rafmacalaba/datause-encoder-data huggingface dataset:
**District Characteristics** We use additional data to control for district heterogeneity. We use population data from the Global Human Settlement Layer (GHSL),<sup>11</sup> land categories from the European Space Agency land cover (see Defourny (2017)), distance from the coast from the Global Self-consistent, Hierarchical,<sup>12</sup> High-resolution Geography Database (GSHHG), distance to the border,<sup>13</sup> access to a city larger than 50,000 inhabitants from the Malaria Atlas Project,<sup>14</sup> temperature from Land Processes Distributed Active Archive Center,<sup>15</sup> and elevation from CGIAR-CSI.<sup>16</sup>

# **4 Empirical Strategy and Results**

## **4.1 Ordinary Least Squares**

Our first empirical strategy uses panel ordinary least squares (OLS) regressions that include year and country fixed effects and a battery of initial district-level controls. The OLS specification is _Sectori,c,t_ is the share of employment in agriculture, manufacturing or services for district _i_ in country _c_ , at year _t_ . _Paved Roadi,c,t_ is a dummy variable that takes a value of one if location _i_ in country _c_ contains a paved road at year _t_ . _Electricityi,c,t_ is a dummy variable that takes a value of one if location _i_ in country _c_ has more than 50% of its population with lights at night at year _t_ . _Interneti,c,t_ is a dummy variable that takes a value of one if location _i_ in country _c_ has a node on the internet backbone fiber network at year _t_ . _Paved Roadi,c,t_ ∗ _Electricityi,c,t_ captures the interaction of the road and electricity infrastructure, and _Paved Roadi,c,t_ ∗ _Interneti,c,t_ the interaction of the road and internet infrastructures. We add interaction effects between the dummies to better understand the complementarities between infrastructures. We do not include an interaction effect for electricity and internet as access to internet is assumed to rely on electricity access. _Controlsi,c,t_ represents the additional location-specific controls, which include initial district population, access to a main city, land size, distance to the coast, distance to the border,

> 11GHSL: Population count from the Global Human Settlement Layer. Based on population data from Gridded Population of the World v4.10 polygons, distributed across cells using the Global Human Settlement Layer global layer. Source data provided in 9 arc-second (250m) grid cells.

> 12Distance to the coast (on land only) is measured in meters. It is derived using World Vector Shorelines (Wessel and Smith, 1996).

> 13Distance to country borders is measured in meters. It is derived using the database of Global Administrative Areas (GADM) 2.8 ADM0 (Country) boundaries.

> 14Data incorporate data from Open Street Map (OSM) and the Google roads database. See Weiss et al. (2018)

> 15Yearly daytime land surface temperature are from Wan and Hook (2015).

> 16Global elevation (in meters) are from Shuttle Radar Topography Mission (SRTM) dataset (v4.1) at 500-meter resolution. See Jarvis et al. (2008).

9
# **1. Introduction**

Since most manufactured and semi-manufactured goods are transported in liner vessels, access to efficient and competitive liner shipping is crucial for a country’s engagement in international trade. In fact, maritime transport costs (MTCs) today matter more than tariffs. _Ad valorem_ MTCs of exports to the United States are on average more than three times higher than the average US tariff, and in New Zealand are more than twice as high.<sup>1</sup> The current perception is that the scope for lowering MTCs through policy reform is limited because the market for maritime liner shipping services is largely free of distortions. Governments now generally desist from both sins of commission, such as reserving cargo for national shipping lines, and sins of omission, such as exempting liner conferences from competition policy. However, a new services trade restrictions database reveals that protection persists. It now takes the form more often of restrictions on foreign investment in maritime transport services than of restrictions on cross-border trade or port services, which have been the focus of the existing literature.

This paper seeks to assess the impact on MTCs and seaborne trade flows of policy measures currently affecting trade in liner shipping services, with a focus on hitherto neglected restrictions on foreign investment or commercial presence – ‘mode 3’ in WTO parlance.’ There are two principal reasons for this focus. First, the most significant barriers to crossborder trade (i.e. ‘mode 1’ in WTO terms) have indeed diminished in significance. Cargo reservations only affect a few specific goods and cover a tiny share of total seaborne trade, and many countries have narrowed the scope of exemptions from competition law for liner transport. Therefore, the total impact of mode 1 measures on MTCs is likely to be small. Second, even though cross-border trade is the key mode of supply for international shipping services, the ability to establish a commercial presence is crucial for an efficient provision of liner shipping services. Thus, provisions governing mode 3 are likely to affect maritime transport costs and trade flows.

