rafmacalaba/datause-encoder-data huggingface dataset:
it loses its statistical significance in column 3 but becomes significant again in column 4 albeit weakly. Panel B and C of Table 5 shows the result of the productivity convergence effect of intermediate imports from the Global North and Global South, respectively. The OLS results suggest that intermediate import from the Global North and Global South exert a significant positive effect on productivity convergence, although the size and sign of the estimated coefficient of intermediate import from the Global North suggest that it is a more robust channel of influence (statistical significance) as well as have a greater contribution (magnitude) on productivity convergence. This result is collaborated by the IV estimate. The IV regression particularly shows that intermediate import from the Global north is a strong predictors and contributor of productivity convergence in Africa. The estimated coefficient is positive and statistically significant at 1 percent significance level. Conversely, although the IV estimated coefficient of imported intermediate from the Global South remains positive, it loses its statistical significance in column 3 but becomes significant again in column 4 albeit weakly at 10 percent. The size of the estimated coefficient also remains small relative to that of intermediate import from the Global North.

Overall, the study underscores the distinct roles of imported capital and intermediate goods from different regions in driving economic transformation – structural change and productivity convergence in Africa. We show that imports from the Global South play a dominant role in promoting structural change in Africa. This trade fosters growth and transformation even with limited local absorptive capacity. For productivity convergence to global best practices, imports from the Global North are more influential. The superior quality of these imports helps close the productivity gap, but African countries need to build absorptive capacity to fully benefit from them. <mark>While South-South trade may trigger growth enhancing structural change even with little or no development of local absorptive capacity, the potentially low-quality nature of such trade may not be sufficient to propel countries in the Global South to benefit from such trade to converge to the productivity of the global best practices. North-South trade is more beneficial in closing the global productivity gap. T</mark> he superior quality of these imports helps close the productivity gap, but African countries need to build absorptive capacity to fully benefit from them. Our findings challenge the idea that regional integration alone can drive Africa’s economic transformation, even in some cases at the expense of trading with non-regional partners. Instead, 22
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# Table 7

# Remittances of Profits. Dividends. Interest and Other Fees from Thailand to Home Country. 1983-87

||Profits|Dividonds|Interest|Fees|Total|
|---|---|---|---|---|---|
|United States|1873.7|5374.0|5850.3|2360.4|15458.4|
|(Z of Total)|(12.1)|(34.8)|(37.8)|(15.3)|(100%)|
|Japan|436.9|1805.0|2144.4|4063.4|8449.7|
|(Z of Total)|(5.2)|(21.4)|(25.4)|(48.0)|(100%)|
|United Kingdom|562.2|439.2|2995.6|707.7|4704.7|
|(Z of Total)|(11.9)|(9.4)|(63.6)|(15.1)|(1OOZ)|
|West Germany|25.6|240.9|904.7|449.6|1620.8|
|(Z of Total)|(1.6)|(14.9)|(55.8)|(27.7)|(1002)|
|Hong Kong|246.1|1198.1|11077.1|464.8|12986.1|
|(Z of Total)|(1.9)|(9.2)|(85.3)|(3.6)|(1002)|
|France|..|13.8|190.8|..|204.6|
|(2 of Total)||(6.7)|(93.3)||(100%)|
|Singapore|36.5|155.9|10023.3|..|10215.7|
|(Z of Total)|(0.4)|(1.5|(98.1)||(100Z)|

Source: Bank of Thailand.
# Figure 6. Predicted crisis probabilities

A. Probability of financial crisis after adverse shock <!-- Start of picture text --> Percent<br>8 Adverse outcome<br>Baseline outcome<br>6<br>4<br>2<br>0<br>Sovereign debt crisis Currency crisis<br>GDP growth decline U.S. interest rate increase<br><!-- End of picture text --> Source: Laeven and Valencia (2018); World Bank.

