rafmacalaba/datause-encoder-data huggingface dataset:
and groundnuts. Our cost estimates, however, focus on those feedstocks which are popularly grown in Zambia.

# **2.1 Data Sources for Estimating Production Costs**

## 2.1.1 Data for Production Costs of Feedstock

For soybeans, Jatropha, groundnuts, sunflower, sugarcane and partially sweet sorghum, feedstock production costs were obtained locally from the Ministry of Agriculture and Livestock, Zambia National Farmers Union (ZNFU, 2010/2011), SEED-CO (2010 – 2011), and the Biofuels Association of Zambia (BAZ). Production costs for other feedstocks were estimated based on data obtained from other countries, as follows: palm oil (Malaysia, Indonesia and Nigeria), sweet sorghum (Philippines, EU and USA), castor (India), cassava (Nigeria and Thailand), and Agave (Australia and Mexico).

Although sugarcane and cassava are popularly grown in Zambia, no information on production costs are readily available. Oil palm is grown at small scale in Luapula Province and also in Mpika in Northern Province where a Zambian company Zamanita has so far established 3,500 Ha plantation out of a planned 20,000 hectares. University of Zambia has undertaken research activities on sweet sorghum, but there is no commercial production yet (Remvos, 2011). Jatropha is wide-spread at small scale in Zambia. Information on this crop was assembled from the Biofuels Association of Zambia including the main author of this article. Agave is grown only as an ornamental crop in Zambia although the country’s climate and agronomic conditions are conducive to grow it in a large-scale. Agave is therefore also included in our analysis.

## 2.1.2 Data for Biofuels Processing Costs

Since no commercial production of biodiesel and bioethanol exist in Zambia, biofuels processing costs are obtained from a study carried out by APEC (Asia Pacific Economic Cooperation) in December 2010 (APEC Secretariat, 2010) for Brazil, Malaysia and the USA. The APEC study estimated processing costs over 10 years and found that the costs are relatively stable over the period.

## 2.1.3 Assumptions for Cost Estimation

We made the following assumptions to estimate production costs of biofuels: 5
Figure 17. Credit Card Ownership <!-- Start of picture text --> Equity Adjust Coverage, (Individual level, FINDEX)<br>2011 2014<br>60 60 54.7 54.9<br>50 45.1 45.1 46.0 50 46.4<br>41.6 42.4 42.9<br>38.9<br>40 35.3 40 36.6<br>32.5 33.2 32.7 33.7 32.8 32.7<br>28.9 29.7<br>30 26.3 30 23.7 26.724.1<br>21.7 21.8<br>20 16.6 13.6 20 15.3<br>10.6<br>10 10<br>0 0<br>Netherlands Spain Turkey Denmark Taiwan, Netherlands Spain Turkey Denmark Taiwan,<br>China China<br>Coverage Dissimilarity EACR<br>Equity Adjust Coverage, (Household-level, LITS)<br>80<br>69.4<br>70 64.4<br>60 52.2 55.4 55.9 57.2<br>50 45.7 42.3 47.4 48.7<br>40<br>30 23.7<br>20 12.4 15.1 14.8<br>7.1<br>10<br>0<br>France Hungary Great Britain Turkey Sweden<br>Coverage Dissimilarity EACR<br><!-- End of picture text --> _Notes:._ FINDEX is an individual level data base and LITS is household level. In FINDEX, 145 countries are sorted on Coverage, and only nearest neighbors in coverage rates are shown. In LITS, 35 countries are sorted on Coverage, and only nearest neighbors in coverage rates are shown. See Appendix for all countries. _Sources_ : FINDEX, LITS Coverage of credit card ownership in Turkey is considerably higher at the household level than at the individual level (Figure 17). This difference is noteworthy since a similar change is not observed for the coverage rate in debit card ownership.

