rafmacalaba/datause-encoder-data huggingface dataset:
- 54 -

22. In the 1988 survey, farmers who used money-keepers were asked what they liked about this system of saving. 62? cited safety or trustworthiness. 18Z said they liked being able to withdraw whenever they chose, 7Z cited proximity of the moneylender, and 13? had other answers. Of course, these reasons mentioned are not mutually exclusive.

23. Whether men should be recruited in preference to women, to reflect local financial practice, or the reverse of each, to pursue political ideals, is an important related ethical question. As so often, foreign agents have here the awkward choice of being sexists or cultural imperialists.

24. One infirmant mentioned the problem of risk. Opinion polling of this sort is only of limited use in most African contexts because informants' finely developed etiquette can produce much second-guessing. Also, of course, this subsample was self-selected as people who liked money-keepers.

25. Little research has been done on attitudes toward money notes and coins themselves and their denominations. Gambian coins in the smallest Gambian denominations (in multiples of bututs, hundredths of a dalasi) are sometimes used by themselves, especially by women and children, but men and others who save large amounts like to convert their money into the largest notes possible, to help protect themselves against temptations. Printing more notes in denominations larger than 25 dalasis might not just serve the convenience of bank clerks and the rich (who queue up in urban banks to count out many hundreds of notes for deposits or withdrawals) but also help and encourage cash saving among poorer people, for better or worse. There would be dangers, however, in problems of innumeracy.

26. The Mandinka term Kafo (corresponding roughly to yirde in Fula, morom in Wollof) is sometimes used by non-Mandinko. In Fula, kafo groups are more commonly called compin, from "company".

27. This discussion of village kafo groups has benefitted from shared preliminary findings of surveys by E. Bazalgette, F. Le Grand, and A. Sillah. Separately, they and I have interviewed on the topic in over 40 Gambian villages. Mandinka villages commonly have three age-graded kafo groups for men and two or three for women. For a cross-ethnic comparison of Gambian age-sets and agegrades, see Sonko-Godwin 1986: 36-50; see also Dunsmore et al. 1976: 277-8. Much more is available in the literature on Senegal.

28. E. Bazalgette and F. Le Grand, personal communications.

29. Some women who have married near their natal homes belong to the kafos of their parents' and husbands' villages.

30. One powerful woman villager interviewed had borrowed several hundred dalasis from a kafo and re-lent the money to other villagers at double the interest.

31. See March and Taqqu 1986 for insights gained in other countries.
variables. Table 3 displays the predicted climate change for the two selected basins by Palmer et al. (2008). Due to the constraints of obtaining natural flow data, the sample areas are upstream sections of the gauging station and represent a portion of the entire river basin. They are around 25% of the entire set of tributaries of the rivers that they represent.

# **Data**

Runoff data with location coordinates were collected from the Global Runoff Data Centre (GRDC). GRDC provided available monthly time-series gauging station data in the international river basins (approximately 3500 stations). While the GRDC is the source of comprehensive data on global gauging station data, the data are available in limited time periods and the distribution of gauge data is not even across the globe; gauges are mainly located on main stem rivers in middle and high income countries.

The model of precipitation data is from the CRU 3.0 Global Climate database downloaded from <u>http://badc.nerc.ac.uk/data/cru/ (accessed 2010) with data from 1901 to June 2006 (in</u> preparation, see Mitchell and Jones 2005). These data have a spatial resolution of 0.5 degree.

# **SSM/I derived surface wetness data description**

The BWI uses observations from the Special Sensor Microwave Imager (SSM/I). It is a seven channel passive microwave radiometer operating at four frequencies (19.35, 22.2, 37.0, and 85.05) and each channel has dual-polarization (except at 22.235 GHz which is V-polarization only). The frequencies flown on the SSM/I are used to dynamically derive the amount of liquid water near the surface. Data are available from 1988 to present except mid-1990 through 1991 and this analysis uses data until 2009.

# **Statistical procedures and calibration of the runoff models**

In order to relate the BWI to the flow gauge data, we use the XL-stat software to run a quadratic univariate regression equation. The independent variable is BWI and the dependent variable is the flow data. Initial results from a simple coincident model did not account for a natural lag that occurs between the period of time when the moisture falls on the surface and the arrival of the water at the downstream gauge in the basin. Therefore, we performed several lagged relationships, realizing that the duration of the lag would depend on the basin‘s size, topography, soil type, and land-use. In performing this analysis we used monthly data, allowing lag to occur 14
relatively less.

