rafmacalaba/datause-encoder-data huggingface dataset:
# **4. Discussion and Conclusions**

Despite the importance of agriculture for rural livelihoods, poverty alleviation, and food security across the developing world, household and farm surveys collecting micro data on agriculture exhibit substantial cross‐country heterogeneity in terms of access policies, use of international best practice survey methods and dissemination standards, and data quality (Carletto et al., 2015). Given the rapid advances in the availability of 10‐meter or sub‐10‐meter spatial resolution satellite imagery, the demand is increasing for understanding how these advances can be leveraged to measure and understand agricultural outcomes with greater accuracy and higher spatial resolution.

Although there is a concerted push to showcase the value of geospatial applications for monitoring and evaluation efforts in the agriculture sector, and for tracking the progress towards the Sustainable Development Goals, multi‐disciplinary research efforts aimed at assessing the accuracy and feasibility of the proposed applications, particularly in smallholder production systems, are scant. If validated, satellite‐based remote sensing, combined with georeferenced household and farm survey data that could serve as “ground truth”, could dramatically enhance not only our ability to fill the data gaps, but also our understanding of the linkages between development and human welfare.

Taking advantage of a unique range of ground‐based plot‐level maize yield measures based on farmer‐reporting, sub‐plot crop cutting and full‐plot harvests that were collected as part of a methodological survey experiment that was conducted in Eastern Uganda, our study showcases the accuracy and empirical utility of satellite‐based approaches to plot‐level maize yield estimation in smallholder production systems with a median plot size of approximately one‐tenth of a hectare.

The satellite‐based yield estimates include those that are (a) anchored in a calibration model that relates maize yields from full‐plot harvests to MTCI values on multiple dates on a subset of pure stand maize plots that were at least 0.1 ha in size; (b) based on the same calibration model that uses sub‐plot crop cut, as opposed to full‐plot, yield; and (c) based solely on crop model simulations, without reliance on any ground‐based yield measure. While (a) and (b) are identified as “calibrated” variants of remotely‐sensed maize yields, (c) is framed as the “uncalibrated” counterpart.

Overall, the accuracy of the satellite‐based maize yield estimates is very encouraging. Having over 200 full plot harvests, which is very rare because of their cost, is a unique situation with which to test satellite estimates, and we find that both calibrated and uncalibrated approaches capture 20
# 1. **Introduction**

Numerous analysts have linked volunteering, working without pay, to positive economic and political outcomes. This possibility has been discussed at least since Tocqueville, but the praises of volunteering and other forms of "participation" in community affairs have been sung ever more loudly in the last decade. Under various guises, volunteering has been proposed as an alternative to "inefficient" government activity (see Weisbrod, 1975, as well as much of the recent literature on the "new federalism"); as a means of ensuring the sustainability of public investments (Uphoff, 1992; Isham et. al., 1995); as an explicit attempt to self-target investments to the poor, whose opportunity cost of time is assumed to be lower (for a general discussion, see Besley and Kanbur, 1990; Ravallion, 1991; Besley and Coate, 1992); and as part of a solution to local collective action problems (Ostrom, 1990, pp. 138-41 et. passim; White and Runge, 1994). Volunteering, it is argued, also nurtures civil society, builds trust, and may be a key ingredient in a virtuous cycle of accountable govemment and high rates of economic growth (Putnam, 1993; Fukuyama, 1995).

The theoretical economics literature on volunteering is extensive (including Tullock, 1971; Arrow, 1974; Becker, 1974; Rose-Ackerman, 1982; Sugden, 1984; Andreoni, 1988). There are also numerous empirical studies which seek to explain the determinants of volunteering in European and North American countries (on Holland, van Jouke, 1993; on the United States, Menchik and Weisbrod, 1987, and Freeman., 1997; on Canada, Vaillancourt, 1994, and Day and Devlin, 1996). In Less Developed Countries (LDCs), much of the empirical work has been qualitative rather than quantitative, or based on much smaller sample sizes (for example, Ostrom; White and Runge).

This paper seeks to identify the detenninants of volunteering in rural Peru: in a 1994 survey, alnost 10% of working-age adults in rural areas in Peru reported they did unpaid work "for the benefit of the community" in the last week. These men and women performed a multitude of tasks: they constructed and repaired local infrastructure, helped with the upkeep of land, cooked for community soup kitchens, cleaned, picked up trash, and volunteered as teachers, doctors, or social workers. On average, voluanteers donated almost seven hours of labor per week, a large amount of time by any standard.

