rafmacalaba/datause-encoder-data huggingface dataset:
variation is between 0.273 and 0.353 and the total processing time between 58 and 70 days, the effect of a 1 percent improvement in σ is in the range of 0.2801–0.3621 percent and that of a 1 percent improvement in _T_ in the range of 0.0733–0.2206 percent. Note that the higher is the coefficient of variation, the lower is the impact of a one percent improvement in the total processing time. In the case of trade from Pakistan to the UK, for example, the high variability of processing time results in very modest improvement in trade when processing time is improved (without reducing the variability).

**Table 6. Illustrative Example of Impacts of Logistics Improvements on Bilateral Trade**

||**Value of****_T_**<br>**(days)**|**Value of**σ**/****_T_**|**Elasticity of**σ<br>**(**∂**_S_/**∂σ**)/(****_S_/**σ**)**|**Elasticity of****_T_**<br>**(**∂**_S_/**∂**_T_)/(****_S_/****_T_)**|
|---|---|---|---|---|
|**Brazil to US**|59.7|0.285|-0.2924|-0.1445|
|**China to US**|58.5|0.273|-0.2801|-0.1480|
|**Pakistan to US**|61.9|0.291|-0.2985|-0.1544|
|**Brazil to UK**|70.1|0.285|-0.2924|-0.2206|
|**China to UK**|60.3|0.315|-0.3231|-0.1181|
|**Pakistan to UK**|59.5|0.353|-0.3621|-0.0733|

Note: The two columns on the right hand side of the table shows the effect of a 1 percent reduction in the logistics variables σ and _T_ .

Source: Authors’ calculations.

Most of the second derivatives are positive; the only one with an unclear sign is ∂<sup>2</sup> _S_ / ∂ _T_<sup>2</sup> . But for reasonably large _T_ values, such as 30–100 days, this second derivative is also positive. The _S_ function is convex in the four variables that make up the logistics index. If _d_ is replaced by _d_ − Δ _d_ , _C_ by _C_ − Δ _C_ , _T_ by _T_ − Δ _T_ , and σ by σ − Δσ , where all the deltas represent decision variables on the magnitudes of logistics improvements, the _S_ function is a concave function of the delta variables. Let _wd_ , _wC_ , _wT_ , and _w_ σ be the respective cost per unit of improvements in _d_ , _C_ , _T_ , and σ . Suppose there is a total budget of _B_ to spend on logistics improvements. Then: _wd_ Δ _d_ + _wC_ Δ _C_ + _wT_ Δ _T_ + _w_ σΔσ = _B_ . (2) An optimal solution to the well-defined nonlinear optimization problem of maximizing (1) subject to (2) would produce the optimal allocation of resources across the possible logistics improvements.

The analysis above assumes that σ is independent of _T_ . In reality, it is possible that σ is a function of _T_ ; indeed, it is likely that σ is an increasing function of _T_ . If so, the explicit relationship between the two needs to be defined, and the impact of these variables on _S_ would 24
# Table 4. Stochastic Frontier Analysis

