rafmacalaba/datause-encoder-data huggingface dataset:
calculating summary statistics within a geographical area. For example, the median road size of a geographical area containing one _track/trail_ (1), one _secondary road_ (3) and one _primary road_ (4), will be 3.<sup>25</sup> Appendix Table A1 presents summary statistics about the number of kilometers per road type and the corresponding share of total kilometers, both for the country as a whole and for our sample. Our sample of 52 sub-counties contains 2,786 km of roads, or about 2 percent of Ugandan roads, and roads are larger in our sample than in the rest of the country: 24 percent of the roads in our sample are _primary roads_ and only 38 percent are classified as _track/trail_ , while the corresponding figures for the country as a whole are 1 and 87 percent, respectively. Reflecting our sampling strategy, this shows that our sample is more urban, and therefore denser, than the average Ugandan geographical area.

# **5 Empirical Strategy**

Our analysis is guided by the conceptual framework of Section 3, which posits several empirical relationships that we are going to test with our data. Specifically, we proceed in three steps. First, we study the relationship between local profitability and pollution, to establish that they are positively related, hence that the trade-off we hypothesized between exposure to pollution and market demand, created by locating near major roads, is present. Second, we study the location choice of managers, to show that they choose to expose themselves to pollution in order to get the benefits from market access on major roads. Third, we study heterogeneity by managerial ability in location choice and adaptation behavior to show that high-skilled managers do not avoid pollution, but better adapt to it.

Before showing the regression specifications for each of these three steps, we describe how we residualize our pollution measurements to net out time effects, and how we create measures of pollution, road size, and firm density at the neighborhood level. Residualizing pollution is essential because we are interested in variation in pollution across space within the city (rather than across time). Creating neighborhood or “grid cell” measures of firm density, pollution, and road size allows us to study the location choice of firms.

## **5.1 Recovering Residual Spatial Variation in PM2.5**

As described in Section 4, we collected measures of PM2.5 concentration from both stationary and mobile monitors. Our goal is to construct measures of spatial variation in pollution at a highly granular level within the city. To do this, we exploit our mobile monitors that continuously recorded pollution and GPS coordinates while moving within Ugandan cities.

> 25A road is defined as a road segment not intersected by any other road. Each road intersection marks the extremity of the intersecting roads, as illustrated in Appendix Figure A17.

16
Vietnamese employers most prioritize “practical” technical skills in both the manufacturing and noneducation service sectors (World Bank 2008). In Indonesia, thinking skills and general skills dominate both industries (diGropello 2011). Filipino employers most value socio-emotional skills, though “technical” was not one of the skills sets measured (diGropello 2010). While in Argentina, Brazil, and Chile, employers in all five industries most valued socio-emotional skills (Bassi et al. 2012).<sup>47</sup> Although the priority ranking by industry within countries is identical, the weight on each priority ranking reveals the expected patterns: behavioral skills are more important in the service sector than the manufacturing sector. For example, 54 percent of Indonesian manufacturing sector employers rate behavioral skills as very important (for their professional staff), as compared to 72 percent of service firm employers (diGropello 2011) (Figure 4). In the Latin American sample, the manufacturing sector (auto) valued socio-emotional skills at 10 percentage points lower than the service sectors (Bassi el al. 2012). Communications skills are slightly more important in the service sector, as observed in the Philippines (11.5 percent of employers identify as very important, as compared to 10 percent of manufacturing sector employers) (diGropello 2010), Vietnam (prioritized by 10 percent of service sector employers as compared to 8 percent of manufacturing sector employers) (World Bank 2008), and Indonesia (English) (diGropello 2011). In the Philippines, when asking employers to only reflect on “core” skills (basic and higher-order cognitive and socio-emotional skills), service sector employers had a higher demand for basic cognitive skills, as compared to employers in the manufacturing sector, and a lower demand for socio-emotional skills (independent work, problem solving) than manufacturing employers (diGropello 2010).

