rafmacalaba/datause-encoder-data huggingface dataset:
**Table 4: Investment in mutual fund Y (decreasing returns) – Islamic fund**

|Dep|endent Varia|ble: Investme|nt in Fund Y||||
|---|---|---|---|---|---|---|
|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 (Islamic fund = 1)<br>181.9*|188.8*|-85.2|-136.2|-25.8|-621.5*|-487.0|
|(97.2)|(101.2)|(131.2)|(342.1)|(135.3)|(357.3)|(366.7)|
|Decision number (1 - 12)<br>-189.1***|-189.1***|-210.2***|-189.1***|-189.1***|-210.2***|-209.3***|
|(6.4)|(6.4)|(9.1)|(6.4)|(6.4)|(9.1)|(13.1)|
|Risk preferences|-94.39*|-94.39*|-149.3*|-88.34|-142.8*|-142.8*|
|7 = Risk-seeking|(55.32)|(55.32)|(78.19)|(54.31)|(76.71)|(76.71)|
|Religious preferences|1.114**|1.114**|1.132**|0.160|0.179|0.228|
|Donation to mosque|(0.52)|(0.52)|(0.52)|(0.65)|(0.65)|(0.85)|
|Treatment X||42.15***|||42.15***|21.460|
|Decision number||(12.84)|||(12.84)|(18.07)|
|Treatment X|||111.3||110.3|110.3|
|Risk preference|||(111.9)||(109.7)|(109.7)|
|Treatment X||||2.377**|2.373**|0.741|
|Religiouspreference||||(1.02)|(1.02)|(1.34)|
|Religious preference X||||||-0.007|
|Decision number||||||(0.08)|
|Treatment X||||||0.251*|
|Religiouspreference X Decision num|ber|||||(0.1)|
|Constant<br>3004.2***|3498.5***|3635.5***|3559.8***|3303.1***|3501.2***|3495.7***|
|(80.47)|(958.40)|(959.30)|(960.50)|(943.50)|(946.40)|(948.30)|
|Additional Controls<br>NO|YES|YES|YES|YES|YES|YES|
|Observations<br>1464|1464|1464|1464|1464|1464|1464|

Notes: Random effects panel (GLS) regressions. Dependent variable is amount (in tokens) invested in mutual fund Y. * 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.1 in the appendix B). Religious preferences are measured in tokens donated to local mosque, with a maximum of 400 tokens. Risk preferences are measured based on the number of times a subject selected the gamble in the risk preference measure. Financial literacy is measured by the number of correct answers the subject provided in the financial literacy questionnaire. Appendix A defines the main variables.
# **1. Introduction**

The value and profitability of politically connected firms have been well documented. But to which extent cronyism affects aggregate economic growth or only has distributional consequences is not clear. Existing research compares the profitability or productivity of connected and unconnected firms. Such comparison, however, cannot address whether connected firms promote or retard economic growth generally. For example, connected firms’ higher productivity might be explained by specific government support such as subsidies, guarantees, or regulatory advantages that they receive at the expense of unconnected firms that do not have such privileges— their impact on aggregate growth could thus still be negative.

It is thus more insightful to assess connected firms’ investments and impact on activities that bring growth externalities such as their contribution to technological change. So far, however, information on firms’ innovation activities and their political connections have not been available. We use a novel World Bank Enterprise Survey from The Arab Republic of Egypt that provides to the best of our knowledge for the first-time information on firms’ political connections and their innovation activities.

We find that connected firms—that have a current or former government official among their managers, owners, or board of directors—innovate less. The finding is robust across different forms of innovation such as introducing a new or significantly improved product, a new production method or organizational process, and investing in quality accreditations. Nevertheless, we find that connected firms are not necessarily less productive due to their higher capital intensity. The latter, however, originates from the generous policy privileges they receive, reducing their marginal cost of capital and diverting economic resources away from innovation activities. The relative policy privileges that connected firms receive tend to be largest when compared to their direct competitors—firms with comparable size and age operating in the same 4-digit sector.