_Dependent Variable = Log of Total Maize Grain Production (KG)_

||**Ove**|**rall**|**S1**<br>|**S**|**2**|**S3**<br>|
|---|---|---|---|---|---|---|
|Highest Membership Grade|MAPS|AFSIS|MAPS|MAPS|AFSIS|MAPS|
|Distance from S1|-0.075***|-0.060***|||||
|Plot Area (Hectares, Logged)|0.993***|0.988***|1.018***|0.977***|0.995***|1.094***|
|Household Labor Days (Logged)|-0.048|-0.050|-0.121*|-0.038|-0.045|-0.080|
|Hired Labor Days (Logged)|0.003|0.007|0.023|0.005|0.001|0.001|
|Inorganic Fertilizer (KG, Logged)|0.023*|0.015|0.008|-0.009|-0.014|0.024|
|KG of Seed Planted (Logged)|0.080**|0.081**|0.041|0.125***|0.114***|-0.199**|
|Intercropping Rate (Logged)|0.200***|0.194***|0.087|0.174**|0.167**|0.110|
|Pure Stand Maize°|0.206***|0.233***|0.374**|0.253***|0.283***|0.169|
|Flowering Season Rainfall (mm, logged)|0.376*|0.737***|†|0.27|0.504*|-0.734|
|Constant|5.072***|2.939**|7.851***|5.017***|3.825**|11.506***|
|_Technical Inefficiency_|||||||
|Manager Completed Primary Education°|-0.011|-0.001|-0.167|0.010|0.01|-0.100|
|Manager Age|-0.008|-0.007|-0.037|0.008|0.003|-0.076|
|Manager Age (squared)|0.000|0.000|0.000|0.000|0.000|0.001*|
|Manager Received Agricultural Extension Services°|†|†|0.284|0.076|0.066|0.570*|
|Dependency Ratio|-0.029|-0.026|0.004|-0.016|-0.029|-0.297**|
|Count of Agricultural Assets|-0.019|-0.019|-0.017|-0.021|-0.02|-0.042|
|CV of Flowering Season Rainfall (1999-2014)|1.303|1.047|-1.836|1.048|2.006**|†|
|Constant|6.262***|6.337***|3.859**|6.387***|5.850***|3.586***|
|_Random Error Term (v)_|||||||
|Constant|-1.949***|-1.857***|-3.140***|-1.807***|-1.718***|-2.033***|
|N|840|840|106|634|736|100|

Notes: Standard errors clustered at EA level; † = insufficient variation for inclusion of variable or high correlation with other covariates; ° = Binary variable; *** p<0.01, ** p<0.05, * p<0.1 18
output to a government consumption shock is positive. Figure 4(a) shows that the size of the response of real output and private consumption to discretionary government investment spending is greater during periods of depressed economic growth. Finally, the response of economic activity in Figure 6(a) shows that there is little di¤erence in the to a 1 percent tax cut in either state of nature.

Fixed coe¢ cient structural VARs tend to predict a rise in private consumption in response to a government spending disturbance. In contrast, event based studies, such as Ramey and Shapiro (1998) and Ramey (2009), report a decline in private consumption to a discretionary spending shock. This, it has been pointed out, is due to ...scal anticipation e¤ects which lead to mismeasurement of the timing of spending shocks. The ...ndings in this paper imply the response of private consumption to discretionary government spending in ...xed coe¢ cient models is dependent upon the component of government expenditure and the nature of the regime. For instance, the estimates show that under an expansionary growth state, discretionary government consumption crowds out private consumption.

2. It is instructive to look at the multiplier values. Comparing the response of real output and private consumption to government investment and government consumption shocks in Figures 4(b) and 5(b), the results show that government stimulus spending is more e¤ective - as measured by the size of the multiplier values - when the focus is on discretionary government investment spending. Conforming to (old) Keynesian theory a 1 dollar rise in government investment spending leads to a more than 1 dollar rise in real output. Figure 4(b) illustrates a peak median real output multiplier of around 2:7 during the recessionary state and a multiplier of around 2 during normal time to a government investment shock. The corresponding multiplier values for private consumption are 1:2 and 1:4, respectively. That the response of private consumption is more damped relative to real output is consistent with the permanent/life-cycle hypothesis. In contrast to Leeper, Walker and Yang (2009), the estimates appear to display little evidence of a negative short-run multiplier in response to a government investment shock.

Figure 5(b) reports the dynamic multiplier values to discretionary government consumption spending. Relative to the response for government investment spending, the long-run con...dence intervals for real output and private consumption are 14
TABLES

**Table 1. Summary statistics**

||a.|statistics of|indicators b|<br>y country and y|ear||b. statistics|of country a|<br>verages||
|---|---|---|---|---|---|---|---|---|---|---|
|Variables (%)|Obs|Mean|Median|Std. Dev.|Max|Obs|Mean|Median|Std. Dev.|Max|
|REM/GDP|2304|5.58|2.13|12.43|94.18|116|5.76|2.67|9.70|79.43|
|FDI/GDP|2304|3.29|1.79|5.50|86.24|116|3.58|2.61|3.52|14.57|
|ODA/GDP|2304|8.78|4.57|11.48|93.83|116|9.61|5.61|11.81|76.95|

