rafmacalaba/datause-encoder-data huggingface dataset:
**Table 4.  The Investment Equation for the Pool sample**

||**tobit**<br>**coef/t**<br>(1)|**OLS**<br>**coef/t**<br>(2)|**tobit**<br>**coef/t**<br>(3)|**OLS**<br>**coef/t**<br>(4)|**OLS+ind**<br>**coef/t**<br>(5)|**IV**<br>**coef/t**<br>(6)|**OLS**<br>**coef/t**<br>(7)|**FE**<br>**coef/t**<br>(8)|
|---|---|---|---|---|---|---|---|---|
|ln(GDP per capita)|0.043|-0.014|0.035|-0.021|-0.037*|-0.040**|-0.019||
||(1.406)|(-0.695)|(1.231)|(-1.089)|(-1.946)|(-2.327)|(-0.984)||
|ln(city pop)|0.058**|0.021|0.054**|0.020|0.009|0.009|0.016||
||(2.132)|(1.427)|(2.108)|(1.362)|(0.612)|(0.581)|(1.091)||
|ln(firm age)|-0.044***|-0.048***|-0.052***|-0.051***|-0.055***|-0.053***|-0.051***||
||(-2.910)|(-4.554)|(-3.428)|(-4.801)|(-4.985)|(-5.606)|(-4.638)||
|Non-state|0.066*|0.055**|0.044|0.042**|0.045**|0.052**|0.039*||
||(1.935)|(2.542)|(1.366)|(1.975)|(2.053)|(2.995)|(1.909)||
|Foreign|0.082**|0.011|0.047|-0.014|-0.018|-0.006|-0.023||
||(2.221)|(0.405)|(1.309)|(-0.545)|(-0.648)|(-0.210)|(-0.879)||
|sales/lagged K|0.008***|0.008***|0.011***|0.010***|0.010***|0.011***|0.011***|0.043***|
||(5.074)|(6.014)|(6.349)|(6.914)|(6.975)|(5.467)|(6.902)|(32.645)|
|CF/lagged K|0.107***|0.089***|0.129***|0.107***|0.105***|0.090***|0.106***|0.089***|
||(5.182)|(4.881)|(5.900)|(5.510)|(5.386)|(3.856)|(5.400)|(8.780)|
|Loan|||0.289***|0.155***|0.155***|0.149***|0.165***||
||||(11.621)|(9.234)|(9.020)|(8.767)|(9.790)||
|Trade credit|||0.103**|0.079*|0.071*|0.058|0.076*||
||||(2.103)|(1.899)|(1.716)|(1.482)|(1.755)||
|UCA|||0.044***|0.032***|0.031***|0.028***|0.031***||
||||(4.432)|(4.475)|(4.570)|(4.248)|(4.399)||
|Lagged sale growth|||||||0.019***<br>(3.095)||
|Tobin’s Q|||||||0.247***||
||||||||(3.721)||
|Ind Q||||||||0.158***<br>(3.205)|
|Number of<br>observations|10,283|10,283|10,207|10,207|10,207|10,032|9,982|22,688|
|Adjusted R2|0.020|0.061|0.031|0.079|0.085|0.080|0.085|-0.744|

*** p<0.01, ** p<0.05, * p<0.10.    Not reported are coefficients of area dummies.

Ind Q is time-varying and thus does not drop out in the FE estimation. For OLS, Tobin’s Q is average (to reduce measurement errors).

45
households.<sup>6</sup> The average household housed 8.2 people, with a range of 3 to 59 people within the house. The sample included twice as many children under age 3 (n=1,387 or 66.7%) as children aged 36-72 months (n=705 or 33.3%), weighting results accordingly.

