rafmacalaba/datause-encoder-data huggingface dataset:
three indicators of financial development show coefficients that are significant at the ten percent level. The coefficients, however, are of smaller size than in the OLS regressions."3 The growth differentials of the TSLS regressions are also more in line with the results obtained by RZ. These results therefore indicate that better-developed financial intermediaries and markets ameliorate market frictions and thereby promote the growth of industries that rely more heavily on external finance.

# **_4.2_** _Financial Structure and Industry Growth_

Thus far, the results confirm those in RZ. Industries that depend heavily on external finance grow faster in economies with higher levels of financial development. We will now turn to the question of whether a specific structure of the financial system - bank-based or marketbased -- enhances growth of these industries.

The results in Table 5 indicate that the financial structure does not have an independent impact on industrial growth patterns across countries. Although the interaction terms with all measures of financial structure show coefficients that are significant at the five- percent level in the OLS regressions, these results are not confirmed by the instrumental variable regressions.

The results in Table 6 support the financial services view and reject the bank-based and market-based views. Specifically, we find that when controlling for the level of financial development, the interaction of external dependence and financial structure does not have a significant impact on industrial growth patterns across countries.'<sup>4</sup> Whereas the interaction terms with all indicators of financial development are significant at the five- percent level, none of the interaction terms with our financial structure measure is significant. These results indicate

> 13 Since _Finance-Dummy is a_ binary variable and because of the findings reported below, we do not attempt to instrument for _Finance-Dummy using_ a probit regression in the first stage.

> 14 In the following we will only present the TSLS results. The OLS regressions yield similar results.

20
# **3.1 Historical Predictors of Rural Non-Farm Employment**

We begin by estimating the following specification to estimate the role of individual characteristics on rural non-farm employment: The unit of observation in equation(1) is the individual _i_ , located in district _d_ , and observed in year _t_ . We aggregate the NSS data across the four rounds in the spirit of a repeated cross-section and restrict the sample to rural individuals aged between 15 and 65. **X** contains a host of individual characteristics such as a quadratic in age, dummies for literacy, secondary education, gender, religion and caste.<sup>2</sup> **Z** contains time-varying district-level predictors of interest such as the level of urbanization, fraction of manufacturing workers, contemporaneous and lagged rainfall shocks, and banking infrastructure.<sup>3</sup> _Y_ denotes our outcome of interest. We consider two categories of outcomes: the first – is broad employment type namely dummies indicating whether the individual is a participant in the labor force (LFP), and conditional on participation, whether the individual is self-employed, or works as a casual or salaried worker. We also use an aggregate indicator of any non-farm employment. In addition to these broad employment categories, we also consider dummies indicating sectoral employment in the farm, manufacturing, trade, construction and service sectors. As all our outcomes of interest in this section are binary, we estimate linear probability models, with the _β_ and _γ_ coefficients capturing the change – in the likelihood of observing a “success” namely, whether the individual is employed in the non-farm employment categories of interest. All specifications include district ( _α_ ) and survey round ( _δ_ ) fixed effects. The former flexibly captures time-invariant district-specific unobservable factors predicting non-farm employment such as soil fertility, societal attitudes or institutional quality in the district. The latter captures time-varying factors which equally affect all individuals in a year such as global demand for farm products or aggregate GDP

> 2 The full set of individual controls are: a quadratic in age; household size; a dummy for whether the individual belongs to the historically marginalized Scheduled Caste (SC) or Scheduled Tribe (ST) communities; dummies indicating if the individual is Muslim, Christian or Sikh; a dummy for whether the individual belongs to a marginal landowning household, with land owned under 0.5 acres; a dummy for whether the individual belongs to a female-headed household; a dummy for whether the individual is literate; and a dummy for whether the individual has completed secondary or higher education.

> 3 The full set of time-varying district covariates are: fraction of rural population; fraction of historically marginalized communities (SCs and STs); fraction of Muslims; fraction of salaried workers; fraction of public employment; fraction of manufacturing workers; fraction of literates; fraction of secondary educated individuals; logged bank branches per capita; logged bank credit per capita; and contemporaneous and 2 lags of rainfall shocks.

