rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 9: Performance Ratings of National Government and Changes over Time**

<!-- Start of picture text --> Overall Ratings of the Performance of National Government<br>Uzbekistan<br>Tajikistan<br>Sweden<br>Azerbaijan<br>Georgia<br>Turkey<br>Belarus<br>Montenegro<br>Kazakhstan<br>Hungary<br>Albania<br>Estonia<br>Mongolia<br>Bulgaria<br>Moldova<br>Great Britain<br>France<br>Macedonia<br>Slovenia<br>Kyrgyzstan<br>Russia<br>Armenia<br>Germany<br>Ukraine<br>Czech Republic<br>Poland<br>Bosnia-Herzegovina<br>Serbia<br>Italy<br>Latvia<br>Slovakia<br>Lithuania<br>Croatia<br>Romania<br>0 20 40 60 80 100<br>Percent of respondents<br>Very Good Good Neither Bad Very Bad<br><!-- End of picture text --> <!-- Start of picture text --> Perceptions of change in performance of national government<br>Georgia<br>Uzbekistan<br>Azerbaijan<br>Tajikistan<br>Turkey<br>Bulgaria<br>Armenia MontenegroHungary Sweden MacedoniaKazakhstanMongolia Belarus<br>Albania<br>Estonia Moldova Poland Kosovo<br>Slovenia<br>Great Britain<br>Ukraine Czech RepublicSlovakiaSerbiaKyrgyzstan<br>Russia<br>Latvia Bosnia-Herzegovina<br>Lithuania CroatiaItaly GermanyFrance<br>Romania<br>-10 -5 0 5 10<br>GDP growth 2009-2010<br>Source: LITS 2006 and 2010, IMF. 2010 growth numbers are estimates (IMF WEO October 2010 Update).<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> Source: LiTS2 (2010).

12
**Table 2. The Product Composition of**<sup>**Middle**East</sup><sup>**Countries' Global Exports.**</sup>

|||||Product|Group as a|Percentage of Total|Exports|||
|---|---|---|---|---|---|---|---|---|---|
||||||||Sele|ct Commodity G|roups|
|Exporting Country (Year)|Total<br>Exports<br>($ million)|All<br>Foods|Agricultural<br>Materials|Fuels|Ores<br>and<br>Metals|All<br>Manufactures|Textiles<br>and<br>Clothing|Chemicals|Transport<br>and<br>Machinery|
|Bahrain (90)|3,415.2|0.6|--|76.9|11.0|11.4|--<br>|_0.5_<br>|2.0<br>|
|Cyprus (89)|793.0|37.7|0.9|1.8|0.7<br>|59.0<br>|29.5<br>|7.6<br>|4.7<br>|
|Egypt (90)|2,582.0|9.3|6.2|46.9|4.0|32.4<br>|35.4|3.3|3.3<br>|
|Iran,Islamic Rep. (90)|14,409.4|2.5|0.9|92.5|0.3|3.7|-|--|0.1<br>|
|Israel (91)|11,8)0.8|7.6|2.6|0.6|1.6|87.5<br>|7.6<br>|14.0<br>|27.3<br>|
|Jordan (91)|878.9|16.0|0.5|--|37.9|45.7|3.7|29.6|1.3<br>|
|Kuwait (89)|11,476.5|1.1|0.4|84.1|0.5|13.7<br>|0.7|1.0<br>|2.4<br>|
|Lebanon (89)|410.0|3.0|27.5|0.2|55.8|13.0|--|0.5<br>|0.4|
|Libya (87)|8,502.7|--|--|97.8|--|2.1<br>|--<br>|1.9<br>|--<br>|
|Oman (89)|3,932.9|2.2||88.9|1.4|5.7|0.3|0.2<br>|5.4<br>|
|Qatar (89)|2,609.7|--|--|70.0|--<br>|17.4<br>|--|13.0<br>|0.4<br>|
|Saudi Arabia (90)|44.062.0|0.5|0.3|89.7|0.6|8.1||4.4<br>|1.7<br>|
|Syria (90)|4,061.6|11.6|3.8|40.5<br>|1.4<br>|42.6<br>|--<br>|10.8<br>|0.2<br>|
|Turkey (90)|12,959.3|22.4|3.0|2.3|4.3|67.9|39.0|5.9<br>|8.2<br>|
|UAE (88)|11,873.0|1.9|0.1|84.5|1.4|11.5|--|0.7|4.3|
|ALL MIDDLE EAST|133,857.0|4.5|1.1|68.7|1.8|23.1|5.4|4.5|4.7|
|**MEMO ITEM:**||||||||||
|ALL DEVELOPING||||||||||
|COUNTRIES|708,947.0|11.6|_3.1_|26.1|4.2|54.0|13.1|4.2|19.8|

