2e-16|
|<br>Robust LMerr||||2407.3|
|p-vlaue||||2.2e-16|
|LMlag||||267.32|
|p-vlaue||||2.2e-16|

_Note:_ Dependent variable is the log of the sales price per parcel for residential properties in Kigali over the 2013-16 period. Standard errors in parenthesis: *** significant at 1%; ** significant at 5%; * significant at 10%. The estimation is done in R. aThe reported test statistics are for the spatial weights defined at a threshold distance band of 435m for which each property has at least one neighbor. Alternative threshold distances (750m, 1000m, 1500m and 5000m) are also used, but the Moran’s _I_ is the highest at 435m. The same is true for the other statistics.

27
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22
Finally since all firms that cross-list via a Level II or Level III ADR have to conform to US GAAP, I investigate if the cash flow to voting rights ratio varies with home country’s accounting standards. I use an _Accounting_ index produced by the Center for International Financial Analysis and Research Inc that is a measure of disclosure requirements and transparency in accounting statements produced by firms in different countries. Poorer the accounting standards, one would expect controlling shareholders to be better able to divert funds and expropriate requiring a lower amount of cash flow to voting rights. I find that the ratio is 0.86 for firms with poor accounting standards, which is significantly lower than the ratio of 0.95 for firms with good accounting standards.

# _3.4. Use of Control Enhancing Features --Prior to cross-listing_

The ways in which cash flow rights can be different from control rights is through the use of dual classes of stock, pyramidal structures and cross shareholdings. I create dummies for each of these categories. _Dual-Class_ equals one if the firm issues multiple classes of shares with different voting rights, _Pyramid_ equals one if the controlling owner exercises control through at least one publicly traded company and _Cross-Shareholding_ equals 1 if the company has a controlling shareholder and owns a percentage of shares in either its controlling shareholder or in any other company in that chain of control. I also define dummies to identify if there is just one controlling owner, _Single_ , and if any person from higher management is also from the controlling family, _Management_ . See Claessens et al (2000) for detailed description of these variables.

Table IV presents details on the prevalence of various control enhancing features in different cross-listing firms. As in Table III, firms are classified on the basis of their home country’s legal rules, law and enforcement, financial development and accounting standards. The use of multiple classes of shares with different voting rights is significantly higher (lower) in firms from French civil law (English common law) countries than in firms cross-listing from other countries. The use of pyramidal structures seems to be quite high (around 50%) in most of 18
# **3 CPHS to benchmark Comparing surveys**

Our starting point is a CPHS dataset containing one observation per household per year, where consumption is reported with a one-month recall and individual level sampling weights reflect the observed population distribution. Nominal consumption expenditures in both the CPHS and NSS surveys are deflated to 2011-12-rupee prices using monthly CPI-IW and CPI-AL price indices for urban and rural observations, respectively. We also adjust for spatial price differences using 2011 PPP exchange rates from the International Comparison Program following Atamanov, et al. (2020).

## **3.1 Non-expenditure variables**

_Demographic characteristics:_ According to Somanchi (2021), the share of children under the age of 10 in CPHS-2019 is 8.9 percentage points lower than the official sample registration survey (SRS) of 2018. This under-coverage is balanced by shares of people aged 40 to 65 years being 11.9 percentage points higher in CPHS-2019 than SRS 2018. CPHS also reports a higher share of households with 2 to 5 members but undercounts households with either a single member or those with more than 6 members. Finally, the CPHS is seen to over-represent Hindu households compared to the benchmark surveys such as NFHS-4.

Figure 2 compares trends in key demographic indicators using the NSS-2011 consumption expenditure survey, the NSS-2014 survey on services and durable goods consumption and the PLFS surveys of 2017 through 2019 as the nationally representative benchmark surveys. The figure shows both the magnitude of the biases observed in the CPHS and the extent to which these biases are corrected by means of reweighting the CPHS. The distribution of household size and its trend estimated using the CPHS now closely match the estimates observed in the nationally representative NSS-surveys. The over-representation of Hindu households is also accounted for. The population shares for other religions similarly match with those observed in the NSS surveys. Biases observed in the composition of scheduled caste, scheduled tribes (and other classes), share of female headed units and households with extended family members living in the same house are also largely resolved through reweighting.