Compared to the benchmark countries, the effect of age on the d-index seems lower across the Turkish population (Figure 18) in 2011. At the individual level, the primary explanatory variables for dissimilarity in credit card usage are gender and age. However, at the household level, the dissimilarity in credit card usage across groups is driven more by the education of the household and its income level.

Between 2011 and 2014, Turkey succeeded in reducing the negative impact of gender disparity in credit card ownership as the contribution of gender fell form 44.4 percent to 31.7 percent. Age became the largest contributor to the d-index by 2014 as the percentage of 15-24 year olds that had a credit card fell from 32 to 12 in three years. This was a sharp reduction, however in comparison to benchmark country groups, 12 percent ownership at the youngest measured cohort was a more expected level for Turkey.

19
Table 3: Summary statistics the Krugman specialization measure

|**Statistics**|**All**|Small|**City size**<br>Medium|Large|
|---|---|---|---|---|
|Mean|0.408|0.442|0.379|0.271|
|Median|0.232|0.249|0.265|0.138|
|Minimum|0.000|0.000|0.000|0.020|
|Maximum|1.958|1.958|1.347|1.021|
|Standard deviation|0.426|0.461|0.359|0.285|
|Number of cities|300|199|66|35|
|**Share of speci**|**alized ci**|**ties**|||
|Status|All|Small|Medium|Large|
|Highly specialized (ksi_>_0.75)|21.00|25.13|15.15|8.57|
|Weakly specialized (ksi_∈_(0.35, 0.75])|20.33|18.09|28.79|17.14|
|Not specialized(ksi_<_0.35)|58.67|56.78|56.06|74.29|
|Total|100.00|100.00|100.00|100.00|

_Notes:_ Authors’ computations using gec data. Mean and median values of the Krugman Specialization Index (ksi) for 300 arrondissements in Cameroon. Small, medium, and large cities are respectively cities with less than 50,000, between 50,000 and less than 100,000, and more than 100,000 inhabitants in 2011. Population data are from Cameroon Census Bureau (bucrep), and the National Institute of Statistics (ins).

have been extensively used in the literature (see Glaeser and Kerr, 2009). The first approach consisted of using (i) the share of startups within a single industry, (ii) the new product introductions, (iii) the venture capital placement, and (iv) the creation of new firms. All these metrics allow to capture the dynamic nature of entrepreneurship. However, obtaining these data and information is difficult, especially in the developing context and for confidentiality reasons. The second approach consists in associating entrepreneurship with the number of people leading independent enterprises. To this end, relevant metrics are (i) self-employment, and (i) average firm size. Hence, in this paper, we define as an informal entrepreneur a self-employed in the informal sector who owns a business that has at least one employee. The definition of formal entrepreneur is the traditional one (that is a self-employed operating in the formal sector) Despite significant improvement in the government willingness to improve the business climate in Cameroon, more than 50% of entrepreneurs are not satisfied with the overall business environment. Table 4 reports, for each 2-digit industry the weighted share of firms that identify a given factor as one of the top five most challenging con15
# **References:**

Anderson, James E. (1979). “A Theoretical Foundation for the Gravity Equation.” _American Economic Review_ 69: p.106-116.

Anderson, James E. and Eric van Wincoop (2003). “Gravity with Gravitas: A Solution to the Border Puzzle.” American Economic Review v93, n1: 170-92.

Asia Pacific Economic Co-operation (APEC) (1999). _Assessing APEC Trade Liberalization and Facilitation: 1999 Update, Economic Committee_ , September 1999. APEC: Singapore.

Asia Pacific Foundation of Canada (1999). _Survey on Customs, Standards and Business Mobility in the APEC Region._ APF Canada: Vancouver.

Balistreri, Edward J. and Russell H. Hillberry (mimeo). “Trade Friction and Welfare in the Gravity Model: How Much of the Iceberg Melts?” U.S. International Trade Commission, Washington, D.C.

Clark, Ximena, David Dollar and Alejandro Micco. (2002). “Maritime Transport Costs and Port Efficiency.” World Bank Working Paper Series # 2781. The World Bank: Washington, D.C.

