rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1.  Selection of business environment indicators and construction of variables**

|RE Category|Series|Variable Name|Direction|Variable Meaning|
|---|---|---|---|---|
|Corruption|ES|_ES Corruption_<br>_Obstacles_|negative|City-industry average of the firm’s answer on whether<br>corruption constitutes an obstacle. ranking from 0 (no<br>obstacle) to 4 (very severe obstacle). 2 (moderate<br>obstacle) and above are treated as firm considering it an<br>obstacle. (The logic is the same for skilled labor<br>availability, access to land, and informal competition.)|
||DB||||
|Basic<br>Protection|GC|_GC Corporate_<br>_Ethics_|positive|Ethics/corruption score, from Executive Opinion<br>Survey: “How do you rate the corporate ethics of<br>companies (ethical behavior in interactions with public<br>officials, politicians, and other firms)?”|
||ES|_ES Security_<br>_Costs_|negative|Share of a firm’s sales paid for security protection|
||DB||||
|Security|GC|_GC Police_<br>_System_|positive|Reliability of police service, from Executive Opinion<br>Survey: “To what extent can police services be relied<br>upon to enforce law and order?”|
||ES|_ES Power_<br>_Outage_|negative|% of firms experience electricity power outrage in the<br>past year|
|Electricity|DB|_DB Get_<br>_Electricity_|positive|Getting Electricity score|
|Infrastructure|GC|_GC Electricity_<br>_Quality_|positive|Electric power transmission and distribution losses as a<br>percentage of domestic supply, from the International<br>Energy Agency|
||ES|_ES Web_|positive|%of firms usinga website to conduct business|
||DB||||
|Internet|GC|_GC Broadband_|positive|Broadband coverage, number of fixed-broadband<br>internet subscriptions per 100 population, from the<br>International Telecommunications Union|
||ES|_ES Skill_<br>_Obstacles_|negative|% of firms reporting skilled labor shortage as an<br>obstacle|
|Human Capital|DB|_WB HLO_|positive|Harmonized LearningOutcome(Angrist et al.,2019)|
||GC|_GC Education_<br>_System_|positive|Quality of education system, from WEF Executive<br>Opinion Survey: how well does the education system<br>meet the needs of a competitive economy?|
||ES|_ES Overdraft _|positive|% of overdraft ability|
|Access to finance|DB|_DB Get Credit_|positive|Gettingcredit score|
||GC|_GC Financial_<br>_Development_|positive|Financial development score, pillar 8|
||ES|_ES Land Access_<br>_Obstacles_|negative|% of firm reporting access to land as an obstacle|
|Barriers to entry/exit|DB<br>GC|_DB_<br>_Construction_<br>_Permit_<br>|positive|Dealing with Construction Permits score<br>|

_Note:_ BE = business environment; DB = _Doing Business_ ; ES = Enterprise Survey; GC = Global Competitiveness index; HLO = Harmonized Learning Outcome; LP = labor productivity; SD = standard deviation; TFP = total factor productivity; WB = World Bank; WEF = World Economic Forum.

27
TANZANIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 18.  Spreading investment over time**

- a. Existing resource envelope

<!-- Start of picture text --> 300<br>200<br>100<br>0<br>10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30<br>number of years needed to attain funding target<br>b. Resource envelope assuming efficiency gains<br>200<br>100<br>0<br>10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30<br>number of years needed to attain funding target<br>% deviation from current envelope<br>% deviation from current envelope<br><!-- End of picture text -->

- b. Resource envelope assuming efficiency gains

_Source:_ Derived from Foster and Briceño-Garmendia, AICD Flagship Report, 2009. _Note:_ The threshold is the index value of 100.

33
Measures taken to ensure human subject protection over the course of this experiment are discussed in Appendix C.

# **Recruitment**

A total of 363 women commuters and 51 men commuters were recruited to participate in the study through online social media and networks, referrals, and flyers distributed at the train stations. The recruiting material invited respondents to download a smartphone application and respond to survey questions regarding their experience with the SuperVia.<sup>9</sup> None of the recruitment material mentioned gender, harassment or the reserved space.