# **Table 2. Stability of Capital Flows by Groups: Averages of Country Coefficients of**

# **Variation, 1980-2007**

# **a. Simple average**

||REM|FDI|ODA|
|---|---|---|---|
|ALL DEVELOPING COUNTRIES|0.75|1.47|0.47|
|East Asia and Pacific|0.78|1.49|0.44|
|Europe and Central Asia|0.82|1.04|0.39|
|Latin America and the Caribbean|0.90|0.86|0.56|
|Middle East and North Africa|0.31|1.67|0.54|
|South Asia|0.58|1.11|0.32|
|Sub-Saharan Africa|0.75|2.16|0.46|
|Low income|0.82|1.88|0.38|
|Lower middle income|0.73|1.35|0.42|
|Upper middle income<br>**b. GDP-weighted average**|0.72|1.20|0.62|
||REM|FDI|ODA|
|ALL DEVELOPING COUNTRIES|0.94|1.12|0.55|
|East Asia and Pacific|1.36|0.98|0.53|
|Europe and Central Asia|0.64|1.13|0.55|
|Latin America and the Caribbean|0.93|0.95|0.60|
|Middle East and North Africa|0.31|1.67|0.54|
|South Asia|0.67|1.40|0.36|
|Sub-Saharan Africa|0.87|1.78|0.61|
|Low income|0.91|1.81|0.66|
|Lower middle income|1.12|1.16|0.45|
|Upper middle income|0.79|1.02|0.62|

21
duty exemptions are applied to some food products to help stabilize prices. In times of crisis, emergency food or cash transfer programs to households have been rolled out. In recent years, a few small, localized nutrition schemes have targeted malnourished children, pregnant women, persons infected with HIV and TB patients. Several school-feeding programs, often targeted to girls, have been implemented in selected poor areas. Recent food-for-work schemes set up by the World Food Program sometimes also target women though the programs have limited coverage. Finally, the government has a program of health fee waivers for the elderly and indigent, and for women without resources.<sup>9</sup> The few, short-term, small and ad hoc targeted policies that exist often try to reach elderly women or school-aged girls, and to be gender sensitive. However, there is no sign that existing social protection schemes or policies in Mali have any focus on widows (whatever their age) or their dependents.<sup>10</sup> Neither the government nor donors appear to be particularly concerned about this group. In the next two sections, I turn to the data to examine whether they should be more concerned.

# **3. Are female-headed households poorer?**

This question is harder to answer than one might imagine, and the attempts to answer it given in the literature to date could well be wrong. A data problem lies at the heart of the issue. Examining whether and how the gender differentiation detailed above affects interpersonal differences in well-being is difficult empirically due to a lack of appropriate data. Individual level data detailing the intra-household allocation of resources, consumption, health, work and time allocation are typically not available. Thus, the poverty status or vulnerability of individual household members cannot be directly calculated from most household surveys. One common approach is to compare female- and male-headed households to infer effects of an individual‘s gender on welfare. However, female-headed households (FHH) can be extremely heterogeneous. Some have remitting adult male migrants, others are headed by poor widows who have no means of livelihood and yet others, by wealthy divorcees and other single women who have careers or other independent means of support. On balance, a majority of studies, though by no means all, find that FHH are indeed poorer in many parts of the world; see Buvinic

> 9   A comprehensive review of safety net policies in Mali is given in World Bank, 2010.

> 10  One exception is given by the small schemes targeted to orphans and their often widowed mothers by the NGO Islamic Relief.

9
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SADC separately. For the East African Customs Union, we took Kenya, Uganda, Rwanda and Burundi. For SADC we took Angola, Botswana, Democratic Republic of Congo, Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia, South Africa, Swaziland, Zambia and Zimbabwe. Trade shares for the “Africa” region in our model are the sum of East Africa Customs Union plus SADC. Rest of the World is the residual.

We mapped two digit sectors from the COMTRADE database into the sectors of our model. The exact mapping is defined in appendix A. We used Tanzania as the reporter country for both exports and imports. Results for both exports and imports are reported in tables A2 and A3 of appendix A.