The one demographic variable for which a bias persists is the share of members aged between 0 and 18 years for which a gap of up to 5 percentage points between the CPHS and the NSS-surveys is observed.

_Asset ownership and access to services:_ Somanchi (2021) also documents that the shares of households with access to electricity, water, toilet and ownership of a television and refrigerator are notably higher in the CPHS -- 2015 and 2019 compared to the 13
18 entrants. Macroeconomic failures to generate enough employment growth led to pressures for the VET system to "do something." The "something" often took the form of the system creating yet another training program to serve a group having trouble obtaining employment.

A brief catalog of employment training programs and initiatives illustrates the diverse and complex range of programs Tunisia created in response to its perceived labor market adjustment problems. The VET system in Tunisia, like that in South Korea, includes vocationally-oriented programs in the school system, public vocational training outsirlA the school system, and training programs run by public enterprises and private training institu. i; The public sector accounts for most lnitiatives.Y Cherif (1990) lists employment training programs in the 1980s for graduates of secondary technical schools, vocational training centers, and higher education. He mentions others for rural development, youth employment promotion, and productive families. The proliferation of employment training programs he cites is indicative of a VET system concerned with employment shortfalls rather than market demand for skilled workers.

Thus, both South Korea and Tunisia exhibit diversity in their VET systems, but with different goals. The goal in Tunisia was to compensate for macroeconomic failures, while that in South Korea was to complement rapid economic growth and macroeconomic successes. The latter creates incentives for private sector involvement on the part of enterprises and proprietary training institutions. As already indicated, the private sector plays an important role in the South Korean VET system, and a much smaller role in Tunisia. Egypt with its strongly inward-focused economy shares many of the same problems experienced by Tunisia. Employment growth in Egypt's modem sector economy was stagnant in the 1980s. The private sector was largely absent from its VET system. A World Bank study (1989) found expansion of public VET in Egypt unrelated to producer demand, and instead, concerned with reducing demand for higher education which was provided tuition-free.

V' Cherif (1990) reports that 9 out of 10 secondary students are enrolled in govemment schools In Tunisia. Outside the school system in 1984, 3 out of 4 trainees were enrolled in govemment vocational training centers. The government was responsible for all employment training program inRtiatives during the 1980s.
# **Figure 8. Government revenue in small states and other EMDEs**

_Although small states have higher average ratios of total government revenue and tax revenue to GDP than other EMDEs, they are more reliant on non-tax revenues. Their revenue sources vary widely, including sovereign rents such as sales of fishing access rights, shipping licenses, and economic citizenship programs. Revenues are particularly high in EAP, and commodityimporting small states, and in micro states. Small states revenues are also more volatile than in other EMDEs._

## **A. Government revenue**

<!-- Start of picture text --> Percent of GDP<br>Small states<br>40 EMDEs excl. small states<br>30<br>20<br>10<br>0<br>2011 2019 2020 2023<br><!-- End of picture text -->

**C. Government revenue, by region, 2011-23**

<!-- Start of picture text --> Percent of GDP<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>EAP small LAC small Other small<br>states states states<br><!-- End of picture text --> <!-- Start of picture text --> B. Composition of government revenue, 2011-<br>22<br><!-- End of picture text --> <!-- Start of picture text --> Percent of GDP<br>Small states<br>25 EMDEs excl. small states<br>20<br>15<br>10<br>5<br>0<br>Tax Grants Other<br><!-- End of picture text --> <!-- Start of picture text --> D. Government revenue, by trade composition,<br>2011-23<br>Percent of GDP<br>50<br>40<br>30<br>20<br>10<br>0<br>Small states: Small states: Small states:<br>Commodity Commodity Tourism-reliant<br>exporters importers<br><!-- End of picture text --> 37
variables are also measured using the number of firms established in different years. Similar to equation 1, we control for the aggregate effect at the region level.

