rafmacalaba/datause-encoder-data huggingface dataset:
Our study also speaks to the literature on the effect of laws and institutions on bank loan contracts. Qian and Strahan (2007) find that contracting costs, as measured by legal formalism, shape the terms of bank loans. In countries where legal formalism is high, domestic banks are the main lender to unrated firms. In these environments, loan maturity tends to be longer and the use of collateral tends to be more common. Later, Haselmann, Pistol, and Vig (2010) also report that improving collateral law has a large effect on increasing the supply of credit. In addition, Bae and Goyal (2009) report that banks respond to port enforceability of contracts by reducing loan amounts, shortening loan maturities, and increasing loan spreads. Sufi (2007) finds that third-party certification by rating agencies increases the availability of debt financing for firms since it reduces information asymmetries. Love, Martinez Peria, and Singh (2013) investigate the impact of introducing credit information sharing systems on firm’s use of bank loans and their maturity using firm level survey data for more than 75,000 firms in 73 countries over the period 2002–13. They find that the introduction of credit bureaus is associated with increased maturity of bank loans. Gopalan, Mukherjee, and Singh (2014) show that, in India, the establishment of new specialized courts, called debt recovery tribunals (DRTs), to process debt recovery cases, led to financial deepening and increased maturity of bank lending. Tasic and Valev (2008) show that credit maturity is longer in countries with strong institutions, low inflation, large financial markets and better information sharing among banks. Tasic and Valev (2010) explain the share of credit with maturity above one year or above five years by the political and institutional environment, the rate of inflation, the levels of economic and financial development and the establishment of credit information sharing institutions.

7
# **3. Econometric Methodology**

The core of our empirical analysis consists in estimating the following production function: where _y_ denotes real output, _k_ and _h_ represent physical capital and human capital, respectively, and _z_ denotes the infrastructure capital. All variables (except human capital) are expressed in log per worker terms (e.g. _kit_ = ln( _Kit_ / _Lit_ ) where _Lit_ represents the workforce) and, in keeping with the majority of earlier literature, constant returns to scale have been imposed. The subscripts _i_ and _t_ index countries and years, respectively;  i and  t capture country-specific and time-specific productivity factors, and  it is a random disturbance that will be assumed uncorrelated across countries and over time.<sup>20</sup>

## **3.1 Panel unit root testing**

Empirical assessments of the output contribution of infrastructure using time-series data have often failed to deal adequately with the non-stationarity of the variables. Here we address the issue using panel unit root and cointegration tests developed in the recent panel time-series literature. Unlike the traditional panel literature, which deals with samples in which the crosssectional dimension _N_ is large but the time dimension _T_ is small, the panel time-series literature is concerned with situations in which both _N_ and _T_ are of the same order of magnitude; see Breitung and Pesaran (2008) for an overview. As a considerable literature has shown, the panel time-series (or multivariate) approach to integration and cointegration testing yields higher test power than separate, conventional tests for each unit in the panel; see, for example, Levin, Lin and Chu (2002).

The first step is to test for the stationarity of the variables under consideration, namely, output, the stocks of physical and human capital, and the composite index of infrastructure capital, all (except for human capital) measured in logs per worker. As a preliminary step, we remove the cross-sectional means from the data, to render the disturbances cross-sectionally

> 20 As noted by Canning (1999), infrastructure appears twice in (1): first as _z_ , and then as part of overall physical capital _k_ . Hence the total elasticity of output with respect to infrastructure capital can be approximated as     , where  is the share of infrastructure in the overall physical capital stock. Evaluation of  requires data on the price of infrastructure, which are not widely available. Nevertheless, calculations based on the data of Canning and Bennathan (2000) suggest that it is a small number. For the countries in our sample with available data, its median is 0.08, and its standard deviation is 0.05.

9
# **Table 4: Simulation results from four steps that bring Turkey’s agricultural policies in line with the EU**

