rafmacalaba/datause-encoder-data huggingface dataset:
|||(<br>1<br>_bias _<sup>_−_1)</sup>|<sup>_×_</sup><br>_Annual Import_|_Gains from_<br>_Variety_|
|---|---|---|---|---|
|_Year_|_Bias_|100|_(% of GDP)_|_(% of GDP)_|
||_(1)_|_(2)_|_(3)_|_(4)_|
|1995|1.000|0.00|39%|0.00|
|1996|0.984|1.66|42%|0.70|
|1997|0.937|6.69|39%|2.55|
|1998|0.911|9.77|37%|3.50|
|1999|0.993|0.75|46%|0.34|
|2000|0.991|0.92|41%|0.38|
|2001|0.994|0.63|41%|0.26|
|2002|0.863|15.92|38%|5.75|
|2003|0.986|1.41|41%|0.58|
|2004|0.981|1.94|60%|1.16|
|2005|0.859|16.39|48%|7.63|
|2006|0.976|2.43|48%|1.16|
|2007|1.008|-0.78|45%|-0.35|
|2008|1.029|-2.84|44%|-1.27|
|2009|0.961|4.01|43%|1.70|
|2010|0.951|5.11|55%|2.79|
|2011|0.869|15.10|59%|8.60|
|2012|0.957|4.53|56%|2.49|
|2013|1.025|-2.43|52%|-1.26|
|2014|0.994|0.62|49%|0.30|
|2015|0.968|3.27|53%|1.72|
|2016|1.009|-0.86|46%|-0.40|
|2017|0.936|6.85|42%|2.82|

20
# **Table 10. The Effect of Titling Firms’ Political Connections on Investment**

The sample consists of all nonfinancial listed firms in the CSMAR database from 2008 to 2017.This table presents parameter estimates from OLS regression of investment on Titling Firm dummy variable and firms’ political connections, measured by either the total number of local bureaucrats on the firms’ boards or the ratio of Communist Party members as the percentage of total employees. The independent variable is the averaged investment between 2013 and 2017 net of investment between 2010 and 2012. _Bureaucrats on Boards_ is the total number of board members of the firms with experience of serving in the government. The _Party Member Ratio_ is the ratio of the total number of employees with communist party membership divided by the number of total employees. All variables are winsorised at the upper and lower 1-percentile. Industry FE denotes whether the industry of the listed firms (categorized according to industry categorization from CSMAR database) is considered in the specification. Exchange FE denotes whether the effect of exchange (Shanghai or Shenzhen Exchange) in which the firms are listed is considered in the specification. The _t-statistics_ are computed using standard errors that are White-corrected. The *,**,*** indicates significance at the 1%, 5% and 10% level respectively.

|||∆(Investm<br>|ent/PP&E)<br>||
|---|---|---|---|---|
||Bureaucrat|s on boards|Party Mem|ber Ratio|
||(1)|(2)|(3)|(4)|
|Titling Firm|0.186***||0.211***||
||(2.682)||(3.093)||
|Bureaucrats on boards|-0.035|-0.035|||
||(-1.251)|(-1.254)|||
|Titling Firm * Bureaucrats on board|0.500***||||
||(2.808)||||
|Party Member|||-0.016|-0.016|
||||(-1.043)|(-1.033)|
|Titling Firm * Party Member|||1.663***<br>(5.227)||
|Titling Firm * SOE||0.271***||0.254***|
|||(3.376)||(3.530)|
|Titling Firm * Non-SOE||0.031||0.149|
|||(0.282)||(0.970)|
|Titling Firm * SOE * Bureaucrats on board||0.064|||
|||(0.932)|||
|Titling Firm * Non-SOE * Bureaucrats on b|oard|1.216***|||
|||(10.706)|||
|Titling Firm * SOE * Party Member||||-0.679|
|||||(-0.107)|
|Titling Firm * Non-SOE * Party Member||||1.594***<br>(5.185)|
|Control Variables|Yes|Yes|Yes|Yes|
|Industry FE|Yes|Yes|Yes|Yes|
|Exchange FE|Yes|Yes|Yes|Yes|
|Adj. R<sup>2</sup>|0.067|0.069|0.073|0.072|
|# of Obs.|1250|1250|1250|1250|