B. Probability of financial crisis after debt buildup <!-- Start of picture text --> Percent<br>3045 Median debt accumulation episode<br>25 No debt accumulation<br>20<br>15<br>10<br>5<br>0<br>Sovereign Currency Banking Currency Currency<br>debt crisis crisis crisis crisis crisis<br>Government Private Combined<br><!-- End of picture text --> Note: Figures show the predicted probability of currency, banking, and debt crises (as defined in Laeven and Valencia (2018)) based on regression results. Whiskers indicate 95 percent confidence intervals.

A. “Adverse outcome” is GDP growth of -1 percent (average EMDE growth during crisis episodes) or U.S. policy interest rate increase of 2 percentage points (cumulative U.S. Fed Funds rate increase from end-2015 to mid-2018). “Baseline outcome” is GDP growth of 4 percent (average EMDE growth outside crisis episodes) and no U.S. policy interest rate increase.

B. Predicted probabilities assuming government debt buildup of 30 percentage points of GDP or private debt buildup of 15 percentage points of GDP or both in the median debt accumulation episode.

23
**How Business Is Done and the „Doing Business‟ Indicators: The Investment Climate When Firms Have Climate Control**<sup>*</sup>

Mary Hallward-Driemeier<sup>*</sup> Lant Pritchett**

> * We would like to thank Bill Battaile, Asli Demirguc-Kunt, Ahmed Galal, Ricardo Hausmann, Stephen O‘Connell, Rita Ramalho, James Robinson, Dani Rodrik, Luis Serven, Andrei Shleifer, Sylvia Solf and David Weil for conversations that were helpful and to Dan Willey for motivation on this topic.  The views expressed here do not necessarily represent the views of the World Bank or its Executive Directors.

> * World Bank;  **Harvard University
studies.<sup>23</sup> On the other hand, the estimated impact of school attainment across the 13 countries is just 0.049 after adjusting the Mincer returns for the literacy scores. This low estimate partly reflects the joint consideration of literacy scores and school attainment. The estimated return to years of schooling without considering literacy scores is 0.06, which is still below the more common estimates of about 0.1. As Figure 2.3 shows, the estimates that adjust for literacy scores are below the unadjusted returns in every country except Poland.

**Figure 2.2: Returns to Cognitive Skills, International Adult Literacy Survey**

<!-- Start of picture text --> 0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>USA ChileNetherlandsSwitzerland FinlandGermanyNorwayHungaryDenmarkSwedenCzech Rep Italy Poland<br>Increase per stnd. dev.<br><!-- End of picture text --> Source: Hanushek and Zhang (2006).

23 The U.S. is noticeably higher than other countries and the previous U.S. studies, perhaps reflecting that these earnings are obtained across the entire work life. The average excluding the U.S. is still 0.08.

15
to the Energy Poverty Advisory Hub's report (2022), the driving forces behind energy poverty can be categorized into contextual and individual factors. Additionally, the report identifies three principal causes responsible for the occurrence of energy poverty: low income, energy inefficiency, and energy prices.

**To better understand what could be associated with energy poverty, we next describe the energy poor, using the multidimensional energy poverty index; characterizing the energy poor can also be helpful for the design of targeting strategies.** Table 4 shows that the energy poor are more likely to reside in households with at least one unemployed, with more than five members, and with at least one pensioner when compared to households that are not energy poor. They are more likely to live in rural areas, be elderly, and disabled. We confirm that these characteristics are correlated with energy poverty incidence rates by estimating a Probit regression, using the probability of energy poverty based on the energy poverty index as a binary outcome variable. Table 5 shows that these groups are still more likely to be energy poor, even when compared with households with similar household characteristics<sup>43</sup> . The fact that these characteristics relate to a higher probability of being energy poor can help with targeting mechanisms, such as proxy mean testing, and generate insights on what might be driving energy poverty in the first place (e.g., increased vulnerabilities, more generally speaking).

