rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1: Likelihood of collateralized borrowing**

|Dependent Variable:|||2005|-2017|||
|---|---|---|---|---|---|---|
|Collateral Required=100|OLS-FE||OLS with|countrychara|cteristics||
|<br>otherwise 0|(1)|(2)|(3)<br>|(4)<br>|(5)<br>|(6)|
|years of operation|-0.0265|-0.116***|-0.127***|-0.115***|-0.101**|-0.0739**|
||(0.0190)|(0.0299)|(0.0324)|(0.0345)|(0.0424)|(0.0353)|
|Small(<20)|-5.518***|-5.723***|-5.921***|-5.241***|-6.826**|-4.635**|
||(1.425)|(1.483)|(1.538)|(1.538)|(2.615)|(2.267)|
|Medium(20-99)|-0.445|-0.652|-0.538|-0.300|-2.754|-1.993|
||(1.046)|(0.950)|(1.068)|(1.024)|(1.688)|(1.596)|
|services (D)|-2.369*|2.174|1.659|1.495|-2.366|-1.655|
||(1.271)|(1.626)|(1.945)|(1.751)|(2.222)|(1.923)|
|loan from a state-owned bank (D)|3.466**|8.413***|10.90***|8.077***|12.01***|5.499*|
||(1.612)|(1.579)|(1.963)|(1.645)|(3.176)|(2.791)|
|loan from a non-bank financial institution (D)|-7.923***|-7.521**|-5.900|-7.424**|-12.10**|-14.74***|
||(2.913)|(3.409)|(3.569)|(3.440)|(4.634)|(4.460)|
|loan from others (D)|-22.95***|-22.08***|-20.74***|-22.40***|-36.42***|-38.89***|
||(4.420)|(4.233)|(4.237)|(4.330)|(8.042)|(8.445)|
|state-owned firm (D)|-8.127**|-1.222|-2.352|-4.353|0.411|-2.821|
||(4.031)|(4.469)|(4.126)|(4.721)|(8.646)|(10.10)|
|foreign-owned firm (D)|-8.040***|-7.629***|-6.502***|-6.787***|-4.701|-4.138|
||(1.629)|(1.818)|(1.952)|(1.908)|(2.951)|(2.651)|
|other firm (D)|-6.014**|-3.038|-2.639|-2.178|-3.905|-1.255|
||(2.826)|(4.482)|(5.003)|(4.751)|(8.039)|(6.730)|
|% of sales in export >50%|0.369|1.071|1.206|1.360|-2.176|0.154|
||(1.127)|(1.235)|(1.245)|(1.183)|(2.039)|(1.926)|
|an internationally recognized certification|-1.124*|-1.167|-1.519|-1.412|-0.0324|0.469|
||(0.677)|(0.892)|(0.924)|(0.960)|(2.108)|(1.654)|
|a checking and/or saving account|1.081|1.165|1.237|0.558|-3.289|-2.529|
||(1.586)|(2.020)|(2.442)|(2.034)|(2.701)|(2.443)|
|overdraft facility|0.534|-3.952**|-3.352|-2.980|-2.945|-2.740|
||(0.903)|(1.814)|(2.063)|(1.950)|(3.425)|(3.006)|
|GDP per capita||-0.000822***||||-0.000812*|
|||(0.000205)||||(0.000460)|
|rule of law|||-5.596**|||7.395|
||||(2.515)|||(5.076)|
|regulatory quality||||-9.602***||-12.62**|
|||||(2.367)||(5.200)|
|depth of credit information|||||-1.938***|-0.278|
||||||(0.499)|(0.562)|
|Constant|69.99***|112.3***|106.3***|105.2***|97.83***|95.97***|
||(2.965)|(3.954)|(4.504)|(4.041)|(4.361)|(6.482)|
|country dummies|Yes|No|No|No|No|No|
|year dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|OLS weighted by|-|-|-|-|-|-|
|Observations|20352|20318|19811|19811|5466|5382|
|R-squared|0.163|0.0663|0.0550|0.0680|0.0632|0.0989|
|Clustered standa|rd errors inp|arentheses,***|p<0.01,**p<|0.05,*p<0.1|||

