rafmacalaba/datause-encoder-data huggingface dataset:
-36-

54 ejidatarios. And while agricultural credit is not the first best in3trument to subsidize consumption, this subsidy, by construction, cannot reach landless rural inhabitants, who are probably among the poorest of the rural poor. Third, credit to the ejidos is made contingent on crop choice, with emphasis on corn, beans and basic cereals. There is almost no credit for livestock, nor for other crops that might be more labor intensive or have higher value per acre<sup>55</sup> . This limits the returns to both ejido land and labor.

Three, the rural labor market suffers from at least five distortions. First, regulations on private agriculture (particularly large farms) can at times depress the demand for labor, since land used for catcle-grazing may not be used for crops (which are probably more labor-intensive), even if this is feasible and profitable. Second, certain practices that ejido owners might engage to reduce risk and diversify their earnings sources are prohibited; in particular, sharecropping is not allowed, nor is the renting of land to 54 "Empirical data suggests that not even the institutional credit channeled through Banrural is reaching a significant portion of the poorest population." (World Bank, 1989a, Vol I, p. 56). Heath, op. cit., also notes a "..tendency to use credit for political rather than economic purposes" (p. 5). In addition, he notes that "State lending policy reflects a confusion between the objective of poverty alleviation and the objective of enhancing agricultural productivity" (p. 40).

55Crop diversification is one of the key mechanisms to increase the returns to land. Yet changes in cropping patterns have been very uneven. In the North and North-West there seems to be a "..remarkable degree of responsiveness to market incentives" (Yates, op. cit., p. 53), but in the Altiplano and Center the dependence on corn and beans has been increasing, although these are among the crops with the lowest value in terms of output per acre. Of course, crop choice depends not only on access to credit, but also on risk considerations, access to water, and timely access to fertilizers and transport; see below, section VI.5.
**Table 1: Summary statistics**

|**Variable**|**Mean**|**S.D.**|**Min**|**Max**|**N**|
|---|---|---|---|---|---|
|Total Revenue|3668|8145|2|200000|8873|
|Log(total revenue)|7.42|1.19|.693|12.2|8873|
|Net Income|1260|3138|-78498|126300|8807|
|Years in Business|8.58|9.37|.167|51|9033|
|Numrber of paid workers|.384|1.08|0.00|15|9036|
|Any paid workers|.193|.395|0.00|1|9036|
|Number of unpaid workers|.232|.601|0.00|5|9036|
|Any unpaid workers|.164|.370|0.00|1|9036|
|Registered with federal treasury|.417|.493|0.00|1|9036|
|Registered with local treasury|.252|.434|0.00|1|9036|
|Amount of taxes paid|78.5|552|0.00|30000|8401|
|Paid any taxes|.346|.476|0.00|1|9036|
|Any paid workers registered with IMSS|.346|.476|0.00|1|1748|
|Number of paid workers registered with IMSS|.318|.451|0.00|1|1748|
|Business registered with guild or association|.225|.418|0.00|1|9036|
|Amount of business organization dues paid|7.04|43.8|0.00|1500|9019|
|Any business organization dues paid|.156|.363|0.00|1|9036|
|Enumerated in the 1989 Census|.331|.471|0.00|1|5220|
|Fraction of all institutions|.279|.301|0.00|1|9036|
|Fraction of all institutions (excluding guild, dues)|.325|.371|0.00|1|9036|
|Types of work site:||||||
|Transportation: people or merchandise|.064|.245|0.00|1|9036|
|Makeshift stand in a public road|.041|.199|0.00|1|9036|
|Fixed stand in a public road|.017|.130|0.00|1|9036|
|Makeshift stand inside a marketplace|.023|.149|0.00|1|9036|
|Fixed stand forming part of a marketplace|.019|.138|0.00|1|9036|
|Door-to-door or street vendor|.045|.207|0.00|1|9036|
|Services offered via vehicle or cart|.034|.181|0.00|1|9036|
|Own home: without specialized equipment|.123|.328|0.00|1|9036|
|Own home: with specialized equipment|.051|.220|0.00|1|9036|
|Other non-permanent work site|.008|.088|0.00|1|9036|
|Fixed work site in a public marketplace|.032|.176|0.00|1|9036|
|Hotel, tavern, inn or hostel|.0006|.024|0.00|1|9036|
|Factory: production or repair services|.095|.293|0.00|1|9036|
|Variety or grocery store|.075|.263|0.00|1|9036|
|Retail service establishment|.131|.338|0.00|1|9036|
|Other permanent work site|.008|.091|0.00|1|9036|
|Home of customer or client|.233|.423|0.00|1|9036|
|Permanent work site|.485|_.500_|0.00|1|6194|
|Family/friends only main customers|.384|.486|0.00|1|9036|

26
These findings raise the question whether relationships also exist between financial development and undernourishment and how these may come about. More specifically, the question arises whether there is evidence of a beneficial relationship between financial development and both Millennium Development Goals (MDG) indicators of poverty, i.e., not just income poverty but also undernourishment; and if so, what are the specific channels through which financial sector development improves undernourishment? In this paper we investigate these two questions.