The focus on policy barriers to foreign investment in the shipping sector addresses a blind spot in the existing literature on the determinants of maritime transport costs. One strand of the literature has studied aspects revolving around infrastructure and connectivity. In their

> 1  The comparison is based on figures of average tariff rates, taken from WITS (2007), and maritime transport costs, taken from OECD (2007), respectively, both in ad valorem terms as of 2007.

2
|**Table 2: Base Regr**<br>Dependent variable: R&D<br>Activity|**ession Resu**<br>**(1)**|**lts: Margin**<br>**(2)**|**al effects fr**<br>**(3)**|**om Logit e**<br>**(4)**|**stimation**<br>**(5)**|**(6)**|
|---|---|---|---|---|---|---|
|Informal Competition|0.251***|0.269***|0.272***|0.284***|0.281***|0.281***|
|(Country-level cell average)|(0.064)|(0.071)|(0.070)|(0.067)|(0.067)|(0.071)|
|GDP per capita (lagged, logs)||0.008|0.010|0.008|0.005|0.004|
|||(0.010)|(0.012)|(0.012)|(0.013)|(0.018)|
|Firm Size|||0.057***|0.051***|0.048***|0.046***|
||||(0.011)|(0.012)|(0.012)|(0.012)|
|HHI|||0.023|0.017|0.004|0.014|
||||(0.073)|(0.073)|(0.074)|(0.073)|
|Age of Firm (logs)||||0.007|0.008|0.007|
|||||(0.012)|(0.012)|(0.012)|
|Manager Experience (logs)||||-0.019|-0.019|-0.019|
|||||(0.014)|(0.014)|(0.014)|
|Exports (proportion of sales)||||0.099***|0.099***|0.097***|
|||||(0.034)|(0.032)|(0.032)|
|Female Top Manger Y:1 N:0||||0.008|0.007|0.010|
|||||(0.021)|(0.021)|(0.022)|
|Multi-firm Y:1 N:0||||0.018|0.011|0.012|
|||||(0.022)|(0.022)|(0.022)|
|Power Outages (day hours)|||||-0.001|-0.001|
||||||(0.002)|(0.002)|
|Finance Obstacle (0-4 scale)|||||0.005|0.003|
||||||(0.007)|(0.007)|
|Overdraft Y:1 N:0|||||0.048***|0.049***|
||||||(0.018)|(0.019)|
|Courts Obstacle (cell average)||||||0.022|
|||||||(0.018)|
|Population (lagged, logs)||||||0.004<br>|
|||||||(0.008)|
|Inflation (%, annual)||||||-0.001|
|||||||(0.001)|
|Gross Tertiary Enrollment Rate||||||0.034|
|||||||(0.069)|
|Gross Primary Enrollment Rate||||||<br>-0.031<br>(0.056)|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of observations|12,980|12,980|12,980|12,980|12,980|12,980|

Standard errors in brackets. All standard errors are Huber-White robust and clustered on the country. Significance is denoted by *** (1%), ** (5%), * (10%).

46
indicating the prestige level, and the other two the educational content of each occupational category.

The dependent variable is regressed on several human capital indicators, which are: general work experience (proxied by age and age squared), years since arrival or since migration (represented by a dummy variable indicating whether migration took place in the first or the second half of the decade),<sup>21</sup> and level of education (captured by the six dummy variables grouping the years of education in: less than five, five to nine, ten to twelve, high-school level, some college, Bachelor’s Degree or higher).

To account for the age-at-migration bias (Friedberg, 1992), the estimation samples include only migrants who earned their degrees before migration. Additionally, age restrictions insure cohort comparability across censuses.<sup>22</sup> Regressions for each of the three OP indices are weighted and run separately by census year _t_ (where _t_ =1980, 1990 or 2000) and decade of arrival _d_ (where _d_ = 1970-1979 or 1980-1989). Altogether, this implies 15 estimations, i.e. 18 (three dependent variables * three Censuses * two decades) minus three, since Census 1980 only captures the 1970s decade.