Recruitment occurred in two waves starting August 2015 and August 2016. After signup, riders were offered both a demographic survey task and the crowdsourcing task. Of those riders, 72.5 percent completed the demographic survey (Table A1). Riders reside along the SuperVia network all around the metropolitan area , spanning a mix of rich and poor areas (Figure A1). Women riders were offered to participate in ride tasks, while men riders were asked to collect platform observations. We now describe these two work streams.

# **Women riders’ tasks**

Each woman rider is offered a series of tasks which entail riding the SuperVia and answering questions before, during and after each ride. Data were collected between September 2015 and February 2017. Figure 1 shows how the ride task is presented in the app and broken down into three sub-tasks: check-in ( _Check-in na esta ̧c ̃ao_ ), ride either the reserved or public space ( _Escolhi viajar no vag ̃ao feminino/carro comum_ ) and check-out ( _Check-out da esta ̧c ̃ao_ ). Total pay-out to complete a ride varies from $4.50-$4.70 per ride.<sup>10</sup><sup>_,_11</sup> These rides can be completed any weekday between 6-9AM or 5-8PM (rush hour window when the reserved space policy is in effect), up to twice per day (once in the morning, once in the evening) and

> 9Our survey of a random sample of commuters indicates that 85 percent of them own a smartphone.

> 10This payment covers the SuperVia transit fare.

> 11Even though the sub-tasks are priced separately, riders must complete all three sub-tasks in the correct order to receive payment.

9
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productive activities, e.g. investing in sugar and oil palm factories, the results were discouraging-the public companies producing those two goods today do so at a cost which is nearly double world prices.

The public investment program was drastically reduced after 1981, fa'ling from 17 to 4% of GDP in 1990 (see Table below). Its composition also changed. The share of agriculture in total investment increased from 31 to 47%, reflecting the difficulty of reducing employment in the extension services. Investment in transport and road construction fell from 23 to 12% of the reduced total and road maintenance is becoming an important problem. Investment in education fell dramatically from 16% of total investment in 1981 to 3% in 1990, for a population growing at 3.5"r . year tnis is hardly sufficient to cover the needs for new enrollments. That is, today the size of public investment program is, in relative terms, around half of its level in 1975, moreover, roughly 30% of what is now defined as capital expenditure consists of salary payments to extension workers, which was not the case in 1975.

The Sectoral Composition of Public Investment (percent)

||1981|<br>1984|1987|<br>1990|
|---|---|---|---|---|
|Agriculture|31|26|28|47|
|Transport and rcads|23|27|29|12|
|Housing and urban devel.|<br>13|18|<br>17|20|
|Education|16|12|6|3|
|Others|17|17|20|18|
|Public Investment/GDP|17|8|5|4|

Source World Bank resident mwission, Abidjan.
number of low value transactions routed through the FMIS is revealing. If, as was the case in Cambodia, these are few in number it raises the question of how these are actually executed. Large project or program advances routed through the FMIS will still give expenditure control at the aggregate level, but the FMIS does not provide for the integrity in expenditure data it otherwise would and cannot guarantee funds were actually spent according to appropriations. Pakistan and Cambodia do not appear to be isolated cases. Low budget capture was also identified as a problem in Bangladesh and Ghana, and the issue relating to advances in Somalia and Sierra Leone.

_Table 9 Transactions Data for a Select Group of Countries_

|**Country**|**Transaction**<br>**type **|**Threshold**|**Share of**<br>**Transactions**|**Share of the**<br>**Budget**|
|---|---|---|---|---|
|China, Central|High value|> US$ 78,250|2.1%|69.0%|
|Government (2015)|Low value|< US$ 1,564|69.0%|2.7%|
|Vietnam (2015)|High value|>US$ 43,800|1.8%|75.0%|
||Low value|< US$ 2,190|78.5%|4.3%|
|Philippines (2017)|High value|> US$ 10,000|7.8%|93.0%|
||Low value|< US$ 2,000|79.8%|2.2%|
|Afghanistan (2017)|High value|> US$ 25,000|8.6%|86.0%|
||Low value|< US$ 1,000|55.0%|1.1%|
|Laos PDR (2017)|High value|> US$25,000|7.7%|70.0%|
|,|Low value|< US$1,000|43.4%|1.2%|

_Source:_ Authors.