**Table 1**

# _<u>Sample Descriptive Characteristics</u>_

|||**Percentage**||
|---|---|---|---|
|**All children (n=2102)**||**(weighted)**|**n (raw)**|
|Child Age||||
||0-35 months|66.67|1397|
||36-72 months|33.33|705|
|Child Gender||||
||Male|49.99|1076|
||Female|50.01|1026|
|Location||||
||Urban*|23.28|749|
||Rural|76.71|1353|
|Birth registration||||
||Birth certificate|69.72|1436|
||Proof of registration|21.65|455|
||Not registered|16.01|324|
|Refugee Status||||
||Yes|5.62|135|
||No|94.38|1967|
|Maternal Education||||
||No education**|58.14|1135|
||Completed pre-primary|4.60|106|
||Completed primary|12.32|258|
||Completed middle|14.87|347|
||Completed Secondary|4.63|120|
||Completed Bachelor’s Degree|2.45|65|
||Completed Graduate Degree|<br>2.72|66|
||ReligiousEducation|.27|5|
|**All children (n=2102)**||**Mean (weighted)**|**Mean (range) raw**|
|Child Age (months)||30.90|31.00 (0.00, 71.88)|
|Household Size||8.25|8.13 (3, 59)|
|Caregiver Age (years)||31.58|31.22 (15, 72)|
|Household Head Age (yea|rs)****|46.41|46.41 (18, 94)|

> 6 <mark>Official estimates on registered and unregistered refugees in Khyber Pakhtunkhwa suggest this might be an overestimate and, as such, caution in interpreting these findings is warranted.</mark>

11
# **<u>Table 11. Average distance between origin and destination countries (km)</u>**

||||**D**<br>|**estination**|**Countries/**|**Regions**||||**Correlation with**<br>|
|---|---|---|---|---|---|---|---|---|---|---|
||||**er**||||||||
|**Countries/Regions of Origin**|**+**<br>**Canada**|**+**<br>**EFTA**|**g**<br>**income**|**LAC**|**ECA**|**MENA**|**AFR**|**EAP**|**SAS**|**numers y**<br>**origin**|
|**USA + Canada**|2,451|7,170|12,624|3,064|8,805|10,566|11,211|12,570|11,604|-0.87|
|**EU15+EFTA**|7,151|937|14,136|9,044|2,417|3,232|6,839|11,340|6,164|-0.38|
|**Other high income**|10,859|11,938|3,627|15,886|7,411|901|9,055|5,226|5,175|-0.42|
|**LAC**|2,815|8,571|14,734|1,386|11,546|11,738|10,560|15,863|14,319|-0.68|
|**ECA**|8,429|1,880|5,674|11,405|3,012|3,177|7,886|9,352|3,754|-0.47|
|**MENA**|10,387|2,253|2,334|10,807|2,932|1,246|3,659|10,924|5,026|-0.60|
|**AFR**|11,718|6,124|4,815|9,017|6,901|3,137|1,272|11,207|7,013|-0.61|
|**EAP**|11,935|10,024|5,167|16,143|6,735|8,668|11,061|4,344|4,212|-0.17|
|**SAS**|12,681|6,968|3,187|14,998|4,155|2,186|6,951|5,680|1,494|-0.58|
|**Correlation with numbers by**<br>**destination**|-0.51|-0.88|-0.47|-0.75|-0.38|-0.59|-0.72|-0.54|-0.48||

Note: "EU15 + EFTA" includes Switzerland. "Other high income" includes Australia, New Zealand, Japan and high income MENA countries. Figures are weighted by the number of migrants. Calculations are based on the geographic coordinates of each country, which are rounded latitude and longitude figures of the approximate geographic centre of a country (source: CIA, The World Fact Book).

28
two columns of Table 5).

Table 12: Gravity model estimation results: robust regression

|**Dep. var. **|**(ln)**<br>To|tal imports|
|---|---|---|
|**Variable**|**Coefci**|**ent**<br>**Std. Err.**|
|Ym|1.305<sup>_∗∗_</sup>|0.363|
|Yx|-0.139|0.225|
|dGDPpc|0.019|0.017|
|GDPsim|0.050<sup>_∗∗_</sup>|0.015|
|dist|-1.255<sup>_∗∗_</sup>|0.030|
|comlang|0.564<sup>_∗∗_</sup>|0.057|
|border|0.390<sup>_∗∗_</sup>|0.120|
|colony|0.446<sup>_∗∗_</sup>|0.156|
|PTA|0.718<sup>_∗∗_</sup>|0.074|
|N||13317|
|R<sup>2</sup>||0.796|
|F (233_,_13083)||219.68|
|Signifcance l|evels :<br>_†_ 10%<br>|_∗_5%<br>_∗∗_1%|

Comparing the above estimates with the GLS results of Table 5, we see that the coefficients and standard errors are quite similar, as are the _t_ and _p_ values. Despite the rejection of normality for GLS residuals, the robust regression estimates are not very different from our baseline result, suggesting that the lack of normality is likely a minor problem.