12
Table 7: Measurement Model Coefficients over Different Values of Political Influence (Θ _i_ )

|Fewer than 20 competitors (Y=100)|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|_β_Θ_,_5_thpercentile_|0.369**|0.331**|0.295**|0.402***|0.381***|
||(0.137)|(0.134)|(0.114)|(0.122)|(0.108)|
|_β_Θ_,mean_|0.357**|0.311**|0.274**|0.383***|0.366***|
||(0.136)|(0.124)|(0.104)|(0.108)|(0.0962)|
|_β_Θ_,_95_thpercentile_|0.345**|0.289**|0.249**|0.357***|0.344***|
||(0.136)|(0.123)|(0.107)|(0.107)|(0.0959)|
|Observations|21,633|21,633|21,518|20,750|20,750|
|Total Sales (log 2009 USD)||||||
|_β_Θ_,_5_thpercentile_|0.0238***|0.0221***|0.0239***|0.0220***|0.0160***|
||(0.00346)|(0.00464)|(0.00497)|(0.00489)|(0.00329)|
|_β_Θ_,mean_|0.0234***|0.0215***|0.0234***|0.0215***|0.0152***|
||(0.00335)|(0.00444)|(0.00472)|(0.00460)|(0.00306)|
|_β_Θ_,_95_thpercentile_|0.0230***|0.0210***|0.0230***|0.0211***|0.0145***|
||(0.00323)|(0.00417)|(0.00443)|(0.00428)|(0.00283)|
|Observations|20,540|20,540|20,424|19,649|19,649|
|Sales per Worker (log 2009 USD)||||||
|_β_Θ_,_5_thpercentile_|0.0792***|0.0728***|0.0749***|0.0743***|0.0624***|
||(0.00551)|(0.00622)|(0.00732)|(0.00779)|(0.00668)|
|_β_Θ_,mean_|0.0775***|0.0704***|0.0724***|0.0716***|0.0589***|
||(0.00540)|(0.00604)|(0.00704)|(0.00752)|(0.00646)|
|_β_Θ_,_95_thpercentile_|0.0757***|0.0678***|0.0698***|0.0688***|0.0556***|
||(0.00527)|(0.00580)|(0.00673)|(0.00724)|(0.00623)|
|Observations|20,687|20,687|20,570|19,796|19,796|
|Labor Costs to Sales Ratio (log)||||||
|_β_Θ_,_5_thpercentile_|-0.0135***|-0.0139***|-0.0154***|-0.0149***|-0.0175***|
||(0.00297)|(0.00421)|(0.00482)|(0.00502)|(0.00336)|
|_β_Θ_,mean_|-0.0131***|-0.0132***|-0.0150***|-0.0146***|-0.0168***|
||(0.00293)|(0.00412)|(0.00464)|(0.00485)|(0.00317)|
|_β_Θ_,_95_thpercentile_|-0.0128***|-0.0127***|-0.0147***|-0.0144***|-0.0163***|
||(0.00288)|(0.00398)|(0.00443)|(0.00463)|(0.00290)|
|Observations|18,653|18,653|18,533|17,724|17,724|
|**_X_** co-variates||✓|✓|✓||
|**_X_** interacted w/Pol. Infuence|||||✓|
|Location FEs|✓|✓|✓|by Sector|by Sector|
|Sector FEs|✓|✓|by Economy|by Location|by Location|

* _p <_ 0 _._ 10 ; ** _p <_ 0 _._ 05 ; *** _p <_ 0 _._ 01 OLS estimates. Robust standard errors, with two-way clustering at the 2-digit ISIC and region level in parentheses, per Cameron et al. (2011). Co-variates (not shown) include the log of age, the years of the manager’s experience, dummy variables for exporters, foreign-owned firms, and those part of a larger firm.