||||**“Deepening**<br>|**” Stages**||
|---|---|---|---|---|---|
|||Agricultural|Agriculture||plus Common|
|||goods free of<br>duties and quotas|included in the<br>Customs Union|plus third-party<br>FTAs aligned|Agricultural<br>Policy adopted|
|||(i)|(ii)|(iii)|(iv)|
|**Economic welfare**||||||
|Overall welfare|million USD|72.26|292.18|842.79|499.89|
|Efficiency gains|million USD|339.08|754.14|1658.91|1208.69|
|Terms of trade|million USD|-266.82|-461.95|-816.13|-708.8|
|**Gross domestic product**||||||
|Volume<sup>a</sup>|percent|0.05|0.12|0.26|0.19|
|Dollar value|percent|-0.28|-0.46|-0.72|-0.88|
|**Agricultural product**<sup>1</sup>||||||
|Volume|percent|-0.42|-1.46|-2.95|1.28|
|Dollar value|percent|-0.93|-2.82|-5.23|4.03|
|**Trade balance (X-M)**|million USD|126.92|122.39|222|-281.18|
|Total Exports||||||
|Volume|percent|1.61|2.23|3.37|2.96|
|Dollar value, fob|percent|1.45|1.94|2.86|2.42|
|Total imports||||||
|<br>Volume|percent|1.00|1.36|1.99|1.96|
|Dollar value, cif|percent|1.00|1.36|1.99|1.96|
|**Private household prices**|Percent|-0.34|-0.59|-0.98|-1.26|
|**Primary factor rents, real**<sup>2</sup>|Market prices|||||
|Overall rents and wages|percent|0.13|0.21|0.33|1.41|
|Land|percent|-2.35|-8.20|-13.75|21.69|
|Unskilled labor|<br>percent|0.11|0.16|0.23|1.21|
|Skilled Labor|percent|0.15|0.31|0.54|1.06|
|Capital|percent|0.17|0.35|0.57|1.29|
|Other natural resources|percent|0.67|1.22|2.22|1.59|
|**Terms of trade**||||||
|Overall terms of trade|percent|-0.16|-0.29|-0.5|-0.53|
|Prices received|percent|-0.16|-0.28|-0.49|-0.53|
|Pricespaid|percent|0.00|0.00|0.00|0.00|

a Deflated by a GDP price index.;1 In value-added terms; 2 Deflated by private consumption prices. Source: Tsigas and Sahin (2013).

26
<mark>TABLE 2. Comparison of Baseline (Predeparture) Characteristics across Households That Do and Do Not Experience Subsequent Departure</mark>

||Permanen<br>|t absence o|f father|
|---|---|---|---|
|Characteristic,Baseline↓|(D)|||
||D = 1|D = 0|P value|
|Age of household head|42.88|42.21|0.182|
|Age of spouse|37.81|37.14|0.110|
|_Education of head_||||
|None|0.282|0.230|**0.015**|
|Some(complete/incompleteprimary)|0.535|0.638|**0.000**|
|High(incomplete secondaryor more)|0.181|0.132|**0.004**|
|_Education of spouse_||||
|None|0.199|0.195|0.846|
|Some (complete/incomplete primary)|0.633|0.661|0.238|
|High(Incomplete secondaryor more)|0.168|0.144|0.171|
|_Household composition_||||
|Avg. # of children ≤ 6years|0.388|0.467|**0.011**|
|Avg. # of boys 7–11years|0.727|0.738|0.775|
|Avg. # ofgirls 7–11years|0.718|0.684|0.366|
|Avg. # of boys 12–17years|0.635|0.641|0.890|
|Avg. # ofgirls 12–17years|0.581|0.590|0.809|
|Avg. # of female adults|1.232|1.244|0.708|
|Avg. # of male adults|1.366|1.396|0.432|
|School enrollment rate of||||
|7- to 14-year-olds in household|0.924|0.899|0.057|
|Household monthlyconsumption|421,286|441,994|0.085|
|Program area|0.700|0.682|0.453|
|Altitude|574.45|601.90|0.451|
|N|426|5,720||

_Source: Familias en Accion_ Evaluation Data.

_<mark>Note:</mark>_ Sample consists of households in which both parents are present at the baseline and there is at least one 7- to 14-year-old child. N denotes the number of households at baseline. P values are based on standard errors clustered at the municipality level. Figures in bold in column (4) indicate that the correlation of the figures in columns (2) and (3) are significant at the 5 percent level or less.

33
on infrastructure investment are quite scarce, the available information suggests that these numbers exceed by a wide margin those observed across the developing world, perhaps with the exception of some rapidly-growing East Asian countries.<sup>25</sup> Furthermore, given the fairly limited involvement of the private sector in infrastructure across the region (with South Africa as the main exception) one can conjecture that the bulk of this additional spending would correspond to the public sector, for which it would pose a heavy burden indeed. In fact, in a number of countries in SubSaharan Africa total government revenue is well below 20 percent of GDP. This means that in those countries a fast-pace infrastructure catch-up would be financially infeasible, and this even ignoring its associated O&M costs. Instead, the acceleration of infrastructure development would have to be spread over a number of years. Most importantly, its benefits would have to be compared with those of other pressing demands on scarce government resources, a task beyond the scope of this paper.