Source: United Nations Series D Trade Tapes.<sup>Import statistics as reported (c.i.f.) by the OECD countries. Product groups are defined as follows: All foods and feeds (SITC</sup> 0+ 1 +22+4); Agricultural materials (2-22-27-28);<sup>mineral fuels (3); ores minerals and nonferrous metals (27 +28+68); all manufactures (5 to 8 less 68): yarns, textiles and</sup> clothing (26+65+84); chemicals (5); transport<sup>and machinety (7).</sup> ON
While this paper highlights the e¤ects of payment system reform for our sample countries, structural changes to the banking system also occurred along other dimensions. Figure 2 provides evidence that privatization resulted in a reduction of government ownership of banking assets in many of the sample countries. Moreover, it shows that the speed of privatization varies across countries. While the government no longer owned a sizable fraction of banking system assets at or soon after the start of the sample period in Estonia, Latvia and Hungary, a gradual process of privatization was still ongoing in some other countries during our sample period. Figure 2 also documents a rising proportion of banking system assets that are foreign-owned. Finally, countries have undertaken major reforms to the regulatory and supervisory framework that banks are subject to. Figure 3 charts the path of a banking system reform index constructed by the European Bank for Reconstruction and Development (EBRD). A higher banking reform index indicates that banking laws, regulations and prudential supervision are closer to the Bank of International Settlements (BIS) standards.<sup>8</sup> In addition to the paths for the share of government ownership, the share of foreign ownership and banking system reform, Figures 2 and 3 contain a line corresponding to the date of introduction of a modern interbank payment system. While in principle, other aspects of structural reforms may represent confounding in‡uences for our analysis, Figures 2 and 3 suggest that payment system reforms are not contemporaneous with other structural changes, but that instead there is independent variation in the timing of payment system reforms – potentially re‡ecting di¤erences in central bank preferences and resources dedicated to payment system reforms – which can be exploited in the regression analysis.

> 8The index takes the following values:

1 Little progress beyond establishment of a two-tier system.

2 Signi...cant liberalisation of interest rates and credit allocation; limited use of directed credit or interest rate ceilings.

3 Substantial progress in establishment of bank solvency and of a framework for prudential supervision and regulation; full interest rate liberalisation with little preferential access to cheap re...nancing; signi...cant lending to private enterprises and signi...cant presence of private banks.

4 Signi...cant movement of banking laws and regulations towards BIS standards; well-functioning banking competition and e¤ective prudential supervision; and signi...cant term lending to private enterprises; substantial ...nancial deepening.

4+ Standards and performance norms of advanced industrial economies: full convergence of banking laws and regulations with BIS standards; provision of a full set of competitive banking services.

7
them to subscribe to a text-message service summarizing the latest government directives and recommendations.

# **3 Results**

We estimate the average impact of the information treatments on respondents’ support for government policy, perceptions of state capacity and trust in the state using the following specification: where _Yi_ is an outcome variable, _Si_ , _Ci_ , and _Ri_ are dummy variables indicating whether respondent _i_ was assigned to one of the three treatment groups (past state effectiveness, citizen cooperation, religious authority) or the control group, _δc_ ( _i_ ) are city fixed effects, _θj_ ( _i_ ) are enumerator fixed effects, and _ωt_ ( _i_ ) is a dummy variable indicating the period after the change in the wording of the public service announcement. _Xi_ is a measure of self-reported, past behavior, an additional control variable that we include only when estimating treatment effects on attitudes towards the government’s Covid-19 related directives on hand-washing, social distancing and forgoing congregational prayers.<sup>13</sup> Standard errors are adjusted to account for heteroscedasticity. The coefficients of interest, _β_ 1, _β_ 2, and _β_ 3, measure the average difference in outcomes between treatment and control groups.