# **4.5  Exporters v. domestic markets**

Exporting firms demand more socio-emotional and higher-order cognitive skills than do firms producing for domestic markets. Latin American exporting firms place a higher value on socioemotional skills than non-exporters, with 57 percent of exporting employers prioritizing socio-emotional skills, as compared to 53 percent of firms selling domestically and 52 percent of firms selling locally (Bassi et al. 2012). LAC firms producing for domestic and local markets put a greater value on cognitive skills than exporters do (30 compared to 26 percent) (Bassi et al. 2012). Indonesian exporting employers demand more of every skill set than do non-exporting employers. They particularly demand English and thinking skills of their managers and basic cognitive skills, thinking (higher-order cognitive), and behavioral skills of their workers (diGropello 2011). Employers in Filipino exporting firms particularly

> 47 Auto, retail, hotel, financial sector, and food industries

21
# **Table 3: Comparison of Firm-level Business Environment Indicators for the Manufacturing Sector**

|Sample|**2003**<br>**2010**<br>Manufacturing(MFG)|**2010**<br>MFG, Overlapping<br>industries*|**2010**<br>MFG, Overlapping<br>industries, weighted**|
|---|---|---|---|
|ln(TFP)|2.50<br>2.83|2.76|3.06|
|Firm introduced new product/process|21.9%<br>24.2%|25.2%|35.1%|
|Has foreign competitor|21.2%<br>-|-|-|
|>10% government ownership|14.7%<br>0.9%|0.7%|1.8%|
|Losses from crime as a percent of sales|0.8%<br>0.2%|0.2%|0.2%|
|Has formal training program|40.6%<br>21.5%|21.9%|39.4%|
|Firm exports|67.7%<br>21.5%|23.0%|46.2%|
|Firm is direct importer|42.5%<br>10.3%|10.2%|19.0%|
|Has >10% foreign ownership|19.1%<br>3.4%|3.0%|5.5%|
|Has internet access (web and/or e-mail)|68.0%<br>40.0%|41.2%|70.2%|
|Share of sales on credit|49.3%<br>38.8%|39.6%|38.4%|
|Share of investment financed externally|33.2%<br>42.9%|44.1%|41.3%|
|Interest rate of loans|13.6%<br>-|-|-|
|Percent of electricityfromgenerators|13.3%<br>1.9%|1.9%|3.1%|
|Sample size|408<br>330|274|274|

Source: 2004 and 2011 WBES Means generated from estimation sample, which is restricted to firms with valid output data.

*Subsample restricted to overlapping industries in 2003 and 2010 surveys.

**Means weighted by 2003 survey sample size distribution.

36
**<u>Table 2: Program characteristics at household and plot levels</u>**

||**Certificat**|**es issued?**|
|---|---|---|
||**No**|**Yes**|
|**Household level data**|||
|A member of the household attended public information meetings|0.78|0.85|
|Number of meetings attended|3.50|3.60|
|Well informed about the program|0.68|0.85|
|Has landholding certificate|35.55|87.47|
|Number of months since certified|8.07|17.15|
|**Plot level data**|||
|Plot area was determined in the field|0.64|0.92|
|Plot area was determined at_kebele_office referring to previous records|0.35|0.05|
|Plot measured using tape and rope, if determined in the field|0.65|0.95|
|Plot measured using eye estimation, if determined in the field|0.35|0.00|
|More than half of the neighbors were present when measured|0.35|0.59|
|Half or less than half of the neighbors were present were present|0.11|0.20|
|Plot has a certificate|0.30|0.75|
|Number of months since certified|8.19|16.93|
|Plot isjointlycertified with head and spouse|0.83|0.77|
|Number of households|481|359|
|Number ofplots|2369|2143|