Dollar, David and Aart Kraay (2001). “Trade, Growth, and Poverty” World Bank Working Paper Series #2615. The World Bank: Washington D.C.

Fink, Carsten, Aaditya Mattoo and Cristina Ileana Neagu (2002a). “Trade in International Maritime Services: How Much Does Policy Matter?” World Bank Economic Review v16, n1 (2002): 81-108.

Fink, Carsten, Aaditya Mattoo and Cristina Ileana Neagu (2002b). “Assessing the Role of Communication Costs in International Trade.” World Bank Working Paper #2929. The World Bank: Washington, D.C.

Frankel, Jeffrey A and Rose, Andrew K. (2000). “Estimating the Effect of Currency Unions on Trade and Output.” _National Bureau of Economic Research Working Paper_ #7857.

Freund, Caroline and Diana Weinhold (2000). “On the Effect of the Internet on International Trade.” International Finance Discussion Papers #693, Board of Governors of the Federal Reserve System.

Hertel, Thomas W., Terrie Walmsley; and Ken Itakura (2001). “Dynamic Effect of the "New Age" Free Trade Agreement between Japan and Singapore.” _Journal of Economic Integration_ v16, n4: p. 446-84.

25
able from January 2008 to March 2009. The savings data include monthly aggregate deposit and withdrawal amounts, for compulsory savings accounts.

As previously described, in our main analysis we study borrowers who have received both individual and group liability loans to overcome the selection problem. Hence, in our dataset, these clients begin with an individual liability loan, and after November 2007, receive a group liability loan. Within Saath’s Behrampura and Vasna locations, we find a sample of 276 such clients, representing 22% of the loan client base in these two branches as of March 2009.

Table 1 provides summary statistics for our sample. Collectively, these clients received a total of 748 loans from Saath, 450 of which are individual liability loans and 298 are group liability loans. The average individual liability loan amount is about INR 10,000 (USD 220), and about INR 18,000 (USD 390) for group liability loans. Figure 1 plots the number of group liability loans that are disbursed over time. As the figure shows, the borrowers in our sample, all of whom received individual liability loans, switched to group liability loans at varying months. Our empirical strategy takes advantage of this staggered timing, comparing individual liability loan clients who have already received group liability loans to future recipients, to identify the impact of group liability on loan repayment behavior and savings discipline.

# **5 Empirical Strategy and Analysis**

## **5.1 Empirical Strategy**

Our first empirical tests measure the extent of assortive matching, using two complementary sets of analysis. First, we use a simple regression framework. For each individual _i_ who belongs to a joint liability borrower group, we define a measure of ‘reliability,’ _ri_ , (discussed in greater detail below), as well as the average reliability of that individual’s group members (i.e., _yi_ = _N_ <u>1</u> _Gi_ � _j∈Gi,j_ = _i_<sup>_rj_,</sup> where _NGi_ is the number of borrowers in the group _Gi_ of which _i_ is a member). We thus regress: clustering standard errors at the borrower-group level.

Our second measure is based on a permutation test. We first randomly assign members into 12
suggest a positive relationship between changes in the real lending rate and financial development, the investment to GDP ratio and per capita income.

The positive relationship between financial development, the investment to GDP ratio and per capita income is driven by the post-FSRP period 1991 to 2005. The results for the subsample 1991 to 2005 closely match those for the overall sample period. Further, the real lending rate is significantly and negatively correlated with financial development, investment activity and per capita income in the post-FSRP subsample. However, the regression results do not suggest a significant relationship between financial development, the investment to GDP ratio and income per capita for the pre-FSRP period 1976 to 1991. Whereas the investment to GDP ratio is positively and significantly related with per capita income over this period, financial development enters the long-run responses of investment-GDP share and income per capita with insignificant and negative coefficients.