The results in Table 7 show that the estimates given in Table 5 are likely robust to measurement error in our index. All coefficients for the mean values of the index lie firmly within the bounds given by estimating Eq. 12 by using the 95th and 5th percentile values of the index; the former providing a bound of the coefficient closer to 0, the latter a larger-magnitude value of the estimates.<sup>48</sup> Similarly, the

> 48For a discussion, see the Data Appendix A.6.

31
4

# **II. Theoretical Mechanisms**

This section sets out a number of possible theoretical mechanisms by which an RTA could have real effects beyond the actual provisions of the RTA. We first start out with a short review of the traditional gains and losses from RTAs.

## **_A. Traditional Gains_**

## **1. Trade Creation and Diversion**

In one of the classic demonstrations of the theory of the second best, Jacob Viner (1950) showed that a move to free trade between two countries that maintained their respective external tariffs toward the rest of the world could leave them worse off. The argument relied on showing that as a consequence of liberalizing trade with only a subset of its trading partners, the country could switch from a relatively lower cost producer to a higher cost producer, potentially decreasing welfare for all. This negative effect on world efficiency was given the term "trade diverting".

Some observers (e.g. Krugman (1991)) argue that something like this has been the effect of EC enlargement on agricultural trade. Southern European countries now buy grain and other products from relatively high-cost European sources rather than from the lower-cost providers on the other side of the Atlantic or Australasia. Northern European countries buy Mediterranean products like olive oil or more labor intensive products from Southern Europe rather than from cheaper sources in Northern Africa. This effect could be even more pronounced in the context of a customs union, which requires the equalization of external tariffs also: indeed, one of the main arguments used against EC entry in the UK was that it would result in price _increases_ for dairy products previously purchased primarily from New Zealand.<sup>6</sup> Nonetheless, Krugman goes on to argue that most RTAs are likely to entail relatively low welfare losses resulting from trade diversion, since the countries involved are often geographical neighbors and hence already engage in a sizable amount of trade. However, for that very reason, RTAs may result in substantial redistributional losses in the form of tariff revenue for the country that is lowering its tariff barriers, as pointed out in 6See Winters (1993) for an evaluation of EC integration.
Table 1b. Sample Statistics

|Programs|# of eligible<br>countries|# of eligible<br>categories|
|---|---|---|
|TOTAL|173|98|
|GSP|127|90|
|FTA|3|98|
|CBI - 1997|21|87|
|CBI - 2001|21|98|
|ANDEAN - 1997|4|88|
|ANDEAN -2001|4|98|
|AGOA|33|98|

Table 1c. Sample Statistics

|Utilization<br>Ratios|# of eligible<br>categories|Mean|Mean weighted<br>by trade volume|Min|Max|
|---|---|---|---|---|---|
|GSP - 1997|11430|0.249|0.137|0|0.901|
|GSP - 2001|11430|0.193|0.107|0|0.761|
|FTA - 1997|294|0.633|0.595|0|0.993|
|FTA - 2001|294|0.581|0.547|0|0.996|
|CBI - 1997|1827|0.459|0.358|0|1|
|CBI - 2001|2058|0.361|0.419|0|0.990|
|ANDEAN - 1997|352|0.333|0.163|0|1|
|ANDEAN -2001|392|0.248|0.175|0|1|
|AGOA|3234|0.004|0.456|0|1|

22
education human capital – e.g. nutrition or health – being invested in children and therefore less earning potential for those individuals as adults.

14 An exercise by Bourguignon (1999) using data for Malaysia from Meerman (1979) shows that with imperfect capital markets, credit-constrained poor households with an intergenerational elasticity of 0.2 would find their welfare lowered by an education program that, under standard incidence analysis, would give them a net benefit equivalent to 25% of initial income. It might be objected that the extraordinary willingness of some parents to sacrifice current consumption for their children’s future welfare illustrates that, empirically, the elasticity of intergenerational substitution is often very high. However, it does not follow that a social planner would have to accept such high elasticities as given. In the same way that societies ban workers from selling themselves into indentured servitude to improve their children’s prospects, a social planner making welfare calculations might choose to assume a lower intergenerational elasticity in making normative judgments..