**Source:** Own computation from AAU/Gothenburg/WB Survey

19
46

# **Table 5 Test of Differences in means between regions and ownership of banks**

|||**Weighted A**|**verages* **||
|---|---|---|---|---|
||**GCC**|**Non-GCC**|**t-test**|**p-value**|
|**SME Loans/Total Loans (%)**|2.02|13.16|4.77|0.00***|
|**Investment Loans/SME Loans (%)**|15.43|26.40|4.38|0.00***|
|||**Un-weighted**|**Averages**||
||**GCC**|**Non-GCC**|**t-test**|**p-value**|
|**SME Loans/Total Loans (%)**|2.08|15.82|6.89|0.00***|
|**Investment Loans/SME Loans (%)**|14.68|30.69|3.64|0.00***|
||**Private**|**State**|**t-test**|**p-value**|
|**SME Loans/Total Loans (%)**|11.34|8.99|0.82|0.42|
|**Investment Loans/SME Loans (%)**|22.43|32.38|1.40|0.18|
||**Domestic**<br>**(Private)**|**Foreign**|**t-test**|**p-value**|
|**SME Loans/Total Loans (%)**|11.67|10.56|0.29|0.77|
|**Investment Loans/SME Loans(%)**|22.80|21.48|0.27|0.79|

***** Total loans for banks are used to construct the weighted averages.
These findings suggest that laws have a much deeper impact on human behaviour than just changing constraints and beliefs. They may also affect what is considered as normatively legitimate, which may directly affect economic outcomes. In Falk, Fehr and Zehnder, the impact of history on subjects‘ reservation wages changes actual wages and employment in the experiment. There are hysteresis effects not, as in the standard model, through _e.g_ . fixed costs, but instead through changes in preferences.

An anomaly that has attracted a great deal of attention is the effect of default options on decisions. Default options appear to have very large consequences for choices that people make over retirement savings, insurance plans, and organ donation. For instance, in Austria, consent to being an organ donor is the default option, so that not consenting requires an active decision to opt out. Here the effective consent rate is 99.98% (Johnson and Goldstein 2003). Just over the border in Germany, consent to being an organ donor requires opting out of the default option, and the proportion of individuals who are organ donors is 12%.

Several factors consistent with the standard economics view that individuals have fixed preferences could contribute to this vast difference in consent rates: Filling out a form to opt out may have a high disutility. The default option may suggest that the norm is to contribute and individuals may value meeting others‘ expectations, or they may implicitly reciprocate a favor if they believe that most others will also be organ donors. Perhaps differences in education, transplant infrastructure, or religion influence contributions. But another possible explanation, outside the classical economics view, is that preferences for organ donation are constructed at the time of decision. In that view, choosing the default option is favored because it avoids the cost of making an active decision.

A test that can provide evidence for the constructed preference view is that individuals who have not made up their mind, but not other individuals, can be nudged to contribute to the public good by a manipulation that makes them think about it. In this symposium, Stutzer, Goette, and Zehnder (2011) test this prediction in a field study of blood donations by the Swiss Red Cross in Zurich. All potential donors are asked in a questionnaire to indicate YES or NO to the question ―Do you feel sufficiently informed about the importance of donating blood?‖ The study randomly assigns individuals to three groups: in the control group there is no intervention; in the ―strong active decision group‖ (hereafter strong AD), individuals are asked to either agree to 15
## <u>Fragile Caribbean (Haiti)</u>

Haiti has very little intra-regional connectivity (Table 15). Traffic has spiked during two significant relief efforts: a series of storms and floods hit Haiti between 2002 and 2006, including Hurricane Ivan, and a significant spike in traffic occurred in 2010 and 2011, after the earthquakes of January, 2010 (Figure 18). The little traffic that exists is with Air Caribe of Guadeloupe between Haiti and Dominica, and with another operator between Haiti and Jamaica.