Third, we use alternative specifications, which are presented in Row A of table 5. Controlling for capital intensity<sup>11</sup> does not alter the pattern of results as shown in columns 1 and 2 in Row A in Table 5. Columns 3 and 4 furthermore demonstrate that the results are robust to inclusion of sector-year dummies, which control for sector specific shocks and also shield against the impact of using inappropriate deflators. Note that the attenuation effect weakens. Thus, it seems that it is partially, but not exclusively, the result of more productive sectors being more severely impacted by the crisis.

Fourth, Row B shows that the pattern of results remains when we include anomalous observations (see Appendix B) that we have excluded from our estimation sample. The results are also robust to dropping all plants for which at least one of the observed values is anomalous (we typically exclude such anomalous observations but do not drop other plant-year observations from plants characterized by such outliers).

Fifth, by setting arbitrary size thresholds for exit and examining how our results change, we assess whether it is likely that our results are due to sample selection bias because we are only observing firms with 20 or more employees. Row C aggravates such sample selection bias by excluding firms with, respectively, fewer than 30 (columns 1 and 2) and 50 (columns 3 and 4) employees and defining a firm as having exited when it either disappears from the data altogether or fails to report having more than, respectively, 29 or 49 employees in any of the subsequent years. The results are robust to using different exit thresholds and, if anything, using higher thresholds for exit reduces the attenuation effect; to the extent there is sample selection bias because we are only observing firms with more than 20 employees it appears to be making it harder to reject cleansing.

Finally, to address the concern that exit is a protracted process and that by focusing on

> 11 Our preferred specifications do not control for capital because controlling since this would introduce sample selection bias as the response rate for capital is far lower for measures of the capital stock than for other variables. It is also missing in the 1996 data, requiring it to be estimated for that year (see Appendix).

23
selection issues related to GPS measurement of plots, as that is bound to matter for specific aspects of the analysis.

One final concern relates to the possibility that larger farms be concentrated in remote and unfavorable agricultural areas, or that market imperfections leading to the IR may be confined to particular regions. We investigated whether that appears to be the case in our data, by running our analysis separately for the four macro-regions for which the UNHS is statistically representative (Central, Eastern, Northern and Western Uganda). Annex Table 3 presents a summary of the analysis. For brevity we only report the coefficient of land size, which is our variable of interest. The specification is the same as that described in model (2) and reported in Table 5 for the full sample, with the same explanatory variables included but not reported. The top panel of Annex Table 3 reports the results of the IR between net revenue of crop production per acre computed using the self-reported land area variable, for each of the 4 regions. The bottom panel shows the corresponding statistics for the specification that uses the GPS area measurement. As in Table 5, we note that in all regions the IR holds regardless of the specification, but that it is stronger when the GPS area measured is used. Our results do not therefore appear to be sensitive to the effect of possible socio-economic or agroecological differences across regions.

# **6. Conclusions**

The hypothesis that the inverse relationship between farm size and productivity may just be a statistical artifact, linked to problems with land measurement error, is rejected by the analysis presented in this paper. Contrary to earlier conjectures, we find that the empirical validity of the IR hypothesis is strengthened, not weakened, by the availability of better measures of land size collected using GPS devices. Lamb (2003) concludes that by controlling for measurement error in land size, the statistical case for the IR disappears. This paper has shown that introducing rigorous controls for land _quantity_ does not affect the evidence concerning the IR, which is in fact strengthened when the better land data are used.

We also explored how farmers‟ self-reporting of plot size varies systematically with the size of their holdings and other household and land characteristics. We conclude that in Uganda small farmers tend to over-report plot size relative to medium-sized land-holders, while the largest farm groups under-report plot size on average. This is consistent with earlier evidence from Mali reported by De Groote and Traorè (2005).

Self-reported measures of land size are notoriously imprecise. In large household surveys in developing countries, however, they have for a long time been the only option available to practically collect data on the physical dimension of the plots owned or cultivated by the household. More 12
Rhee, H.C., Chung, H.S., 2006. Change in CO2 emission and its transmissions between Korea and Japan using international input-output analysis. Ecological Economics 58 (4), 788-800.

Saikku, L., Rautiainen, A., Kauppi, P.E., 2008. The sustainability challenge of meeting carbon dioxide targets in Europe by 2020. Energy Policy 36 (2), 730-742.

Schipper, L., Murtishaw, S., Khrushch, M., Ting, M., Karbuz, S., Unander, F., 2001. Carbon emissions from manufacturing energy use in 13 IEA countries: long-term trends through 1995. Energy Policy 29 (9), 667-688.

Shrestha, R.M., Marpaung, C.O.P., 2006. Integrated resource planning in the power sector and economy-wide changes in environmental emissions, Energy Policy 34 (18), 3801-3811.

Shrestha, R.M., Timilsina, G.R., 1996. Factors affecting CO2 intensities of power sector in Asia: a Divisia decomposition analysis. Energy Economics 18 (4), 283-293.