# **4. Concluding remarks**

Poor infrastructure is commonly viewed as one of the key obstacles to economic development in Sub-Saharan Africa. In this paper we have provided an empirical evaluation of the potential contribution of improved infrastructure to growth and equity in the region. Our assessment is based on the estimation of infrastructure-augmented growth and income inequality regressions using a large data set comprising 100 countries over the period 1960-2005. The empirical approach encompasses different core infrastructure sectors, considers both the quantity and quality of infrastructure services, and employs instrumental variable techniques to account for the potential endogeneity of infrastructure and non-infrastructure determinants of growth and inequality.

We find robust evidence that infrastructure development – as measured by an increased volume of infrastructure stocks and an improved quality of infrastructure services – has a positive impact on long-run growth and a negative impact on income inequality. The evidence also suggests that these impacts are not different in Sub-Saharan Africa vis-à-vis other regions. A variety of specification tests and robustness checks lend support to our empirical experiments. Since most African countries are lagging in terms

> 25 See Calderón, Odawara and Servén (2008).

29
2011). Multinationals can require strict productivity and production standards of their suppliers, and may share new technologies or encourage the adoption of new production methods to achieve this (Criscuolo and Timmis, 2017). To ensure the robustness of our instrument, we use ex-ante trade networks at the start of the sample period for the year 2000. We also only consider trade networks with other world regions (i.e. excluding trade with the home country’s region) – to exclude common regional shocks that may contaminate the analysis. In addition, the relevance of the instrument withstands a barrage of fixed effects including exporter*HS 10-digit product and exporter*year fixed effects, in order to control for potential trends across countries overtime, as well as potential time-varying multilateral resistance terms highlighted by the trade literature (Anderson and van Wincoop, 2003).<sup>6</sup> The existing literature on robotics, and technology more generally, often pertains to developed economies (Acemoglu and Restrepo, 2020; Presidente, 2017; Graetz and Michaels, 2018; Cardona et al 2013). A priori it is unclear how quality upgrading gains relate to country development levels. On the one hand, developing countries typically produce lower-quality exports and so have larger potential to catch-up through automation (Hallak and Schott, 2011; Henn et al., 2020). On the other hand, developed countries are more likely to have access to the complementary factors needed to realize the potential quality gains from robotics, such as the management skills to reorganize production (e.g. Brynjolfsson and Hitt, 2000, Bresnahan et al., 2002). Our study uses data that spans 59 countries allowing us to identify important heterogeneity in quality gains from robotics across developed and developing countries.<sup>7</sup> We also leverage new data on robots types – e.g. laser guided cutting vs mechanical cutting – to examine whether certain robots are more crucial for quality upgrading than others.

Export quality has attracted a great deal of attention because of its importance for the development and growth (Hirschman 1958; Grossman and Helpman 1991; Hidalgo et al 2007). At the firmlevel, businesses that produce higher quality goods have higher revenue and employment, pay higher wages, are more productive and sell their goods to a greater variety of markets (Verhoogen 2008; Manova and Zhang 2012; Crozet et al 2012). Producing high quality exports is also often a

> 6 We also add control variables for (log) industry employment and real value added per worker.

> 7 Including developing countries in our sample necessitates using data from 2000, whereas for advanced economies data is available from 1993 (as in Graetz and Michaels, 2018).

5
The four treatments implemented in Andhra Pradesh were: (i) whether the household is visited by an insurance educator; (ii) whether the educator is endorsed by an LSA, (iii) whether the educator presents the additional education module, and (iv) whether the visited household receives a high reward (Rs. 100 rather than Rs. 25). Because endorsement took place in twothirds of villages, we include as an additional treatment the interaction of whether the village was one in which endorsements took place and whether the household received a visit, to identify spillovers from endorsement.

Results are presented in Table 5. We use data from all 1,047 households, and since treatment compliance is not perfect, the results should be interpreted as intent-to-treat effects. The unconditional insurance take-up rate is 28%. Basic treatment effects are reported in Columns (1)-(3). Column (1) includes only the treatment variables. Column (2) also includes village fixed effects, while Column (3) includes both village fixed effects and a set of household covariates (specific controls are listed in the table notes).<sup>17</sup> In each of these three columns, being assigned a household visit, even if not combined with other treatments, increases take-up by 11.5 to 17.2 percentage points, while a high reward increases take-up by 39.4 to 40.8 percentage points. Each of these estimates is statistically significant at the 1% level. Individual LSA endorsement alone is positively signed and marginally statistically significant (t-stat between 1.5 and 1.7). However LSA-endorsement and the village endorsement variable are jointly significant at the 2% level in columns (2) and (3), which control for village fixed effects. Finally, the effect of the education module on demand is economically small and statistically insignificant.