We use a similar specification to estimate experimenter demand effects: where _Si_<sup>_′_,</sup><sup>_C_</sup> _i_<sup>_′_,and</sup><sup>_R_</sup> _i_<sup>_′_aredummyvariablesindicatingwhetherrespondent</sup><sup>_i_wasassigned</sup> to one of the three treatment groups or the experimenter demand group, and all other variables are defined as before. The coefficients _γ_ 1, _γ_ 2 and _γ_ 3 capture the extent to which respondents in each treatment group update their beliefs above and beyond respondents in the experimenter demand group, who received a clearly worded favorable opinion of the government. We use these estimates to net-out any potential experimenter demand effects from estimated treatment 13The empirical specifications were pre-specified in the AEA RCT registry (RCT-ID: AEARCTR-0005744).

11
Table C5: Heterogeneous Impacts of GDP Decline in Firms’ Destinations by Worker Characteristics

||Average months worked|Average real wage|
|---|---|---|
||Test eq. of coef.|Test eq. of coef.|
||Coefcient<br>t-test<br>p-value<br>(1)<br>(2)|Coefcient<br>t-test<br>p-value<br>(3)<br>(4)|
|GFC frm shock*Low previous formal sector attach.|-0.0270***<br>(0.0097)|-0.0146***<br>(0.0051)|
|GFC frm shock*High previous formal sector attach.|-0.0377***<br>-1.0231<br>(0.0082)<br>(0.3063)|-0.0082***<br>1.2577<br>(0.0017)<br>(0.2085)|
|Observations|3,046,074|3,030,524|
|R-squared|0.8541|0.5698|
|GFC frm shock*AKM below median|-0.0444***<br>(0.0084)|-0.0077***<br>(0.0027)|
|GFC frm shock*AKM above median|-0.0260***<br>-2.2585<br>(0.0077)<br>(0.0239)**|-0.0078***<br>0.0105<br>(0.0029)<br>(0.9916)|
|Observations|2,298,119|2,297,958|
|R-squared|0.8729|0.7082|
|2-digit industry X Year FE|Yes|Yes|
|State X Year FE|Yes|Yes|

_Note:_ Robust standard errors clustered at the firm level in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. All specifications control for vectors of worker and firm characteristics as of 2007 described in Section III. AKM stands for worker fixed effects estimated following Abowd et al. (1999) as described in Appendix Section B. FE stands for

# **D. Additional Firm Results**

49
# **Table A6.2 The Effect of PMT on Height-for-Age of Children 12-24 Months: Using Binary Indicator for Program Exposure**

||Poole|d OLS|Com<br>Fixed|munity<br>-Effects|
|---|---|---|---|---|
|Year (2000=1)|0.001|-0.035|0.109|0.121|
||[0.231]<br>|[0.235]<br>|[0.241]<br>|[0.257]<br>|
|Program since ‘98 (Yes=1)*Year|-0.106|-0.063|-0.096|-0.089|
||[0.225]|[0.231]|[0.265]|[0.280]|
|Gender (boy=1)|-0.121|-0.124*|-0.207**|-0.202**|
||[0.074]|[0.075]|[0.088]|[0.090]|
|Age|-0.043***|-0.043***|-0.034***|-0.034***|
||[0.011]|[0.011]|[0.012]|[0.012]|
|Mother education (years)|0.008|0.008|-0.007|-0.007|
||[0.008]|[0.009]|[0.009]|[0.009]|
|Father education (years)|0.004|0.002|0.002|0.001|
||[0.009]|[0.009]|[0.010]|[0.010]|
|Mother height (cm)|0.025***|0.024***|0.029***|0.030***|
||[0.007]|[0.007]|[0.008]|[0.008]|
|Father height (cm)|0.032***|0.032***|0.038***|0.038***|
||[0.008]|[0.008]|[0.009]|[0.009]|
|Head of HH (male=1)|0.009|0.021|-0.134|-0.141|
||[0.123]|[0.123]|[0.135]|[0.138]|
|Number of children 5-14 yo in HH|-0.003|0.001|0.022|0.021|
||[0.037]|[0.038]|[0.044]|[0.045]|
|HH owns private toilet (Yes=1)|0.137*|0.134*|0.147|0.143|
||[0.080]|[0.081]|[0.104]|[0.105]|
|HH has sanitation (Yes=1)|0.220**|0.188*|0.108|0.081|
||[0.097]|[0.113]|[0.162]|[0.172]|
|Type of community (urban=1), C|-|0.149||0.174|
|||[0.104]||[0.211]|
|Distance to district capital (km), C|-|-0.001||0.000|
|||[0.002]||[0.003]|
|Distance to bus station (km), C|-|<br>0.009||<br>0.015|
|||[0.006]||[0.012]|
|Land w/ technical irrigation (%), C|-|0.366*||-0.321|
|||[0.211]||[0.425]|
|Comm. has asphalt road (Yes=1), C|-|-0.110||-0.001|
|||[0.103]||[0.187]|
|C h Y=1 C||0096||0092|
|omm. as sewerage system (es),|-|-.||-.|
|||[0.086]||[0.167]|
|Comm. has piped water (Yes=1), C|-|<br>0.070||<br>0.004|
|||[0.094]||[0.161]|
|Constant|-10.318***|-10.164***|-11.826***|-11.921***|
||[1.607]|[1.613]|[1.842]|[1.875]|
|R-squared|0.08|0.09|0.40|0.40|

