rafmacalaba/datause-encoder-data huggingface dataset:
positively affects greenfield FDI by the most diversified firms, suggests that if MNEs are able to absorb discontinuous risk, entry into conflict countries might even increase earnings. As such, MNEs might want to consider entry into conflict countries – taking into account sector and firm attributes, despite the large level of risk posed, with the intention of obtaining a competitive advantage.

The main limitation of this study is that the empirical analysis cannot establish a causal relationship between FDI and political violence; therefore, the results should be interpreted as conditional associations. Although it is unlikely that total FDI flows have a direct effect on the number of battle-related deaths, FDI in the natural resource sector might affect political violence – particularly separatist violence – by intensifying grievances or increasing the perceived gains of secession. However, finding sources of exogenous variation in political violence that can be exploited in a panel format is challenging. Therefore, we address the endogeneity problem by including fixed effects and a large set of control variables, including income and quality of institutions. In the robustness analyses, we also control for the discovery of large oil and gas reserves because it is likely that the discovery of valuable resources rather than the involvement of an MNE fuels conflict. This additional control variable does not change our main results.

Another limitation lies in the data sources used. The data on greenfield FDI flows are collected through Financial Times newswires, internal information sources, other media sources, project data acquired from industry organizations and investment agencies and data purchased from market research and publication companies. MNEs investing in conflict countries might actively avoid publication of the investment project in the media to avoid a public outcry. This selection effect could drive our results on the effect of political conflict on total greenfield FDI flows. To our knowledge, however, there are no FDI data sets that are not based on media coverage while simultaneously covering an equally large set of countries and allowing for disaggregation at the sector level. Nevertheless, it is unlikely that our results on sector heterogeneity are driven by sample selection bias. It could be expected that particularly resource-related FDI is sensitive to public outcry because this type of investment is generally believed to have negative environmental, economic and institutional development effects (Collier & Hoeffler, 1998; Fearon and Laitin, 2003; Hodler, 2006; Ross, 2004; Sachs and Warner, 1995). Non-resourcerelated greenfield FDI, however, is widely considered a vehicle for economic development and 41
# **<u>Sensitivity analysis: Restricted model</u>**

**Dependent Variable: Sensitivity analysis: LN Total cost of tax compliance (+50% across the board)**

||Coeff.|Std. Error|Sig.|
|---|---|---|---|
|(Constant)|7.575|0.370|0.0%|
|LN full-time employees|0.082|0.036|2.4%|
|LN of q55 after imputed values were introduced (imputed "no|0.190|0.028|0.0%|
|answer")||||
|Share of activities completely outsourced|-1.133|0.133|0.0%|
|Share of activities partially outsourced|-0.344|0.129|0.8%|
|Guateng provincial dummy|0.152|0.077|4.9%|
|Western Cape provincial dummy|0.028|0.100|78.2%|
|sole proprietor dummy|-0.511|0.118|0.0%|
|close corporation dummy|-0.167|0.089|6.2%|
|Manufacturing|-0.206|0.107|5.4%|
|VAT|0.200|0.123|10.4%|
|PAYE|0.253|0.104|1.5%|
|Adj. R2|0.465|||
|Mean of the dependent variable|10.059|||

|**Dependent Variable: Sensitivity analysis: LN Total cost of tax**<br>**board)**|**compliance (**|**+40% acros**|**s the**|
|---|---|---|---|
||Coeff.|Std. Error|Sig.|
|(Constant)|7.510|0.368|0.0%|
|LN full-time employees|0.082|0.036|2.2%|
|LN of q55 after imputed values were introduced (imputed "no<br>answer")|0.190|0.028|0.0%|
|Share of activities completely outsourced|-1.084|0.132|0.0%|
|Share of activities partially outsourced|-0.323|0.128|1.2%|
|Guateng provincial dummy|0.155|0.077|4.4%|
|Western Cape provincial dummy|0.034|0.099|73.1%|
|sole proprietor dummy|-0.514|0.117|0.0%|
|close corporation dummy|-0.173|0.089|5.2%|
|Manufacturing|-0.203|0.106|5.7%|
|VAT|0.203|0.122|9.7%|
|PAYE|0.250|0.103|1.6%|
|Adj. R2|0.462|||
|Mean of the dependent variable|10.024|||

|**Dependent Variable: Sensitivity analysis: LN Total cost of tax**<br>**board)**|**compliance (**|**+30% across**|**the**|
|---|---|---|---|
||Coeff.|Std. Error|Sig.|
|(Constant)|7.440|0.366|0.0%|
|LN full-time employees|0.083|0.036|2.0%|
|LN of q55 after imputed values were introduced (imputed "no|0.190|0.027|0.0%|
|answer")||||
|Share of activities completely outsourced|-1.032|0.132|0.0%|
|Share of activities partially outsourced|-0.300|0.128|1.9%|
|Guateng provincial dummy|0.158|0.076|3.9%|

41
about the impact of community heterogeneity on project choice or on the distribution of benefits since they have little to say about non-participants.