The basic conclusions of the paper are two. First, volunteers in rural Peru do not appear to have a low opportunity cost of time. They are more educated, and are more likely to hold a job. Other household characteristics, such as gender, marital status, length of residence, and ethnicity, are all important predictors of the probability of volunteering. Second, controlling for household characteristics, there are large differences across communities in rural Peru in aggregate volunteer 2
Estimation results are reported in Table 2 as average marginal effects, showing the change in the probability of being in the labor force associated with a unit change in the explanatory variable (for categorical variables this is the difference with the reference category). First of all, the U-shape in education appears even stronger in the marginal effects than in the average unconditional participation rates in Figure 2. Even conditional on variables that could have explained the unconditional U, such as partner’s income and education, caste, or religion, participation declines as education increases up to intermediate levels.

# **Table 1.** **<u>Sample Means</u>**

||**1987 **|**1999**|**2004 **|**2009**|**2011**|
|---|---|---|---|---|---|
|Labor force|0.18|0.18|0.20|0.17|0.18|
|Illiterate|0.42|0.32|0.28|0.23|0.22|
|Literate|0.11|0.08|0.07|0.07|0.07|
|Primary|0.15|0.12|0.13|0.12|0.11|
|Middle|0.11|0.14|0.16|0.15|0.15|
|Secondary|0.14|0.20|0.22|0.26|0.27|
|Graduate|0.07|0.13|0.14|0.17|0.18|
|Log income|3.63|3.99|4.08|4.36|4.47|
|Salaried employment|0.52|0.48|0.46|0.46|0.46|
|Hh head Illiterate|0.21|0.18|0.16|0.15|0.14|
|Hh head Literate < prim.|0.14|0.10|0.10|0.08|0.08|
|Hh head Primary|0.17|0.12|0.13|0.11|0.11|
|Hh head Middle|0.14|0.15|0.16|0.15|0.16|
|Hh head Secondary|0.22|0.26|0.28|0.30|0.29|
|Hh head Graduate|0.13|0.18|0.18|0.21|0.22|
|Hindu non-SCST|0.66|0.65|0.65|0.66|0.65|
|SCST|0.14|0.17|0.17|0.16|0.16|
|Muslim|0.14|0.14|0.14|0.14|0.15|
|Other social group|0.05|0.05|0.05|0.04|0.04|
|Age|35.6|36.1|36.6|36.7|36.8|
|Children 0–4|0.69|0.53|0.50|0.42|0.39|
|Children 5–14|1.59|1.37|1.20|1.05|1.05|
|Agriculture|0.07|0.06|0.05|0.05|0.05|
|Construction|0.07|0.10|0.10|0.12|0.12|
|Manufacturing|0.27|0.23|0.24|0.22|0.23|
|White-collar services|0.20|0.18|0.18|0.19|0.19|
|Other services|0.38|0.43|0.43|0.41|0.41|

19
**Table** 2: **Estimated Effect of Trade Structure on Growth Panel Data (System Estimator), 1980-1999 Countries, Observations:65, 143**

|||**Natural**|**Resour**|**ce Depen**|**dence**|||**Exp**|**ort Con**|**centratio**|**n**||**Intra-**|**Industry Tr**|**ade**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Net N**|**RX/Labor F**|**orce**||**NRX/GDP**||**Expo**|**rt Herfind**|**ahl**|**NRX**|**/Total Exp**|**orts**|**Grub**|**el-Lloyd In**|**dex**|
|||Sargan|Senal||Sarg<br>Corr|CSenral<br>Corr||Sga|**Corr.**||Sagan<br>|Serial<br>~~~~~|~~~~~|Sargan<br>~~Con|C<br>Corr|
|**Basic Conditioning**<br>+<br>|1.74<br>(1.84)<br>|*<br>0.20|0.27|2.77<br>(0.89)|0.34|0.27|-4.57<br>(-1.35)|0.18|0.31|-2.67<br>(-3.77)|t<br>0.04|0.22|10.49<br>(2.87)|*<br>0.21|0.41|
|**Capital Accumulation**<br>+<br>|2.22<br>(205)|*<br>0.39|0.53|4.00<br>(186)|*<br>0.29|0.49|-6.08<br>(-3.72)|**s<br>0.14|0.66|-1.65<br>(-194)|*<br>0.06|0.41|2.09<br>(072)|0.22|0.57|
|**Growth of Terms of Trade**|2.16<br>|**<br>0.45|0.48|5.01|**<br>0.45|0.48|-2.93|*<br>0.19|0.61|-1.03|0.18|0.52|2.91|0.15|0.54|
|+<br>|(213)<br>|||(244)|||(-1485)|||(-1.46)|||(111)|||
|**Macro Stability**|2.11<br>|**<br>0.50|0.56|2.78|0.42|0.56|-5.59|***0.38|0.51|-0.59|0.32|0.56|6.76|*<br>0.21|0.71|
||(242)|||(161)|||1 (-3 85)|||(-0 90)|||(332)|||
||||||Ad|ditional|Controls|||||||||
|**NRX/GDP**||||||||||||||<br>||
|<br>|||||||-6.00<br>(-5 14)|***<br>0.43|0.61||||8.08<br>(4 78)|*<br>0.39|0.72|
|**Leamer Index**|||||||-5.42<br>(-4 31)|**t<br>0.42|0.61||||8.33<br>(5 80)|*<br>0.33|0.71|
|**Export Herfmdahl**<br>|3.49<br>(363)|**<br>0.42|0.61|1.88<br>(118)|0.43|0.61||||-0.36<br>(-072)|0.21|0.62|0.08<br>(039)|0.37|0.69|
|**NRX/Total Exports**|||||||-5.91<br>(-5 50)|*<br>0.21|0.62||||10.55<br>(5 6)|0.15|0.68|
|**Intra-Industry Trade**<br>|3.25<br>(440)|*t*<br>0.33|0.71|4.47<br>(2 48)|**<br>0.39|0.72|-3.47<br>(-2 22)|**<br>0.37|0.69|-0.57<br>(-0 87)|0.15|0.68||||
|**HT+Export Herfindahl**|3.07<br>(4.47)|***<br>0.32|0.66|2.04<br>(152)|0.51|0.69|l|||-0.34<br>(-0 62)|0.14|0.65|__|||