The insignificant relationship between finance, investment and income for the period of financial repression matches cross-country experience discussed below and carries some first important policy conclusions. Specifically, a financial system that relies heavily on nationalized banks, interest rate controls and directed credit cannot fulfill its important role in channeling society’s savings into investments and fostering economic growth.

# **3. Bangladesh’s financial system in international comparison**

The development and efficiency of Bangladesh’s banking system is at a comparable level with other South Asian countries and at a higher level than the average for low-income countries, while other segments of the financial system, such as the insurance sector and the stock market, are substantially less developed than in comparator countries. Further, the banking sector has 6
- 3 -

As a result, in many developed countries and rapidly growing emerging economies, firm agglomerations and industrial clusters have been established (e.g., Yusuf et al. 2008). In Africa, there are only a few examples of this occurring, such as the textile sector in East Africa (Otsuka and Sonobe 2011). Africa has lagged behind in the global manufacturing market since the 1960s. Among others, low labor productivity and lack of land access are significant constraints in Africa (Dinh et al. 2012). Poor infrastructure is another constraint (Harrison et al. 2011).

Empirically, there are methodological challenges to exploring the relationship between firm location, productivity, and transport infrastructure. First, infrastructure investment is often endogenous. Firms tend to be located where transport connectivity is better, but causality is less obvious than it would appear. Firms can be more productive because of better mobility of goods and people, but at the same time, governments may invest more in transport infrastructure where firms already exist. Thus, causality remains unclear. Recent publications propose different instrumental techniques to avoid this endogeneity problem (e.g., Chandra and Thompson 2000; Banerjee et al. 2012; Jedwab and Moradi 2012).

Another challenge is spatial autocorrelation. Transport infrastructure, by nature, forms a network. Thus, any improvement in transport connectivity has local and spillover effects. The extent to which firms would be affected is normally considered to depend on proximity to the improvement. Tobler’s (1970) first law of geography says that, “everything is related to everything else, but near things are more related than distant things.” Spatial autoregressive models address this problem. Wang, Kochelman, and Damien (2012) used the spatial autoregressive probit model to analyze land use in Austin, Texas. Other authors have incorporated possible spatial autocorrelation into the count data regression context (e.g., Bhat, Paleti, and Singh 2014).

The current study examined whether transport connectivity affects firms’ decisions on where to locate and how firms’ decisions affect those of other firms. Using the firm registry data in
in the individual circumstances generates the randomness in the treatment assignment. A necessary condition for a quasi-experiment analysis is that there should be no systematic variation in the unobservable characteristics between the pre and post-treatment groups.

The natural experiment approach employed in this paper is based on the use of crosssectional data from two time periods to compare the employment of children who would have been affected by the implementation of the EoCA 1991, with that of a similar group who were not affected by it. The identification strategy relies on a regression discontinuity framework applied to the pre and post-treatment period and the data used, with two pre-treatment and two post-treatment groups allows us to control for the differential trends by structuring the regression discontinuity design on a difference-in-difference approach to isolate the effect of the law on employment of children from other factors determining the labor supply decision. A direct comparison of the two groups would not be appropriate to identify the causal effect of the law since even in the absence of the law, younger children have a lower probability of working than older children, or there may be age-specific unobservables that could affect the results. The difference-in-difference estimator compares the difference in the employment probability of children in the two groups in a period after the implementation of the Act with the equivalent difference in an earlier period when the EoCA 1991 was not introduced.

