rafmacalaba/datause-encoder-data huggingface dataset:
The decline in employment rates was not even for men and women. Between 2005 and 2015, the male employment rate in India decreased by 1 percent per year, while the female employment rate decreased by nearly 5 percent per year (Figure 7). In Bhutan, the male employment rate decreased at 0.2 percent per year, while the female employment rate decreased by over 3 percent per year. However, not all countries in the region display the same pattern. In Bangladesh, male employment declined by close to 1 percent per year, whereas female employment remained constant. And in Pakistan, female employment even increased at 0.6 percent per year – though from a very low level.

As economies develop, individuals usually move out of agriculture into more productive, non-agricultural employment. South Asia is not an exception to this pattern, but the pace of structural transformation varies across countries. Such transformation took place at a rapid pace in Bhutan, where there was a clear shift in employment from agriculture to services between 2005 and 2015 (Figure 8). While agricultural employment decreased by 20 percentage points, services sector employment increased by 8 percentage points. The transformation was slower in Bangladesh and India. The agricultural employment rate decreased by around 7 percentage points in both countries; but while manufacturing employment increased by 3 percentage points in Bangladesh, it did not vary in India. In Pakistan, structural changes in employment have been almost inexistent.

**Figure 8.** Change in employment rates by sector of activity from 2005 to 2015.

<!-- Start of picture text --> Percentage points<br>15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>-20<br>-25<br>Bangladesh Bhutan India Pakistan Sri Lanka<br>Agriculture Manufacturing Construction Services<br><!-- End of picture text -->

**Source:** South Asia Employment Dataset.

The region has not experienced a considerable creation of more regular employment between 2005 and 2015. As shown in Figure 9, the only strong expansion in regular employment happened in Bangladesh, where the share of regular wage jobs in the working-age population increased by 4.5 percentage points. Regular wage employment also increased in Bhutan, but only by 2 percentage points. In terms of other types of employment, casual work and unpaid employment declined across the region in the same period. In Pakistan, for example, casual employment decreased by almost 8 percentage points. In India and Bangladesh, unpaid employment decreased by 4 and 5 percentage points respectively.

Worldwide, a vast majority of jobs is created by the private sector. And the public sector can create only a limited number of jobs in countries where government revenue is modest, as is the case in South Asia. In Bangladesh and Pakistan, the share of public sector employment in the working-age population declined 13
# **<u><mark>Table 2A – Impact of FD Law on States’ Fiscal Balance Components Given Their Public Debt</mark></u>**

This table reports OLS estimates of the impact of the FD Law on fiscal balance components of states of varying ex ante public indebtedness. On the public expenditure side, the variables _TotalExpendituress,y_ , _Infrastructures,y, SocialServicess,y_ and _DebtServicings,y_ correspond to the ratios of total public expenditures and expenditures on infrastructure projects, social aid, and debt servicing over the GDP of state _s_ in year _y_ . On the public revenue side, the variables _Transferss,y_ and _TaxRates,y_ correspond to the amount of federal government transfers and tax income obtained by state _s_ in year _y_ and are calculated as ratios over the state yearly GDP. _DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _Posty_ is an indicator variable that equals one from 2016 onwards. Observations at the state-year level. Standard errors are reported in parentheses and are doubled clustered at the state and year levels. *, ** *** <u>, denote significance at the 10, 5 and 1 percent levels. Detailed variable definitions are provided in Table A1 in the Appendix.</u>

||Total Expe<br>|ndituress,y<br>|Panel<br>Infrastr<br>|A. Public<br>uctures,y<br>|Expenditu<br>Social S<br>|res<br>ervicess,y<br>|Debt Ser<br>|vicings,y<br>|Pan<br>Transf<br>|el B. Pub<br>erss,y<br>|licReven<br>Tax<br>|ues<br>Rates,y<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Posty|-0.002||-0.001||0.001||0.002||-0.003**||-0.024||
||(0.002)||(0.001)||(0.001)||(0.001)||(0.001)||(0.016)||
|Posty*DebtStates,16Q1|-0.011**|-0.012**|-0.003*|-0.004*|-0.003*|-0.005*|0.003*|0.002|-0.002|-0.002|0.070*|0.068*|
||(0.005)|(0.005)|(0.002)|(0.002)|(0.002)|(0.003)|(0.002)|(0.003)|(0.003)|(0.003)|<br>(0.039)|<br>(0.038)|
|Observations|174|174|174|174|180|180|174|174|174|174|150|150|
|R-squared|0.990|0.990|0.682|0.703|0.981|0.982|0.471|0.506|0.991|0.992|0.886|0.890|
|Macro Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|

45
- 23 -

competition tends to be limited. Electricity projects are typical. In most cases of our sample, only two or three bidders participated in each auction.