61
54 on credit access confronting the ejldos, ejidatarlos probably fared marginally worse than small private farmers.

(c) Estimating post-1970 trends. By the early 1970s, agricultural growth had slowed to the point where It was inferlor to the rate of growth of the population; food kuports began to rise sharply (Heath,1988; YfLnez Naudo,1988). Sbnltaneously, thero was an outbreak of widepread rural unrest. Th1 Mexican government responded to these developments by Increasing the flow of subsidized state credit to the raln-fed reglons, Introducing a marketing-subsidies programme for the ejidos (PACE) and placing more emphasis on small-scale irrigatlon schemes (many of these located on the previously-neglectod altiplano of central Mexico). Data on regional shares of national crop output value suggest that, by the mid19809, the central and southern regions (where most ejldos are concentrated) were making a much larger contribution than they had done in the early 1960s: betwoen 1949-51 and 1960-62 the Centre's share In crop revenue fell from 30 percent to 25 percent; however, between 1980-62 and 1983, the Centre's share rose from 25 percent to 35 percent (Venezian & Gamble,1967; INEGI,1985).

120. State subsidies to tractor production led to a significant growth In tractor use after 1970; one study Indicates that the adoption rate was higher for the ejido sector than for private farms (SPP-UNIDO,1986). While there are no data In the 1981 Agricultural Cansus on the number of tractors by tenure sector It Is possible to Infer the kIportance of tractor use from the proportion of the cattle herd employed as work anknals (for ploughing and carting). In 1970, 12 percent of cattle In ejidos were used as work animals, compared to 4 percent In the private sector; by 1981, the proportlons had decilned respectively to 5 percent and 2 percent; moreover, on farms up to five hectares, the proportion was the same In
# **1 Introduction**

Recent trade theory stresses the importance of market-share reallocations in increasing aggregate productivity following a trade liberalization (Bernard, Eaton, Jensen and Kortum 2003, Melitz 2003). In contrast, the earlier literature emphasized the idea that trade could improve average productivity among existing firms (Corden 1974, Grossman and Helpman 1991, Helpman and Krugman 1985, for example). Yet there are few empirical studies that quantify the relative importance of average productivity gains versus gains from market-share reallocations in the wake of a major trade liberalization.

In this paper, we use a comprehensive, firm-level dataset that allows us to examine the role played by market-share reallocations in aggregate productivity growth in India’s organized manufacturing sector from 1985 to 2004.<sup>1</sup> In 1991, India embarked on a series of reforms, including a major trade liberalization. We confirm that for the organized manufacturing sector as a whole, market-share reallocations were an important source of productivity growth in the years immediately following the start of the 1991 reforms, but not during other periods.

We document three distinct phases in India’s manufacturing productivity during the period from 1985 to 2004. During this time, aggregate productivity (defined as output-weighted, mean firm productivity) grew by nearly 20%. From 1985 to 1990, the growth in aggregate productivity was driven by “learning” - that is, an increase in unweighted, average firm productivity. This measure of learning captures the change in productivity for the average firm, and therefore includes not only changes in productivity among surviving firms, but also changes in average productivity that can be attributed to firm entry and exit. In the period immediately following the start of the reforms (1991-1994), the “stealing” of market share - that is, the reallocation of market share from less productive to more productive firms - became more important than learning in driving aggregate productivity growth. In the longer run (1998-2004), learning once again became the more important factor in aggregate productivity growth, with stealing (reallocation) contributing

> 1The organized (formal) manufacturing sector in India consists of firms that are registered under Sections 2m(i) and 2m(ii) of the Factories Act; all firms with 20 or more employees (10 if power is used) are required to register. The organized sector accounts for approximately 80% of manufacturing sector output, though only 20% of employment.