Columns (4)-(6) interact these treatments with three household variables in turn: an indicator for whether the household reports being unfamiliar with BASIX, an index of household wealth, and the log of per capita food consumption. Column (4) shows that LSA endorsement has sharply different effects depending on whether the household is familiar with BASIX, and thus is likely to have had past interactions with the LSA. For households familiar with BASIX, LSA endorsement increases take-up by 10.1 percentage points, statistically significant at the 5% level. In contrast, endorsement has no net effect on insurance demand amongst households unfamiliar with BASIX (the net effect is 10.1 - 17.1 = -7.0 and statistically insignificant). The other notable interaction is that in both columns (5) and (6) the effect of the high cash reward on

> 17 Because treatments are randomly assigned to households, estimates of the treatment effects are consistent with or without these controls. But including them may reduce error variance, leading to more precise parameter estimates.

14
and growth. Industrialization, growing populations, and the use of motorized vehicles in cities contribute to increase the levels of pollution. Cities are hence faced with a two‐pronged challenge. On the one hand, they want to attract industries and firms that can help their city grow and develop. On the other hand, the agglomeration of certain types of industries can increase air and water pollution, and the concentration of people in cities is often met with increased used of motorized transport, both key sources of air pollution. Ensuring the benefits of economic growth outweigh their costs, including air pollution, is often the main concern of city leaders.

Although all types of pollution are important and need attention, in this paper we focus on air pollution for its critical impact on health and economics. Air pollution is the fourth leading cause of death worldwide. In 2012, the WHO estimated that approximately 7 million people died prematurely due to air pollution. This means that air pollution is responsible for 1 in 10 deaths worldwide, killing more people than AIDS, Tuberculosis, and Malaria combined. From all the different pollutants that contribute to contaminated air, particulate matter affects more people than any other pollutant. Particulate matter “...consists of a complex mixture of solid and liquid particles of organic and inorganic substances suspended in the air. The most health‐damaging particles are those with a diameter of 10 microns or less, which can penetrate and lodge deep inside the lungs”.<sup>8</sup> The WHO estimates that 87% of the premature deaths due to air pollution occur in low and middle‐income countries, particularly in Western‐Pacific and South East Asia regions.<sup>9</sup> Because outdoor air pollution is not influenced by individual behavior, the role of the national and local government is key.

# Pollution and the Relationship with City Competitiveness

We have identified some of the characteristics that make a city competitive. Fast growing cities that are outperforming their nations, with the ability to create jobs, increase productivity, and attract foreign direct investment fall in this category. We have also described pollution management as one of the challenges these cities face in their journey to become competitive, and briefly mentioned how pollution

> 8 “Ambient (outdoor) air quality and health”. WHO Fact sheet. September 2016. www.who.int.

> 9 According to World Health Organization global update 2005, air pollution guidelines are the following:

> ‐ PM 2.5: 10 μg/m<sup>�</sup> annual mean; 25 μg/m<sup>�</sup> 24‐hour mean

> ‐ PM 10: 20 μg/m<sup>�</sup> annual mean; 50 μg/m<sup>�</sup> 24‐hour mean

> ‐

> Ozone: 100 μg/m<sup>�</sup> 8‐hour mean

> ‐ NO2: 40 μg/m<sup>�</sup> annual mean; 200 μg/m<sup>�</sup> 1‐hour mean

> ‐

> SO2: 20 μg/m<sup>�</sup> 24‐hour mean; 500 μg/m<sup>�</sup> 10‐minute mean.

12
# Figure 3. Growth prospects for low-income countries

# A. Global

# B. Per capita growth

<!-- Start of picture text --> Percent Percent Non-FCV LICs<br>8 5 FCV LICs<br>6 LIC Total<br>4<br>4<br>2 3<br>0 2<br>1<br>0<br>-1<br>LICs Industrial- Non-resource-<br>commodity- intensive LICs -2<br>exporting LICs 2003-08 2010-12 2013-15 2016-18 2019-21<br>2000-08 2010-12 2013-15 2016-18 2019-21 2000-08 2010-12 2013-15 2016-18 2019-21 2000-08 2010-12 2013-15 2016-18 2019-21<br><!-- End of picture text --> Source: World Bank.