In practice, if one can determine the threshold of transactions that would yield high budget coverage (e.g. 90-95%), then it would be possible to target ex-ante control to those transactions. This would ensure high coverage, and provide confidence in a government’s commitment to fiscal expenditure rules.

The following provides two examples of how this information could guide an effective expenditure management reform.

- (i) **A risk based and sequenced deployment strategy.** Formulate system deployment and control strategies that focus on high value transactions. Such a strategy would enable expedited results during the implementation of FMIS systems early in the roll-out phase. Once the principle of this selective control is agreed the following steps could be pursued:

- Ensure that all transactions generated at the central MOF such as fiscal transfers, subsidies, and debt service payments, are routed through the central FMIS system;

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# **1. Introduction**

Traditionally, trade performance and diversification are studied from a product perspective. This perspective lost focus due to large-scale offshoring trends with countries trading tasks rather than products, carrying out different activities in global value chains (Grossman and Rossi-Hansberg 2008; Baldwin and Robert-Nicoud 2014; Antrás and Chor 2022). The aim of this paper is to explore patterns of export specialization from an activity perspective. To aid intuition, table 1 provides an illustrative example. It shows the distribution of activities in exports of a particular product class, textiles, for four countries at different levels of development. Activities are identified in the data by cross classifying the occupational class of workers and their industry of work. It shows that activities of machine operators in the textile industry make up more than twothirds of domestic value-added exports in Türkiye. Instead, in Italy much more value is added by managers, engineers and other professionals outside the textile industry. In Pakistan agricultural workers provide most value in textile exports as cotton is a major input in domestic textile production, whereas in Vietnam workers in wholesale trade services are major contributors. Activities in textiles exporting thus differ widely across countries, suggesting potential for new insights when using an activity perspective on trade.<sup>1</sup> Exploiting a new panel data set on export activities covering countries at a wide range of income levels, we document four main empirical findings. First, production activities typically account for the majority of export value at low levels of GDP per capita. As countries grow richer, incomes from engineering, managerial, and services support activities grow and eventually account for the majority of export value. Second, poorer countries specialize along the extensive margin, shifting export activity across industries within occupational classes. Advanced countries mostly specialize along the intensive margin, shifting export activity between occupational classes within industries. Third, the probability that a country gains new comparative advantage in a particular activity is positively related to the proximity of this activity to the initial export basket. Proximity appears to be particularly predictive for new specializations along the extensive margin. Fourth, some countries specialize in new activities that are only weakly related to their initial basket. A higher

> 1 The need and potential for an activity perspective has been stressed before, see for example Lederman and Maloney (2012). Wolff (2003) provides an early empirical example of the activity perspective on trade.

2
Zhengzhou, the capital of Henan Province, which we have chosen because its population, income and air pollution level are representative for China's large cities.<sup>2</sup> 3Zhengzhou's industry pours approximately 45,000 tons of S02 into the atmosphere every year, contributing to an ambient S02 concentration of 90 ug/m<sup>3</sup> . At this level of air pollution, Xu's dose-response function predicts that over 400 people are dying annually from S02related pollution. Thousands more are undoubtedly suffering from serious respiratory illness.

Because Zhengzhou is a representative large city, we use it to develop an estimate of the optimum air pollution levy for urban China. The optimum levy should be set at the point where the incremental benefit of abatement is equal to its incremental cost. To identify this point, we simulate pollution control alternatives by reducing Zhengzhou's S02 emissions in 10% increments until 90% abatement is reached. We follow the method used for Beijing to estimate reductions in atmospheric S02 concentration and mortality from respiratory disease. We value mortality reductions at $8,000 per 'statistical life,' yielding the marginal benefit (MB) schedule in Figure 29.