Not all quantitative studies, however, find that economic inequality has a monotonically negative impact. Somanathan, Prabhakar, and Mehta (2002) analyze the effect of collective action on forest conservation in India using matched-comparison difference-in-difference techniques. They find that communities with more equal land ownership conserved pine forests somewhat better than other communities. However, they find no impact of caste heterogeneity on conservation efforts. They also find that more equal land ownership does not translate into better conservation of broadleaf forests, which are of much greater importance to villagers for firewood and fodder. Some in-depth case studies of participatory projects using qualitative methods also suggest a more complex role for social heterogeneity (see, for example, Vedeld 2000.

Dayton-Johnson (2000) develops a model of cooperation in small irrigation systems and tests the model with data from a survey of Mexican irrigation societies. He finds that social heterogeneity is consistently and significantly associated with lower levels of maintenance. Landholding inequality and the proportional sharing of water in accordance with landholding size also tend to decrease maintenance, but in a nonmonotonic way. Khwaja (2001) finds empirical evidence for a U-shaped relationship between land inequality and project maintenance, with both highly equal and highly unequal communities performing better than moderately equal ones.

_Capture by local elites._ The frequent tendency for participatory projects to be dominated if not captured by local elites is highlighted by several case studies. Katz and 40
private sector as a percent of GDP (World Bank FinStats, end-2019), and iv) adoption of the capital requirements of the Basel III framework (which include the creation of new buffers and the need for more and higher-quality capital) (World Bank Bank Regulation and Supervision Survey, 2016). However, the country sample size decreases significantly, so we report regression results in Appendix C as they need to be interpreted with more care.

# **4 Determinants of the Financial Sector Policy Response to COVID-19: Empirical Results**

# _4.1 Time until the first policy response is taken: Kaplan-Meier survival estimates_

Table 2 reports the descriptive statistics on the time taken (measured in days) to implement the first policy measure by category.<sup>14</sup> With a median of 50 days, _Banking Sector_ and _Liquidity and Funding_ measures were implemented faster compared to other categories. By the end of March 2020, around 21 percent of countries had taken their first measure in the _Banking Sector_ and _Liquidity and Funding_ categories. In contrast, by that time, around 10 percent of countries had implemented measures in the _Financial Markets and NBFIs_ and _Payments Systems_ categories.

**Table 2. Descriptive Statistics: Time Until First Policy Measure by Category (in days)**

|**Category**|**Countries**|**Mean**|**Std. Dev.**|**Min**|**Median**|**Max**|
|---|---|---|---|---|---|---|
|Banking Sector|154|56.5|31.8|2|50|216|
|Financial Markets and NBFIs|154|128.6|80.7|20|97|216|
|Liquidity and Funding|154|63.6|46.7|6|50|216|
|Payments Systems|154|128.7|77.2|21|101|216|

Note: Standard deviation, minimum, median, mean, and max of the distribution of the number of days taken to put in place a policy in the four categories described in section 2.1.

The panels in Figure 5 each display two Kaplan-Meier survival curve estimates corresponding to a group of lower- and higher-income countries, respectively.<sup>15</sup> In doing so, we split the country sample on median 14 The descriptive statistics in Table 2 reflect the fact that countries are right-censored, i.e. we assign to these countries the maximum value (216) to the time until the first policy measure was implemented. In ancillary regressions not shown in the paper we drop from the sample those countries that did not implement a measure in a specific category over the period in analysis. Results, available from the authors upon request, remain qualitatively the same.

15 For the sake of brevity, we just report the KM survival curve estimated for GDP per capita as this variable shows the strongest univariate relationship with the time of implementation of the first policy. Results for the other determinants are available from the authors upon request.