To be more specific, assume that _Di=1_ if a treatment is introduced and zero in the absence of the treatment. Let _Y0i_ be the outcome for individual _i_ in the absence of the treatment and _Y1i_ be the outcome for the individual in the presence of the treatment. However, for any single individual only one of these outcomes is ever observed. Since both _Y0i_ and _Y1i_ are never 12
**Table 1. Indonesia Human Capital Index by gender (2018)**

|Component|Boys|Girls|Overall|
|---|---|---|---|
|HCI|0.52|0.55|0.53|
|Survival to Age 5|0.97|0.98|0.97|
|Expected Years of School|12.2|12.4|12.3|
|Harmonized Test Scores|398|408|403|
|Learning-adjusted Years of School|7.8|8.1|7.9|
|Adult Survival Rate|0.8|0.86|0.83|
|Not Stunted Rate|0.65|0.67|0.66|

Source: World Bank Human Capital Project Globally, girls tend to do better than boys on international standardized tests of reading, including the Programme for International Student Assessment (PISA) (Stoet and Geary, PLOS ONE, March 2013). Indonesia is on-trend with this global statistic, with girls outperforming boys by a wide margin (Figure. 1).

**Figure 1. Trends in reading, PISA 2000-18, by gender**

<!-- Start of picture text --> 425 419<br>415 410 409<br>402<br>405<br>394<br>395<br>383<br>380<br>385<br>386<br>375 384 383 382<br>365<br>369<br>355<br>360<br>358<br>345<br>2000 2003 2006 2009 2012 2015 2018<br>Year<br>Boys Girls<br>Reading Score<br><!-- End of picture text --> Source: PISA, OECD, various years In mathematics, boys’ mean performance in OECD countries is on average five points higher than girls’, and girls outperformed boys in science just by two score points (OECD 2019, PISA Insights and Interpretations). In Indonesia, however, girls scored higher than boys in mathematics by 10 score points, and girls outperformed boys in science by seven score points (OECD 2019, Indonesia Country Note). These gendered trends in performance start early in Indonesia. Based on a sample of more than 10,000 children between the ages of 6 and 9 in rural Indonesia, a World Bank study found higher levels of cognitive skills for girls on language and mathematics test scores. For example, at age 6, girls on average have a significantly higher score than boys by 0.25 standard deviation in Bahasa Indonesia and 0.24 standard deviation in mathematics. The same study found that 8-year-old girls perform significantly better than boys in non-cognitive skills, with higher scores on the social competence (0.25 standard deviation) and 3
## **Figure 27. Uganda’s pattern of capital investment in infrastructure differs from that of comparator countries, 2001-2009**

<!-- Start of picture text --> Investment in infrastructure sectors as percentage of GDP, by source<br>3.0 Uganda LIC-Non-Fragile<br>2.0<br>1.0<br>0.0<br>Public ODA Non-OECD Private<br>% of GDP<br><!-- End of picture text --> _Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009). _Note:_ Private investment includes self-financing by households.

ODA = official development assistance; OECD = Organisation for Economic Co-operation and Development; ICT = information and communication technology; GDP = gross domestic product; WSS = water supply and sanitation; LIC = low-income country.

# How much more can be done within the existing resource envelope?

_Inefficiencies in Uganda divert as much as $307 million in resources (table 18)._ By far the largest source of inefficiencies is the power sector, where underpricing and distribution losses _each_ hemorrhage more than $100 million a year. The same types of inefficiencies are also pronounced in the water sector, but the volume of resources involved is much smaller at under $40 million in total. Another significant issue is underexecution of capital budgets in the transport sector, leading to losses of $24 million a year.

**Table 18. Uganda’s potential gains from greater operational efficiency, 2001-2009**

US$ million

||ICT|Irrigation|Power|Transport|WSS|Total|
|---|---|---|---|---|---|---|
|Under-recovery of costs|—|n.a.|103|n.a.|21|124|
|Overstaffing|n.a.|—|8|—|5|13|
|Distribution losses|—|—|116|—|11|127|
|Undercollection|—|n.a.|15|n.a.|0|15|
|Low budget execution|0|0|1|24|2|27|
|Total|0|0|243|24|39|307|

_Source_ : Derived from Foster and Briceño-Garmendia (2009). _Note:_ WSS = water supply and sanitation; ICT = information and communication technology. — = Not available. n.a. = Not applicable.