**<u>Table 15: Haiti’s Connections within the Caribbean</u>**

|**Country**|**Seats 2013**|**Share**|**Annual**<br>**Growth**<br>**01-13**|**Annual**<br>**Growth 08-**<br>**13**|
|---|---|---|---|---|
|Dominican Republic|2,262|65.9%||-13%|
|Jamaica|1,170|34.1%|-2%||
|Total|3,432|100.0%|7%|-6%|

_Source: Authors’ calculation based on data by Diio (2014)._

**Figure 18: Haiti’s Seat Capacities and Direct Airport Pairs with the Caribbean**

<!-- Start of picture text --> 45 5<br> 40<br> 35 4<br> 30<br>3<br> 25<br> 20<br>2<br> 15<br> 10 1<br> 5<br> - 0<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>Year<br>Estimated Seats Airport Pairs<br>Estimated Seats, thousands<br>Intra-Regional Airports Served<br><!-- End of picture text --> _Source: Authors’ calculation based on data by Diio (2014)._

# _iv. Extra-Regional Routes_

## <u>Overview</u>

The extra-regional routes are the key suppliers of airlift for the tourism sector in the Caribbean, especially for the island states. Flights are generally direct, without the use of any hubs in the region. The extraregional capacity of this group has been declining, which may have to do with the success and draw of the resorts and hotels in the larger hub countries, especially Jamaica and the Dominican Republic, which are known for their extensively developed properties. The extra regional capacity of the Small Island States has grown at roughly 1 percent annually over the last 12 years, but growth has reversed recently, with rates of almost minus 4 percent annually since 2008. The rest of the region, comparatively, has grown above 3 percent annually, and even their most recent growth rates, though somewhat lower, has remained positive, even for fragile countries like Haiti (Table 16).

47
**Table 4. Expectations of others' behavior**

||**U**<br>|**K**<br>|**Keny**<br>|**a**<br>|**Ugan**<br>|**da**<br>|
|---|---|---|---|---|---|---|
||Mean|N.obs.|Mean|N.obs.|Mean|N.obs.|
|Percentage of subjects responding '<br>|yes' when<br>|asked whe<br>|ther other w<br>|ill...<br>|||
|Burn their endowment|29.6|144|42.4|198|51.5|145|
|Steal their endowment|76.9|144|53.6|126|54.7|145|
|Give to them|12.9|144|39.9|126|52.0|145|
|Percentage of subjects responding|`yes' when|asked whe|ther others|expect them|to give.||
|Giving norm|17.9|144|47.0|126|45.4|145|
|Ratio Expectation to Part 1 play|||||||
|Burning|3.8||8.8||4.8||
|Stealing|2.1||2.3||2.1||
|Giving|18.4||8.7||6.2||

Source: Authors analysis based on data described in the text. Note: Some expectation questions were not asked to Kenyan participants in the first two sessions because of a technical glitch, hence the smaller number of observations. Differences between Oxford and the two African samples are statistically significant using either a t-test, or a joint significance test in regressions of answers on country dummies with session clustering.
- 21 -

And while these effects could be deemed favorable for trade, they sent many carriers into a tailspin. In growing numbers shipowners were forced to reduce rates to defend their share in the cargo market. But in the wake of such pricing practices actual rate levels barely covered operating cost. With the renewed slowdown in the seatrade markets during the early 1990s, the freight earnings in most segments of the international ocean transport industry were considerably below the break-even levels which reflect the minimum charter rates or freight tariffs to cover all cost of financing, managing, and operating a cargo vessel--and which would yield a modest return on investment (see Figure 5). Between 1989 and 1992 the average internal rates of return on investments in shipping assets shrank from 9.5 percent to almost zero. In real terms the freight rates in most segments of the seatrade markets have been on a declining trend during most of the time since the beginning of the 1980s (see Table 4 on page 22).