19
11

# 4. **Fiscal Guarantees and Coordination**

Among infrastructure projects, there are typically a lot of positive externalities (see Murphy, Shleifer, and Vishny 1989; Paternostro 1997; Oh 2011; Government of India 2012). The new road that will be operated by a toll system is more likely to be successful if the residential township at the end of the road is constructed; and the residential township being contemplated by the developer is more likely to be successful if the road gets built. The government, by giving some carefully orchestrated guarantees to investors, can ensure that all these projects will come to fruition, thereby raising the probability of success of all these projects. The fact that government investment often leads to greater private investment, especially in low-income countries (see, for instance, Eden and Kraay 2014), is indirect evidence of this being true. What follows is a simple model that draws on Basu (2012) to illustrate this idea.

Let us suppose that there is a set _X_ ≡ { 1,..., _n_ } of _n_ potential infrastructure projects. Each project is run by an independent private entrepreneur. To undertake the project entails an up-front cost of _C_ ( _>0_ ). The project will yield results after a longish time. If it succeeds, it gives a return of _S_ and if it fails, it gives _0_ .

Let _p_ denote the probability of this project succeeding. I shall assume that the probability of success depends on how many projects in _X_ are undertaken. If _m_ ≤ _n_ projects are undertaken, then the probability of success is given by _p(m)._ It is possible to argue that, quite apart from its dependence on _m_ , _p_ could vary merely by the fact of government giving a guarantee. Suppose an international investor puts money in a private Indian firm building an airport. The investor, who has little legal jurisdiction—certainly not one without considerable cost—in India, could face a high risk of default. I am assuming that the investments that are made take the form of nonrecourse loans (Basu 2010). If, instead, the government of India gives a guarantee, then by virtue of the government’s power, the probability of default could decline. This is broadly the idea that was pursued
migration, with households with well established networks in Italy being less likely to send temporary migrants (to Greece).

**Table 3** _Maximum Likelihood Migration Duration Models (log-logistic hazard function)_

||**any migr**|**ation**|**perm migrati**|**on only**|**temp migrat**|**ion only**|
|---|---|---|---|---|---|---|
||**coef **|**rob.sd**|**coef **|**rob.sd**|**coef **|**rob.sd**|
|origin of spell|-0.008**|0.004|-0.016***|0.005|0.029***|0.01|
|gender (female=1)|0.673***|0.025|0.283***|0.025|1.562***|0.067|
|coastal region|-0.197***|0.033|-0.221***|0.039|-0.132|0.082|
|central region|-0.071**|0.034|-0.091**|0.041|-0.046|0.081|
|mountain region|0.114***|0.035|0.057|0.042|0.204**|0.082|
|urban areas|-0.024|0.021|-0.090***|0.026|0.102*|0.053|
|age (in years)|0.049***|0.001|0.052***|0.002|0.046***|0.003|
|years of education|-0.123***|0.018|-0.095***|0.02|-0.162***|0.046|
|years of education squared|0.005***|0.001|0.004***|0.001|0.007***|0.002|
|no. of household members age 0-14|0.013*|0.008|0.039***|0.01|-0.035**|0.017|
|no. of males in household age 15-35|-0.006|0.009|-0.099***|0.011|0.124***|0.028|
|no. of females in household age 15-35|-0.050***|0.012|-0.016|0.015|-0.060*|0.035|
|no. of males in household age 36-55|-0.118***|0.013|-0.298***|0.018|0.224***|0.03|
|no. of household members age >55|-0.095***|0.016|-0.235***|0.021|0.313***|0.045|
|migrant network in Greece|-0.322***|0.022|-0.240***|0.021|-0.245***|0.067|
|migrant network in Italy and beyond|-0.234***|0.019|-0.235***|0.019|0.175***|0.062|
|household-level shocks|||||||
|pyramid collapse|0.007|0.056|0.054|0.063|-0.375***|0.126|
|property loss|-0.002|0.05|-0.016|0.061|0.026|0.126|
|job loss|0.001|0.019|0.086***|0.023|-0.139***|0.042|
|illness/death of household member|0.029**|0.014|0.021|0.015|0.046|0.048|
|wealth index in 1990<br>|-0.018***|0.005|-0.006|0.007|-0.041***|0.013|
|regularization programs (epoch dumm|||||||
|y<br>>1998)|0.180***|0.026|0.174***|0.03|||
|constant|2.099***|0.115|2.624***|0.129|2.570***|0.299|
|logarithm of gamma|-0.996***|0.019|-1.052***|0.028|-0.533***|0.026|
|gamma|0.36|9|0.349||0.587||
|no. of subjects|12,75|3|12,753||12,75|3|
|no. of failures|2,95|5|1,971||984||
|total time at risk|147,7|57|155,42|8|158,29|8|
|log-likelihood|-6,04|1|-4,304||-3,40|0|
|chi2|4,91|6|3,664||1,036||
|prob.|0||0||0||
|note: .01 - ***;.05 - **;.1 - *|||||||