The specification is related to the econometric models of wage determination previously used in the migration literature for estimating earnings assimilation. As claimed by Borjas (1995) and LaLonde and Topel (1990) there are three types of major biases that can affect the accuracy of assimilation estimates. These are: cohort effects, period effects and the return migration effect. Cohort effects refer to unobservable changes in the quality of cohorts entering the US at different times. Period effects reflect modification of labor market conditions from one census to another. To eliminate period effects, studies focusing on earnings measure assimilation of migrants relative to a comparison group expected to have been influenced similarly by the changes in the economy. A third source of bias is the non-random cohort attrition related to either mortality or return migration.

> 21 Usage of intervals is justified by the fact that Census 1980 and Census 1990 do not provide the individual years of migration.

> 22 For example, the youngest individual in the 1980s decade is 16 years old as of Census 1990, therefore the youngest individual who arrived in the 1980s is 26 years old as of Census 2000.

13
then redirect its exports to the Northern partner, a trade deflection that may hurt actual or potential South-South trade.

The size of this trade deflection is an empirical question that depends, _inter alia_ , on how specific and stringent the standards are. When the Northern partner’s standard is idiosyncratic (national or regional), the Southern partner’s adaptation to that particular standard may make it costlier to also produce for other markets with different standards or, at least, may not help in those markets. For instance, adopting a standard imposed by the European Union (EU) does not necessarily facilitate clearance of the product in the United States (US). This effect may be mitigated when harmonization takes place on the basis of international standards. Although there is no theoretical argument establishing a cost hierarchy to standards and regional standards are not necessarily costlier to implement than international ones, a number of papers (see, e.g., Otsuki et al. 2001; Wilson and Otsuki 2004) have empirically shown that international standards are less trade-inhibiting than domestic or regional ones, with a smaller negative trade impact and even, in some cases, a positive one.<sup>1</sup> Accordingly, the main objective of our paper is to assess whether the liberalization of technical barriers to trade (TBTs) in North-South EIAs contributes to reinforcing hub-andspoke trade patterns centered on large Northern blocs, which are potentially damaging for South-South trade integration. Put differently, the hypothesis is that as a result of the deep integration associated with standards provisions included in the EIA, the Southern partners’ trade expands with the North, but at the expense of their trade with non-bloc Southern partners. The net effect of these two opposite forces is _a priori_ indeterminate and subject to empirical estimation.

> 1 However, there are counterexamples where even international standards impose adaptation costs that stifle trade. For instance, Jensen and Keyser (2012) show how the adoption by the East African Community of dairy standards based on the international _Codex Alimentarius_ led to requirements so tight that regional trade in dairy products was largely stifled.

4
the number of new schools constructed in a district under the Inpres program. The allocation rule decided the number of new schools in proportion to the number of children of appropriate age group not enrolled in primary school in 1971 (Aziz (1990)). The over time variation comes from comparing birth cohorts that were exposed to the new schools with those who completed schooling before the construction of the Inpres schools. A major concern here is whether the timing of the program implementation can be treated as exogenous. If the school constructions were undertaken by the government in response to some shocks to the domestic economy with dierential eects across districts, then the same shock could aect the educational outcomes of children through family income independent of the eects of Inpres schools. The public funds for such a massive school construction program were generated by an external shock in the international market for crude petroleum (gasoline). The dramatic increase in the oil prices owing to the 1973 OPEC oil shock created a huge windfall for the Government of Indonesia, and the size of the Indonesian government budget increased 2.5 times from 1973 to 1975. The Inpres school construction under the second ve year plan was thus not related to any domestic economic factors.

Following the inuential work of Duo (2001, 2004), the eects of Inpres school expansion have been studied by many papers, and we have a wealth of accumulated evidence on the validity of the quasi-experimental design originally developed by Duo (2001).<sup>28</sup> The evidence reinforces and enhances the credibility of the research design in a variety of contexts using dierent data sets. Some of this evidence is directly relevant for the validity of the research design in our application. In the context of our analysis, an important issue is whether the high Inpres intensity districts were experiencing higher growth in educational outcomes in the pre-program period because of factors unrelated to school construction. Mazumder et al. (2019) use 1985 intercensal population survey (SUPAS 1985) data and show that there are no signicant dierences in the trend of primary completion rates across districts for the cohorts that completed primary schooling before Inpres school construction. This allays the concern of dierential underlying trends across districts. In addition, we nd that treatment intensity is not correlated with father's education which is informative about whether the program

> 28See, for example, Martinez-Bravo (2017), Mazumder et al. (2019), Jung et al. (2021), Ashraf et al. (2020), Akresh et al. (2018).