The dependent variable **is** the GDP per capita growth rate. Basic conditioning set includes log of initial income of the period and a measure of openness (S&W) Capital accumulation includes average ratio of investment/GDP and log of years of schooling. Growth of terms of trade refers to the growth of the ratio of exports price index to import price index over the period. Macro stability includes the standard deviation of the real exchange rate over the period. Sargan refers to the p-value of the Sargan test for the validity of instruments, where the null hypothesis is the no-correlation between the instruments and the errors. Serial Corr. refers to the p-value of a second order serial correlation test, where the null hypothesis is the non-existence of second order serial correlation. Time dummies are included in all the regressions T-statistics shown **in** parenthesis * significant at 10%, ** significant at 5%, *** significant at 1%.
**Figure 18 NORTH AMERICA: SHARE OF INTRAREGIONAL TRADE, 1965-1995**

<!-- Start of picture text --> 100 -<br>9 0 0 -<br>90<br>70<br>30 -<br>20<br>10<br>0-<br>1965 1970 1975 1980 1985 1990 1995<br>-,- AGRICULTURE<br>| -- MA N UFACTUR E|<br><!-- End of picture text -->

increase after 1980, the magnitude is much smaller than that in Europe. Note that while European integration has been making progress both in coverage and in depth for many years since the establishment of the EC, comparable development toward regional integration has not been observed in Asia and North America until very recently. Probably time is needed before any such progress can be identified in these regions.

# _- V. Regional Integration and Japanese Rice Imports An Ex Anti Analysis_

In this section, we will conduct an _ex ante_ analysis of the effect of regional economic integration on agricultural trade flows. Using the framework developed in Section III, we will 43
right panel shows our smearing to get the ambient population. The Landscan figure has an obvious degree of smoothing, with reduced peak heights and assignment of lots of people into low-density grid squares. Of course, it could be that Landscan is allocating people during commuting times to roads and to shopping areas, and that is the reason for the smoothing. Overall it seems Landscan may do a reasonable job: the simple correlation coefficients of Landscan numbers with population, employment and our replication numbers are respectively 0.55, 0.60, and 0.60.

For Nairobi in Figure 2, we note our employment patterns lack the sharp peaks of Kampala, in part because we smear employment into non-residential buildings, including public buildings. If we just smeared into commercial and industrial buildings we would get sharper peaks near the center, but that does not mean it is a better choice. The Landscan figure again exhibits a degree of smoothing, missing the sharper peaks we see in our replication, as well as missing high-density slum areas to the south-west of the city center. However, Landscan does seem to do a better job of capturing low-density grid squares near the city center in Nairobi than it does for Kampala. For Nairobi, the simple correlation coefficients of Landscan numbers with population, employment and our replication numbers are higher at respectively 0.65, 0.56 and 0.69.

For Nairobi, we also know exactly the footprint (or ground cover) of all buildings. We can calculate what would happen if we just smeared total urban area population by share of each grid square in total urban area built cover, hence replicating what Landsat-based smearing exercises seem to be trying to do. The bottom panel of the figure shows the result. Inferred density is basically flat throughout the city. As in Henderson et al. (2018), the figure implies built cover per grid square is basically flat throughout the city (while building volume and height decline sharply with distance from the center). This clearly shows that smearing population into built cover to calculate within-city density variation would be more problematic than using Landscan data.