Notes: n is 1042. (C) indicates community-level variables. Robust standard errors are in parentheses. (*), (**) and (***) indicate significance respectively at 10%, 5%; and 1%.

38
# **Figure 7. EAP: Infrastructure indicators**

_Despite significant progress, providing adequate transport networks, power, water and other facilities remains a challenge across much of the region. EAP economies are confronted by environmental problems that threaten to undermine future growth and regional stability._

**A. Ranking of overall infrastructure**

<!-- Start of picture text --> Ranking<br>2011-12 2016-17<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>C. Population living in slums<br>Percent of urban population<br>60 Latest EAP LAC SAS<br>50<br>40<br>30<br>20<br>10<br>0<br>E. Air pollution, mean annual exposure:<br>regions<br>Micrograms of PM2.5 per cubic meter<br>50 Latest World<br>40<br>30<br>20<br>10<br>0<br>Malaysia China Thailand Indonesia Lao, PDR Vietnam Cambodia Mongolia Philippines<br>Cambodia Mongolia Myanmar Philippines Vietnam China Thailand Indonesia<br>SAS EAP MENA SSA ECA LAC<br><!-- End of picture text -->

# **B. Environmental performance**

<!-- Start of picture text --> Index 2010 2014<br>60<br>40<br>20<br>0<br>D. Quality of port infrastructure<br>Score<br>7 2007 2016-17 OECD U.S.<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>F. Air pollution, mean annual exposure:<br>countries<br>Micrograms of PM2.5 per cubic meter<br>60<br>1990 Latest OECD U.S.<br>50<br>40<br>30<br>20<br>10<br>0<br>Malaysia Thailand Vietnam Indonesia Philippines China Lao, PDR Cambodia Myanmar<br>Malaysia China Thailand Indonesia Cambodia Vietnam Philippines Lao, PDR<br>China Myanmar Vietnam Thailand Cambodia Indonesia Malaysia Philippines Mongolia Fiji<br><!-- End of picture text --> Sources: Environmental Performance Index; World Economic Forum; World Development Indicators, World Bank. A. Ranking of 140 countries according to the quality of their infrastructure. 1= best, 140 = worst.

B. The Environmental Performance Index (EPI) is constructed through the calculation and aggregation of 20 indicators reflecting national-level environmental data, including child mortality, wastewater treatment, access to drinking water, access to sanitation, and air pollution average exposure to PM2.5. These indicators use a “proximity-to-target” methodology, which assesses how close a particular country is to an identified policy target. Scores are then converted to a scale of 0 to 100, with 0 being the farthest from the target (worst observed value) and 100 being closest to the target (best observed value).

C. Latest data are for 2014.

D. 1= extremely underdeveloped; 7= well developed and efficient by international standards.

E.F. This measures the average level of exposure of a nation's population to concentrations of suspended particles measuring less than 2.5 microns in aerodynamic diameter, which are capable of penetrating deep into the respiratory tract and causing severe health damage. Exposure is calculated by weighting mean annual concentrations of PM2.5 by population in both urban and rural areas. Latest data are as of 2013.