Another interesting finding relates to the differential effect of various types of shocks on the different types of migration. The collapse of the pyramid schemes at the end of 1996 appears to have triggered a massive exodus of migration which, however, in most cases ended up being only temporary. As also suggested by the descriptive statistics, the sudden loss of savings created a strong push for less suited individuals, such as older and 20
wages, and their pensions (the letter calculated according to specific policy rules or through replacement rates).<sup>6</sup> The model also opens the possibility to introduce unemployment insurance but the option is not used in the Moroccan case.

In this section we focus mainly on the innovations of the model while describing very briefly the standard applied general equilibrium specifications.

**Table 1: Typology of Skills in the Model SELMA**

|**Typology of Labor Categories**|**Diplomas and Specializations Included**|
|---|---|
|Unskilled workers (un)|No formal education or only Initial Training (i.e.,<br>VT diploma obtained where the only condition is<br>toread and write).<br>|
|Low skilled workers type A (lsa)|Primary education + VT (_specialilsation_) or 9<sup>th</sup>+<br>VT.(_qualification_) orSecondary educationonly<br>|
|Low skilled workers typeB(lsb)|Only primary educationor9<sup>th</sup> grade|
|Skilled workers (sk)|Secondary<br>education<br>+<br>VT<br>(_Techniciens_<br>_specialisés_)|
|Highly skilled workers type A (hsa)|Graduates from_Grandes Ecoles_and faculties<br>withtechnicalspecializations.|
|Highly skilled workers type B (hsb)|University graduates (except physicians) without<br>a VTdiploma|

Source: Authors’ design. The number of workers in each skill category by economic sector was provided by the High Council for Planning on the basis of the labor force survey.

# **_The production and factors demand block_**

The economy is disaggregated into 20 sectors. Within each sector, production factors are subdivided in 7 items: capital plus the six labor categories described above. The production function is a nested one. At the highest level we assume that the production of each sector is a Leontief function of value added and total intermediate consumption. The demand for capital and the 6 skills levels is modeled through a nested CES function at 5 levels, which allows to have differentiated elasticities of substitution between the different factors. Capital and highly skilled labor have been modeled relatively complementary, following the Fallon-Layard hypothesis (1975) which has been confirmed by various empirical studies. Concerning the other bundles we have aggregated factors by pairs in CES bundles so as to have higher substitution elasticities between similar factors (for example

> 6 The basis of the template is the World Bank model PROST (see World Bank. 2005).

7
the exception of sales where self-employment predominates. These patterns are con fi rmed in the fi ner classi fi cation of occupations and sectors. Evidence in Table 1 thus shows that for both occupational and sectoral classi fi cations of non-farm activities, every sub-category includes a number of activities with returns ranging from below the median agricultural wage to well above median agriculture wage rates. Analysis based on these classi fi cations is likely to be less informative in highlighting the role of farm and urban linkages in stimulating pro-poor nonfarm growth.

For the empirical analysis, we de fi ne our dependent variable as the employment status of an individual where the status variable indicates multiple choices. Speci fi cally, the dependent variable takes the value of 1 if an individual is employed in agriculture (our base outcome), 2 if employed in low-return wage employment in the nonfarm sector, 3 if employed in high return wage jobs in the non-farm sector; and 4 if self employed in the non-farm sector. The high return nonfarm jobs are de fi ned as those activities which pay higher than median agricultural wage in the village. The low return nonfarm jobs are those that pay less than or equal to median agricultural wage. We also test robustness of our econometric results using other de fi nitions of nonfarm activities (e.g. production workers vs service workers) as suggested by Foster and Rosenzweig (2003a & b). Since the dependent variable includes unordered multinomial responses, we use standard multinomial logit estimation technique for the empirical analysis.

# 3 The Data

The main data source for our empirical analysis is the Household Income and Expenditure Survey (HIES) 2000 of Bangladesh. The survey drew a nationally representative sample of 7440 households in 442 primary sampling units (PSUs) which are equivalent to census enumeration areas. It also contains detailed information on about 39,000 individuals including data on 12
# US Historical Settlement Patterns

Columns 9 and 10 of Table S1a provide the largest cities for each country’s ancestry population. Due to their large overall size and high immigration shares, cities like New York, Los Angeles, and Miami appear frequently. Nonetheless, there is substantial heterogeneity. East and West Coast cities are more likely to link respectively to European and Asian ancestries, for example, while southern parts of the United States link more to Latin American ancestries. The migration from Nordic countries to the mid-west is evident (e.g., Finland’s presence in Minnesota).