Table 4 reports the regression results for equation 2 separately for light manufacturing, heavy manufacturing, “other industry,” WRT services, and “other services,” and by north versus south. To establish the causal effect of the growth of firms in mining on the growth of firms in other sectors, we conduct placebo tests. We consider the lead effects of new mining firms for two periods, _NewMFirmd_ , _t_ + 1 and _NewMFirmd_ , _t_ + 2 . In the north (panel a), we find weak evidence of the relationship between the entry of new firms in other sectors and in mining, as the results are mostly statistically insignificant. This is primarily due to the limited expansion of mining in the north. In the south (panel b), both contemporaneous and lagged effects of new mining firms on the entry of heavy manufacturing firms are positive and statistically significant. The entry of mining and heavy manufacturing firms in the south appears causal based on the results of the placebo tests, as the lead effects are statistically insignificant for heavy manufacturing. For other sectors, the placebo test results do not support a causal interpretation for the increase in the number of firms due to an increase in the number of mining firms. Appendix table A4 reports the regression results for equation 2 at the national level. For heavy manufacturing, the results at the national level reflect a combination of the results obtained at the regional level.

To conclude, the district-level results reinforce the results in Section 3 based on regional I-O tables that mining has a stronger effect on heavy manufacturing through production networks in the south than in the north.

# **Firm-Level Analysis**

The district-level evidence suggests that employment in heavy manufacturing has grown in mining districts between 2003 and 2013. An increase in the number of heavy manufacturing firms could generate this result, without an increase in average employment in heavy manufacturing firms in mining districts. We test whether average log employment in different sectors (heavy manufacturing, in particular) is higher in mining districts in the south, based on the following model: ′ log _Empi_ = α +φ _Southi_ +γ _MDisti_ + δ _DDSouthi_ × _MDisti_ +θ _Xi_ + ε _i_ . (3) We estimate the double-difference parameter ( δ _DD_ )in equation 3 using 2014 IBES data, controlling for various firm characteristics (<sup>_X_</sup> _i_ ) including informality status, type of ownership, and legal organization.

15
journal of clinical nutrition, 48(1):5871.

- Headey, D. D. and Hoddinott, J. (2015). Understanding the rapid reduction of undernutrition in nepal, 20012011. PloS one, 10(12):e0145738.

- Jha, H. B. (2008). Economics of conict and peace with focus on nepal. Dhobighat, Lalitpur. Centre for Economic and Technical Studies (CETS). Paper submitted to "New Dynamics of Development: Challenges and Prospects".

- Justino, P., Leone, M., and Salardi, P. (2014). Short- and Long-Term Impact of Violence on Education: The Case of Timor Leste. World Bank Economic Review, 28(2):320353.

- Kreuttner, T. R. (2008). The maoist insurgency in nepal, 1996-2001: Implicating for us counterinsurgency doctrine. https://apps.dtic.mil/docs/citations/ADA505200.

- León, G. (2012). Civil Conict and Human Capital Accumulation: The Long-term Eects of Political Violence in Perú. Journal of Human Resources, 47(4):9911022.

- Libois, F. (2016). Households in Times of War : Adaptation Strategies during the Nepal Civil War. Working Papers 1603, University of Namur, Department of Economics.

- Luo, Z. and Karlberg, J. (2000). Critical growth phases for adult shortness. American journal of epidemiology, 152(2):12531.

- Macours, K. (2011). Increasing inequality and civil conict in Nepal. Oxford Economic Papers, 63(1):126.

- Mahat, P. S. (2006). Socio-economic transformation of rural areas for peace and democracy.