_Underpricing of utility services is a significant problem for Uganda, although this problem is even more serious among Uganda’s peers_ . Mispricing of power costs Uganda about $103 million each year, or around 0.7 percent of the country’s GDP. It is estimated that the average total cost of producing electricity 45
|P132101|2013|2016|2/2/2016|6 Closed|Madagascar|
|---|---|---|---|---|---|
|P132101|2013|2017|8/30/2016|7 Closed|Madagascar|
|P132101|2013|2017|2/23/2017|8 Closed|Madagascar|
|P132101|2013|2018|9/22/2017|9 Closed|Madagascar|
|P132101|2013|2018|3/21/2018|10 Closed|Madagascar|
|P132101|2013|2019|7/5/2018|11 Closed|Madagascar|
|P132116|2014|2014|10/31/2013|1 Closed|Yemen,Republic of|
|P132116|2014|2014|5/6/2014|2 Closed|Yemen,Republic of|
|P132116|2014|2015|11/19/2014|3 Closed|Yemen,Republic of|
|P132116|2014|2015|5/27/2015|4 Closed|Yemen,Republic of|
|P132116|2014|2016|11/20/2015|5 Closed|Yemen,Republic of|
|P132116|2014|2016|5/26/2016|6 Closed|Yemen,Republic of|
|P132116|2014|2017|12/2/2016|7 Closed|Yemen,Republic of|
|P132119|2014|2017|7/26/2016|1 Closed|Marshall Islands|
|P132119|2014|2017|5/17/2017|2 Closed|Marshall Islands|
|P132119|2014|2018|12/7/2017|3 Closed|Marshall Islands|
|P132119|2014|2018|6/15/2018|4 Closed|Marshall Islands|
|P132119|2014|2019|7/31/2018|5 Closed|Marshall Islands|
|P132119|2014|2019|12/18/2018|6 Closed|Marshall Islands|
|P132119|2014|2019|6/26/2019|7 Closed|Marshall Islands|
|P132123|2015|2015|6/25/2015|1 Active|Eastern Africa|
|P132123|2015|2016|12/24/2015|2 Active|Eastern Africa|
|P132123|2015|2016|6/23/2016|3 Active|Eastern Africa|
|P132123|2015|2017|12/28/2016|4 Active|Eastern Africa|
|P132123|2015|2017|6/23/2017|5 Active|Eastern Africa|
|P132123|2015|2018|1/4/2018|6 Active|Eastern Africa|
|P132123|2015|2019|7/10/2018|7 Active|Eastern Africa|
|P132123|2015|2019|10/16/2018|8 Active|Eastern Africa|
|P132123|2015|2019|2/25/2019|9 Active|Eastern Africa|
|P132123|2015|2020|9/10/2019|10 Active|Eastern Africa|
|P132123|2015|2020|4/7/2020|11 Active|Eastern Africa|
|P132134|2016|2019|5/7/2019|1 Dropped|Pacific Islands|
|P132138|2013|2013||0 Closed|Bangladesh|
|P132154|2013|2013||0 Closed|China|
|P132154|2013|2014|9/15/2013|1 Closed|China|
|P132154|2013|2014|2/10/2014|2 Closed|China|
|P132154|2013|2015|9/1/2014|3 Closed|China|
|P132154|2013|2015|1/18/2015|4 Closed|China|
|P132154|2013|2016|10/8/2015|5 Closed|China|
|P132154|2013|2016|3/14/2016|6 Closed|China|
|P132154|2013|2017|7/26/2016|7 Closed|China|
|P132154|2013|2017|12/17/2016|8 Closed|China|
|P132154|2013|2017|5/8/2017|9 Closed|China|
|P132154|2013|2018|12/8/2017|10 Closed|China|
|P132154|2013|2018|6/25/2018|11 Closed|China|
|P132154|2013|2019|1/1/2019|12 Closed|China|
|P132154|2013|2019|6/25/2019|13 Closed|China|