2
# **6 Modeling Results**

# **6.1 Impact on emissions**

Figures 6.1 and 6.2 present summary results of changes in emissions across the different scenarios. The figures show that, as expected, emission reductions increase over time as the price on carbon increases. Depending on the recycling scenario, the reduction ranges from 1.1 million tons to 1.5 million tons relative to BAU in 2030, when the carbon tax reaches its maximum value. The differences across scenarios are due to the differences in growth of the economy due to the different revenue recycling mechanisms considered, which in turn influence the demands for fossil fuels.

Comparing across the scenarios, the least emission reduction is achieved in the sales tax (STAX) reduction scenario. This is because its effect on the general economy results in the least growth impact (Figs 6.1 and 6.2 below). Direct tax reduction (DTAX), on the other hand, leads to more significant reductions in emissions.

Similar comparisons follow when we compare the resulting reductions in emissions from all emission sources. These will be larger than the reductions in emissions in fossil fuels, because the changes in fuel prices will have effects on the quantities of other emissions-producing activities in the economy. By 2030, as compared to the base case, the STAX reduction scenario will lead to a reduction of total emission by 1.7 million tonnes while the DTAX reduction scenario will lead to a reduction of total emission by about 3.8 million tonnes (see Figs 6.3 and 6.4).

_Figure 6.1: Reduction in fossil fuel emission (millions of tonnes of CO2) from tax on fuel and kerosene compared to the base case_ <!-- Start of picture text --> Reduction in fossil fuel emission from tax on fuel and kerosene<br>compared to the base case<br>2018 2022 2026 2030<br>(5.0 USD/ton) (13.3 USD/ton) (21.7 USD/ton) (30.0 USD/ton)<br>0.0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>-1.0<br>-1.2<br>-1.4<br>-1.6<br>DTAX STAX SAVINGS CORPOR TRANS<br>Millions of tonnes of CO2<br><!-- End of picture text --> 14
22 _Central America and NAFTA_ both internal and external real shocks. Ideal macro economic management in principle can eliminate the first, but there is nothing that a single country can do about the second. Toward this goal we present regressions of the RER uncertainty on the termsof-trade (TOT) uncertainty. We take the TOT uncertainty as an imperfect measure of the uncontrollable fundamental and treat the residual as controllable with ideal macro economic management.

82. Relatively high TOT variability in Central America compared with Asia does contribute to its relatively unstable RER. The TOT volatility in Central America comes from an export mix that is heavily concentrated on several specific agricultural commodities, which tend to have more volatile prices than exports of industrial goods. Asian exports, on the other hand, include relatively large amounts of rather diversified mnanufactures'. In particular, booms and busts in international coffee prices (the major export crop in Central America) have been common in recent Central American economic history, bringing about sharp swings in the RERs'o 83. Table E. 12 presents some measures of TOT uncertainty in Central America and several Asian countries during 1970-90. These measures are one-year and five-year forecast standard errors from a simple autoregressive forecasting equation analogous to the measures of the RER uncertainty in Section III. Excepting Indonesia, the TOT in Asia show smaller uncertainty than Central America's. This is compatible with our viewpoint that the RER in Central America is relatively unpredictable due, to a large extent, to relatively high variability in the TOT shocks which comes from a highly specialized export mix, compared to Asia.

84. These data (excepting the outlier Nicaragua) plus data for Argentina, Brazil, Cyprus. Kenya. Peru, Philippines. Tanzania, Thailand, Tunisia and Turkey and the cor-r-esponding regression line are graphed in Figure E. 11 which clearly illustrates the relationship between RER uncertainty and TOT uncertainty. In this figure the Asian countries are clustered together with low RER and low TOT uncertainty, whereas the Central American countries display both higher RER and higher TOT uncertainty. The 23 observations have been used to estimate the regression (standard error in parenthesis): sRER = 0.08 + 0.89 sTOT: R-= 0.26 (0.06) (0.32) Harbergcr (1988) generalizes the argument, pointing **out** that the supply elasticity of tradables (exportables and importabies) in Asia is much higlher than its counterpart in Latin America. Therefore, oiven a similar exterinal shock. the RER in Asia has to undergo a smaller and quicker adjustment (as compared to the one needed in Latin America) in order to restore internal and external equilibrium.