15 Indeed, the failure to improve the quality of education more rapidly was cited as a cause of nationwide demonstrations by Chilean students in June 2006.

16 It must be stressed that the presence of alternatives to public schooling may modify slightly the preceding analysis. Parents sending their children to private school and incurring the cost _c_<sup>_pro_</sup> when they could send them to public school for nothing are implicitly transmitting wealth to their children and cannot be considered as fully liquidityconstrained.

17 In applying this test, adjustment for quality would be very important. Otherwise, a program that expanded access to low-quality schooling for the poor while simultaneously ramping up schooling quality (but not access) for rich children would incorrectly be identified as highly progressive, in incidence terms.

16
# (Ratio ofregularto casual nonfam wage)

<!-- Start of picture text --> 3.00—§ ASS<br>24 —- OO<br>+—_... _——<br>2.29—§ $NAi Avaa<br>1. x |<br>1983 1993-94 1999-00 2004-05 2009-10<br>—+— mean —a— median<br><!-- End of picture text -->
In a similar vein, the traffic information problem can now be addressed using Google Maps, which provides real-time traffic reports for the Dar es Salaam metro region. Although desktop monitors do not show this, Google Traffic continuously produces a map large enough to cover the entire metro region at 100 m resolution. However, Google provides no facility for georeferenced downloads. To mobilize this huge information resource, one of the authors has developed an algorithm that maps region-scale Google Traffic displays into a panel database of traffic measures for a reference grid with arbitrary cell size.

Google Traffic provides a four-color measure of vehicle speed along each 100-meter road link, with separate speed measures for traffic flows in two directions on primary roads. For this study, we match information from Google Traffic with time-compatible NO2 imagery within a grid of spatial cells with a resolution of .01 decimal degrees (approximately 1.1 km). Since Google Traffic data are available continuously, we also construct a database of traffic measures by day and hour for the period March 20 - May 23, 2019. We convert these files to geoTIFF format for grid-based spatial computations. Using the Dar es Salaam region shapefile provided by OpenStreetMaps (OSM), we divide roads into two classes, primary (OSM classes primary and trunk) and secondary (all other OSM classes). Then we overlay Google Traffic’s color-coded pixels on the OSM map and estimate vehicle speeds by road class and color code using trip routings for a sample of realtime Google Traffic information. Table 1 reports our results for two road classes and four color codes.

**Table 1:  Google Traffic indicators and speeds (km/hr) in Dar es Salaam**

|Color|OpenSt<br>Roa|reetMaps<br>d Type|
|---|---|---|
|Indicator|Primary|Secondary|
|Green|45|24|
|Orange|23|16|
|Red|11|7|
|Purple|7|4|
|Source: Goog|le Traffic||

Figure 2 provides a composite summary of traffic data for the metro region. Since the database is formatted at grid scale, it could also produce the same display for each grid cell. Figure 2 displays regional hourly median and 1st-quartile traffic speeds on primary roads. Several features of the display are particularly evident. First, driving on primary roads is much faster on Sunday, the least congested day, and somewhat faster on Saturday Second, on weekdays traffic speed declines steadily from its midnight peak to around 8 AM; remains roughly in the same range until midafternoon; declines rapidly through 6-7 PM; and then increases rapidly until midnight. Third, the path of Q1 shows that the worst traffic conditions display more volatility than median conditions. On weekdays, Q1 speed falls twice daily from about 43 km/hr near midnight to 25-30 km/hr during the morning and evening peak traffic hours.

7
- Prüss-Üstün, Annette, and Carlos Corvalán. 2006. _Preventing Disease through Healthy Environments: Towards an Estimate of the Environmental Burden of Disease_ . Geneva: World Health Organization.