56
# **Abstract**

Recent research suggests that isolation from regional and international markets has contributed significantly to poverty in many Sub-Saharan African countries. Numerous empirical studies identify poor transport infrastructure and border restrictions as significant deterrents to trade expansion. In response, the African Development Bank has proposed an integrated network of functional roads for the subcontinent. Drawing on new econometric results, this paper quantifies the trade-expansion potential and costs of such a network. We use spatial network analysis techniques to identify a network of primary roads connecting all Sub-Saharan capitals and other cities with populations over 500,000. We estimate current overland trade flows in the network, using econometrically-estimated gravity model parameters, road transport quality indicators, actual road distances, and estimates of economic scale for cities in the network. Then we simulate the effect of feasible continental upgrading by setting network transport quality at a level that is functional, but less highly-developed than existing roads in countries like South Africa and Botswana. We assess the costs of upgrading with econometric evidence from a large World Bank database of road project costs in Africa. Using a standard approach to forecast error estimation, we derive a range of potential benefits and costs. Our baseline results indicate that continental network upgrading would expand overland trade by about $250 billion over 15 years, with major direct and indirect benefits for the rural poor. Financing the program would require about $20 billion for initial upgrading and $1 billion annually for maintenance. We conclude with a discussion of supporting institutional arrangements and the potential cost of implementing them.

1
**Figure 5. Decomposition of total gender pay gap into composition and wage structure effects, by percentile in 2011**

<!-- Start of picture text --> 0.40<br>0.30<br>0.20<br>0.10<br>0.00<br>5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95<br>‐0.10<br>Total wage gap<br>‐0.20<br>Composition<br>Wage structure<br>‐0.30<br><!-- End of picture text --> _Note:_ Based on Oaxaca–Blinder decompositions similar to those presented in Table A3b for each percentile indicated in the Y-axis.

Thus, the fact that the composition effect decreases along the wage distribution indicates that women are more subject to discrimination or lack access to jobs that pay as well as men’s jobs, given their qualifications. The policy recommendation of this finding would be to help women at the bottom of the earnings distribution increase their labor market skills, since equalizing their characteristics to those of men at the bottom of the earnings distribution would reduce the gender pay gap by half. Meanwhile, for women at the top of the distribution, policies should be designed to help them access jobs that remunerate their skills as much as men.

Second, some of the covariates inside the composition effect also show a very nonmonotonic pattern along the wage distribution. The most striking result is that the importance of industry decreases along the wage distribution. The problem for women at the bottom of the distribution (10th percentile) is that they are employed in low-wage industries, though performing similar occupations as men (or occupations slightly better paid than men as the occupation line falls below zero). If women in the 10th percentile were employed in the same economic sectors as men, their gender wage gap would decrease by half. Meanwhile, women at the top of the distribution (90th percentile) are working in high-wage occupations: If women were employed in the same occupations as men, the gap in pay would be 37 percent larger. Lastly, all women, and in particular those at the bottom of the distribution, are more educated than men who hold similar jobs and are in a similar position in the earnings distribution.

20
Table 6- Regression results explaining the determinants of risk premium

|Explanatory variables|equation I|equation 2|equation 3|equation 4|equation 5|
|---|---|---|---|---|---|
|Intercept|-0.9613|-1.3104|-1.2403|-1.1550|-0.0823|
||(-0.9300)|(-1.1820)|(-1.0740)|(-1.0790)|(-0.0960)|
|Inflation|2.9171**|3.2725**|3.2995**|2.7781**|2.9362**|
||(3.6490)|(3.7090)|(3.6160)|(3.4140)|(3.5430)|
|ln(Extemal debt/export)|0.2321|0.2333|0.2267|0.2988||
||(1.4050)|(1.2590)|(1.1820)|(1.7040)||
|ln(GDP per capita)|-0.1013||-0.0524|-0.0440|-0.1234|
||(-0.9750)||(-0.4530)|(-0.4040)|(-1.1590)|
|Ln(Short term debt/Reservs)|0.2544|0.0963|0.1491|0.0381|0.3562*|
||(1.2520)|(0.5470)|(0.6910)|(0.1950)|(1.8110)|
|Reserves /Import|0.0904|0.0495|0.0738|0.0426|0.1077|
||(1.3200)|(0.9420)|(0.9670)|(0.5870)|(1.5420)|
|ln(Project size (investment))|-0.0967||||-0.0999|
||(-1.7470)||||(-1.7410)|
|Project leverage ratio, %||-0.0020|-0.0015|||
|||(-0.4370)|(-0.3170)|||
|Roads|0.1694|0.0251|0.0227|0.1140|0.1147|
||(0.7720)|(0.1100)|(0.0960)|(0.5740)|(0.5120)|
|Power|-0.2071|-0.2125|-0.2483|-0.2172|-0.2665|
||(-1.2350)|(-1.0140)|(-1.0780)|(-1.3170)|(-1.5830)|
|IndexType|0.0311|0.0189|-0.0037|0.1237|-0.0332|
||(0.1240)|(0.0670)|(-0.0130)|(0.4810)|(-0.1300)|
|Intercept Inflation Dummy|-1.9616**|-2.3082**|-2.321 9**|-1.9226**|-1.9774**|
||(-3.2000)|(-3.6450)|(-3.5490)|(-3.2110)|(-3.1110)|
|R-square|0.761|0.7170|0.7190|0.6670|0.5480|
|Number of observations|23|23|23|26|23|