11
Table 4: Counterfactual 1): 10% decrease in border trade costs

|(10 x change)|Welfare|1<br>_ξ_ <sup>log</sup><sup>_πF_</sup><br>_ii_|DX|D|D*|N|N*|M share|VAX|
|---|---|---|---|---|---|---|---|---|---|
|USA|0.094|0.070|-0.517|-0.083|0.057|-0.051|0.036|-0.121|-0.073|
|SGP|0.464|0.426|-0.598|-0.335|-0.029|-0.160|-0.005|-0.080|-0.076|
|JPN|0.139|0.094|-0.546|-0.086|0.052|-0.022|0.048|-0.116|-0.074|
|TWN|0.381|0.307|-0.624|-0.083|0.001|-0.007|-0.020|-0.092|-0.075|
|KOR|0.313|0.236|-0.627|-0.027|0.032|-0.014|-0.001|-0.108|-0.072|
|MYS|0.427|0.373|-0.553|-0.161|-0.013|-0.065|-0.016|-0.092|-0.076|
|THA|0.411|0.343|-0.508|-0.132|-0.008|-0.065|-0.014|-0.098|-0.078|
|IDN|0.407|0.298|-0.458|-0.038|0.025|-0.050|0.006|-0.136|-0.056|
|CHN|0.188|0.110|-0.475|-0.059|0.024|0.041|0.050|-0.087|-0.077|
|PHL|0.455|0.405|-0.485|-0.204|-0.023|-0.101|-0.003|-0.109|-0.074|

# **5.4.2 Increasing productivity in China**

<u>1</u> Table 5 reports results from a 10% shock to productivity in China, _TCHNξ_<sup>.Thisisauniform</sup> shock that improves productivity at all points in the chain. Our interest is in seeing how such shocks affect China’s relative position in chains, and the degree to which such shocks spill over into other countries.

Table 5: Counterfactual 2): 10% increase in productivity in China

|(10 x change)|Welfare|1<br>_ξ_ <sup>log</sup><sup>_πF_</sup><br>_ii_|DX|D|D*|N|N*|M share|VAX|
|---|---|---|---|---|---|---|---|---|---|
|**CHN**|0.968|-0.047|-0.289|-0.190|-0.052|-0.112|-0.009|-0.047|-0.002|
|USA|0.014|0.011|0.188|0.052|0.028|0.012|0.002|0.046|0.003|
|SGP|0.036|0.040|0.191|0.154|0.029|0.079|-0.012|0.026|0.005|
|JPN|0.015|0.012|0.164|0.066|0.027|0.019|-0.001|0.038|0.004|
|TWN|0.030|0.029|0.174|0.146|0.029|0.065|-0.012|0.027|0.005|
|KOR|0.025|0.023|0.173|0.135|0.033|0.049|-0.010|0.031|0.005|
|MYS|0.035|0.037|0.148|0.143|0.026|0.051|-0.013|0.022|0.005|
|THA|0.032|0.034|0.140|0.135|0.030|0.041|-0.013|0.026|0.004|
|IDN|0.040|0.034|0.176|0.188|0.049|0.039|-0.010|0.034|0.005|
|PHL|0.038|0.039|0.145|0.149|0.031|0.039|-0.012|0.024|0.005|

The welfare changes reported in column 2 show that the vast majority of the welfare gains accrue to China, which sees a nearly 10% increase in welfare from a 10% productivity shock. The gains elsewhere are limited, and reasonably similar across countries. The most notable changes have to do with changes in relative position in supply chains. Changes in China’s _DX_ , _D_ and _D_<sup>_∗_</sup> indices indicate that the technology shock moves Chinese production significantly 48
# List of Tables