- Martorell, R., Khan, L. K., and Schroeder, D. G. (1994). Reversibility of stunting: epidemiological ndings in children from developing countries. European journal of clinical nutrition, 48:S4557.

- Media Foundation (2011). Healing the wounds: Stories from nepal's transitional justice process. Technical report, Media Foundation, Kathmandu, nepal. https://www.mfnepal.org/ publication/healing-wounds-2010/.

29
**Table 9. Differences in Effects of Objective Financial Access Variables on Employment Growth by Firm Size and Age**

||<br>**by F**<br>|<br>**irm Size and Ag**<br>Dependent va<br>|<br>**e**<br>riable: employment<br>|<br> growth<br>|
|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|
|||**Credit**|||
||**Loan**|**constraint**|**Sales credit**|**External finance**|
|Financial access variable<br>(_FC_)|0.091***|0.053***|0.106***|0.105***|
||(0.004)|(0.004)|(0.012)|(0.006)|
|Small*_FC_|<br>-0.040***|<br>-0.057***|<br>-0.043***|<br>-0.039***|
||(0.002)|(0.002)|(0.007)|(0.004)|
|Medium*_FC_|<br>-0.051***|<br>-0.065***|<br>-0.057***|<br>-0.056***|
||(0.003)|(0.004)|(0.008)|(0.004)|
|Large*_FC_|-0.069***|-0.078***|-0.066***|-0.073***|
||(0.003)|(0.007)|(0.009)|(0.005)|
|Mature*_FC_|<br>-0.035***|<br>-0.026***|<br>-0.042***|<br>-0.030***|
||(0.004)|(0.004)|(0.012)|(0.006)|
|Older*_FC_|-0.061***|-0.044***|-0.062***|-0.051***|
||(0.004)|(0.004)|(0.012)|(0.006)|
|Multi|<br>0.008***|<br>0.006***|<br>0.003|<br>0.004**|
||(0.002)|(0.002)|(0.002)|(0.002)|
|Manuf|-0.005***|-0.006***|-0.009***|-0.009***|
||(0.001)|(0.001)|(0.001)|(0.001)|
|Exporter|<br>0.009***|<br>0.003|<br>0.001|<br>0.004*|
||(0.002)|(0.002)|(0.002)|(0.002)|
|Foreign|0.003|0.003|0.000|0.001|
||(0.002)|(0.002)|(0.002)|(0.002)|
|Govt|<br>-0.035***|<br>-0.041***|<br>-0.046***|<br>-0.042***|
||(0.005)|(0.005)|(0.005)|(0.005)|
|Constant|<br>0.045***|<br>0.055***|<br>0.055***|<br>0.048***|
||(0.001)|(0.001)|(0.001)|(0.001)|
|Adjusted_R_-squared|<br>0.090|<br>0.065|<br>0.095|<br>0.078|
|<br>Number of observations|34,894|35,641|36,722|36,722|
|Number of countries<br>_Note:_Standard errors (in par|95<br>entheses) are ro|96<br>bust to heteroske|96<br>dasticity and clust|96<br>ered on countries.|

_<mark>Note:</mark>_ <mark>Standard errors (in parentheses) are robust to heteroskedasticity and clustered on countries.</mark> Model: _EG_ = _b_ 0 + _b_ 1 _FC_ + _b_ 2Small* _FC_ + _b_ 3Medium* _FC_ + _b_ 4Large* _FC_ + _b_ 5Mature* _FC_ + _b_ 6Older* _FC_ + _b_ 7Multi + _b_ 8Manuf + _b_ 9Exporter + _b_ 10Foreign + _b_ 11Govt + Country Fixed Effects + _e_ . * Significant at the 10 percent level. ** Significant at the 5 percent level. *** Significant at the 1 percent level.

_<mark>Source:</mark>_ <mark>Authors' estimations based on data from World Bank Enterprise Surveys (2006-10).</mark> 29
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