- Prüss-Üstün, Annette, Robert Bos, Fiona Gore, and Jamie Bartram. 2008. _Safer Water, Better Health: Costs, Benefits and Sustainability of Interventions to Protect and Promote Health_ . Geneva: World Health Organization.

- Rubin, Donald B. 2001. “Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation.” _Health Services and Outcomes Research Methodology_ 2 (3–4): 169–88.

- Senn, Stephen. 2013. “Seven Myths of Randomisation in Clinical Trials.” _Statistics in Medicine_ 32 (9): 1439–50. doi: 10.1002/sim.5713.

- United Nations. 2016. “Transforming Our World: The 2030 Agenda for Sustainable Development.” <u>https://sustainabledevelopment.un.org/post2015/transformingourworld.</u>

- Waddington, Hugh, Birte Snilstveit, Howard White, and Lorna Fewtrell. 2009. _Water, Sanitation and Hygiene Interventions to Combat Childhood Diarrhoea in Developing Countries_ . New Delhi: International Initiative for Impact Evaluation.

- WHO (World Health Organization). 2004. _Evaluation of the Costs and Benefits of Water and Sanitation Improvements at the Global Level_ . Geneva: World Health Organization.

- World Bank. 2016 _._ “How Sustainable Are Sanitation Outcomes in Clean Village Prize Winners? Findings of a Large-Scale Rapid Assessment in India.” Water and Sanitation Program Learning Note, World Bank, Washington, DC.

- Ziegelbauer, Kathrin, Benjamin Speich, Daniel Mäusezahl, Robert Bos, Jennifer Keiser, and Jürg Utzinger. 2012. “Effect of Sanitation on Soil-Transmitted Helminth Infection: Systematic Review and Meta-Analysis.” _PLoS Medicine_ 9 (1): e1001162.

39
- 2 -

# **I. INTRODUCTION**

Agrobusinesses, such as input suppliers, collectors, processors and exporters, play an important role to facilitate agricultural growth in many developing countries (e.g., World Bank, 2013). Especially in Africa where the production system remains largely subsistence farming, a large number of rural farmers live under the poverty line and do not have their own transport means. To purchase advanced inputs, such as fertilizer and improved seeds, and access the output market to sell harvested crops, transportation services must of necessity be provided by agribusinesses or trucking companies. Unavailability of affordable transportation has been recognized as one of the significant constraints in many African countries (Teravaninthorn and Raballand, 2009). In Ethiopia, for instance, transport costs account for 64-80 percent of fertilizer farmgate prices (Rashid et al. 2013).

Despite its clear importance, agrobusiness is still hardly available to many farmers in Africa. First of all, regardless of sectors, the formal private sector remains generally thin in the region. This is not a unique problem for the agrobusiness industry. Even though there are some agrobusinesses in a country, they are often only located in major urban areas or even abroad. In Rwanda, for instance, 47 percent of agrobusinesses are located at the primary city, Kigali, while about 90 percent of the country’s total population live elsewhere (Iimi et al., 2015). While West African countries account for about 70 percent of the global cocoa production, the EU accounts for 40 percent of the world’s cocoa processing from cocoa beans to cocoa paste and butter (World Bank, 2013). A natural question is why agribusinesses are not located in local production areas despite clear benefits from close proximity to their inputs.

The economics literature has long been discussing where a firm should be located. In general, the traditional location theory initiated by Moses (1958) suggests that the optimal location of a firm can be determined by the production and demand functions and transportation rates of inputs and outputs. One of the major conclusions in the early literature is that the optimal location is independent of the demand in the output market as long as the production function
- 12 -

term is included to allow the model to be flexible enough.<sup>8</sup> As discussed above, the firm’s optimal level is expected to decrease with purchasing costs, largely depending on transport infrastructure conditions.

Transport accessibility can be measured in many ways. Three measurements are considered: (i) local connectivity (ii) distance to the nearest port, and (iii) lowest transport cost to the port. First, local connectivity is measured by road density around each city and denoted by _CONN_ 1. While traffic congestion in urban areas is becoming increasingly heavy in Africa, higher road density is by and large expected to reduce local transportation costs and increase proximity among firms within the city. As the result, firm inventory could be reduced.