_Note:_ t-ratios are in parentheses ** significant at the 0.05 level * significant at the 0. 1O level 21
**Figure 9.  Cohort Size in the Brazilian Censuses from 1960 to 2000**

<!-- Start of picture text --> 4.0<br>3.5<br>3.0<br>2.5<br>2.0<br>2000 census<br>1991 census<br>1.5<br>1980 census<br>1970 census<br>1.0 1960 census<br>0.5<br>0.0<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000<br>Year of Birth<br>Number Reported in Census (millions)<br><!-- End of picture text --> 35
that current income per capita growth does not affect the growth rate of the share of working-age population in the past. Although it can be argued that this approach deals with reverse causality, it does not necessarily address the omitted variable problem.

This paper uses different approaches to dealing with the problem of endogeneity. First, it shows the association between �� and �� by providing the results based on a first-difference estimation. Then, in order to deal with time-invariant unobservable factors that could simultaneously affect �� and ��, a panel fixed effects estimation is used. Finally, to deal with other potential endogeneity issues related to omitted variables that could simultaneously affect �� and ��, a system-GMM estimation strategy, with the share of working age population lagged up to 40 years, is used to identify a causal relationship between international aid and growth, in the spirit of Loyaza et al. (2000), Rajan and Subramanian (2008), and Murtin (2013). Similar approaches were adopted to estimate the effect of change in the share of the working-age population on growth, savings, and poverty. Yet, particularly for the analyses on per capita growth, the coefficients related to the changes in the share of working age population should be interpreted cautiously.

Another important component of the effect of a larger share of working age population relates to the human capital embedded in them. The simple fact of a larger share of working age population may have an effect on growth through the channel of labor supply, as previously discussed. But an increase in the share of working age population may occur in parallel with human capital accumulation, which may affect workers’ productivity (��).<sup>15</sup> Therefore, the paper’s estimations incorporate years of schooling as a proxy for human capital in order to control for its effect on productivity growth per worker. Years of schooling are also interacted with the working age population share in order to capture information related to quality of labor supply.

Since the demographic determinants of growth may also affect savings and poverty, we simply replace the GDP per capita growth dependent variable with changes in the domestic savings as a share of GDP and poverty rate, in order to analyze the effects of demographic change on these on savings and poverty.

# **4. Data, trends, and descriptive statistics**

Several data sources covering the 1950-2010 period are combined in order to analyze the effect of demographic change on growth per capita and savings. First, the UN World Population Prospects 2015 Revision is used to provide cross-country information on population by different age groups. We use information on GDP per capita from the World Bank (WDI) and the Penn World Table (version 8.1). We also use average years of schooling by country, provided by Barro and Lee

> 15 Murtin (2013) suggests that increasing access to primary education leads to a reduction in the fertility rate.

11
# **Figure  3.   Demand for Representative Dixit-Stiglitz (IRTS) good g in Regional Market r**

**Dixit-Stiglitz Aggregate of IRTS good g in RMr**

<!-- Start of picture text --> CES<br>σ = 3 = esub a /<br>Imports of g from Goods g from<br>Goods g from Goods g from<br>Rest of World RM q<br>RM 1 RM m<br>CES<br>CES CES<br>CES<br>σ =3<br>σ =3 σ =3 σ = 3 = esub<br>Rest of World firms Firms in RM m<br>Firms in RM 1 Firms in RM q<br>selling g in RMr selling g in RMr selling g in RM selling g in RM<br><!-- End of picture text -->

> **<u>a/</u> We take σ = 3, except based on Ivanova (2005), we take σ = 3.1 in MWO; σ = 2.6 in TPP; σ = 2.5 in CNM; and σ = 1.8 in OTI.**

54
characteristics include specific age dummies, gender, and birth order which is calculated over all children aged 18 and under in each household. For Vietnam we also include a dummy for whether the child is the head's child. At the household level we include log consumption per capita, log household size, demographic composition (shares by age group), the gender of the head and dummies for urban and regional location. For Vietnam we enter the father and mother’s age, age squared and total years of education; for Morocco, we have age and agesquared only for the father, and education for both the father and mother is measured as dummy variables for various attainment levels rather than years. In addition, for Vietnam we include a dummy variable for whether the head is from an ethnic minority, a group that tends to be poorer and less likely to be schooled. In Morocco we bring in controls for migration (whether the father has lived somewhere else for the last 12 months, whether he was born in the current residence and number of years father has been living in the current residence). Geographic variables consist of the mean consumption and inequality (as measured by the mean log deviation of consumption per person) of the community of residence, as well as distance to school.