# **Tariff Data—Collected Rates at the Tariff Line Level**

We were fortunate to receive unusually detailed collected tariff data from the Tanzania Revenue Authority. That is, we received data on collected import duties (tariffs) and import values at the eight digit tariff line level. The collected tariff rates for the sectors in our model are obtained by first aggregating the eight digit tariff line level tariff collections and import values to the sectors of our model. The ratio of tariff collections to import values for each sector of our model is then calculated to give estimates of the collected tariff rates, which in turn are incorporated into our SAM. The tariff rates are shown in Table 4. Applying these tariff rates across all sectors implies that tariff revenue in the revised database is about 1.3% of GDP, which is consistent with collected revenues in Tanzania.19 The SAM has some detail on taxes, which include direct taxes on households and enterprises, import tariffs, producer taxes, indirect (sales) taxes and factor taxes. The data for import tariffs are replaced with collected tariff rate data for the year 2006.

Given that Tanzania participates in preferential trade areas with the East Africa Customs Union and the South African Development Community, it was necessary to make further adjustments. That is, since, in principle, tariff rates should be zero within these preferential trade areas, we set tariff collections on imports from SADC and EACU (our Africa region) at zero. We

> 19 For the year 2006, aggregate data from Tanzania show that tariff collections are 1.47 percent of GDP.

33
Table 8: Geo-referenced Roads

||EQ1|EQ2|EQ3CF|
|---|---|---|---|
||(1)|(2)|(3)|
|ACD Confict|0.311<sup>_∗∗_</sup><br>(0.131)|||
|TI Corruption||0.372<sup>_∗∗∗_</sup><br>(0.072)||
|Length>than 50km|-.135<br>(0.087)|-.140<sup>_∗_</sup><br>(0.084)|-.192<sup>_∗∗_</sup><br>(0.08)|
|Population Density|0.015<sup>_∗∗_</sup>|0.012<sup>_∗∗∗_</sup>|0.005|
||(0.006)|(0.005)|(0.005)|
|Log Ruggedness|-.019|0.014|0.019|
||(0.027)|(0.019)|(0.025)|
|Log of Rainfall|0.057|-.212<sup>_∗_</sup>|0.162|
||(0.111)|(0.122)|(0.135)|
|Log distance to city|-.002|0.00009|-.004|
||(0.016)|(0.014)|(0.014)|
|Log distance to port|0.032|0.015|-.038<sup>_∗_</sup>|
||(0.02)|(0.021)|(0.021)|
|Estimate|0.292<sup>_∗∗∗_</sup>|0.362<sup>_∗∗∗_</sup>|0.271<sup>_∗∗∗_</sup>|
||(0.089)|(0.085)|(0.095)|
|Contract|0.026|0.063|0.061|
||(0.049)|(0.049)|(0.056)|
|World Bank|-.061|-.260<sup>_∗∗∗_</sup>|-.509<sup>_∗∗∗_</sup>|
||(0.097)|(0.097)|(0.131)|
|Bilateral Donor|-.106|-.275<sup>_∗∗∗_</sup>|-.285<sup>_∗∗∗_</sup>|
||(0.101)|(0.096)|(0.108)|
|Obs.|941|941|941|
|_R_<sup>2</sup>|0.933|0.935|0.947|

_Notes_ : Dependent variable is the log of cost per km; column (1) includes country fixed effects; column (2) includes country-year fixed effects; all models control for work activity fixed effects, year fixed effects, an interaction between work type and five-year period fixed effects; base categories are actual costs; robust standard errors in parentheses, clustered at a one degree country level grid;<sup>_∗_</sup> ,<sup>_∗∗_</sup> ,<sup>_∗∗∗_</sup> denote significance at 10%, 5% and 1% levels. _Source_ : Authors’ analysis based on data described in the text.

37
Morgan, Dilys, Cedric Mahe, Martin J. Okongo, Billy Mayanja, and J. A.G. Whitworth. 2001. "Genital Ulceration in Rural Uganda: Sexual Activity, Treatment-Seeking Behavior, and the Implications for HIV Control." Sexually Transmitted Diseases 28(8): 431—36.