|Table 1.<br>Table 2.<br>Table 3.<br>Table 4.<br>Table 5.<br>Table 6.<br>Table 7.<br>Table 8.<br>Table 9.<br>Table 10.<br>|Classification of causes of death and corresponding International<br>Classification of Diseases, Injuries, and Causes of Death (ICD)<br>categories ...........................................<br>Estimated and projected mortality rates (per 100,000), by major<br>cause, sex, and region, 1970-2015.....................<br>.<br>Deaths by major and specific cause (in thousands), by region,<br>1985 ..............................................<br>Age-standardized mortality rates (per 100,000) for major and<br>specific causes, by region, 1985.......................<br>Comparison of regression estimates of mortality rates and deaths<br>from particular causes and expert assessments, all developing<br>countries, 1985.................. .............<br>Ratio of deaths from circulatory system diseases to deaths from<br>infectious and parasitic diseases, by region, 1970-2015.... ..<br>Percentage distribution of deaths by major cause in 1985 and<br>incremental changes expected by 2000 and 2015 from three<br>factors, world and developed and developing countries ...<br>.....<br>Percentage distribution by major cause of all deaths, deaths under<br>65, and potential years of life lost, world and developed and<br>developing countries, 1970-2015.............<br>Percentage of regional population covered by reports to WHO or<br>sample surveys, by year ................................<br>Predicted and reported percentage distributions of deaths by<br>cause for reporting countries grouped by region, 1985.... ..<br>|2<br>...<br>7<br>10<br>....<br>11<br>.......<br>12<br>.....<br>15<br>..<br>16<br>19<br>.....<br>20<br>|
|---|---|---|
|Table Al.<br>Table A2.<br>Table A3.<br>TableB1<br>Table B2.<br>Table B3.<br>Table B4.<br>Table B5.<br>Table B6.|Regional grouping of countries and territories .................<br>Countries or territories with reliable reports on causes of death,<br>and year of data used, by region ..........<br>..<br>..............<br>Countries or territories with less reliable reports on causes of<br>death, year of data used, and indices of coverage and data<br>quality, by region .................<br>.PopulationiLmillions by age and sex, major world regions, 1970-<br>2015 ......................<br>..................<br>Estimates and projections of fertility, mortality, and population<br>growth for major regions in selected quwnquennia .....<br>.........<br>Mortality rates per 100,000 from major causes, by age, sex, and<br>region, 1970-2015................<br>............<br>Mortality rates per 100,000 from major causes, by age, sex, and<br>region, 1970-2015................................<br>Mortality rates per 100,000 from specific causes by age, sex, and<br>region,1985<br>.................**.**...............<br>Deaths in thousands from specific causes by age, sex, and region,<br>1985.........................................|22<br>24<br>25<br>......<br>29<br> <br>31<br>.32<br>....<br>44<br>......<br>56<br>.....<br>62|

ii
Figure E.6 <!-- Start of picture text --> Net Exports Per Worker Net Exports Per Worker<br>China - 1965 China - 1988<br>08 -- CAP U 30 ~~~~~~~~~~~~~~~~~~~~~LAB<br>084| * LAB<br>2PETRO A FOR TRC | CER MACH C ET 1 PROP<br>MAT~~~~~~~~~~~~~~~~~A CTRM PANL p<br>0. - - I MAT ~~~~ ~~~~~~~~~~FOR AN CAP MACH CHEM<br>0<br>6 4 -10<br>-06<br>0 8 -20<br>Net Exports Per Worker Net Exports Per Worker<br>USA- 1965 USA- 1988<br>MACH *o0<br>flD0 C _ER<br>200<br>CHEM<br>* *3 ! | ^HEM XCER 2 ETRO MAT FOR TRFOP ANL X CAP MAC<br>CER ~ ~~~CE<br>PETRO MAT -20 ~A FOR * TROP ANL LAM3 P I 2-0Q P * CAP I M<br>ZC2-600<br>41, ~~~~~~~~~~~~~~~~~~~~~~~600<br><!-- End of picture text -->
49

# **TABLE 3 PARTICIPATION IN EXPORT MARKETS BY SURVEYED JEPARA FIRMS**

|Annual Value<br>of<br>|Expo<br>|rt as Percen<br>|tage of Firm<br> [|Sales<br>|Total<br>Number of<br>|
|---|---|---|---|---|---|
|Export Sales|<50%|50-74%|**799%**||**00%**|Firms|
|US $1 million or more|-|2|2|**3**|**7**|
|US $600,000-999,999||-|3||**3**|
|US $250,000-599,999|||-<br>-|||
|US $100,000-249,999|-|-|3|1|4|
|**.US**Less than $100,000|5|3|**1**|1|10|
|Total Numberof Firms|**5**|5|9||24|
ure 2). For instance, the trade-weighted average of Column 2 tariffs on steel products is about 23.7 percent, very much in line with the newly introduced steel tariffs of 25 percent. A potential 25 percent tariff on autos and auto-parts would be commensurate with Column 2 average tariffs at 32.6 percent and much higher than the current average MFN rate of 4.1 percent. Withdrawing PNTR status from China or any other member of the WTO would violate U.S. commitments. However, a potential U.S. withdrawal from the multilateral trading system and the WTO could be accompanied by a shift of all U.S. tariffs to Column 2 rates.