Consumption is believed to be measured well in the living standards surveys. It incorporates the value of own production and the imputed value of housing expenditures, and is expressed in real 1998 prices. The reference used is January of 1998 for Vietnam and 1998 for Morocco. We will also test a measure of predicted expenditures which we calculate using the parameters of a regression of actual consumption on a wide array of household level characteristics that are likely to influence household consumption.

Using these data we will calibrate the regression models described above and implement the decompositions. We begin by studying the change in school attainments over time, both in the aggregate and by gender. Next we present a cross-sectional comparison of the sources of the 15
# **E Impacts on Other Outcomes**

Figure E1: Impacts on Number of Products and Countries Exported <!-- Start of picture text --> Number of Products 2019 Number of Countries 2019<br>Diffuse Posterior Diffuse Posterior<br>Literature Posterior Literature Posterior<br>Literature Prior Literature Prior<br>Firm Posterior Firm Posterior<br>Firm Prior Firm Prior<br>Policymaker Posterior Policymaker Posterior<br>Policymaker Prior Policymaker Prior<br>Academic Posterior Academic Posterior<br>Academic Prior Academic Prior<br>2019 Treatment Estimate 2019 Treatment Estimate<br>-2 0 2 4 6 -2 0 2 4 6<br>Treatment Effect Treatment Effect<br>Number of Products 2020 Number of Countries 2020<br>Diffuse Posterior Diffuse Posterior<br>Literature Posterior Literature Posterior<br>Literature Prior Literature Prior<br>Firm Posterior Firm Posterior<br>Firm Prior Firm Prior<br>Policymaker Posterior Policymaker Posterior<br>Policymaker Prior Policymaker Prior<br>Academic Posterior Academic Posterior<br>Academic Prior Academic Prior<br>2020 Treatment Estimate 2020 Treatment Estimate<br>-2 0 2 4 6 -2 0 2 4 6<br>Treatment Effect Treatment Effect<br><!-- End of picture text -->

**Notes** : Treatment estimates and associated red line show frequentist ITT estimate and associated 95 percent confidence intervals. Treatment effect units are in terms of the change in the number of products or countries. Control Mean is 4.16 products and 2.33 countries in 2019, and 4.05 products and 2.37 countries in 2020. Light blue lines show 95 percent prior intervals elicited from academics, policymakers, and firms, and derived from the literature, with circles indicating median of prior distributions. Bayesian posteriors and dark blue lines show median and 95 percent intervals from the estimated Bayesian posterior distributions that update the associated prior with the data from the experiment. Diffuse posterior is the Bayesian posterior from using a (non-informative) diffuse prior..

We use additional data sources to examine impacts on secondary outcomes. We did not collect priors on any of these outcomes, so present frequentist treatment impacts only.

## **Impacts on Survival and Employment Using the PILA**

Our employment data come from the _Planilla Integrada de Liquidación de Aportes_ (PILA) (Unified Register of Contributions), which is the national information system used by firms to file the mandatory contributions to health, pensions, and disability insurance paid for workers. Of the 200 firms that applied for the program, 198 were able to be matched to the PILA, 195 have data in early 2018 (pre-intervention), and 185 are found to be reporting data in December 2020.

Panel A of Table E2 examines the impact of the program on firm survival. Columns 1 and 2 define survival as showing up in the PILA, and look at survival until the end of 2019 and end of 2020 respectively. We see 4 percent of control firms have died by the end 57
40