We provide a wide range of evidence supporting a causal interpretation of the findings. The results are robust to the five different innovation measures provided in the survey. The differences relative to non-connected firms tend to be largest if we restrict the comparison to the non-connected direct competitors operating in the same 4-digit sector, demonstrating that the policy privileges are firm specific and not specific to the activities connected firms selected into. Consistent with our results, we also find that connected firms’ primary business strategy is to sell in (protected) domestic markets while comparable non-connected firms focus on international 2
# **7.2 Access to markets and connectivity**

Since the homelands were spread across the country, the effects of their progressive integration in the South African economy might be expected to depend on their proximity to markets and connectivity (Harris, 1954; Krugman, 1991; Hanson, 1996; Fujita et al., 2001; Redding and Sturn, 2008; Donaldson, 2015). Failing to account for these mechanisms might bias our estimates, notably if initial employment in mining were correlated with proximity to markets and roads of the corresponding communities. Even though we initially accounted for the distance of the community to the border of the homeland, this measure does not necessarily capture the effect of the integration of former homeland communities in the South African economy.

To capture a community’s access to markets, we calculate its distance to the ten largest cities in terms of population in South Africa.<sup>27</sup> The linear distances are weighted by the city’s population. Figure 9 describes this measure for the sample of communities near the former homeland borders. Communities located inside homeland areas appear to have worse access to major cities, though there is substantial variation inside and out.

In a similar way, we account for the connectivity of each community, defined as proximity to main road. Formally, this measure is defined as the linear distance in kilometers of a community to the nearest main road (in 2001 as defined by Statistics South Africa). Figure 10 describes this measure and shows a similar pattern to that found with distance to major cities. However, the difference between communities just-inside and outside the homelands is substantially larger in this case.

Table 7 presents regression results that account for the introduction of these variables. In column 1 we include the measure of distance to major cities and the interaction with the homeland indicator. Both covariates are statistically significant. The closer a community is to a major city, the stronger is its growth in income. This effect is even more pronounced for communities located inside homeland areas. A potential concern is that this measure might not properly account for heterogeneity in transport infrastructure across regions. To account for this, in column 2 we include distance to main roads and its interaction with the homeland indicator. We observe that these measure are not statistically distinguishable from zero. Finally, column 3 includes both measures together and the results remain unaltered. These results provide further evidence supporting the importance of market access for economic growth. Furthermore, notice that the estimates for our variables of interest remain unchanged throughout.

> 27In order, the cities are Cape Town, Durban, Johannesburg, Soweto, Pretoria, Port Elizabeth, Pietermaritzburg, Benoni, Tembisa, and East London.

19
**Table 11: Determinants of Cross-Province Price Differentials**

|**Dep Var:**<br>**Price Difference 'i-j'**|**Rice**<br>**Coef.**|**Soybeans**<br>**Coef.**|**Maize**<br>**Coef.**|**Sugar**<br>**Coef.**|**Cooking Oil**<br>**Coef.**|
|---|---|---|---|---|---|
|**Remoteness 1**|0.375|4.607|1.134|0.600|0.073|
||(6.57)***|(11.67)***|(3.8)***|(14.28)***|(1.68)***|
|**Remoteness 2**|0.100|-0.586|-0.262|0.035|0.001|
||(0.38)|(-3.27)***|(-0.91)|(0.41)|(0.01)|
|**Contiguity**|-0.195|-0.028|0.029|-0.303|0.102|
||(-1.52)|(-0.14)|(0.17)|(-3.04)***|(0.64)|
|**Remote*Infra 1**|-0.162|-3.493|-1.073|-0.217|-0.110|
||(-2.64)***|(-11.92)***|(-4.89)***|(-4.97)***|(-1.96)*|
|**Remote*Infra 2**|-0.020|0.494|0.241|-0.038|0.191|
||(-0.1)|(3.09)***|(0.96)|(-0.45)|(2.49)**|
|**Income PC 1**|0.300|-1.030|-0.258|-0.176|-0.033|
||(3.14)***|(-9.33)***|(-1.76)*|(-4.1)***|(-0.5)|
|**Income PC 2**|0.075|0.092|-0.281|-0.088|0.114|
||(1.11)|(0.63)|(-1.62)|(-1.38)|(2.3)**|
|**Land Productivity 1**|-0.156|1.420|-2.118|||
||(-0.88)|(2.2)**|(-4.35)***|||
|**Land Productivity 2**|0.181|0.758|0.237|||
||(0.62)|(1.55)|(0.49)|||
|**Output of Rel Comm 1**|0.210|-0.146|-0.081|||
||(1.56)|(-3.09)***|(-1.24)|||
|**Output of Rel Comm 1**|0.425|0.038|0.189|||
||(2.92)***|(0.56)|(1.83)*|||
|**Constant**|-80.706|-74779.590|95522.850|173.907|427.005|
||(-0.61)|(-1.35)|(4.12)***|(9.01)***|(7.02)***|
|**Obs.**|**300**|**91**|**91**|**300**|**300**|
|**Prob> F**|**0.000**|**0.000**|**0.000**|**0.000**|**0.000**|
|**R-Squared**|**0.3605**|**0.7217**|**0.6043**|**0.4405**|**0.0752**|
|**Notes:**|**(1) *** indi**|**cates signifi**|**cance at 1%,**|||