11
Do Improved Biomass Cookstoves Reduce PM2.5 Concentrations? If So, for Whom? Empirical Evidence from Rural Ethiopia Randall Bluffstone<sup>*</sup> , Daniel LaFave, Alemu Mekonnen, Sahan Dissanayake, Abebe Damte Beyene, Zenebe Gebreegziabher, Michael A. Toman Key Words: Household Air Pollution, Indoor Air Quality, Ethiopia, Improved Stove JEL Codes: I15, Q55, Q23, Q42

> *Corresponding author: Bluffstone, Portland State University, Portland, OR, USA bluffsto@pdx.edu Dissanayake: Portland State University, Portland, OR, USA; Beyene: Ethiopian Development Research Institute, Addis Ababa, Ethiopia; Gebreegziabher, Ethiopian Development Research Institute and Mekelle University, Addis Ababa and Mekelle, Ethiopia; LaFave: Colby College, Waterville, ME, USA; Mekonnen, Ethiopian Development Research Institute and Addis Ababa University, Addis Ababa, Ethiopia; Toman, The World Bank, Washington DC, USA. Financial support for this research from the World Bank and the Knowledge for Change Trust Fund is gratefully acknowledged.
Jacoby, H, Guo, L, and S. Rozelle (2002), “Hazards of Expropriation: Tenure Insecurity and Investment in Rural China”, _American Economic Review_ , December, 2002.

Klein, R and F. Vella (2009), “A Semiparametric Model for Binary Response and Continuous Outcomes under Index Heteroscedasticity”, Journal of Applied Econometrics, v. 24, iss. 5, pp. 736-62 Klein, Roger and Francis Vella. (2010) “Estimating a Class of Triangular Simultaneous Equations Models Without Exclusion Restrictions.” _Journal of Econometrics_ .

Lewbel, A (2012), “Using Heteroskedasticity to Identify and Estimate Mismeasured and Endogenous Regressor Models”, Journal of Business and Economic Statistics.

Mulligan, C and Y. Rubinstein (2008), “Selection, Investment, and Women’s Relative Wages Over Time”, Quarterly Journal of Economics, vol. 123. no. 3.

Newman, P (1965): Malaria Eradication and Population Growth: With Special Reference to ceylon and British Guyana, School of Public Health, University of Michigan.

Peebles, Patrick, 2006, _A History of Sri Lanka_ , Greenwood Publishing Group. Rustomjee, J (1944): Observations Upon the Epidemiology of Malaria in Ceylon, Sessional Paper 24, 1944, Government Press, Colombo.

Schaffner, J. A (2002), “Heteroskedastic Sample Selection And Developing-Country Wage Equations”, _The Review of Economics and Statistics_ , vol. 84, issue 2.

Sen, A (1981), “Public Action and the Quality of Life in Developing Countries”, _Oxford Bulletin of Economics and Statistics_ , 43, 287-319.

Wijesundera, M (1988), “Malaria Outbreaks in New Foci in Sri Lanka”, Parasitol Today, 1988. World Bank (2008), _Land Reforms in Sri Lanka: A Poverty and Social Impact Analysis_ , South Asia Region, World Bank, Washington DC.

29
price offered in the _mandis_ , the composition effect. If farmers with good quality soybean have a greater tendency to sell directly to the private company, the effect of the hub on the _mandi_ price is _a priori_ ambiguous, and is ultimately an empirical question.

The location and installation date of each internet kiosk and hub, available from the private company, provide the spatial and time patterns of the implementation of the intervention in the state of Madhya Pradesh. The outcomes, monthly wholesale price and volume of crops sold in government regulated _mandis_ in the state from April 2000 to September 2005, are available from the Madhya Pradesh State Agricultural Marketing Board. Measuring output response to this intervention is crucial for understanding the effect of this intervention on farmers’ behavior. Annual district level data on area cultivated, production and yield of crops from 1998 to 2004 is available from the Commissioner of Land Records, Madhya Pradesh.