Without doubt competition is the most important factor toward efficiency and anti-corruption. However, the degree of competition that one can expect is closely related to bidders’ entry decision and auctioneer’s decision on how to assess non-price (technical) attributes in the bid evaluation process. The current paper casts light on the potential interactive effects among quality, entry and competition, by estimating the system of simultaneous equations with data on procurement auctions for large electricity projects in developing countries.

It is found that the decision to adopt the technical evaluation method is endogenously made while potential contractors are considering whether they would enter the auction process. If certain technical aspects are taken into consideration regardless of prices, the equilibrium bids would increase significantly due to extra costs required to meet higher quality standards. In addition, high technical requirements would reduce the number of participants. When the expected competition is going to be limited, auctioneers may have an incentive to loosen the thresholds. But this feedback is found insignificant because the quality is of particular importance in large-scale infrastructure projects. In general, technically large electricity projects are not only costly by nature but also can attract only a few potential bidders, resulting in less efficiency. Moreover, the technical requirements are more likely to be imposed on large projects so that bidder participation would be even more limited. This is the revealed underlying mechanism for limited competition in procurement auctions for large electricity projects.
<!-- Start of picture text --> Google Maps<br><!-- End of picture text -->

# Google Maps

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# **References:**

Campa, J. M. and L. S. Goldberg (2002). "Exchange Rate Pass-Through into Import Prices: A Macro or Micro Phenomenon?" NBER Working Papers 8934, National Bureau of Economic Research, Inc COMISIÓN ECONÓMICA para AMÉRICA LATINA y el CARIBE (CEPAL), (2001), _Panorama social de América Latina 2000-2001_ , Santiago de Chile.

Deaton, A. and J. Muellbauer. (1980). _Economics and Consumer Behavior._ Cambridge: Cambridge University Press..

Deaton, A. (1997). _The Analysis of Household Surveys. A Microeconometric Approach to Development Policy_ , The World Bank and The Johns Hopkins University Press.

Edmonds, E. and N. Pavcnik (2003). “The Effect of Trade Liberalization on Child Labor”, mimeo, Darthmund College.

Feenstra, R. C. (1989), “Symmetric Pass-through of Tariffs and Exchange Rates under Imperfect Competition”, _Journal of International Economics_ . 27: 27-45.

Feenstra R. and Hanson G., (1997). "Productivity Measurement and the Impact of Trade and Technology on Wages: Estimates for the U.S., 1972-1990," NBER Working Papers 6052, National Bureau of Economic Research, Inc Goldberg P. and M. Knetter. (1997). “Good Prices and Exchange Rates: What Have We Learned?”, _Journal of Economic Literature_ ; 35:1243-1272.

Goldberg, P. and N. Pavcnik (2003). "The Response of the Informal Sector to Trade Liberalization," _Journal of Development Economics_ , forthcoming.

Hanson G. (1997). “Increasing Returns, Trade and the Regional Structure of Wages”, _The Economic Journal_ ; 440, 113-133.

Hanson G and Harrison A. (1999). "Who Gains from Trade Reform? Some Remaining Puzzles," NBER Working Papers 6915, National Bureau of Economic Research, Inc Hanson G. (2003). “What happened to Wages in Mexico Since NAFTA?” NBER Working Papers 9563, National Bureau of Economic Research, Inc Hadri, Kaddour. (2000) “Testing for stationarity in heterogeneous panel data”. _The Econometrics Journal_ , 3, 2000, 148-161 Hsiao C. (1986). _Analysis of panel data_ . Cambridge ; New York : Cambridge University Press.

32
lines and more than 200 bus routes (3,300 new buses).<sup>7</sup> Similar expansions are happening in India, Mexico, and many other emerging economies.<sup>8</sup> Although it requires massive funding, this type of investment can also stimulate economic activities and facilitate trade (Redding & Turner 2015).

With the goal of reducing traffic-related air pollution and traffic congestion, supply-side policies such as expanding the public transit network can create two countervailing forces on air quality. Improving the public transit network can divert commuters from driving private vehicles to public transport (Mohring 1972). This traffic diversion effect (the “Mohring effect”) could potentially reduce traffic congestion and vehicle emissions. However, improving transportation infrastructure (by increasing road capacity or expanding public transit) can reduce the cost of travel by reducing congestion and increasing accessibility, and generate more economic activity, resulting in an increase in travel demand and driving (Vickrey 1969). The net effects of increase in public transit supply may depend on the time horizon. In the short run, the diversion effect could play a more important role as there is spare capacity in the transportation system. In the medium to long run, induced demand may offset the diversion effect, causing congestion and more pollution. For example, Duranton & Turner (2011) find that traffic volume increased as a result of the expansion of highway capacity in US cities between 1983 and 2003. Although the expansion of road capacity can initially reduce traffic congestion and air pollution, it increases travel demand in the long run, eroding the initial improvement in traffic conditions.