# **1.3 Defining Urbanized Areas**

As noted earlier, the problem with typical definitions of urbanized areas – from the United Nations or economic censuses of different countries for instance – is that they employ 8
<!-- Start of picture text --> Grid capacity<br>30<br>25<br>20<br>15 eee= Benchmark<br>== Counterfact<br>-10<br>; 05 we www ww ww ww www www ewww wow nn-<br>00<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> SectoralGVA shares<br>75<br>70 yes<br>65<br>60<br>5055 ems. lll =>. ee Bence farming<br>45 wee Benc industry<br>2 _, j= Bene services<br>| 39 [7 ==----Coun farming<br>3 aad = ==----Coun indusry<br>15 | -----Coun services<br>0540 ee<br>00<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br><!-- End of picture text -->

<!-- Start of picture text --> Average no. of workers per establishment<br>55 .<br>¢<br>45 /<br>oLK 7<br>35 - Ss, 4¢<br>7 a Y wee Bere farming<br>25 4af“ sSoe?- je Benc industry<br>we Bence services<br>155 ent ao=====sennSS 55 SSS ===> --* ===poo=== CunnesyCounfamingCounsevices<br>-51970. 1975 1980 1985 1990 1995. 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Predicted sectoral real GVA<br>12<br>10<br>—— Benc farmi<br>0.8 os MN Ing<br>06 - --" =ween~~J} ‘ON ann = Bene -industry<br>94 \‘.FN.et oo----- BereCoun SESfarming<br>0.2~“ ooowETSeennn nner ee Sear =----Counindustry<br>0.0 a cssittninininininkind tad he<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Normalized GDP per capita<br>4<br>2 === Counterfact<br>Benchmark<br>moe<br>-wee? women<br>1 i ee<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Predicted expenditure shares<br>09<br>——_ ——— = -<br>07 Benc priv inv<br>0.6 eee Bec pub! inv<br>05 a Benc fixfix cost<br>04 === Councons<br>0.3 === Coun priv inv<br>=----—<br>02o nN_---?% an GP« >} pr—==== CounCoun publinwfk cos<br>00 SSS==<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Sectoral Consumption shares<br>80<br>7570 ve><br>65 ast yA fo<br>6055 = ia eee Bence farming<br>50 = Benc industry<br>45<br>40 wee Ber services<br>3530 ee === Coun faming.<br>2520 ewsNa nynN - <= Coun indusry.<br>15 === Coun services<br>1005 ee,<br>00<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br><!-- End of picture text -->
performance during economic crises. In doing so, our analysis helps to disentangle some of the ambiguities in previous ...ndings.

Also exploring the recent crisis, Tong and Wei (2009) examine whether the volume and composition of capital ‡ows a¤ected the degree of credit crunch faced by manufacturing ...rms. Using data on 3,823 ...rms in 24 emerging countries, the authors ...nd declines in stock prices to be, on average, more severe for ...rms intrinsically more dependent on external ...nance. The volume of capital ‡ows had no signi...cant e¤ect on the severity of the credit crunch, but the composition of capital ‡ows mattered: pre-crisis exposure to non-FDI capital in‡ows worsened the credit crunch whereas exposure to FDI alleviated liquidity constraints.

In this paper, we investigate both whether and why FDI could lead to better establishment performance during the crisis. We explore the role of production, as well as ...nancial, linkages between MNC subsidiaries and parent ...rms, and evaluate how the relationships led to di¤erential establishment responses to the negative demand and ...nancial shocks. We also compare the results during the crisis with non-crisis years to examine whether foreign ownership exerts a di¤erent e¤ect during economic crises.

# 3 Data Description

## 3.1 The Global Financial Crisis and Establishment Performance

The recent Global Financial Crisis, regarded as the worst ...nancial crisis since the Great Depression of the 1930s, is notable for its speed, severity, and international span. The crisis was triggered by a liquidity shortfall in the U.S. banking system, starting with the collapse of the U.S. subprime industry in 2007 (including the bankruptcy of some of the largest mortgage lenders such as New Century Financial and American Home Mortgage Investment Corporation), and quickly resulted in failures of large ...nancial institutions (such as the ...re sale of Bear Stearns in March 2008 and the bankruptcy of Lehman Brothers in September 2008).<sup>6</sup> The crisis was soon spread to non-...nancial sectors and economies around the world. Almost all industrial countries and a large number of developing and emerging economies were in‡icted in 2008, witnessing substantial declines in total output and employment. Between 2007 and 2008, world GDP fell by 5 percent, unemployment rates rose to 9 percent in industrial nations, and read world exports plunged by 17 percent.