E. EAP is East Asia and Pacific; ECA is Europe and Central Asia, LAC is Latin America and Caribbean; MENA is Middle East and North Africa; SAS is South Asia; SSA is Sub-Saharan Africa.

15
which is the reference category. The coefficients of the three subsidy variables are negative and statistically significant at the 1% level, showing that adoption is lower when subsidy decreases or price increases. The magnitude of the coefficients shows that the effect is stronger at higher prices or lower subsidy levels.

**Table 5.** OLS Regression of the Effect of Information and a continuous Price on Adoption of Solar Lantern

||Without c<br>|ontrol|With co<br>|ntrols|
|---|---|---|---|---|
|Variables|Coef.|se|Coef.|se|
|Randomly drawn price|-0.004***|0.000|-0.004***|0.000|
|Information treatment|-0.005|0.037|-0.008|0.039|
|Information treatment X Randomly drawn price|0.000|0.000|0.000|0.000|
|Gender of household head (1=Male, 0=Female)|||0.112|0.095|
|Marital status (1 if married, 0 otherwise)|||-0.111|0.087|
|Education of the household head in years|||0.005|0.005|
|Age of household head in years|||-0.001|0.001|
|Household size|||-0.003|0.007|
|Value of total assets in Birr|||0.000|0.000|
|Number of rooms in the house|||0.013|0.019|
|Household connected to grid (1 if yes, 0 otherwise)|||-0.246***|0.068|
|Household uses solar light (1 if yes, 0 otherwise)|||0.016|0.041|
|Household uses kerosene for lighting (1 if yes, 0 otherwise)|||0.076**|0.033|
|Household uses fuel wood for lighting (1 if yes, 0 otherwise)|||0.063|0.063|
|Access to credit (1 if access to 400 Birr or more, 0 otherwise)|||0.030|0.033|
|Distance to market|||0.004*|0.002|
|Distance to nearest road|||0.000|0.000|
|Distance to nearest town|||-0.000|0.001|
|Village has access to grid (1 if yes, 0 otherwise)|||0.072*|0.039|
|Constant|1.109***|0.026|1.010***|0.100|
|Observations|808||806||
|R-squared|0.399||0.423||

Note: *** p<0.01, ** p<0.05, * p<0.1; Adoption =1 if household purchased at randomly drawn price and =0 otherwise 25
Pag 45 investors had in the performance of the license. Assuming that TO.' disburses 4% of its real equity in the form of dividends, then a discount rate of 20% ir4plies a stock price equal to US$.187 In March 198B8. Now, _in_ 1988 the average prime lending rate was 23%, while the rate of devaluation for the year was zero implying a real rate of interest (in US$) of 23% minus the US inflation rate (about 4%). Thus, a real discount rate of 19% for the private sector was quite reasonable for Jamaica in 1988. A real discount rates of 19% would imply a value for TOJ shares as of March 1988 of US$.212, a bit higher than the price of the public sale, but exacty equal to the price paid by C&W In the two latest acquisitions." The similarity between the market valuation of TOJ and an estimate of the value of TOJ's stock on the assumption that the license holds for its complete period, provides support to the hypotheses that the 1987 regulatory regime was seen as credible by investors.

80. Impact on performance. The movement towards the creation of TOJ and the introduction of the 1988 licenses has implied large changes in the way the sector operates. First, Figure 4 shows that since the negotatons for the creation of TOJ were started in 1985, the real price of international calls has ceased to decline, and has remained more or less constant Second, the profitability of the companies has been systematically high (see Figures 11-13), but well within the license-prescribed range. lThis high level of profitability has allowed the companies to increase their level of investment. The increase in the number of main lines has been quite rapid, as has been the increase in the value of the network's fixed assets. See Figures 1 and 5 to 10. As can be seen in Figure 9, furthermore, the increase in profitability has allowed JTC to finance a large part of its investments through long tenn debt.