_Table 4: Descriptive Statistics of Energy Poor and Non-Energy Poor_

||(1)<br>Energy poor<br>Mean|sd|(2)<br>Non-energy<br>poor<br>mean|sd|
|---|---|---|---|---|
|Members|2.56|1.31|2.55|1.10|
|Adult equivalent|2.73|1.61|2.64|1.15|
|Female-headed|0.45|0.50|0.43|0.50|
|HH with +1 unemployed|0.25|0.43|0.12|0.33|
|Single-elderly HH|0.13|0.33|0.06|0.24|
|HH with +1 child|0.39|0.49|0.41|0.49|
|HH with +1 disabled|0.59|0.49|0.57|0.50|
|HH with +5 members|0.08|0.27|0.05|0.22|
|HH with +1 pensioner|0.41|0.49|0.38|0.48|
|Social-aid recipient HH|0.90|0.30|0.89|0.31|
|Urban|0.64|0.48|0.72|0.45|
|Female|0.45|0.50|0.43|0.49|
|Unemployed|0.09|0.29|0.04|0.20|
|Age|44.23|23.43|42.61|22.04|
|Elderly|0.25|0.43|0.19|0.39|
|Child|0.14|0.35|0.15|0.35|
|Disabled|0.28|0.45|0.25|0.43|
|Pensioner|0.28|0.45|0.24|0.43|
|Social aid|0.62|0.48|0.60|0.49|
|Observations|7,008||9,617||

_Notes: We define energy poverty using the multidimensional energy poverty index. "SD" is the standard deviation._ _Source: Own estimates based on EU-SILC (2020)._

> 43 Unfortunately, we do not have sources of heating and cooking in the data to control for these characteristics.

30
One notable omission from this list is the district’s profile in terms of scheduled tribe and caste populations. Iyer et al. (2011) show that castes and tribes vary in their overall rates of entrepreneurship, and castes and tribes also differ in their social norms on the appropriate role of women. Unreported tests analyze whether including these profiles influenced the gender results presented below. These inclusions are not important in our context when also controlling for intermediate variables like the female literacy rate, and so we adopt the more parsimonious specification that is also more easily comparable in the future to other contexts outside of India. We believe, nonetheless, that the role of these social norms is important for future research with respect to female participation in the Indian economy.

Beyond these basic demographics, we consider threeadditional local traits that may affect female entrepreneurship: quality of local physical infrastructure, travel time to one of India’s ten biggest cities, and stringency of a state’s labor laws. While these traits do not constitute an exhaustive list of local conditions, they are motivated by the literatures on entrepreneurship and India’s development.

Basic infrastructure services like electricity are essential for all businesses, but new entrants and the informal sector can be particularly dependent upon local infrastructure (e.g., established firms are better able to provision their own electricity if need be). Aghion et al. (2011) provide a theoretical model of this dependency, and Ghani et al. (2011c) and Mukim (2011) find that infrastructure strongly links to overall entrepreneurship levels in India. Lall (2007) links infrastructure investments to regional growth in India. The population census provides figures on the number of villages in a district which have telecommunications access, electricity access, paved roads, and access to safe drinking water. We calculate the percentage of villages that have infrastructure access within a district and sum across the four measures to create a continuous composite metric of infrastructure which ranges from zero (no infrastructure access) to four (full access to all four infrastructure components).<sup>5</sup> India’s economy is undergoing dramatic structural changes (Desmet et al. 2011). From a starting point in the 1980s when the government used licensing to promote industrial location in regions that were not developing as quickly, the economic geography of India has been in flux as firms and new entrants shift spatially (e.g., Chari 2008, Fernandes and Sharma 2011). One feature for a district that is important in this transformation is its link to major cities. We thus include a measure from Lall et al. (2011) of the driving time from the central node of a district to

> 5In six districts (major cities) which were not further subdivided into separate geographic units, these indicators were not reported in the Census data. In these cases we assign the infrastructure access components as 100%. Our results are robust to excluding these major cities from the analysis sample.

9
> - 109 -

to be regulated is an important one in the domestic economy, the directive regulation's costs are likely to outweigh its benefits, and it therefore will not be the optimal policy approach.<sup>119</sup> For directive regulation to be welfare enhancing, it is almost certainly a necessary condition that the natural monopoly to be regulated be economically important; however, that is not a sufficlent condition.