This is the first attempt to collect _mandi_ level data on price and volume of crops to examine the impact of a change in the procurement strategy of a private buyer on the functioning of rural markets in India. Using differential timing in the introduction of kiosks and hubs across the districts of the state, the paper finds an immediate and significant increase in the average price of soybean after the introduction of kiosks, lending support to the predictions of the theoretical model. The presence of kiosks in a district is associated with an increase in the monthly _mandi_ price of soybean by 1-3 percent, taking into account _mandi_ and month fixed effects and district-specific time trends. The presence of hubs appear to have no effect on average price implying that the composition effect perhaps offsets the competition effect, pushing the estimate of the impact of the hub on _mandi_ price towards zero. In addition, the dispersion of soybean prices across the affected _mandis_ in Madhya Pradesh decreased after the intervention. The increase in price and the reduction in dispersion appears to influence farmers’ planting decisions. There is a significant increase in the area under soy cultivation due to this intervention.

This paper makes two contributions. First, the results contribute to the substantial economic literature emphasizing that information is critical for the efficient functioning of markets. A series of theoretical papers including George Stigler (1961), Steven Salop 4
# Efficiency and Equity in Urban Flood Management Policies: A Systematic Urban Economics Exploration

Charlotte Liotta,<sup>a,b</sup> Paolo Avner,<sup>c,d</sup> Stéphane Hallegatte<sup>c</sup> a Center for International Research on Environment and Development (CIRED) b Technische Universität Berlin, Berlin, Germany c The World Bank d Global Facility for Disaster Reduction and Recovery (GFDRR) Keywords: urban floods, urban economics, land use zoning, subsidized insurance, risk-based insurance, welfare, inequality JEL Codes: R14, R13, C63, H21, H23, Q54 Acknowledgments: This study was financially supported by the Global Facility for Disaster Risk Reduction (GFDRR).
- 16 -

the 2013 data are used for our 2010 data. In addition, urban and rural areas are differentiated by global spatial urban-rural data, the Global Rural-Urban Mapping Project (GRUMP) data set.<sup>6</sup> Thus, the systematic difference in agricultural employment share across regions and between urban and rural areas is taken into account. Using these data, population data are divided into two: Agricultural labor force, _LAG_ , and other population, _LNON_ .

The results are shown in **Table 6** : While the main results remain unchanged, the coefficient of agriculture-specific labor _LAG_ is much smaller. As expected, _LNON_ , which is expected to be a proxy of local non-farmer consumption, also has a significantly positive coefficient. This supports the view that the original global population data may not be an accurate measurement of the agricultural labor force. Still, there is a risk of measurement errors and bias. Unfortunately, there are no detailed spatial data to distinguish agricultural and nonagricultural labor force at the more granular level.

**<u>Table 6. Fixed effect and spatial autocorrelation regression with agriculture labor force used</u>**

||<br>OLS (differ<br><br>|<br>ence)<br>||<br>Spatial regre<br>|<br>ssion<br>||
|---|---|---|---|---|---|---|
||Coef.<br>|Std.Err.||Coef.|Std.Err.||
|Δln_LAG_|0.363|(0.043)|***|0.227|(0.043)|***|
|Δln_LNON_|0.012|(0.043)||0.132|(0.044)|***|
|Δln_R_|0.406|(0.006)|***|0.403|(0.003)|***|
|Δln_I_|0.094|(0.016)|***|0.070|(0.007)|***|
|Δln_F_|0.013|(0.002)|***|0.010|(0.001)|***|
|Δln_TC1_|0.642|(0.166)|***|0.243|(0.238)||
|Δln_TC2_|-0.261|(0.051)|***|-0.096|(0.041)|**|
|constant|0.017|(0.060)||-0.347|(0.057)|***|
|Obs|4624|||4624|||
|F statistic|1055.7||||||
|Wald2||||18409.1|||
|R-squared|0.820||||||
|Spatial param|eters:||||||
|_λ_||||0.079|(0.010)|***|
|_ρ_||||0.303|(0.004)|***|

Note: The dependent variable is Δln _Y_ . Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.

6 SPAM cells are considered as urban areas, when their centroids are within 20 km of the urban areas defined by GRUMP.
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