XThis number is gneraeod as follows: Take K, to be the value (in USS) of TOJ equity per share as of Mardc 1991. The price of a **_share_** in TOJ is given by: P a ZZ65,d/(1 )+14.C(1 .6), **whore** 6 is the discoum rate. K, Is the value of equity as ot peiod t. , K,,'(l+.175)-d, where d, reprosenrt th dividend distribution in period **t,** with dA=K,,04.

**n** Slghy lower raes of discount Imply substaWially higher prices. For example. a rate of discunt ot 15% would Imply a 3188 pran of USS37, almos _100%_ above the public sale price
<!-- Start of picture text --> Water: Hidden Costs - Total<br>Percent GDP<br>5.0<br>4.5<br>4.0<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>2000 2001 2002 2003<br>Average 2000 Average 2001 Average 2002 Average 2003<br>ArmeniaAzerbaijan Belarus BulgariaCzech Republic Estonia GeorgiaKazakhstanKyrgyz Republic Latvia Moldova Poland Romania Russia Tajikistan Ukraine<br><!-- End of picture text -->

# **Going Forward**

This exercise demonstrated that estimating the order of magnitude of hidden costs is worth pursuing. In the energy and water sector, the intention is to continue to update this dataset as information becomes available through external sources for additional countries for 2004 and onward. Future updates will be available through the website _http://ecadata-worldbank.org_ .

10
year that damages will be zero and a 97% probability that damages will be below 10% of GDP. The isolated point at the top indicates that there is a 0.0003% probability that damages will be 91% of GDP or higher. The hurricane damages used in the simulations were drawn from the grey fitted line, because a functional relationship is much more computationally efficient than drawing damage events from the discrete data.

<!-- Start of picture text -->
1.00<br>0.90<br>0.80<br>0.70<br>0.60<br>0.50<br>0.40<br>0.30<br>0.20<br>0.10<br>0.00<br>0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00<br>Probability range<br><!-- End of picture text -->

_Figure 6: Probability of hurricane damage in Jamaica: actual data (blue dots) and fitted equation (grey line). Source: (World Bank, 2018)._ Figure 7 presents the outcomes of these stochastic simulations under probabilistic hurricane shocks with insurance as the disaster risk financing strategy. The dark blue line represents the median result, and the 25<sup>th</sup> to 75<sup>th</sup> percentiles boundaries are the edge of the dark blue area, while the 10<sup>th</sup> to 90<sup>th</sup> percentiles boundaries are shown by the edge of the light blue area.

As with the deterministic scenarios, Figure 7 illustrates that GDP, potential GDP and consumption would be lower under a scenario with hurricanes than under a scenario without. While individual hurricane shocks are softened by insurance payouts, the opportunity cost of foregone productive investment required to pay insurance premia reduces potential output and, with it, GDP and consumption. Importantly, the GDP impacts are larger than the consumption impacts, reflecting the use of lower productive investment to pay for hurricane financing. Note also that GDP impacts are skewed to the high side, while consumption effects are skewed to the downside. In both cases the bulk of scenario outturns fall within a relatively narrow range.

21
# **What Makes Cities More Competitive? Spatial Determinants of Entrepreneurship in India** Ejaz Ghani, William R. Kerr and Stephen O‘Connell

**Keywords:** Entrepreneurship, agglomeration, structural transformation, employment, urbanization, informal sectors, economic growth, edcuation, infrastructure, manufacturign policy, India, South Asia.

**JEL Classification:** L10, L26, L60, L80, M13, O10, R00, R10, R12

Sector Board: Economic Policy, Urban, Education, Social Development _Author institutions and contact details:_ <u>Ghani: World Bank, Eghani@worldbank.org; Kerr: Harvard University, Bank of Finland, and NBER, wkerr@hbs.edu</u> (corresponding author); <u>O‘Connell: World Bank and CUNY Graduate Center, soconnell@gc.cuny.edu.</u> _Acknowledgments_ : We thank Ahmad Ahsan, Sarah Elizabeth Antos, Mehtabul Azam, Rajeev Dehejia, Gilles Duranton, Lakshmi Iyer, Henry Jewell, Shanthi Nataraj, Parth Tewari, and Hyoung Gun Wang for helpful comments. We thank the World Bank's South Asia Labor Flagship team for providing the primary datasets used in this paper. Funding for this project was provided by World Bank and Multi-Donor Trade Trust Fund.