Regulation cannot be expected to be welfare enhancing unless the regulatory agencies can be staffed by technically competent and ncn-corrupt personnel. Absent such personnel, regulation should not be attempted.

Even if the sector to be regulated is an important one and regulatory personnel requirements can be satisfied, directive regulation still may not be optimal. Some market conditions are particularly conducive to regulatory inefficiency, and here too, directive regulation is a poor policy choice.

Efficient directive regulation is not easy to achieve even in industries producing one or a few homogenous goods under relatively static conditions. As our case studies of the U.S. air passenger transport and telephony industries illustrate, it is almost impossible to effectively regulate industries producing a complex mix of products/services or industries 119 For example, even if provision of cable television services is a natural monopoly, LDCs are unlikely to find this sector to be a good candidate for directive regulation.
are nearly all concentrated in big cities, with not a single tier 1 private college in any small city. Meanwhile, the lowest quality heis are disproportionately located in small cities, with nine out of the ten bottom tier public colleges located in small cities. We anticipate that given this spatial distribution in quantity and quality of hei institutions in Colombia, individuals in small cities would seek to attend college in big cities while for the most part, only the most talented or with sufficient means would be able to move for college.

## **_Our sample vs. Colombian population_**

We compare demographics in our sample to those in the Socio-Economic Database for Latin America and the Caribbean (sedlac), a nationally representative household survey of Colombia. We restrict the sedlac data to workers with a bachelor’s degree employed in the formal sector who are aged 20-35. Our sample is remarkably similar to the sedlac sample. For instance, in the sedlac sample 55.5% are female (vs. 57% in our sample), aged 29 on average (vs. 26.6 in our sample), and earn raw average annual wages of 1,692,957 Colombian pesos (vs. 1,647,482 in our sample). Most importantly, the spatial distribution of the sedlac sample is similar to ours: 62.5% are in a big city for work (vs. 64.5% in our sample), 22% are in a medium city (vs. 17.2% in our sample), and 15.4% are in a small city (vs. 18.2% in our sample).

We also use sedlac data to examine labor force participation and incidence of work in the formal sector among young college graduates. The majority (78%) of those aged 20-35 with a bachelor’s degree in Colombia are employed in the formal sector, only 2.5% are in the informal sector, while 20% are not employed. It is notable that of the 20% not employed, the proportion who is female is quite high at 75.8%, while the proportion of female workers among those who are active is 57% (as in our sample). Further, conditional on being employed, women are just as likely to work in the formal or the informal sector. Again, importantly for our purposes, formal and informal workers are similarly distributed across city sizes. In particular, 55, 21, and 23 percent of formal workers are located in big, medium, and small cities, whereas the corresponding percentages are 53, 19, and 28 percent for informal workers.

We are thus confident that our sample is highly representative of the population of interest, that is, young college graduates aged 20-35 in Colombia. Although Colombia has a large informal sector ranging between 55% to 59% in 2009–2013 (International Labour Organization, 2014), the incidence of informality is substantially lower in our sample. As a result, we observe the great majority of college graduates when they start their careers.

# **3. Results**

## **_Agglomeration effects_**

Our first finding is that there is a work city size wage premium in Colombia. In column (1) of table 3, we estimate an elasticity of wages with respect to city population of 0.052 for college graduate workers, in line with previous estimates for the more general population in Colombia 8
quality in India, finds that pollution in Delhi is positively associated with lung function deficits and with respiratory ailments (CPCB 2008a; CPCB 2008b). Guttikunda and Goel (2013) estimate that particulate matter present in Delhi in 2010 led to premature deaths ranging from 7,350 to 16,200 per year and to 6 million asthma attacks per year. As Delhi continues to grow, population and vehicle densities are bound to increase further, making it all the more important to examine whether the expansion of the DM has had an impact on the city’s air quality.