Given the high cost of transportation infrastructure and potential countervailing forces at play, empirically estimating the impact of supply-side policies on air quality is important. The central challenge lies in finding exogenous variation in public transit infrastructure, which could be confounded with other unobserved factors. For example, urban planners may situate public transit (such as the subway) in areas where population and economic activities are projected to grow. In this case, air quality in those areas may have deteriorated in the counterfactual scenario of no expansion of public transit. The issue of endogenous location could bias the true impact of the subway expansion in the empirical analysis.

> 7 Beijing Transport Annual Report (www.bjtrc.org.cn).

> 8 _India Road Investment: Mexico Transport Infrastructure Development_ .

> https://oxfordbusinessgroup.com/overview/full-steam-ahead-investment-major-transport-infrastructureupgrades-continues-despite-tighter-budget.

11
Urban/major city location is associated with a higher risk of HIV infection – this finding is consistent across regions. Mean population age and share of women who report knowledge of HIV prevention practices appear significant only in the Central region.<sup>22</sup> We find a positive correlation between the share of people living below the poverty line and the probability of being HIV-positive in the North and South regions.

The regression results for all districts show an inverted U-shape relationship between the HIV infection status and age – the probability of being HIV-infected first increases to about age of 30, and then decreases. This is consistent with the “benchmark” 2004 Malawi DHS data which show that HIV prevalence peaks for women and men age 30-34 (NSO, Malawi and ORC Macro, 2005).

Education level is found to be correlated positively with the risk of being HIVinfected in the North and Central regions. This correlation is also validated by the 2004 Malawi DHS data, which show higher HIV prevalence among women with secondary or higher education. Interestingly, the 2004 Malawi DHS showed no significant differences in the testing refusal rates among women with various education levels.

The share of orphans in the TA is positively correlated with positive HIV status in the regression specifications for all regions of Malawi. That is not surprising given that higher HIV prevalence in a sub-district results in the larger death toll and hence in the higher share of orphans.

There is a negative correlation between the mean age at first sex and the mean age at first marriage (for women) in the TA and the individual probability of being HIVpositive. We also find a negative correlation between the share of women in TA who report knowledge of HIV prevention methods and the probability of being infected in the Central region.

All in all the results described above appear broadly consistent with our expectations and with prior work on HIV in Malawi.

> 22 Note that if the knowledge of HIV prevention practices is almost universal across TAs in the region, this variable is not expected to be statistically significant.

13
35 THE INTENSIVE MARGIN IN TRADE to the Melitz-Pareto model.

What happens if trade liberalization is asymmetric? We consider an extreme case in which, for each origin, trade costs decrease only for exports to the destination with the highest number of exporters – formally, we consider 13 separate shocks, one for each Latin American country ˆ and China as an origin, with the shock for origin _i_ being that _τij_ = 0 _._ 25 if _j_ = arg max _l Nil_ and ˆ _τij_ = 1 otherwise. Since the trade elasticity should be low for the affected pairs, we expect the Melitz-lognormal model to deliver smaller welfare gains than the Melitz-Pareto model. This is confirmed in Figure 18. However, the differences in welfare gains between the two models are small. Again, as in the analysis with symmetric trade cost declines, we see bigger differences regarding the effects on trade flows, as shown on Figure 19.

It is interesting to compare our results to those in Melitz and Redding (2015). They find that the ACR ex-post formula for welfare evaluation does a poor job of capturing the true welfare changes from a decline in trade costs in a symmetric Melitz model with a truncated Pareto distribution. In contrast, we find that the ACR formula does a good job in approximating welfare changes in the estimated Melitz-lognormal model. The difference comes from how much the trade elasticity varies in the two models: whereas it falls from 15 to 5 as trade costs decline in the Melitz-Redding exercise, the trade elasticity shows little variation in our Melitz-lognormal model. In particular, three quarters of bilateral trade elasticities lie between 4 and 4.8. Note that after trade liberalization, elasticities can never fall below 4, and thus the local trade elasticities are a good approximation to the average trade elasticities during the liberalization episodes. We discuss this further in Appendix H, in which we show that we can reproduce the Melitz and Redding (2015) results but only by setting parameters to values far from those we estimate.

# **6. Conclusion**

The canonical Melitz model of trade with Pareto-distributed firm productivities has a stark prediction: conditional on the fixed costs of exporting, all variation in exports across partners should be due to the number of exporting firms (the extensive margin). There should be no variation along the intensive margin (exports per exporting firm), again conditional on fixed costs.