# **Appendix B: Definitions and Sources of Variables Used in Regression Analysis (cont.)**

|**Variable**|**Definition and Construction**|**Source**|
|---|---|---|
|Net Foreign Income|||
|Taxes on Non-traded<br>Goods|Log [1+taxes/GDP]|World Development Indicators, 2004,<br>Africa Live Database, 2004, and<br>Government Finance Statistics, 2004|
|**Variables Used in**<br>**Growth Regressions**|||
|GDP per capita growth|Log difference of real GDP per capita.|Authors' construction using data from<br>World Development Indicators (WDI),<br>The World Bank (2006).|
|Initial GDP per capita|Initial value of ratio of total real GDP to total<br>population.|Authors' construction WDI, The World<br>Bank (2006).|
|Initial GDP per Capita<br>Cyclical Component|Difference between the logarithm of actual GDP per<br>capita and the logarithm of potential (trend) GDP. The<br>Hodrik-Prescott filter was used for the decomposition|Authors' calculations using data from<br>WDI (2006)|
|Inflation|.<br>The logarithm of 100 plus the inflation rate.|Author’s calculations with data from WDI<br>(2006)|
|Government<br>Expenditures|Ratio of government expenditures (in local currency) to<br>GDP (in local currency).|Data come primarily from International<br>Financial Statistics (IFS), 2006; when<br>missing, they are complemented with data<br>from WDI (2006) and UN National<br>Accounts Statistics (2006)|
|Human Capital<br>Investment|Ratio of total secondary enrollment, regardless of age,<br>to the population of the age group that officially<br>corresponds to that level of education.|Easterly and Sewadeh (2002), WDI<br>(2006), UNESCO (2006).|
|Rule of Law|Presence of law and order. Range is between 0 and 6.|International Country Risk Guide (ICRG),<br>Political Risk Services.<br>www.icrgonline.com|
|Trade Openness|Ratio of exports and imports (in local currency) to GDP<br>(in local currency).|Data come primarily from International<br>Financial Statistics (IFS), 2006; when<br>missing, they are complemented with data<br>from WDI (2006) and UN National<br>Accounts Statistics (2006)|
|Real Exchange Rate<br>Misalignment|Percentage difference between real effective exchange<br>rate and its estimated equilibrium value.|Authors' calculations. See Appendix C for<br>the methodology.|
|Post-conflict Episodes|Peace onset: dummy variable equal to 1 for the period<br>when the war ended and 0 otherwise; post-conflict 1 and<br>2 are the two periods immediately after the end of the<br>war|Authors’ construction using conflict data<br>from Sambanis (2004) and<br>complementing them with data from<br>armed conflict dataset of PRIO for the<br>years 2002-04. www.prio.no|
|Financial Depth|Ratio of liquid liabilities to GDP. Liquid liabilities are<br>also known as broad money or M3.|WDI (2006)|
|Period-specific Shifts|Time dummyvariables.|Authors’ construction.|
3 tourism less price sensitive. Engaged heavily in such activity are Caribbean governments and lobbies in those countries connected with the tourism industry. Jobst’s (2002) data summaries of foreign lobbying (Jobst, 2002, Table 4) indicate that Latin American and Caribbean countries accounted for over 25 percent of total foreign lobbying in the US over the 1990-2000 decade. Our own FARA entries data indicates that the 13 Caribbean destinations used in our sample represent 6 percent of total foreign lobbying in the same period, of which, almost 60 percent was spent on tourism promotion.

The paper empirically investigates whether these efforts are effective. More specifically, are foreign lobbies successful in achieving their aim of increasing tourism? What is the quantitative impact of lobbying on tourism? Does foreign lobbying confer market power by making the demand for tourism less price-inelastic? If the results show lobbying to be effective, they have a practical and straightforward policy implication for foreign countries. Countries endowed with natural amenities can, through legal lobbying channels, increase tourism by earning endorsements from the client country’s government.

In the economics literature, the only other empirical studies of foreign lobbying of which we are aware are Gawande, Krishna, and Robbins (2006), which studies the impact of foreign lobbying on US protectionism, and Kee, Olarreaga and Silva (2007) that studies whether South American lobbies succeeded in lowering US tariff preferences against those countries. Both studies indicate that foreign lobbying benefits countries that engage in it. An important difference is that in those studies, foreign lobbying is presumed to take the form of quid pro quo payment for services; thus, foreign lobbying “buys” reduction in a partner’s protectionism.<sup>3</sup> This paper, on the other hand, views foreign lobbying as informational lobbying designed to shift a rationally ignorant policymaker’s position on an issue. These assumptions are consistent with the facts. The rollback of US protection confers large rents to foreign exporters, and those exporters (via the help of FARA agents) initiate the lobbying efforts. Since domestic producers indulge in quid pro quo lobbying of legislators via PACs contributions, these foreign exporters must also compete in the political market. Foreign lobbying in tourism, on the other hand, involves agencies of foreign governments like tourism boards and there is little or no competition

> 3 FARA rules prohibit campaign or other monetary contributions to US politicians by foreigners, either directly or via FARA agents. However, this rule is easily circumvented by agents, whose payments, while explicitly recorded under their own names, are made implicitly on behalf of their principals.