**** significance at 5%, * significance at 10%. (2) T-statistics in brackets.**

34
# **Table A26: Second stage: Effects of refugee presence on household welfare disaggregated by initial landownership, with analysis at cluster level and the distance -** **<u>weighted refugee index at 100km from the clusters (Panel 2009 2012)</u>**

|||||L|og of welfare|(consumption|aggregate per a|dult equivalen|t)||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Log of refugees (100km) predicted by Log of<br>Distance IV (mean border distance)|0.13256<br>(0.09587)|0.13022<br>(0.09547)|0.14257<br>(0.09774)|0.15208<br>(0.09341)|Panel A|(Own <=medi|an land size“2|.5 acres”)|||||
|Log of refugees (100km) predicted by Log of<br>Distance IV (square of mean border distance)|||||0.17510*<br>(0.10472)|0.17440*<br>(0.10458)|0.18535*<br>(0.10859)|0.19085*<br>(0.10427)|||||
|Log of refugees (100km) predicted by Log of<br>Distance IV (square-root of mean border<br>distance)|||||||||0.09602<br>(0.09552)|0.09220<br>(0.09489)|0.10512<br>(0.09381)|0.11992<br>(0.09003)|
|Observations|2,124|2,124|1,981|1,980|2,124|2,124|1,981|1,980|2,124|2,124|1,981|1,980|
|Kleibergen-Paap rk Wald F|11.99|11.98|12.16|11.34|14.09|14.07|14.18|13.12|9.454|9.448|9.877|9.292|
|Root MSE|0.530|0.529|0.510|0.384|0.533|0.532|0.514|0.388|0.528|0.527|0.508|0.381|
||||||Panel B|(Own > medi|an land size“2.|5 acres”)|||||
|Log of refugees (100km) predicted by Log of<br>Distance IV (mean border distance)|0.02393|0.03411|0.04195|0.05320|||||||||
||(0.08700)|(0.08647)|(0.08249)|(0.07956)|||||||||
|Log of refugees (100km) predicted by Log of<br>Distance IV (square of mean border distance)|||||0.04010|0.04961|0.05214|0.06666|||||
||||||(0.08872)|(0.08802)|(0.08516)|(0.07989)|||||
|Log of refugees (100km) predicted by Log of<br>Distance IV (square-root of mean border<br>distance)|||||||||0.03614<br>(0.08444)|0.04647<br>(0.08457)|0.05621<br>(0.08235)|0.06798<br>(0.07872)|
|Observations|2,170|2,168|2,124|2,122|2,170|2,168|2,124|2,122|2,170|2,168|2,124|2,122|
|Kleibergen-Paap rk Wald F|9.561|9.544|9.240|9.008|10.88|10.87|10.40|10.10|8.639|8.624|8.362|8.185|
|Root MSE|0.507|0.505|0.492|0.339|0.507|0.505|0.492|0.339|0.507|0.505|0.492|0.339|
|**_Included controls_**|||||||||||||
|Exogenous controls (age, agesq, sex<br>“male==1”)|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|

43
|**Exposure**| Examine the location of surveyed<br>households in relation to the<br>flood-prone locations| GIS based maps created to<br>identify the location of surveyed<br>households vis-à-vis the flooding<br>spots|
|---|---|---|
|**Vulnerability and**<br>**Impacts**| Stratified random sampling of<br>households in flood-prone<br>locations and possible<br>interventions areas for flood<br>control<br> Administering questionnaires to<br>sample households to capture<br>vulnerabilityand impacts| Sampling frame determined<br>using the flood-prone locations<br>and important intervention sites<br>identified by the CMC engineers|
|| Examine the impacts on poor and<br>non-poor households separately<br> Examine welfare impacts such as<br>health, productivity and savings<br>in assets that get exposed to<br>floods easily| Primary data collected from 250<br>households in selected areas in<br>Colombo city|
|**Ability to respond**| Identify household adaptation<br>options, their costs, source of<br>finance and decision-making<br>process<br> Examine the option of relocation,<br>influencing factors and<br>willingness to relocate| Primary data collected from 250<br>households in selected areas in<br>Colombo city|