Sun, J. W., 2000. An analysis of the difference in CO2 emission intensity between Finland and Sweden. Energy, Volume 25 (11), 1139-1146.

Wang, C., Chen, J., Zou, J., 2005. Decomposition of energy-related CO2 emission in China: 1957-2000. Energy 30 (1), 73-83.

Wood, R., Lenzen, M., 2006. Zero-value problems of the logarithmic mean divisia index decomposition method. Energy Policy 34 (12), 1326-1331.

World Bank, 2007. Growth and CO2 emissions: how do different countries fare? Environment Department. The World Bank. Washington, D.C.

Wu, J.H., Chen, Y.Y., Huang, Y.H., 2007. Trade pattern change impact on industrial CO2 emissions in Taiwan. Energy Policy 35 (11), 5436-5446.

34
**<u>Table 6: Village level evidence on registration of subsequent transactions</u>**

||**Total**<br>**Sample**|**Tigray**|**Amhara**|**Oromia**|**SNNPR**|
|---|---|---|---|---|---|
|**Inheritances**||||||
|Any inheritances since certificates issued (%)?|57.14|75.00|78.26|50.00|36.84|
|If yes, formal change was made (%)|34.32|44.44|42.58|26.32|23.81|
|Have to pay to get inheritances registered (%)|18.07|50.00|16.67|18.75|5.26|
|If yes, amount (B)|19.92|7.75|28.75|20.00|33.00|
|Have to pay for new book/certificate (%)|46.59|100.00|4.35|63.89|42.86|
|If yes, amount (Birr)|3.99|2.25|4.60|5.56|2.33|
|**Leases and other interests**||||||
|Any leases registered in 2005|50.62|75.00|54.55|48.39|40.00|
|Have to pay to get leases registered|22.73|50.00|14.29|20.83|20.00|
|Ifyes,amount(Birr)|11.13|5.75|15.00|15.60|9.00|

_Source:_ Own computation from 2006 EEA/WB Land certification survey.

26
power test. This should serve as a cautionary tale to others attempting similar panel data IV estimation strategies.

# **IV. Conclusions**

This paper calls into question NQ’s empirical findings that US food aid shipments cause conflict in recipient countries. We focus on the NQ results because they have been widely publicized and inform an intensely debated policy issue that is especially timely as the future of US foreign assistance and food aid in particular under the next Farm Bill are under serious scrutiny. If a policy commonly labeled “humanitarian” actually causes violent conflict, that policy should be revisited. We show through a series of placebo tests that their results appear to result from longerrun, spurious trends and then use Monte Carlo simulation to demonstrate that their results could actually arise from a data generating process in which food aid is independent of or even reduces conflict, contrary to their core claims that it prolongs and thereby increases the incidence of conflict.

The broader methodological point, however, is that a panel data IV estimation strategy that has become popular among researchers may be subject to heretofore unrecognized inferential errors. An instrumental variable constructed as the interaction of two variables, one that plausibly meets the exclusion restriction but has limited time series variation, and another that has greater cross-sectional variation in the sample but may be endogenous generates a continuous DID estimator that is subject to the same parallel trends assumption as any other DID estimator. In the presence of nonlinear non-parallel trends, standard fixed effects controls may not suffice to isolate the exogenous inter-annual variability that is intended to identify the causal effect of interest. Much like Bazzi and Clemens (2013), we offer a caution about instrument validity and strength in panel data IV estimation, and like Bertrand et al. (2004), we offer a caution about inference based on DID methods.

21
# _Bhutan_

The business environment in Bhutan (which was only surveyed once, in 2009) impinges on firms quite differently from elsewhere in the region. The top-ranked constraints are transport, access to skilled labor regulation, and tax administration. Of least concern are the courts, telecoms and political instability. The contrast between Bhutan and Nepal in the importance of electricity as an obstacle is particularly striking; across the SAR countries, the constraint is lowest in Bhutan and second highest in Nepal. This is not explained simply by the abundance of hydropower in Bhutan (about 4/5ths of total generation), since Nepal has the second-most abundant hydropower in the region (over 1/4<sup>th</sup> of total generation). The size of the Bhutan sample was relatively small, however – about 250 firms were used to calculate the country conditional means – so the above results should be treated cautiously.

# **9.  How do the SAR countries compare with those outside the region?**

It is useful to compare the constraints reported by firms in the SAR countries with those outside the region but at similar levels of GDP per capita. The non-SAR country conditional means are constructed using the same set of controls as for the SAR countries, but in a single regression with country-survey fixed effects. The fixed effects correspond to the conditional means for individual country surveys. For compatibility with the results reported above, we continue to use a single conditional mean for each SAR country, i.e., we do not disaggregate by survey.