It is important to also considler the imlport-induced variability in the terms of trade. However, when comparing the recent economic history of Cetitral America and Asia. we notice the fact that the hardest implort prices shocks felt by the two regions (i.e., the two oil shocks) were common and therefore points to export-ill(LucedC variability in the terms on trade.
SYS-REVIEW/HI-LMIC insurance, which can be an efficient way to obtain care. In contrast to the possibility of adverse selection, given the low costs of entry for most insurance, it may simply be that betterinformed individuals enroll. Better-informed individuals may also be more educated, may have larger incomes, and may be healthier than those who do not adopt insurance.

Thus, if one examines the average impact of insurance on those who adopt insurance (i.e., average treatment on the treated), then a simple comparison of insured and noninsured individuals does not yield appropriate results. The comparison is flawed because the noninsured group may not have had an opportunity to enroll, and this group includes those who would adopt insurance if offered as well as those who would not adopt insurance. The insured and noninsured groups may differ with regard to the factors that may affect outcomes. In light of the possibility of selection into insurance, the threat to validity is high. As a result, the impact may differ significantly when no adjustment is made for this type of selection, especially if average treatment on the treated is reported (Imbens and Wooldridge 2009).<sup>3</sup> Inclusion criteria have focused on studies with counterfactuals, we now discuss identification issues applied to the inclusion criteria. We briefly describe statistical procedures to obtain average treatment on the treated in the present context. As indicated, 19 studies properly addressed identification issues.

# **<<B>>** _Randomized Studies_

Even if insurance is offered through random means when uptake is low, it cannot be assumed that people who adopt insurance are similar to those who do not. An adjustment is needed

> 3 Yip and Berman’s (2001) study was among the early empirical papers on health insurance for developing countries that recognized the selection problem. They addressed the issue through simulation.

10
# **The Impact of Policies to Control Motor Vehicle Emissions in Mumbai, India**

# **I. Introduction**

# **_A. Motivation and Purpose_**

Mumbai, like many Indian cities, has a serious air pollution problem caused, at least in part, by mobile sources. Between 2000 and 2002, annual average PM10 was approximately 80 μg/m3 (World Bank 2005), higher than in Mexico City.<sup>3</sup> In many ways, however, Mumbai is more fortunate than other Indian cities. It has an extensive rail and bus system and a much smaller vehicle fleet than Delhi, a city of comparable size and income. The problem facing Mumbai is to reduce emissions from diesel trucks and buses, as well as taxis and auto-rickshaws—and to prevent rapid growth of the private vehicle fleet.<sup>4</sup> In this paper we examine the impacts of measures to reduce emissions from passenger transport; specifically, buses, cars and two-wheelers. These include the possibility of converting diesel buses to compressed natural gas (CNG), as the Indian Supreme Court required in Delhi, which would necessitate an increase in bus fares to cover the cost of pollution controls. We also consider raising the price of gasoline, which should affect the ownership and usage of cars and two-wheelers, as well as imposing a license fee on cars, to retard growth in car ownership. The impact of each policy on emissions depends not only on how the policy affects the mode that is regulated, but on shifts to other modes. For example, a “clean bus” policy might actually increase

> 3 The National Environmental Engineering Research Institute (NEERI) monitors RSPM (respirable particle) levels, which are approximately equivalent to PM10.  It should be noted that annual average RSPM has been declining steadily since 1997, largely as a result of the closing of textile mills in the city. 4 Some measures along these lines have already been taken:  by 2003 all highly polluting taxis and autorickshaws were required to be converted to natural gas.  The sulfur content of diesel fuel has been reduced from 2500 to 500 ppm (World Bank 2005).

2
effectiveness, which could contribute to improving the country’s economic competitiveness, thus supporting structural change in Guinea.