<!-- Start of picture text --> Vol ati le Freig h t Ear n i ng s<br>Volatile Freight Earningsfreight<br>PANAMAX BULKER NEWBUILDING<br>Spot Return, Time-Charter Basis<br>US$, thousands/day<br>1-<br>e8000 OWT costing USs 32.5 million<br>10 percent return assvmed<br>Amortiation oera years<br>13-<br>BREAK EVEN<br>1 \ US$ 11,350/day<br>9<br>7 -<br>Source: Sealrade Week<br>5,<br>A S 0 N D J F M A M J J 32.<br>1991 1992<br>SECONDHAND SUEZMAX TANKER<br>Spot Return, Time-Charter Basis<br>5 US$ thousands/day<br>152,000 DWT. built 1977<br>23 bought in July 1991 at US$1826 million<br>1O percent return assumed<br>21 Ahortization to Scrap over three yeara<br>19<br>17 -<br>is.5BREAK EVEN *<br>US$ 14,500/day<br>13\<br>119<br>33.<br>7Source: Seersde Wek<br>5 A SON S 0 , D J 0, F F M A A M M J 9 J<br>Figure 5<br><!-- End of picture text --> 32. The outlook for improved commercial performance of shipping assets is not encouraging. In some specialized market segments, such as the container trades, unchecked ordering of newbuildings has led to such severe overtonnaging that the medium-term prospects for high load factors are modest (see Table _5_ on page 23). The idicators m this table pomt to another important fact. International statistics on the degree of tonnage utilization do not reflect load factors, which is particularly critical in the case of liner trades. Thus, the high utilization rates reported (above) for the general cargo and container fleets should be interpreted with caution. The _actual_ tonnage utilization-appropriately adjusted for load factors-is therefore usually lower than the statistics suggest.

33. Since the trend in container shipping on the main trade routes has been such that the ten key carriers now effectively control more than 70 percent of the market-with further concentration of their market shares predicted-it has become possible for these carriers to 'rationalize' freight I__ rates and the level of tonnage deployed. For exampie, in the trans-Atlantic and trans-Pacific
<!-- Start of picture text --> Mineral Hybrid Multiple Hiring<br>SKILLS ors Manure "<br>Fertilizer Seeds Weeding Labor<br>Cognitive skills (IRT) 0.01 0.03 -0.00 -0.04* 0.01<br>(0.019) (0.018) (0.021) (0.023) (0.024)<br>NC Factor 1 0.01 -0.00 0.01 -0.01 0.00<br>(CESD) (0.012) (0.011) (0.012) (0.013) (0.016)<br>NC Factor 2 0.00 0.02* 0.01 0.04*** -0.02<br><!-- End of picture text -->

|SKILLS|Mineral<br>ors<br>Fertilizer|Manure|Hybrid<br>Seeds|Multiple<br>"<br>Weeding|Hiring<br>Labor|
|---|---|---|---|---|---|
|Cognitive skills (IRT)|0.01|0.03|-0.00|-0.04*|0.01|
||(0.019)|(0.018)|(0.021)|(0.023)|(0.024)|
|NC Factor 1|0.01|-0.00|0.01|-0.01|0.00|
|(CESD)|(0.012)|(0.011)|(0.012)|(0.013)|(0.016)|
|NC Factor 2|0.00|0.02*|0.01|0.04***|-0.02|
|(Consciensiousness/Tenacity )|(0.013)|(0.013)|(0.015)|(0.015)|(0.015)|
|NC Factor 3|0.01|0.00|-0.01|-0.01|0.02|
|(LOC/Metacog/Openness)|(0.014)|(0.018)|(0.014)|(0.015)|(0.019)|
|<br>NC Factor 4|0.00|-0.04**|0.01|-0.00|0.02|
|(Causes of poverty, negative items)|(0.014)|(0.019)|(0.021)|(0.021)|(0.019)|
|NC Factor 5|0.01|-0.00|0.03**|0.02|0.03**|
|(Attitudetowards change / LOC_va)|(0.013)|(0.016)|(0.014)|(0.013)|(0.015)|
|NC Factor 6|0.01|0.02**|-0.00|0.02|-0.01|
|(CESD positive / Self-esteem / Risk av.)|(0.014)|(0.012)|(0.013)|(0.014)|(0.015)|
|Technical skills (IRT)|0.02|0.01|0.04***|0.00|0.04**|
||(0.013)|(0.017)|(0.015)|(0.015)|(0.017)|
|Observations|890|890|890|890|890|
|R-squared|0.521|0.325|0.377|0.317|0.273|
|Mean|0.679|0.606|0.460|0.576|0.564|
|R2 Adj. (w/o controls)|0.054|-0.001|0.090|0.023|0.019|
|F Test (Cog)|0.691|0.143|0.850|0.086|0.768|
|F Test (NonCog)|0.564|0.124|0,119|0.004|0.125|
|F Test (Tech)|0.135|0.520|0.005|0.993|0.033|
|TestNCdiff.|0.964|0.088|0.338|0.034|0.167|