Morgan, Dilys, Cedric Mahe, Billy Mayanja, Martin J. Okongo, Rosemary Lubega, and James A.G.Whitworth. 2002. "HIV-1 infection in rural Africa: is there a difference in median time to AIDS and survival compared with that in industrialized countries?" AIDS 6: 597—603.

Mulder, Daan, Andrew Nunn, Anatoli Kamali, and Jane Kengeya-Kayondo. 1995. "Decreasing HIV-1 seroprevalence in a rural Ugandan cohort." British Medical Journal 311: 833—36.

Mulder, Daan W., Andrew J. Nunn, Hans-Ulrich Wagner, Anatoli Kamali, and Jane-F. KengeyaKayondo. 1994. "HIV-1 incidence and HIV-1-associated mortality in a rural Ugandan population cohort." AIDS 8: 87—92.

Murphy, Kevin M., and Topel, Robert. 2001. "The Economic Value of Medical Knowledge." Forthcoming in Robert H. Topel and Kevin M. Murphy, eds., Exceptional Returns: The Economic Value of Medical Research. Chicago, Ill.:University of Chicago Press.

Nunn, Andrew J., Jane F. Kengeya-Kayondo, Sam S. Malamba, Janet A. Seeley, and Daan W. Mulder. 1994. "Risk factors for HIV-1 infection in adults in a rural Ugandan community: a population study." AIDS 8: 81—86.

Nunn, Andrew J., Daan W. Mulder, Anatoli Kamali, Anthony Ruberantwari, Jane-Frances Kengeya-Kayondo, and Jimmy Whitworth. 1997. "Mortality associated with HIV-1 infection over fi ve years in a rural Ugandan population: cohort study." British Medical Journal 315: 767—71.

Over, A. Mead. 1997.Confronting AIDS. Public Priorities in a Global Epidemic. World Bank Report. Oxford: Oxford University Press.

Philipson, Tomas J., and Richard A. Posner. 1993. Private Choices and Public Health. The AIDS Epidemic in an Economic Perspective. Cambridge, Mass.: Harvard University Press.

Rao Vijayendra, Indrani Gupta, Michael Lokshin, and Smarajit Jana. 2003. "Sex Workers and the Cost of Safe Sex: The Compensating Differential for Condom Use among Calcutta Prostitutes." Journal of Development Economics 71: 585—603.

37
- 73 -

award practices of contractors. Weather and other exogenous factors will also affect the bidding behavior, contractors’ performance and the quality of roads delivered. All these issues need to be considered when evaluating the performance of public road procurement.

<!-- Start of picture text --> Figure 44. Share of paved roads by region<br>80 73.4 70.6<br>70 64.4<br>60<br>50 47.0 47.3<br>41.3<br>40<br>30<br>20 9.9 12.9<br>10<br>0<br>Share of paved roads (%)<br>the Caribbean Sub-Saharan Africa South Asia East Asia & Pacific Middle East & North Africa Europe & Central Asia Developing countries High-income countries<br>Latin America &<br><!-- End of picture text --> Source: World Development Indicators, accessed in March 2011.

# **REFERENCE**

- Alexeeva, Victoria, Gouthami Padam, and Cesar Queiroz. 2008. ―Monitoring Road Works Contracts and Unit Costs for Enhanced Governance in Sub-Saharan Africa.‖ Transport Papers TP-21, World Bank, Washington, DC.

- Anderson, WIllam, Arthur Jacoby. 2000. Measuring economic impacts of federal-aid highway projects. _Public Roads_ , Vol. 64.

- Athias, Laure, and Antonio Nuñez. 2006. ―Number of Bidders and the Winner’s Curse in Toll Road Concessions: An Empirical Analysis.‖ CNI-Working Paper No. 2006-16, Center for Network Industries and Infrastructure, Berlin.

Blancas, Luis, Laura Chioda, Tito Cordella, Alexandre, Oliveira, Felix Vardy. 2011. Do procurement rules impact infrastructure investment efficiency? An empirical analysis of _Inversão das Fases_ in São Paulo State. Policy Research Working Paper No. 5528, the World Bank.