To estimate the marginal cost (MC) schedule, we turn to our cross-provincial econometric results for the S02-intensity of industrial production. The levy response elasticity in this equation (which is always estimated with the appropriate sign and high statistical significance) registers the overall result when enterprise managers weigh incrementa **l** evy payments for pollution against the incremental cost of abatement. At the equilibrium level of pollution intensity, the effective levy rate should be just equal to the incremental cost of abatement for industry as a whole, whether the abatement is achieved by process change or installation of end-of-pipe treatment. Solving the estimated S02 intensity equation 'in reverse' for the effective levy rate (or, in equilibrium, the incremental cost of abatement), we obtain an expression for incremental abatement cost as a function of industrial S02 intensity. We have plotted this MAC schedule in Figure 29.

23 Zhengzhou's 1993 population was 1.8 million; it average industrial wage was 3,350 RMBIyear.

35
A potential explanation of these results is alcohol induced physical intimate partner violence (AIPIPV). Existing evidence from Psychology shows that Intimate Partner Violence (IPV) is a major predictor of post-traumatic stress disorder in abused women (Pico-Alfonso, 2005) and can drive negative interactions between mother and children (Taylor et al, 2009), directly affecting child development (Bedoya et al, 2020). Using YL data, Bedoya et al (2020) find that IPV is one of the main forms of violence against women in Peru. The authors also find that early-life exposure to AIPIPV is indeed associated with lower test scores in vocabulary.

A second explanation is budget constraints. Allocating household income to consume alcohol reduces the availability of resources in the household for other needs, including inputs that help to support child development. This creates a vicious cycle for mothers with mental health issues, since it can impose additional stressors.<sup>16</sup>

# **5. Discussion and conclusions**

In this paper, we explore the extent to which maternal depression affects child cognition. The identification strategy exploits variation in the exposure to a particular exogenous shock between pregnancy and when the child was 1 year old in Peruvian households. Exposure to the shock can affect maternal mental health and children’s vocabulary development. The paper’s main results indicate that exposure to a crop or livestock loss in 2002 increases maternal depression in that period. Moreover, a standard deviation decrease in maternal mental health in 2002 negatively affects the child’s vocabulary by up to 0.54 standard deviation when children are 5 years old, a result that fades out by time the children are 8 years old. That is, our results suggest the negative effects of maternal depression on child receptive vocabulary do not persist beyond children’s early school years. However, given that vocabulary size in kindergarten and earlier predicts reading comprehension throughout school and into early adulthood, facilitating the development of other cognitive skills (Powell and Diamond 2012), and potentially socio-emotional skills as well, we cannot rule out the possibility that exposure to maternal depression during early life undermines other markers of child development in the medium to long run.

> 16 Finally, we explore whether child gender plays a role in determining the effect of maternal mental health on child cognition by conducting separate estimations for boys and girls. We present results in Table A3. While, on average, maternal mental health does not appear to affect the accumulation of vocabulary during childhood for boys at age of 5, PPVT Z-scores of girls whose mothers had worse mental health by 1 standard deviation are around 1 standard deviation lower, a result that is statistically significant at the 1 percent level. The difference between point estimates for boys and girls is statistically significant (not shown).

17
requirements may reflect an attempt to achieve an industrial revolution on the cheap. If so, the strategy may have turned out to be more expensive in the longer run.

If investment requirements during the First Industrial Revolution were really modest, it should have been reflected in relatively low capital-output ratios. Table 22 collects the evidence for Britain 1800-1860 and for a number of industrial revolutions that have followed in the wake of the First. While we have the ingredients necessary to calculate both average capital-output ratios (ACOR) and incremental capital-output ratios (ICOR), the late 19th and 20th century estimates are limited to ICORs only. Furthermore, all of the figures in Table 22 are for fixed capital.