Robustness checks use the spatial distributions present in the 1990 Census of Populations. These patterns are constructed from the 5% sample in a manner that parallels the 1980 distribution.

# US Patenting Data

The addresses listed on inventor records are used to group patents to the cities identified in the 1980 census. This procedure uses city lists collected from the Offi ce of Social and Economic Data Analysis at the University of Missouri, with a matching rate of 99%. Manual recoding further ensures that all patents with more than 100 citations and all city names with more than 100 patents are identified. Some smaller metropolitan areas identified in the patent data are excluded since they do not link to places identified in the 1980 census. Only patents with all inventors living in the United States at the time of their patent application are included, and multiple inventors are discounted so that each patent receives the same weight when measuring inventor populations.

Second, the USPTO issues patents by technology categories rather than by industries. The work of Johnson (1999), Silverman (1999), and Kerr (2008) develops concordances that link the USPTO classes to ISIC3 industries in which new inventions are manufactured or used. The main estimations focus on industry-of-use, affording a composite view of the technological opportunity developed for an industry. Studies of advanced economies find accounting for these inter-industry R&D flows important (e.g., Scherer 1984, Keller 2002b). Estimations using the alternative categorization of technologies to industry of manufacturer are also presented below. Cohen (2011) discusses the larger literature on industry-level mappings and evidence regarding patents, R&D, and productivity.

With respect to ethnic name matching, Kerr (2007) documents specific algorithms, lists frequent ethnic names, and provides extensive descriptive statistics. This paper also discusses 6
<!-- Start of picture text --> s<br>os<br>- i Ps<br>0-11% ;<br>MMMl 32-57 11-32% 57-81% % *<br>MMM 81- 100%<br><!-- End of picture text --> <!-- Start of picture text --> ; * “ooh.<br>oo<br>- sal<br>yo: bi<br>+ ke<br>€ he<br>Ds<br>0-11% 3<br>Ml 11-32%<br>MM 32-57 57-81% %<br>MMM 81- 100%<br><!-- End of picture text --> <!-- Start of picture text --> +, 2 ; » &<br>pithy Lseo<br>ey<br>0-11% | a 3<br>11-32% "<br>MM 32-57%<br>ME 57-81<br>MH 81 - 100% %<br><!-- End of picture text -->
**A1:  Evolution of millet, maize, rice and sorghum production in Burkina Faso since 1961**

<!-- Start of picture text --> 2500000<br>2000000<br>1500000 Millet<br>Maize<br>1000000<br>Rice<br>Sorghum<br>500000<br>0<br><!-- End of picture text -->

**_<u>Source: FAO, Data viewed on January 06, 2015</u>_**

26
Integrated Rural Development Program in India (Pulley 1989) also focuses on output and income effects of investments financed of credit in beneficiary households. Formal postevaluations of credit programs such as those carried out in India by the National Bank for Agriculture and Rural Development (1981 et sequa) are more sophisticated. Their approach is also discussed in David and Meyer, 1980. These studies compare the output and costs of farms that receive credit under a specific program with farms that did not. A more sophisticated version of cross-sectional comparison are production function studies (for an early review see David and Meyer, 1980). All studies based on such cross-sectional comparisons will overestimate the benefits of credit if credit goes selectively to better farmers, or to farmers with better endowments in terms of land, machinery and liquid capital. Said otherwise, the cross-sectional estimates are likely to suffer from selectivity bias.

An approach that can overcome both the problems of fungibility and of selectivity is supply function analysis, where credit enters as an independent variable in the supply function. However, credit can favor one crop at the expense of others. Applying the techniques to an individual crop could therefore over- or underestimate the supply effects, if effects on other crops are not taken into account. A simple way to circumvent this problem is to estimate the impact of credit expansion on aggregate output. This is the approach to the estimation of benefits taken in this paper. Other major econometric problems associated with

**benefit-cost ratios. Underestimation of the benefit cost ratio is also possible in the** less **likely event that additional credit may induce more** self-finance, **because it eliminates a credit constraint for a lumpy investment and thereby induces more, rather than less, self finance.**

**2**