Figures 1A through 1E present the pollution picture at _ITO_ during our study period, 2004 to 2006. Each figure shows the 8- or 24-hour average for a specific pollutant along with the corresponding upper limit prescribed by the CPCB.<sup>5</sup> There are some noticeable gaps in each series due to missing observations. In spite of this we see a clear seasonal pattern for nitrogen dioxide ( _NO_ 2 ), carbon monoxide ( _CO_ ), and particulate matter ( _PM_ 2.5 ), with their levels being higher in winter (November through January) than in summer (April through June). Further, in the cases of _NO_ 2 , _CO_ , and _PM_ 2.5 , there were a large number of occurrences when their levels exceeded prescribed limits, while there were fewer violations for sulphur dioxide ( _SO_ 2 ) and ozone ( _O_ 3 ). During this period, _NO_ 2 , _CO_ , and _PM_ 2.5 , exceeded limits 85, 48, and 78 percent of the time, respectively, while the corresponding figures for _SO_ 2 and _O_ 3 were much lower at 3 and 0.1 percent, respectively.<sup>6</sup> Given that _SO_ 2 and _O_ 3 were within permissible limits most of the time, our analysis focuses on _NO_ 2 , _CO_ , and _PM_ 2.5 .

> 5. Appendix table S1.1 in the supplementary appendix S1 presents the CPCB limits along with those prescribed by the WHO. WHO (2000) maintains that its limits are to be interpreted as guidelines, and individual countries may have different standards based on prevailing exposure levels, and social, economic, and cultural considerations. We measure violations vis-à-vis the CPCB limits, as they should be more relevant within the Indian context.

> 6. During the same period at _Siri Fort_ , _NO_ 2 , _CO_ , _SO_ 2 and _O_ 3 , exceeded prescribed limits, 13, 26, 0, and 5

> percent of the time, respectively. _PM_ 2.5 was not recorded at _Siri Fort_ .

4
- **8** -

# **IV. ECONOMETRIC SPECFICATION AND EPRICAL RESULTS**

The econometric model most suitable for estimating<sup>the output supply tunction with the</sup> data avallable to **us Is** the switching regression<sup>model</sup><sup>**with**an endooenous criterion</sup> function described in Maddala (pp. 223-228). The model postulates fo'r any observation I where X1;, X2; and Zi are vectors of exogenous or predetermined<sup>varlables,</sup><sup>_Pl, p2,_and7are</sup> the corresponding vectors of parameters, and U11<sup>, U</sup> 2<sup>1 and U</sup> 1<sup>are random disturbances.</sup> 'ii and Y2i are two possible values of the dependent variable,<sup>only one of which is actually observed</sup> for any given household, depending on the value<sup>of the (unknown) criterion function</sup><sup>**7**Z</sup> 1<sup>+ Ui.</sup> The random disturbances are assumed to have<sup>a trivariate norma; distribution, Identically and</sup> independently distributed across households. Applied<sup>to the particu!ar issue at hand, equations</sup> (1) and (2) may be viewed as the output supply<sup>equations under a non-binding and binding</sup> iquidity constraint respectively. The criterion for<sup>whether liquidity is binding or not is whether</sup> the demand for credit exceeds credit sapply, and the<sup>criterion function7Z + U, in our case, is</sup> the excess credit demand function (i.e., demand minus<sup>supply). Excess credit demand is not</sup> directly observable. However, from the survey responses,<sup>we know whether a given household</sup> is constrained or unconstrained by !iquidity. Using data on<sup>the dichiotomous responses, the</sup> vector of parameters 7 can be estimated up to a proportionality<sup>constant by a probit procedure.</sup> The estimated parameters are then used to generate Mills<sup>ra.-.s which are incorporated in tne</sup> second stage estimates, where the equations<sup>(1) and (2), with their Mills ratio corrections, are</sup> estimated by a linear regression. Under model<sup>assumptions, the estimated coefficients are</sup> consistent and asymptotically normal, and with appropriate<sup>corrections to their estimated</sup> variance-covariance matrix (dua to the heteroscedasticity of the<sup>stochastic disturbance terms</sup> in the second stage estimates) can be subjected to<sup>statistical tests based on normality.</sup>