Panel A offers provisional support for the modest-investment-requirements view: Britain's ACOR underwent a spectacular drop from 5.21 in 1800 to 3.55 in 1860, the biggest fall by far taking place in the first three decades of the 19th century. No diminishing returns to capital, it appears. Or if there was diminishing returns, it was dominated by capital-saving technical change (von Tunzlemann, 1981, pp. 160-161). At the margin, therefore, Britain's investment requirements during the industrial revolution were far below 18th century averages. Indeed, the economy-wide ICOR up to 1830 was only 2.65, far below Simon Kuznets' ten-country average for the 1950s (Panel B), and even below that for low-income countries in the 1950s and Meiji Japan (the latter viewed by most analysts to have been the classic example of capital-saving development).

In this sense, Deane, Cole, Landes and Nathias are correct: Britain's investment requirements on the margin were modest during the industrial revolution, and they stay modest well into the late 19th century. However, we believe the inferences which they draw from that observation are incorrect.

First, the case for .itain's unique "modesty" of investment requiremerts

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come from Global Payment Systems Survey 2008, conducted by the World Bank.<sup>17</sup> While a broader regulatory framework might make the remittance market more transparent and more competitive, greater exposure to regulations can also increase the costs on the regulated institutions, so that the impact is a-priori ambiguous.<sup>18</sup> Among remittance receiving country the index averages 2.2, while it averages 2.3 among remittance sending countries.

The correlations in Table 2B indicate that the average costs are lower in corridors with a higher number of migrants, lower GDP per capita, smaller share of rural population, no exchange rate variability, and lower level of financial development. Also, costs are lower in corridors where there is a higher degree of competition and a lower share of bank participation in the remittance industry. Finally, costs are lower in corridors where sending countries have a broader regulatory framework for remittance service operators. We also note that some of the explanatory variables are highly correlated with each other. For instance, GDP per capita levels in receiving and sending countries are significantly correlated with the levels of financial development, competition among providers, the share of rural population, branch penetration and the breadth of regulations for remittance service providers.

# **IV. Empirical results**

Table 3 shows that, across all providers in 119 corridors, remittance costs are significantly associated with a number of factors, most notably: the number of migrants in the corridor, the level of income in remittance sending and receiving countries, the extent of competition among providers (measured either by the number of respondents or the H-statistic for the banking sector in receiving countries), and the extent of bank participation in the remittance market. Specifically, corridors with higher income levels in both sending and receiving country and a greater bank participation in the remittance market exhibit significantly higher average remittance costs, while corridors with a higher number of migrants, a higher number of market players, and greater bank competition in the receiving country exhibit significantly lower average costs. There is also some evidence that receiving countries with a higher share of rural

> 17 The report can  be found at:

> http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTFINANCIALSECTOR/EXTPAYMENTREMMITT ANCE/0,,contentMDK:21813290~noSURL:Y~pagePK:210058~piPK:210062~theSitePK:1943138,00.html

> 18 Note that the index does not measure the severity of regulations, but only the scope of the regulatory framework.

11
**Table 11. Description of variables used to explain variation in WTP/hh/y**

|**Variable**<br>(Question number; according to appendix 1);<br>Description, and messurement unit|**Mean**|**Std. dev**|
|---|---|---|
|(J 1)<br>How close is Delphi estimate to true value<br>(Likert scale 1-10, where 10 is very close)|6.3|1.6|
|(J 2)<br>How difficult to implement CV study on Amazon<br>(Likert scale 1-10, where 10 is very difficult)|5.6|2.5|
|(J 3)<br>How difficult to implement CV study on domestic<br>biodiversity<br>(Likert scale 1-10, where 10 is very difficult)|5.4|2.4|
|(B1)<br>Familiarity with CV literature.<br>(Likert scale 1-10, where 10 is very familiar)|8.4|1.5|
|(B2)<br>Familiarity with CE literature<br>(Likert scale 1-10, where 10 is very familiar)|7.4|2.0|
|(B5)<br>Number of CV and CE surveys on biodiversity done|5.6|5.8|
|(B10)<br>Number of papers on CV and CE published, last 5 years|6.6|6.4|
|(B11)<br>Number of papers on CV and CE reviewed, last 5 years|12.1|18.5|
|(B 8)<br>Fraction of research time spent on CV and CE, ( %)|47.9|25.0|

19