In this paper, the baseline (business-as-usual) scenario is defined as one in which pre-2018 U.S. tariff rates are maintained. The impact of all counterfactual scenarios is reported relative to the baseline scenario. All counterfactual scenarios assume an increase in U.S. tariffs to Column 2 rates and retaliation in kind by major trading partners. Counterfactual scenarios differ in terms of the response of developing countries, which are defined as follows:

- **Join the trade war** : the United States imposes non-MFN tariffs on imports from all regions; major trading partners retaliate. In this scenario, developing countries also retaliate against higher U.S. tariffs.

- **No action** : the United States imposes non-MFN tariffs on imports from all regions; major trading partners retaliate. In this scenario, there is no change in developing countries’ trade policy.

- **RTAs with non-US** : the United States imposes non-MFN tariffs on imports from all regions; major trading partners retaliate. In this scenario, developing countries pursue regional trade agreements with all non-US regions. There is no change in developing countries’ trade policy vis-a-vis imports from the United States.

- **Turn the other cheek** : the United States imposes non-MFN tariffs on imports from all regions; major trading partners retaliate. In this scenario, developing countries pursue regional trade agreements with all non-US regions and they fully liberalize imports from the United States.

Some additional considerations are noteworthy. When Smoot-Hawley tariffs were imposed in the 1930s, the United States and the other industrialized nations dominated world commerce, so that the extreme levels of protection among these countries caused serious disruptions to world trade and exacerbated the Great Depression.<sup>4</sup> Since then, the share of the United States in global goods exports has declined from about 17 percent in 1950 to 8.7

> 4Seer Irwin (2017) for an extensive discussion of trade policy during that period.

6
_International health and human rights_ 13 (1): 30.

- Santibanez, Tammy, Abigail Shefer, Elizabeth Briere, Amanda Cohn, and Amy Groom. 2012. “Effects of a nationwide Hib vaccine shortage on vaccination coverage in the United States.” _Vaccine_ 30 (5): 941–947.

- Steele, Fiona, Ian Diamond, and Sajeda Amin. 1996. “Immunization Uptake in Rural Bangladesh: a Multilevel Analysis.” _Journal of the Royal Statistical Society_ 159 (2): 289–299.

- Suk, Jonathan, Pierluigi Lopalco, and Lucia Pastore Celentano. 2015. “Hesitancy, Trust and Individualism in Vaccination Decision-Making.” _PLOS Currents Outbreaks_ , vol. 1.

- Sutherland, Kim, Sheila Leatherman, and John Christianson. 2010. _Paying the patient: does it work? A revew of patient-targeted incentives_ . London, UK: The Health Foundation.

- Thysen, Sanne, Stine Byberg, Marie Pedersen, Amabelia Rodrigues, Henrik Ravn, Cesario Martins, Christine Benn, Peter Aaby, and Ane Fisker. 2014. “BCG coverage and barriers to BCG vaccination in Guinea-Bissau: an observational study.” _BMC Public Health_ 14 (1): 2037.

- UNICEF. 2001. _Combatting Antivaccination Rumors: Lessons Learned from Case Studies in East Africa_ . New York: UNICEF.

- ~~.~~ 2010. _Maternal and Neonatal Tetanus Elimination Initiative_ . New York: UNICEF.

- Weaver, Tim, Nicola Metrebia, Jennifer Hellier, Stephen Pilling, Vikki Charles, Nicholas Little, Dilkushi Poovendran, Luke Mitcheson, Frank Ryan, Owen Bowden-Jones, John Dunn, Anthony Glaspe, Emily Finch, and John St. 2014. “Use of contingency management incentives to improve completion of hepatitis B vaccination in people undergoing treatment for heroin dependence: a cluster randomised trial.” _Lancet_ 38 (9938): 153–163.

- WHO. 2006. “Maternal immunization against tetanus.” _WHO Standards for Maternal and Neonatal Care_ , pp. 1–6.

- ~~.~~ 2013. “Weekly epidemiological record.” _WHO_ 88:477–488.

- ~~.~~ 2014. “Immunization coverage.” _WHO_ , no. Fact sheet N378.

31
important than those in the quantity of schooling. In the absence of any variable capturing the quality of schooling in the earnings function one was left with a nonsignificant coefficient for the education variable.