We focus on undernourishment which is defined as: “the condition of people whose dietary energy consumption is continuously below a minimum dietary energy requirement for maintaining a healthy life and carrying out a light physical activity” (FAOSTAT, 2006). Reducing undernourishment can be regarded as the first and foremost development objective, since not being undernourished defines a person’s chances of living. Measuring the prevalence of undernourishment involves the comparison of actual household food consumption (expressed in terms of calories per person per day) with a minimum dietary energy requirement (also expressed in terms of calories per person per day) and then the classification of those individuals with per capita calorie consumption levels below the minimum requirement as being undernourished. Obviously, undernourishment is related to the prevalence of extreme poverty, but there are distinctive differences. Whereas the measure of the prevalence of undernourishment is based on the distribution of actual household food consumption and availability, the (World Bank) measure of the prevalence of extreme poverty is based on the distribution of household income (or consumption), adjusted for purchasing power. Undernourishment and poverty can differ, not only because of relative prices which mean a certain income level does not translate in an ability to attain oneself of sufficient food, but also because of insufficient availability of food, as when faced with permanent or temporary insufficient local production.

Our findings can be summarized as follows. Using cross-country OLS, instrumental variables and panel regressions for the period 1980-2003, we find a causal relationship from financial sector development to reduced undernourishment. Our results imply that a 3
a bank account (3.2% of total).<sup>13</sup> Panel A of Table 5 summarizes the program implementation progress until May 2015.

Qualitative information collected during program implementation<sup>14</sup> with beneficiaries suggests that the program was implemented following the study protocol and in particular that the formalization process with the _entreprenant_ status was considered as simple and fast and was free. Panel B of Table 5 shows quantitative data from follow up survey and confirms that the formalization process was fast and cheap for businesses in treatment groups. More than 85 percent of businesses that benefited from the program declared that they did not pay anything to formalize (those who paid something in the treatment groups formalized with another status than the _entreprenant_ status).

Qualitative work conducted few days or weeks after the businesses received a visit from the CGA suggests that the program understanding was relatively good, given the complexity of the intervention. However, data from the midline survey suggest that after some time, most businesses had forgotten the program. Only 36 percent of businesses in groups 2 and 3, and 24 percent of those in group 1, remembered the _entreprenant_ program. Moreover, only 21 percent in groups 2 and 3, and 11 percent in group 1, were able to describe correctly what it is.

In the control group, only 6 percent of the businesses declared that they had heard about the _entreprenant_ program, and 2 percent were able to describe it correctly. It suggests that only marginal externalities were generated by the program on those not directly targeted. This is consistent with qualitative interviews conducted with informal businesses not targeted by the program.

For businesses in group 3 that formalized, tax mediation services were implemented by CGA. Some _entreprenants_ reported that the tax administration requested for tax payments higher than expected. The CGA advisors helped them to solve these issues as they arose. Only 2 cases happened in the first year of program implementation.

> 13 For Orabank, data are available until April 2015, and for BoA until November 30th, 2014.

> 14 22 semi-structured qualitative interview with program participants after they received the first of second visits; 29 field visit with the CGA advisors during the first or second visits; 8 interviews with business owners who decided to formalize; 61 interviews with informal businesses not selected for the program; focus groups with all implementing agencies.