<!-- Start of picture text --> (Consciensiousness/Tenacity ) (0.013) (0.013) (0.015) (0.015) (0.015)<br>NC Factor 3 0.01 0.00 -0.01 -0.01 0.02<br>(LOC/ Metacog / Openness) (0.014) (0.018) (0.014) (0.015) (0.019)<br>NC Factor 4 0.00 -0.04** 0.01 -0.00 0.02<br>(Causes of poverty, negative items) (0.014) (0.019) (0.021) (0.021) (0.019)<br>NC Factor 5 0.01 -0.00 0.03** 0.02 0.03**<br>(Attitude towards change / LOC_va) (0.013) (0.016) (0.014) (0.013) (0.015)<br>NC Factor 6 0.01 0.02** -0.00 0.02 -0.01<br><!-- End of picture text --> <!-- Start of picture text --> Technical skills (IRT) 0.02 0.01 0.04*** 0.00 0.04**<br>(0.013) (0.017) (0.015) (0.015) (0.017)<br>Observations 890 890 890 890 890<br>R-squared 0.521 0.325 0.377 0.317 0.273<br>Mean 0.679 0.606 0.460 0.576 0.564<br>R2 Adj. (w/o controls) 0.054 -0.001 0.090 0.023 0.019<br><!-- End of picture text --> Note: Dependent variables are the averages of binary variables calculated over the 4 seasons (short rain 14 to long rain 16). Controls include education, literacy, gender, age and age squared of the farmer, land and cattle ownership, size and quality of the house, household size, whether the farmer is the household head, household head’s gender, village fixed effects and enumerator-assignment fixed effects. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
<!-- Start of picture text --> oj VX \ ) | ODN RY SAE A DSN IVA AY<br>| ! )<br>-2<br>-.4<br>|<br>-.6<br>-.8<br>1995 1998 2001 2004 2007 2010 2013 2016<br>———— _ Common factor: All countries ———__ VIX<br>— Common factor: Advanced countries<br><!-- End of picture text -->
**Small Plants, Pollution and Poverty: New Evidence from Brazil and Mexico**

# **by**

**Susmita Dasgupta Robert E.B. Lucas David Wheeler***

DECRG **Infrastructure/Environment Group**

* Respectively Economist, DECRG; Professor of Economics, Boston University; and Lead Economist, DECRG. This paper would not have been possible without the generous assistance of our colleagues in Instituto Nacional Ecologia (INE) in Mexico's Environment Ministry (SEMARNAP); IBGE, Brazil's National Census Bureau; and the World Bank's Environment Department. The paper and supporting data on Mexican industrial air pollution intensities are available at the Website, 'New Ideas in Pollution Regulation' (NIPR), www.worldbank.org/nipr. Financial support was provided by the World Bank's Research Committee under RPO #68266, the Poverty, Growth and Environment Trust Fund, and by operational support funding from the World Bank's Brazil and Mexico Departments. For useful comments and suggestions, we are grateful to Joachim von Amsberg, Peter Lanjouw, Gordon Hughes, Richard Ackermann, Kseniya Lvovsky, Muthukumara Manii, Sergio Margulis and Paul Martin.