The vulnerability and impacts of floods on households and their ability to respond in Colombo city have been captured through the primary survey carried out in the flood-prone areas, in particular, near the 15 intervention sites, which were further combined into 11 Micro Drainage Intervention Catchments - P1 to P11 – depending on the value of the financial package. The details of these catchment locations are also given as Annex 2 to this paper. Stratified random sampling has been used to select households across these 15 sites. The survey locations have been chosen after assessment of information available with CMC on flood prone locations and consultations with CMC officials. The CMC engineers categorized the catchment areas into high, medium and low population/density zones and the sample has been distributed accordingly. A detailed primary survey of 250 households has been carried out in the identified areas, the details of which are given in Table 3 below.

_Table 3: Details of surveyed areas_

|**Flooding Locations**|**Areas surveyed**|**No. of**<br>**households**|
|---|---|---|
||**District -01**||

10
# **6 Robustness**

## **6.1 Different Types of Capital Controls?**

In this section we check the robustness of our key results using alternative measures of capital controls or financial openness. In Figure 10, we regress interest rate responses (both point estimates, and the 68% confidence band) at each horizon, on different capital control measures, controlling for all the other country factors including EA, FXI, an indicator for EM, the deposit dollarization (DOL) measure constructed by Christiano et al. (2021), and the U.S. trade share (USTR) as defined in Miniane & Rogers (2007).<sup>15</sup> As seen from the graph, a higher capital restriction (or lower financial openness) suggests a lower response to U.S. monetary policy shocks, and this conclusion holds for a variety of capital control measures.

## **6.2 Endogenous Capital Controls?**

Conclusions concerning the relationship between capital controls and U.S. spillovers rely on the assumption that capital controls are exogenous. If countries enact more stringent controls in response to undesirable external shocks, the statistical findings will be biased (downward for high KC groups in particular). Table 6 reports countries’ transition probabilities among KC quintiles. Clearly, there is a strong tendency that KC stances maintain rather than jumping around from time to time. This evidence of hysteresis in capital controls provides some support for the exogeneity assumption.

Table 6: Transition Probabilities Among KC Quintiles

||_Nqt_ = 1|_Nqt_ = 2|_Nqt_ = 3|_Nqt_ = 4|_Nqt_ = 5|
|---|---|---|---|---|---|
|_Nqt_+1 = 1|0.9375|0.0740|0.0105|0.0041|0.0022|
|_Nqt_+1 = 2|0.0473|0.8603|0.1477|0.0062|0.0043|
|_Nqt_+1 = 3|0.0152|0.0603|0.7996|0.0372|0.0043|
|_Nqt_+1 = 4|0.0000|0.0055|0.0380|0.8822|0.1041|
|_Nqt_+1 = 5|0.0000|0.0000|0.0042|0.0702|0.8850|

Note: Markov transition matrix of countries moving from the _i_ th capital control quintile at time _t_ to the _j_ th quintile at time _t_ + 1, estimated by maximum likelihood from annual data of 99 countries.

To address the potential endogeneity in capital controls, we estimate state-dependent impulse

> 15Data resource: the Direction of Trade Statistics (DOTS).

22
# 4. Empirical results

## _4.1. Changes in Voting Rights_

In this section, I document the change in voting rights of cross-listing firms after they cross-list on a major exchange in the US via a Level II or Level III ADR program. I first examine these changes by classifying firms by their home country’s legal tradition. Table III shows that there is a significant difference in cash flow and voting rights, prior to cross-listing, between firms belonging to different legal traditions and levels of shareholder protection. It is therefore worthwhile to examine if the changes in the legal and regulatory environment that cross-listing firms are subject to, upon issuing an ADR, translate into changes in ownership concentration as predicted by the rent protection theory of ownership. Finally, I also assess if the changes in voting rights are different for different groups of shareholders. The purpose is to examine if a US listing affects certain groups of controlling shareholders more than other groups.