Note: Based on LICs as defined in 2018 and includes 28 economies. A. Aggregate growth rates calculated using 2010 U.S. dollar GDP weights. Industrial commodity-exporting countries include energy and metal exporting-economies.

B. FCV = fragility, conflict, and violence. Weighted averages of country groups.
viduals above compared to those below the median individual. The effect is almost the same for the reference group defined by region and education, whereas it differs for the reference group defined by the age and education.

Table 5: Subjective ranking and relative income

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||De|p. variable:|perceived e|conomic de|cile|
|Ln(Consumption per capita)|0.0403**<br>(0.00650)|-0.0582**<br>(0.00714)|-0.0929**<br>(0.00787)|-0.0582**<br>(0.00714)|-0.0914**<br>(0.00789)|
|rank Reg-Edu||1.232**<br>(0.0369)||||
|rank Age-Edu|||1.293**<br>(0.0429)|||
|"Rich" distance Reg-Edu||||1.239**||
|(distance from above)||||(0.0672)||
|"Poor" distance Reg-Edu||||-1.223**||
|(distance from below)||||(0.0715)||
|"Rich" distance Age-Edu|||||1.479**|
|(distance from above)|||||(0.0688)|
|"Poor" distance Age-Edu|||||-1.066**|
|(distance from below)|||||(0.0785)|
|In full-time work|0.236**|0.181**|0.175**|0.181**|0.174**|
||(0.0204)|(0.0205)|(0.0205)|(0.0205)|(0.0205)|
|In good health|0.360**|0.355**|0.354**|0.355**|0.355**|
||(0.0113)|(0.0113)|(0.0113)|(0.0113)|(0.0113)|
|Ln(Age)|-0.160**|-0.152**|-0.137**|-0.152**|-0.138**|
||(0.0282)|(0.0282)|(0.0283)|(0.0282)|(0.0283)|
|Female|0.0520**|0.0505**|0.0464**|0.0505**|0.0470**|
||(0.0185)|(0.0185)|(0.0185)|(0.0185)|(0.0185)|
|Has university degree|0.604**|0.645**|0.650**|0.645**|0.650**|
||(0.0238)|(0.0238)|(0.0238)|(0.0238)|(0.0238)|
|Ln(Household size)|0.117**|0.406**|0.345**|0.406**|0.348**|
||(0.0222)|(0.0238)|(0.0235)|(0.0239)|(0.0235)|

26
is found to be substantial, and much larger in unionized enterprises than non-unionized, a finding that questions the actual power of unions.

(4) Union: Union power does not seem to influence wages directly. _Union_ is insignificant in the analysis that includes all observations. When unionized firms are analyzed separately, and a proxy for the degree of unionization is included, the proxy ( _density_ ) turns out to be statistically insignificant. Union influence and power seem mostly to come through tenure. Tenure is very important in unionized enterprises and not at all so in non-unionized enterprises. Union power also seems to be protecting “vulnerable” minorities in the Ivorian society— probably mostly “other Africans.” The union-premium may be significantly higher if there is a spill-over effect to the nonunionized firms included in our sample, since this will bias the union premium downwards. Data that include manufacturing firms along with more informal sector firms are welcomed to cast light over this issue.

(5) Establishment-size: Effects from establishment size on wages are pronounced. Workers with comparable observable human capital characteristics earn widely different wages, depending on firm size (in a non-linear fashion). The effect is found much higher for whitecollar workers than for blue-collar workers. Both results are in sync with the literature, and may be explained by efficiency wage considerations.

As previously mentioned, these results do not extend to the entire economy but only hold for the limited and highly selected group of workers employed in the manufacturing sector.

# _Policy implications and recommendations_

Basic education is not sufficient, and does not lead to higher wages among the “golden league” of workers that the manufacturing sector employees constitute. But since both high education and occupational groups are found very important in generating personal income, formal education is highly recommendable, but should go beyond basic education. Intellectual capital is important, and more of it may lead to a reduction in wage inequality.

Further, setting aside for a moment the possible significant spill-over effects from unionized to non-unionized firms (and hence downward bias in the union premium), _labor market distortions do not seem to be of primary concern in the manufacturing sector_ . The unions show no sign of “monopoly union” power, where—in the extreme case—the unions quote 21