19
**Table 1: Variables in the Chilean municipal level data base**

|**Variable**|**Definition**|**Unit**|**Source**|
|---|---|---|---|
|Total population|The number of inhabitants in the<br>_comuna_.|-|2002 Census|
|Urbanization rate|The share of the population who<br>lives in urban areas, where urban<br>is defined as human settlements<br>that has more than 2000<br>inhabitants_or_that has between<br>1,001 and 2,000 inhabitants with<br>more than 50% of the population<br>economically active in secondary<br>ortertiary activities.|-|2002 Census|
|Years of education|Average number of years of<br>education of the population aged<br>15 or more.|Years|2002 Census|
|Life expectancy|Life expectancy at birth for each<br>_comuna_in 1998. This variable<br>was derived from the health index<br>of UNDP’s Human Development<br>Index.|Years|UNDP Chile<br>(2000)|
|Per-capita income|Average household income per<br>capita in each_comuna_, as<br>estimated from the CASEN 2006<br>survey, which is representative at<br>the_comuna_-level except for a few<br>sparsely populated_comunas_.<br>Pesos were converted into USD<br>using the exchange rate 530.28<br>pesos/dollar.|USD/month|CASEN 2006|
|Latitude|Latitude of the main city in the<br>_comuna_.|Degrees<br>south of<br>equator|Google Earth,<br>GeoMaker|
|Longitude|Longitude of the main city in the<br>_comuna_.|Degrees west<br>of the Prime<br>Meridian|Google Earth,<br>GeoMaker|
|Elevation|Elevation of the main city in the<br>_comuna_.|Kilometers<br>above sea<br>level|Google Earth,<br>GeoMaker|
|Normal average<br>annual temperature|The average annual temperature<br>in the main city of the_comuna_as<br>measured over a reference period<br>(typically 1951-1990).|oC|Chilean<br>Meteorological<br>Department and<br>www.worldclimate<br>.com<br>|
|Normal annual<br>precipitation|The average annual precipitation<br>in the main city of the_comuna_as<br>measured over a reference period<br>(usually 1951-1990).|Meters|Chilean<br>Meteorological<br>Department and<br>www.worldclimate<br>.com<br>|

4
the relationship of the migrants to the household.<sup>11</sup> However, because these variables are measured at the household level it is easier to think of arguments that can invalidate them as instruments. In particular, there could be a selection effect where the types of households that send migrants for longer periods of time or who send closer relatives abroad may differ in many ways from other households, including differing in financial access. Hence, though Table A.1 and A.2 show that these instruments pass the exclusion restrictions and our main results are confirmed when using these instruments, we prefer to focus on what we think are likely to be more exogenous instruments: economic conditions in US states where Salvadoran migrants reside.

Table 7A and 7B present the results of the first stage estimations where the likelihood of receiving remittances and the amount of remittances received per capita, respectively, are instrumented with the weighted average of real GDP per capita and/or the poverty rate for US states where Salvadoran migrants reside. We use these instruments separately and jointly. Both in Tables 7A and 7B, the size of the Cragg-Donald F-statistics exceed the Stock and Yogo (2005) critical values, indicating that we do not have a weak instruments problem.

We find that GDP per capita in US states has a positive effect on the likelihood that the household receives remittances (Table 7.A columns (1), (4) and (7)) and on the amount of remittances received (Table 7.B columns (1), (4) and (7)) when this variable is included on its own, without the poverty rate. In turn, the poverty rate has a negative impact on the likelihood that the household receives remittances (Table 7.A columns (2), (3), (5), (6), (8) and (9)) and on the amount received (Table 7.B column (2), (3), (5), (6), (8) and (9)), whether it is included on its own or together with GDP per capita.

Table 8A and 8B show the second stage estimations where we instrument the likelihood of receiving remittances and the amount of remittances received per capita, respectively. We use the municipality level weighted GDP per capita for US states where migrants reside and the corresponding poverty rates individually and jointly as instruments. The overidentification tests reported in both tables 8A and 8B suggest that economic conditions in US states where

> 11 Following Rodriguez (1996), we expect the length of time spent by migrants abroad to have a non-linear inverted U shape relation to remittances and for remittances to increase with the closeness of the relationship of the migrant to the household.