Our results suggest that Guinean authorities should also aim at reducing the growth rate of the labor tax and contribution rate. This could be done by expanding the tax base, thus reducing the tax burden on a few modern enterprises. Beyond the labor tax rate, Guinean authorities should also aim at facilitating other aspects for more labor market flexibility, including those related to hiring conditions and working hours. For instance, the maximum length probationary period for a worker with a formal contract is about three to twelve months, but renewable once, which is more restrictive in Guinea than in many developing countries, including in SSA. Likewise, the premium for overtime work is about 30 percent of hourly pay in Guinea, which is largely higher than in comparator countries such as Benin, Burkina Faso and Côte d’Ivoire where the premium is 12, 15 and 23.8 percent, respectively. Furthermore, the paid annual leave for a worker with 1 year of tenure (in working days) is about 30 days in Guinea, which is higher than in many comparator countries.<sup>19</sup> Though the data show that the dynamics of the primary commodities’ share of merchandise exports has not been unfavorable to structural change, Guinean authorities should pursue efforts for getting the maximum of benefit out of the mining sector for the country’s development. Indeed, mining revenues could be a source of financing infrastructure development (power, roads, railways, and port), which is highly needed in Guinea.<sup>20</sup> Recently, mining has been utilized to back the development of energy infrastructure, as illustrated by the increased access to electricity in the country. Such experience could be replicated for developing other infrastructure that the country needs, provided that transparency and good governance rules are followed. Guinean authorities should also pursue efforts for diversifying the economy. Indeed, primary commodities account for about 90 percent of merchandise exports, which is a very high level of concentration, exposing the country to external shocks and limiting the possibility for structural change. Agribusiness with

> 19 According to 2019 Doing Business data. It is only recently that data on labor market conditions have been collected for the Doing Business report; thus, available data does not allow comparison over the analysis period.

> 20 For instance, of 140 countries around the world, the _2015-16 Global Competitiveness Report_ ranked Guinea the least performing country in terms of quality of infrastructure.

22
Table A.5: Effects of mining on main outcomes with different cut-off distances.

|VARIABLES|(1)<br>Working|(2)<br>Service|(3)<br>Sales|(4)<br>Agriculture|Obs.|
|---|---|---|---|---|---|
|active (5 km)|0.038|0.023|-0.017|-0.008||
||(0.025)|(0.016)|(0.017)|(0.033)||
|inactive (5 km)|0.214***|-0.022|0.034|0.133***||
||(0.043)|(0.014)|(0.032)|(0.023)||
|p value: active-inactive=0|0.000|0.030|0.157|0.001|518,705|
|active (10 km)|0.020|0.028***|0.010|-0.049***||
||(0.017)|(0.011)|(0.013)|(0.019)||
|inactive (10 km)|0.154***|-0.012|0.015|0.113***||
||(0.033)|(0.011)|(0.025)|(0.021)||
|p value: active-inactive=0|0.000|0.007|0.845|0.000|515,839|
|active (15 km)|0.018|0.024***|-0.000|-0.024*||
||(0.012)|(0.007)|(0.009)|(0.015)||
|inactive (15 km)|0.136***|-0.001|-0.002|0.111***||
||(0.024)|(0.009)|(0.019)|(0.023)||
|p value: active-inactive=0|8.92e-06|0.0304|0.937|8.13e-07|514,396|
|active (25 km)|0.023***|0.018***|0.003|0.001||
||(0.009)|(0.005)|(0.007)|(0.011)||
|inactive (25 km)|0.064***|0.000|-0.014|0.045*||
||(0.017)|(0.004)|(0.014)|(0.023)||
|p value: active-inactive=0|0.035|0.007|0.285|0.082|509,875|
|active (50 km)|0.039***|0.004|-0.013**|0.043***||
||(0.008)|(0.003)|(0.006)|(0.010)||
|inactive (50 km)|0.024*|0.000|-0.012|0.025||
||(0.012)|(0.002)|(0.012)|(0.018)||
|p value: active-inactive=0|0.293|0.304|0.969|0.382|493,501|

Robust standard errors clustered at the DHS cluster level in parentheses. All regressions control for year and region fixed effects, regional time trends, living in an urban area, age, years of education, and religious beliefs. Please see Table 3 for more information about coefficients of interest.*** p<0.01, ** p<0.05, * p<0.1 33