Second, distance to the seaport may be a good proxy to measure port connectivity and therefore firm inventory costs. As discussed, many firms in East Africa are involved in regional and international trade and business. A bottleneck is often connectivity to the seaports, and thus, global markets, especially in inland cities and landlocked countries. To calculate the road distance to the nearest seaport ( _CONN_ 2), a spatial technique is used with the national road network data merged within the region (Figure 9). In this paper, only two major seaports in the region are considered: Dar es Salaam and Mombasa.

Finally, the lowest possible transport cost per ton-km to the port is calculated (denoted by _CONN_ 3). Physical distance may not well capture the actual transport connectivity. What really matters may be the transport cost that firms have to shoulder. This should include not only road user costs and fees and charges paid for transport operators but also opportunity time costs to wait at transport nodes, such as borders and ports. “Economic borders” are often thick in Africa (World Bank 2009). Therefore, regional trade and transport costs may be high even between neighboring countries in the same region.

> 8 In reality, firms need to hold certain level of inventory, if the purchasing costs are small. This is simply because firms prefer to prepare for unanticipated shocks and not to miss possible business opportunities. On the other hand, holding too much inventory is also unrealistic because of physical and financial reasons.
Figure 2: Secondary Education Inequality and GDP <!-- Start of picture text --> 2010 Ratio of Female to Male Secondary School Attainment 2010 Ratio of Female to Male Secondary School Attainment<br>Ratio of Female to Male Regression Coefficient:4.27<br>Percentage of over-25 Population Standard Error:0.44<br>With Secondary Education<br>Median=0.926<br>.2 .4 .6 .8 1 1.2 .2 .4 .6 .8 1 1.2<br>Ratio of Percentage of Over-25 Female Population Ratio of Percentage of Over-25 Female Population<br>With At Least Secondary Education With At Least Secondary Education<br>To Corresponding Male Number To Corresponding Male Number<br>.3 12<br>10<br>.2<br>8<br>.1<br>Fraction of Countries 6<br>Log of 2009 Per-Capita GDP<br>0 4<br><!-- End of picture text --> From the 2010 United Nations Development Programme Human Development Report: we find the female(male) secondary school attainment, equal to the percentage of the over‐25 female(male) population with at least secondary school. We then take the ratio of female to male secondary school attainment.
Policy Research Working Paper 5932

# **Abstract**

The paper explores existing patterns of green innovation and presents an overview of green innovation policies for developing countries. The key findings from the empirical analysis are: (1) frontier green innovations are concentrated in high-income countries, few in developing countries but growing; (2) the most technologically-sophisticated developing countries are emerging as significant innovators but limited to a few technology fields; (3) there is very little South-South collaboration; (4) there is potential for expanding green production and trade; and (5) there has been little base-of-pyramid green innovation to meet the needs of poor consumers, and it is too early to draw conclusions about its scalability. To promote green innovation, technology and environmental policies work best in tandem, focusing on three complementary areas: (1) to promote frontier innovation, it is advisable to limit local technology-push support to countries with sufficient technological capabilities—but there is also a need to provide global technology-push support for base-ofpyramid and neglected technologies including through a pool of long-term, stable funds supported by demandpull mechanisms such as prizes; (2) to promote catch-up innovation, it is essential both to facilitate technology access and to stimulate technology absorption by firms— with critical roles played by international trade and foreign direct investment, with firm demand spurred by public procurement, regulations and standards; and (3) to develop absorptive capacity, there is a need to strengthen skills and to improve the prevailing business environment for innovation—to foster increased experimentation, global learning, and talent attraction and retention. There is still considerable progress to be made in ranking green innovation policies as most appropriate for different developing country contexts—based on more impact evaluation studies of innovation policies targeted at green technologies.

This paper is a product of the Economic Policy and Debt Department, Poverty Reduction and Economic Management Network. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The author may be contacted at mdutz@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team