13
|**Table 9:**|**IV regression**<br>**Dependen**|**results for th**<br>**t variable: La**|**e level and g**<br>**bor Product**|**rowth rate of**<br>**ivity (logs)**|**labor produc**<br>**Depende**|**tivity**<br>**nt variable:**<br>**Produ**|**Growth Rate**<br>**ctivity**|**of Labor**|
|---|---|---|---|---|---|---|---|---|
||**Full**<br>**sample**|**Full**<br>**sample**|**SME=1**|**SME=0**<br>**(Large**<br>**firms)**|**Full**<br>**sample**|**Full**<br>**sample**|**SME=1**|**SME=0**<br>**(Large**<br>**firms)**|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
||<br>**Pan**|<br>**el A: Second**|<br>**stage IV regr**|<br>**ession result**|<br>**s**||||
|Ethnic Fractionalization IV|-1.775*|-0.509|-1.892*|-1.508|-36.358**|-24.231|-39.781**|-21.659**|
||(1.039)|(1.121)|(1.124)|(1.040)|(15.727)|(15.409)|(17.383)|(9.532)|
|Ethnic Fractionalization IV*SME Y:1 N:0||-1.473**||||-14.710**|||
|||(0.680)||||(6.286)|||
|SME Y:1 N:0||0.462||||3.071|||
|||(0.293)||||(2.778)|||
|Initial Labor Productivity (logs)|||||-6.249***|-6.181***|-6.360***|-7.008***|
||||||(0.600)|(0.639)|(0.681)|(0.727)|
|Log of Number of workers (lagged)|0.156***||0.220***|-0.055|2.699***||3.579***|0.910|
||(0.036)||(0.044)|(0.083)|(0.559)||(0.768)|(0.838)|
|GDP per capita (logs)|0.436***|0.433***|0.477***|0.190|1.681|1.575|2.014|0.812|
||(0.142)|(0.164)|(0.143)|(0.166)|(1.988)|(1.960)|(2.103)|(1.876)|
|Age of firm (logs)|0.136**|0.193***|0.095|0.327***|-0.544|0.443|-0.725|1.175|
||(0.058)|(0.059)|(0.061)|(0.064)|(0.993)|(0.924)|(1.072)|(0.978)|
|Manager experience (years, logs)|-0.007|-0.018|-0.008|-0.028|0.459|0.280|0.267|0.890|
||(0.061)|(0.059)|(0.062)|(0.064)|(0.994)|(0.983)|(1.066)|(1.058)|
|Exports (proportion of sales)|0.211*|0.349***|0.341***|0.312*|0.962|3.199*|3.630|-2.129|
||(0.125)|(0.117)|(0.116)|(0.179)|(1.987)|(1.674)|(2.245)|(1.783)|
|Foreign Ownership Y:1 N:0|0.191*|0.272***|0.254**|0.130|1.612|2.834*|1.809|1.793|
||(0.101)|(0.099)|(0.113)|(0.172)|(1.661)|(1.549)|(2.162)|(1.899)|
|Firm is Financially Constrained Y:1 N:0|-0.218***|-0.235***|-0.183**|-0.579***|1.485|1.161|2.015|-3.766|
||(0.077)|(0.089)|(0.081)|(0.213)|(1.731)|(1.880)|(1.792)|(2.454)|
|Female Top Manager Y:1 N:0|-0.203***|-0.227***|-0.198***|-0.246|0.627|0.339|0.734|-2.353|
||(0.068)|(0.078)|(0.071)|(0.173)|(1.220)|(1.284)|(1.351)|(2.197)|

61
# **Table 5. Three-stage Logistics Index Estimation**

Dep. variable first and third stages: Log of bilateral exports - <u>Dep. variable second stage: First stage residuals</u>

|Variable|First stage|Second stage|Third stage|
|---|---|---|---|
|Log of exporter's GDP|1.200<br>(61.67)||1.285<br>(76.1)|
|Log of importer's GDP|0.888||0.925|
||(46.04)||(55.55)|
|Exporter's Corruption Perception Index|0.298||0.253|
||(24.09)||(22.86)|
|Importer's Corruption Perception Index|0.252||0.164|
||(17.85)||(12.5)|
|Regional trade agreement<br>dummy variable|1.005<br>(11.5)||0.346<br>(4.87)|
|Log of distance||-1.307<br>(-34.61)||
|Total time (freight plus ports)||-0.007<br>(-3.74)||
|Coefficient of variation for total time||-0.947<br>(-4.56)||
|Log of total processing cost||-0.232<br>(-6.02)||
|Logistics index|||1.083<br>(41.13)|
|_K_|6|5|7|
|_R_-squared|0.620|0.226|0.713|
|Adjusted_R_-squared|0.620|0.225|0.713|
|Observations|5149|5149|5149|
|_F_-statistic|1710.0|385.0|2317.0|

Note: Dependent variable for first and third stages is total bilateral exports (in logs) in 2003 or latest year available. Dependent variable for second stage is first-stage residuals. Corruption Perception Index is for 2004, from Transparency International. Figures in parentheses are _t_ -statistics (based on robust standard errors). Constant terms are not shown. Source: Authors' calculations.