The dramatic shift of the world economy in 2007-2008 constitutes an ideal context for examining micro economic responses to large negative economic shocks. Our analysis draws from this unique episode and exploits its broad coverage as well as considerable heterogeneity, both of which help disentangle the role of foreign ownership in establishment performance

> 6 The National Bureau of Economic Research o¢ cially determined December 2007 as the end of the economic expansion that began in the U.S. in November 2001 and the beginning of the recession.

7
households, business and retail enterprises with electricity can continue operating and keeping their stores open during the evening. It is the purpose of this paper to rigorously examine whether these assumed program benefits are real or not.

# **3. Research methods and survey data**

This study is based on a survey initiated by REB with the financing from the World Bank in 2004. The survey was titled “The Socio-Economic Monitoring and Impact Evaluation of Rural Electrification and Renewable Energy Programme in Bangladesh.” The purpose of the survey was to develop practical methods and guidelines to monitor, and evaluate rural electrification programs of REB. The task involved undertaking a large baseline survey to assess the socioeconomic impacts of the rural electrification program of REB and to develop quantitative and qualitative methodologies to assess benefits of rural electrification. In order to carry out the monitoring and evaluation work, the study also aimed to enhance the internal capacity of REB and its affiliated PBSs. The actual survey was carried out in 2005 by Bangladesh Engineering and Technological Services Ltd. (BETS) and Bangladesh Unnayan Parishad (BUP).

The survey administered detailed questionnaires for domestic, commercial, industrial and irrigation units with and without electricity. Domestic questionnaires included information on household characteristics, consumption, income, energy use pattern and appliances. The energy questions in the survey covered electricity use, grid connection and quality of service, use of solar home systems, and also household perception about the quality of services. There were also qualitative questions on women’s empowerment and general health awareness. Questions in commercial enterprises and industry survey involved the nature of the businesses including their operation, cost and revenue, and consumption of various types of energy including electricity. For the irrigation survey there were questions on pumps, usage patterns, cost of operation, crop production and energy consumption. All this information is useful to determine the energy use pattern of both domestic and nondomestic units and to assess impacts of rural electrification. However, in this study we concentrate solely on the impacts of household use of electricity.

The survey covers a wide geographical area covering all six divisional regions of Bangladesh that are under the operation of REB. In fact 45 out of a total of 70 PBSs operating in Bangladesh were covered by the survey. A stratified random sample was drawn 6
to read and write, have higher Raven’s test scores (which is a proxy for raw intelligence), and have higher food security than non-beneficiaries.

In addition, around 18 and 16 percent of respondents reported, respectively, being related and socializing with government officials or village influentials. Interestingly, beneficiaries are comparable to non-beneficiaries in both lottery and non-lottery villages (p-values of t-tests that individuals are related or socialize with government officials or influentials are 0.486 and 0.558, respectively in lottery villages and 0.467 and 0.731, respectively in non-lottery villages). This result suggests an absence of elite capture in the targeting process, as we will argue more precisely in Section 5.

More generally, a joint F-test that all the characteristics reported in Table 3 do not predict beneficiary status in lottery villages is not statistically significant at conventional levels (p-value is 0.903) but it is highly significant in non-lottery villages (p-value is 0.008).

This indicates that in lottery villages there are no statistical differences between beneficiaries and nonbeneficiaries, as randomization would suggest. In contrast, in non-lottery villages where the VVC chose beneficiaries among eligible, beneficiaries are systematically different from non-beneficiaries in a way that the VVC appears to be targeting more educated and better off farmers.

The last 3 columns of Table 3 compare the characteristics of eligible households in villages which held public meetings to identify eligible farmers (column 11) to those that did not (column 12). As column 13 suggests, none of the characteristics appear significant, suggesting that conducting the public meeting did not affect the pool of individuals identified as eligible. The p-value associated to the joint F-test that all variables jointly determine eligibility with a public meeting is 0.946.<sup>7</sup> This result is perhaps unsurprising because according to the MAFC team that conducted the meetings, few changes were made to the list of eligible farmers prepared by the VVC in all study villages. We conclude that the public meeting did not alter the identification of eligible households significantly.

# _4.2 Impacts_

By virtue of the experimental design, and given that the village meeting had no impact on the

> 7 Due to differences in the number of observations between baseline and endline surveys we replicate the results of Table 1 restricting the sample to 427 of the 460 households surveyed at both baseline and endline and find similar results.

15