The SAR and non-SAR conditional means are reported for the main constraints of interest in Figures 1-9 below (results for the other constraints are reported in Appendix 2). The log of GDP per capita in PPPs is on the horizontal axis and the reported cost of the constraint on the vertical. Heteroskedasticity-robust confidence intervals of 5% are shown individually for the SAR countries; these are simply the confidence intervals for the estimated constants in Table 13. The figures also show a regression line based on a quadratic in log GDP per capita for the country-survey conditional means for the rest of the world. A 95% confidence interval based on the 31
for the higher attrition among the non-stunted group. Table 5 presents results in the same fashion as Table 3: Panel I examines the observed sample, Panel II uses imputed values for missing data, and Panel III focus on data for non-migrants only. The _p_ -values represent one-sided tests for the hypothesis that the differences between the two groups are null versus the alternative hypothesis of the non-stunted group has higher earnings. The conditional differences in log-earnings between the non-stunted group and the stunted treatment group are never statistically significant and average around zero.

The panels of Figure 2 displays kernel density estimates of the densities of earnings for the non-stunted comparison group and the stunted treatment group. The figures generally show little separation between the earnings densities for the two groups, as confirmed the Kolmogorov-Smirnov tests. These results are consistent with the findings reported in Table 5.

In contrast, the stunted control group remains behind. Table 6 presents the mean differences between the non-stunted comparison group and the stunted control group. The table shows that the non-stunted comparison group consistently earns more than the stunted control group, with most differences in mean earnings being statistically significant.

# **7 Pathways to Earnings**

## **7.1 Parental Investment**

The stimulation intervention was designed to improve the maternal-child interaction, i.e. the quality of parenting. We begin by examining the extent to which treatment resulted in more maternal investment in stimulation at home during the experimental period when the children are very young. Although we cannot attribute a causal link between the increase in the measures of the quality of the home environment and the outcomes, these results suggest a possible mechanism.

We analyze the effects of treatment on a modified version of the Caldwell index of stimulation of the home, the infant toddler HOME inventory (Caldwell, 1967; Caldwell and Bradley, 20
Agricultural and rural households in low-income settings often have higher illiteracy rates than the general population, for example. This is illustrated in Figure 4, which presents the literacy rates for households with no income from agriculture vis-à-vis those earning more than 30% of total income from agriculture for 26 low- and middle-income countries.<sup>9</sup> If a meaningful share of respondents can be expected to have literacy difficulties, text message-based and Interactive Voice Response (IVR) phone survey modes are likely unsuitable as these usually require the respondents to read and/or write.

_Figure 4. Adult literacy rate (ages 15+), by share of total income from agriculture._ <!-- Start of picture text --> Income from agriculture > 30% No income from agriculture<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> _Source: Authors’ calculation based on the FAO Rural Livelihoods Information System (RuLIS) database_

# _Response fatigue and incentivization_

Fatigued respondents may cease to answer truthfully, refuse to answer questions, or stop participating in a survey over time. Various factors can contribute to the respondent burden, including survey complexity and length. For instance, Ambler et al. (2021), through an experiment in Ghana, find that the roster position of a household member has implications for activities reported in the labor module, with the movement of an individual down in the roster by one position reducing the number of their reported productive activities by 2.2%. In a recent experimental study, Abay et al. (2021) show that delaying a module on dietary diversity by 15 minutes led to underestimation of the dietary diversity score due to respondents’ fatigue in a phone survey in Ethiopia.

Recall length also contributes to respondent burden and has been found to affect data quality (Beegle et al., 2012a; Wollburg et al., 2020). As discussed in the previous section, phone surveys can be used to shorten recall periods through higher frequency data collection (Arthi et al., 2018; Gaddis et al., 2021). Shorter lags between survey rounds tend to also lower the chances that respondents attrite (Gourlay et 9 Households earning between 0% and 30% of income from agriculture are excluded for presentation purposes. Only countries in the RuLIS database with data on adult literacy rates since 2010 are included. The year of data collection by country is as follows: Armenia - 2013; Bangladesh - 2010; Burkina Faso - 2014; Cameroon - 2014; Ecuador - 2014; Ethiopia - 2016; Ghana - 2013; Guatemala - 2014; India - 2012; Iraq - 2012; Kyrgyzstan - 2013; Malawi - 2017; Mali - 2017; Mexico - 2014; Nepal - 2011; Nicaragua - 2014; Niger - 2014; Nigeria - 2019; Pakistan – 2014; Peru - 2019; Rwanda - 2014; Senegal - 2011; Sierra Leone - 2011; South Africa - 2015; Tanzania - 2015; Uganda - 2016.

19