21
During this period, four rounds of data gathering collected detailed information on land allocation to different crops. For the purposes of this paper, we focus, first, on the share of each plot allocated to coffee. Coffee is a tree crop requiring a long-term investment perspective. Coffee trees only start yielding about 3-4 years after planting, reaching full potential only after 8 years. Then, trees can maintain high production levels for several decades. Cutting down trees yields virtually no return, so this is a clear irreversible investment. For the purposes of our analysis, only four PAs in woredas in SNNP have agro-climatic conditions conducive to growing coffee<sup>4</sup> . The second crop to focus on is eucalyptus. This tree crop is rather different in that it can yield a return after only a few years, either by cutting it down entirely or simply cutting by branches, and hence is more of a medium-term investment. It would be possible to recoup a reasonable part of the investment; still, it is likely to have to occur at a sub-optimal time for the household. It is in general grown both for providing ‘subsistence’ firewood as well as for cash. To measure the impact of security and transferability on the share of land allocated to eucalyptus we use data from the same villages as for coffee. Finally, we consider chat. Chat (or q’at) is a relatively drought-resistant evergreen shrub, somewhat resembling tea plants, and cultivated as a cash crop. The young leaves of this plant are widely appreciated in Ethiopia and neighboring countries for their effects as a stimulant with mild narcotic impact, resembling the effects of amphetamines. The shrub is a perennial that starts yielding substantial return after about 2-3 years. As a shrub, it only has limited use as a source of firewood or building material when cut down. It would appear that eucalyptus and chat do not have similar sunk costs and long gestation periods as coffee; still, they are investments with medium-term horizon. Secure property rights are likely to be relevant for all these investments.

Tenure security and transfer rights are likely to matter for investment in these crops. Obviously, other factors will matter as well – including whether it is profitable to do so irrespective of security concerns, requiring any regression analysis to appropriately control for other factors. Planting trees may also have other effects beyond direct profitability concerns. Tree cover has further environmental effects, including increasing biomass and providing ground cover. In most of the coffee growing areas – typically with relatively high rainfall and fertile land suitable for

> 4 They are Cheha (near Imdibir, Gurage), Kedida (in Kembata), Bule (near Dilla in Sidamo) and Boloso (about 30 km from Sodo).

9
# 5.5 Composition of the innovation portfolio

Product innovations in the South Asia region are mainly related to quality upgrading and new functions, while a small percentage of them is associated with the introduction of cheaper products (except in Pakistan) (Table 13). Indeed, more than 90 percent of the firms introducing new goods in Bangladesh, Nepal, and Pakistan increase the quality of their products. Innovations related to products that include different inputs, a new technology or design are relatively more important in countries such as Nepal and Pakistan.

<u>Table 13. How innovative are new products? (% of product innovation firms)</u>

|**Types of Innovation**|**Bangladesh**|**Pakistan**|**India**|**Nepal**|**ECA**|**Africa**|
|---|---|---|---|---|---|---|
|New Function|63%|95%|66%|76%|67%|64%|
|Cheaper|40%|76%|22%|37%|-|42%|
|Better Quality|94%|96%|74%|99%|-|89%|
|Different Inputs|59%|77%|54%|70%|68%|58%|
|New Tech or Design|46%|75%|41%|81%|54%|54%|

Source: Author’s elaboration from Enterprise Survey (2014) There is a lot of heterogeneity in the region in terms of the type of process innovation that firms conduct (Table 14). For example, in Bangladesh, most of the innovations are related to the introduction of a more efficient technology or the adaptation of a new technology; while in India, they are geared to automate manual processes or adapt a new technology. Almost 100 percent of processes innovations in Nepal are related to the adaptation of a new technology, while in Pakistan, most of them focus on the introduction of a more efficient technology.

<u>Table 14. How innovative are new processes? (% of process innovation firms)</u>

|**Types of Innovation**|**Bangladesh**|**Pakistan**|**India**|**Nepal**|**Africa**|
|---|---|---|---|---|---|
|Automate Manual Process|77%|65%|67%|74%|67%|
|Adapt Used Technology|35%|64%|36%|78%|37%|
|Adapt New Technology|88%|72%|68%|94%|78%|
|More Efficient Technology|87%|89%|55%|64%|50%|

Source: Author’s elaboration from Enterprise Survey (2014) Although there are several reasons why firms may decide to introduce new goods and processes, product innovations are usually conducted with three objectives: (i) extending the product portfolio, (ii) increasing the market share or introducing new markets, and (iii) escaping competition (Table 15), while process innovation are aimed at: (i) increasing the quality of existing products, and (ii) increasing production speed and scale (Table 16). The pattern is in line with what has been observed in other regions such as Africa.

<u>Table 15. Main Reasons for Product Innovation (% of product innovation firms)</u>

|**Reasons**|**Bangladesh**|**India**|**Nepal**|**Pakistan**|**Africa**|
|---|---|---|---|---|---|
|Replace Product|63%|15%|10%|69%|34%|
|Extent Range|90%|94%|100%|95%|90%|
|New Market/Increase share|59%|91%|100%|74%|78%|
|Reduce costs|32%|26%|10%|50%|34%|
|Compete same products|83%|75%|82%|50%|68%|
|Comply standards|45%|48%|13%|72%|47%|
|Decrease in demand|69%|43%|41%|58%|51%|

20