Panel A and B of Table V report the percentage changes in voting rights at the two cutoff levels of 20% and 10% ownership concentration during a six-year period (-1,+5) around the cross-listing year. The statistics reported are group means for different groups of home countries of the cross-listing firm classified on the basis of legal origin. I also report statistics for a comparison sample of benchmark firms from each country that do not cross-list.

A perfect control sample would be the Level I ADRs that trade on OTC markets because while these firms cross-list, they are not subject to SEC enforcement or exchange listing rules. However, the lack of SEC filings of these companies makes it extremely difficult to find their ownership information. To construct the comparison sample, for each cross-listing firm, I find a firm matched by industry (four-digit SIC) and nearest asset size from Worldscope. If there is no match at the four-digit SIC level, I look at three or two-digit SIC codes before applying the size criterion. The final restriction for the non cross-listing firm to be included in the sample is the availability of good ownership information from Mergent International and country specific 20
**_<u>________________________________________________________</u>_**

- Regulatory environment: designed to measure the economy’s approach to regulations `o` Transparency of government policy is satisfactory (WCY); `o` Control of Corruption (KKZ);

- Service sector infrastructure: designed to measure the extent to which an economy has the necessary domestic infrastructure (such as telecommunications, financial intermediaries, and logistics firms) and is using networked information to improve efficiency and to transform activities to enhance economic activity.<sup>3</sup> `o` Speed and cost of internet access (GCR);

- Effect of internet on business (GCR).

**(** <u>Annex E</u> provides the ranking of countries in each trade facilitation measure.)

# _<u>Trade Agreements Database and Archive, Tuck School of Business at Dartmouth</u>_

The Tuck Trade Agreements Database contains the most comprehensive collection of texts of bilateral and regional free trade agreements available on a single online site as well as the only database of free trade and customs union agreements text-searchable by provision or keyword. The database contains searchable versions of all bilateral and regional free trade agreements and customs union agreements notified to the WTO as of June 1, 2003, and are in force, plus many that have not been notified to the WTO. The agreements in the database have been indexed in a format developed by CIB that is a greatly expanded version of the "standard format" for notification to the WTO. The archive contains the full texts of all of the agreements in the database, which can be downloaded in PDF format.

# _<u>European Union (EU) Market Access database</u>_

The Market Access Database is an important operational tool of the European Union's Market Access Strategy, supporting a continuous three-way exchange of information between: the EU institutions, Member States and European business. The Market Access Strategy is part of the EU's trade policy approach to reduce obstacles faced by European exporters of goods and services. The Market Access Database provides nformation on:

- Sectoral and Trade Barriers

- Information on export and investment conditions in non-EU countries, including all our major trading partners. In addition, you can identify trade barriers affecting you in the individual countries.

- Applied Tariffs By simply entering an HS code or product description, this section will provides duties and taxes applicable to your products, allowing you to calculate a landed cost.

- Exporters' Guide to Import Formalities Information on import procedures and documents required for the import of a particular product is available by simply entering an HS code or product description.

> 3 For further discussion of the relationship between domestic infrastructure and e-commerce, see Mann, Eckert, and Knight.

13 ____________________________________________________________________________________________________________
Migrant Opportunity and the Educational Attainment of Youth in Rural China<sup>♠</sup> Alan de Brauw John Giles International Food Policy The World Bank Research Institute & IZA Key Words: Migration, Educational Attainment, Rural China JEL Codes: O12, O15, J22, J24

> ♠ This is the May 2015 revision of our original 2008 working paper. A version similar to the original paper can be found at http://ftp.iza.org/dp2326.pdf. We are grateful for thoughtful discussions on this version and several earlier versions of the paper with a number of scholars, including three anonymous referees, Dwayne Benjamin, Loren Brandt, Eric Edmonds, Andrew Foster, Belton Fleisher, Steven Haider, Hongbin Li, David McKenzie, Xin Meng, Berk Ozler, Albert Park, Steve Pischke, Nancy Qian, Martin Ravallion, Scott Rozelle, T. Paul Schultz John Strauss and Yaohui Zhao among others. We are grateful to Xiaohui Zhang, Liqun Cao, and Changbao Zhao from the Research Center for the Rural Economy (RCRE) at China’s Ministry of Agriculture for assistance with the design and implementation of a supplemental survey to match RCRE’s ongoing village and household panel surveys. We are also grateful for financial support from the Knowledge for Change Program at the World Bank, the National Science Foundation (SES-0214702), and the Michigan State University Intramural Research Grants Program.