# 5.2 Quality of Education

It has been known for sometime from detailed analysis done in Latin America that when school quality varies widely across time and space, years of schooling may be a very imperfect indicator of human capital attained (Behrman and Birdsall; Harbison and Hamushek). The general model which emerges from these discussions is that the impact of education on earnings is best captured in a two-equation system. The first equation traces the deterninants of earnings to human capital attainment, not years of schooling. The second equation attempts to estimate the level of human capital attainment as a function of both quantity and quality of schooling. Algebraically the two equations are as follows:

- _(1) In W = g(Hf E, A)_ where W is wage, H a measure of human capital attained, E is experience, and A individual ability unrelated to H;

(2) _H = h (S,_ Q, _A, B)_ where S is years of schooling (quantity), Q is quality of schooling, A is individual ability, and B is family background.

In this model the impact of schooling on earnings is spelled out as working indirectly through an enhancement of human capital attained, along with other factors. The total effect of schooling years on earnings can be determined by combining the partial derivatives from the two equations: _(3) 8 In W/8dS = a In W/aH x aff/aS_ To implement this model empirically, we need a measure of H, human capital attained. Glewwe (1996) in the work on the 1988-89 survey uses scores of 'cognitive skills' available in the data set. The survey data contained the results of three tests of 'cognitive skills' applied to a sub-sample of individuals in the _9-55_ age category. These covered abstract reasoning (Raven's Colored Progressive Matrices), mathematics and reading (English) comprehension. The last two are used as measures of H, and the first as a measure of A in equation (1).

Turning to equation (2) above, in the absence of direct data which might measure school quality, Glewwe tries to control for variation in school quality by specifying interaction terms between years in school and the geographic region and age variables. "The former should control for variation in school quality across regions and the latter across age cohorts" (p.278).

In his estimation of equation (1) Glewwe finds that in the private sector reading skills have a significant impact on earnings, while in the public sector the mathematics score has a strong positive effect. Turning to the results of equation (2), "years of schooling alone is insignificant, but when interacted with age, Raven's score and 32
- 20 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity

**Table 2.** Variables for describing urban form (vectors **S** , **T** and L).

|**Dimension**|**Sub-**<br>**dimension **|**Variable**|**Description**|**Data source for calculation**|
|---|---|---|---|---|
|I. Urban<br>Shape (**_S_**)|Roundness|nExchange_1996|Share of the total area of the urban<br>extent that is inside the equal-area<br>circle about its center of gravity<br>(Angel et al., 2010).|Urban extents from deblurred and corrected<br>DMSP-OLS NTL RC 1996 image|
||Smoothness<br>of perimeter|nPerimeter_1996|Ratio of the perimeter of the equal-<br>area circle and the perimeter of the<br>shape (Angel et al., 2010).|Urban extents from deblurred and corrected<br>DMSP-OLS NTL RC 1996 image|
||Fullness|Fullness_1996|Fraction of the total area of the<br>urban extent that is built-up.|Urban extents from deblurred and corrected<br>DMSP-OLS NTL RC 1996 image;<br>Built-up raster layer from GHS<br>(GHS_BUILT_LDS1990_GLOBE_R2016<br>A_54009_250_v1_0 at 250 meters of<br>|
|||||spatial resolution)|
|II. Urban<br>Structure<br>(**_T_**)|Urban<br>structure|Circuity_avg_1996|From the street network: the average<br>ratio between an edge length and the<br>straight-line distance between the<br>two nodes it links (Boeing, 2017)|OpenStreetMap street network data within<br>the urban extents from deblurred and<br>corrected DMSP-OLS NTL RC 1996<br>image.|
||Connectivity|Intersection_density<br>_1996|From the street network: the node<br>density o the set of nodes with more<br>than one street emanating from them<br>(Boeing, 2017)|OpenStreetMap street network data within<br>the urban extents from deblurred and<br>corrected DMSP-OLS NTL RC 1996<br>image.|
||Connectivity|Street_density_199<br>6|From the street network: the sum of<br>all edges in the undirected<br>representation of network graph<br>divided by the urban extent area|OpenStreetMap street network data within<br>the urban extents from deblurred and<br>corrected DMSP-OLS NTL RC 1996<br>image.|
|III. Land<br>use (_L_)|Sprawl|Sprawl_1996|Normalized difference between the<br>share of areas with population<br>density below the regional average<br>density and the share of areas with<br>population density above the<br>regional average density (Fallah et<br>al., 2011)|1990 Population count at pixel level from<br>from GHS (GHS_POP_GPW41990_G<br>LOBE_R2015A_54009_250_v1_0 at 250<br>meters of spatial resolution) within the<br>urban extents from deblurred and corrected<br>DMSP-OLS NTL RC 1996 data.|