We use the World Bank’s _Exporter Dynamics Database_ plus China to test this prediction. Compared to existing studies, this data allows us to look for systematic variation in the intensive and extensive margins of trade — allowing for year, origin, and destination components of fixed trading costs.

We find that at least 40% of the variation in exports occurs along the intensive margin. That is,
||<br>**Table **|<br>**11: Selected e**|<br>**conomic and h**|**uman develo**|<br>**pment variable**|**s as function **|<br> **of country-g**|**roups -poole**|<br>**d OLS regress**|**ion**||
|---|---|---|---|---|---|---|---|---|---|---|---|
||**Agriculture**|**Industry value**|**Service value**||**Fixed capital**||**Current**||**Dependency**||**Aid per capita**|
|**Variable**|<br>**value added**|<br>**added**|**added**|**Trade**|<br>**formation**|**Savings**|**account**|**Debt**|<br>**ratio**|**Life expectancy**|<br>**($)**|
||**Model 1**|**Model 2**|**Model 3**|**Model 4**|**Model 5**|**Model 6**|**Model 7**|**Model 8**|**Model 9**|**Model 10**|**Model 11**|
|AA|_-16.80_|_8.17_|_8.60_|_39.73_|_4.94_|_6.17_|_2.71_|_-27.36_|_-.07_|_7.92_|_34.88_|
|BA|2.06|_6.17_|_-8.23_|_18.50_|_9.15_|3.28|_-4.28_|_22.47_|_-.10_|2.58|11.53|
|AB|_5.11_|.94|_-6.05_|-6.63|_-7.06_|-.45|3.30|-3.45|_-.03_|-1.29|-7.63|
|R<sup>2</sup>|.33|.09|.18|.26|.11|.03|.03|.20|.19|.26|.14|
|F test|_122.06_|_27.3_|_56.16_|_89.66_|_30.86_|_8.70_|_7.51_|_60.80_|_64.97_|_40.51_|_42.54_|
|N|1,252|1,252|1,252|1,239|1,227|1,227|1,025|1,167|1,328|567|1,270|
|Wald test that<br>groups' coefficients<br>are equal|_116.4_|_9.10_|_89.57_|_54.90_|_46.04_|_4.37_|_8.52_|_15.16_|_9.61_|_26.18_|_22.21_|
|Wald test that<br>groups' coefficients<br>are zero|_195.8_|_40.48_|_93.24_|_142.60_|_48.86_|_13.33_|_9.87_|_19.80_|_79.80_|_61.72_|_48.25_|

Note: Time and time square included in all models. Dependent variable presented above each model #. BB is the base country-group. Dependent variables of models 1 to 8 are measured as percentage share of GDP. Coefficients in italics significant at the 5% level.

31
# **References**

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- **Castillo, Ramiro, Luciano Dalla Rosa, Walter Garc ́ıa Diaz, Lauro Madureira, Mariano Gutierrez, Lu ́ıs V ́asquez, and Rolf Koppelmann.** 2019. “Anchovy distribution off Peru in relation to abiotic parameters: A 32-year time series from 1985 to 2017.” _Fisheries Oceanography_ , 28(4): 389–401.

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47
an investigation or duty was applied, the variable NTB only takes on a value of 1 or 0. In future research efforts, the UNCTAD database could be replaced by a more comprehensive datas-t with additional information on the value I the preliminary dumping/subsidy margin or the value of the final duty imposed.

The 41 industry sample was selected by drawing only complete price series from the BLS import price series, which is available at the 4-digit level for selected SITC categories and selected years. For 1981-1986, a complete time series was available for 60 SITC groups. The frequency and types of non-tariff barriers across these categories are shown in Table 1. The frequency measure simply records the number of times a particular NTB appeared in the database, and reflects the number of countries affected as well as the number of different instances each measure was applied over 1981-1986.

Table 1 shows that quotas and voluntary export restraints were the most prevalent type of NTB applied. In next order of importance were investigations, which accounted for over 30 % of all non-tariff barrier cases. These were followed by duties, which represented 12 % of all NTBs. LFV cases represented an important type of non-tariff barrier for the sample industries.

To ensure that we would not confuse the effect of a countervailing or antidumping duty with other NTBs, all industries subject to any of the other types of non-tariff barriers were excluded from the sample. This reduced the sample from 60 to 45 industries. Most of the SITC categories eliminated were steel products, which have been subject to multiple NTBs (such as voluntary export restraints and trigger prices) in the 1980s. The sample was further reduced to 41 sectors, eliminating products with insufficient observations to provide a the impact of LFV cases.

13