- Brannman, Lance,   Douglass  Klein and Leonard W. Weiss.  1987. "The Price Effects of Increased Competition in Auction Markets," _The Review of Economics and Statistics_ , MIT Press, vol. 69(1), pages 24-32, February

- De Silva, Dakshina, Timothy Dunne, and Georgia Kosmopoulou. 2003. ―An Empirical Analysis of Entrant and Incumbent Bidding in Road Construction Auctions.‖ _The Journal of Industrial Economics_ 51: 295–316.
# **Does Access to Finance Matter in Microenterprise Growth?**

# **Evidence from Bangladesh**

## <u>1. Introduction</u>

Recent studies have pointed to the importance of the rural nonfarm sector (RNF) in developing economies. Rural nonfarm growth helps expand employment and income, provides forward and backward linkages with both the farm and modern sectors, and thereby leads to broad-based poverty reduction. Growth in the farm sector, with improved seed and other agricultural innovations, has been a major source of rural poverty reduction (Becerril and Abdulai, 2010, Mwabu1, Mwangi and Nyangito, 2006). However, this is not enough to absorb the burgeoning rural labor force in many countries where the modern sector is growing slowly. Therefore, development economics is paying increased attention to expansion of the rural nonfarm sector in order to generate additional productive employment before the modern sector of the economy can absorb the surplus labor (Timmer, 2002, Chawanote and Barrett, 2012). Moreover, given the increased pressure on land due to increasing population density, labor-intensive nonfarm activities can provide avenues for poverty reduction, without further stressing the land. In many East Asian economies, for example, the rural nonfarm sector has been the driver of overall economic growth during the early stage of development (Estudillo et al, 2012; Lanjouw and Lanjouw, 2001; Lin and Yao, 1999; McCulloch, Timmer and Weisbrod, 2007).

In a setting such as Bangladesh where the farm sector traditionally dominates, the RNF sector seems to be playing an important role in the growth of its rural economy (World Bank, 2007, Mahmud, 1996; Sen, 1996; Bhattacharya, 1996). While it is difficult to capture all of the activities that are considered part of the RNF sector, it is usually defined as all activities located in villages, 2
**Figure 2: Trends of import volume and price of diesel in Nepal**

<!-- Start of picture text --> 800 120<br>Diesel import (Million Liter)<br>700<br>100<br>Diesel Price (NRs/Liter, Nominal Price)<br>600<br>80<br>500<br>400 60<br>300<br>40<br>200<br>20<br>100<br>Year<br>0 0<br>Diesel Price (NRs./Liter)<br>Diesel import (Million Liter)<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> Source: Nepal Oil Corporation (http://www.nepaloil.com.np) Existing studies show good potential for jatropha in Nepal (AEPC, 2010). It can be grown in all regions except in mountainous areas (Shrestha et al., 2013). <mark>Both governmental and nongovernmental organizations in the country are encouraging jatropha cultivation for biodiesel. T</mark> he National Planning Commission (NPC) and Alternative Energy Promotion Centre (AEPC) have been playing the leading roles to promote jatropha-based biodiesel. The <mark>government has implemented a National Biofuel Program since fiscal year 2008/09 through the AEPC. T</mark> he major activities under the program include training to potential farmers and entrepreneurs, pilot projects in local communities, establishment of quality test labs for biofuel and support to research and development activities. The program has established 20 modern jatropha nurseries that have produced and distributed 1.25 million jatropha saplings to the farmers, and has established two processing plants to produce biodiesel from jatropha (Jhumsa, Palpa and Ramnagar, Chitwan) each with a production capacity of 1000 liters per day (K.C. et al., 2011).

The AEPC through the National Biofuel program is establishing a seed collection center in Palpa, Mahottari and Dhangadi districts. The government plans to promote a public-private partnership model through AEPC by motivating private institutions and local people through trainings and awareness programs to cultivate jatropha in community forests and degraded private lands. Earlier, the AEPC selected seven partner organizations representing different development regions to provide training to at least 200 farmers on jatropha plantation techniques (AEPC, 2009). In many 4