rafmacalaba/data-use-mentions-extended huggingface dataset:
7 issues the vouchers to the approved farmers and also monitors the use of inputs by voucher recipients.

The purported objective of the program is dual: 1) increase overall maize and rice output, and 2) increase access to modern inputs among poor and vulnerable smallholders. Yet, as indicated above, conflicting targeting rules may derive from these dual objectives. To reduce poverty, subsidized inputs should be targeted to the poorer smallholders. If increasing aggregate output is the objective, subsidies should be targeted to those with the highest marginal input productivity. These often include non-users (Rickert-Gilbert, Jayne, and Black, 2009), and may or may not coincide with the poorer farmers.

During the pilot, which forms the basis for our empirical analysis here, the villages were advised to target literate farming households willing to use the input vouchers for the purported crops and able to meet the co-financing requirement. Application of these criteria in most villages would lead to an exclusion of the poorest (those considered unable to cofinance input purchases). However, the rules were not formally presented and enforced, and actual implementation was left to the discretion of the VVC and the village assembly.

They were however further refined and more formally introduced during the expansion of the program in 2009,<sup>9</sup> when it was also formally established that village leaders had to be excluded from the VVCs and that the VVCs needed to have equal representation of men and women. The fertilizer voucher was further reduced to one bag of urea to reduce the required top up and facilitate participation by poorer farmers. Use of the pilot data thus presents an opportunity to analyze how communities identify beneficiaries in less prescribed environments, the extent to which the advice was adhered to, and whether the adjustments in the targeting rules were well advised.

> 9 One important adjustment was the requirement that the beneficiaries should not be cultivating more than one hectare of maize and rice.

villages within its area and other members elected by the village assembly. The term of office
we find that urbanization affects each variable positively and significantly, except for the share of the pulse area, which is affected negatively.

We check the relative contribution of each channel to the poverty-reducing effect of urbanization by adding each variable in turn to the specification in column 1. According to this analysis, approximately three-quarters of the poverty-reducing effect of urbanization is accounted for by consumer linkages. Intradistrict rural-urban migration accounts for less than one-fifth, and the rural land/labor ratio and the rural nonfarm employment account for 4 percent and 3 percent, respectively. The small contribution of the latter channel is particularly surprising because rural nonfarm employment is often an important cause of rural poverty reduction (Berdegue et al. 2001; Lanjouw and Shariff 2002). This surprising result could be explained by considering that urbanization affects rural nonfarm employment positively and that the latter has an inverted-U effect on rural poverty in India.

## **<mark><<Table 5 about here>></mark>**

Taken at face value, these results suggest that the other channels that we were not able to properly capture (i.e., rural consumer and land prices) are not likely to account for any of the poverty-reducing effect of urbanization on the rural areas within the district. This implication seems consistent with the ambiguous effects of these channels on rural poverty that were discussed in section 2.

# **<<A>>Conclusions**

Do the poor in rural areas benefit from the population growth in urban areas? If so, what is the size of the benefit? Despite the importance of these questions, little empirical evidence is available to provide adequate answers. We have attempted to address this gap by analyzing the effects of urbanization on rural poverty. Using data 33
calculating length of the land modules for individuals who own land versus those with no land). We are also able to provide estimates on the extent to which interview length for sampled households rises with the number of household adult members targeted for individual interviews. On average, having one additional interview with an adult household member increases total household interview time by about 37 to 48 minutes in Cambodia. In Ethiopia, this number is 41 minutes when moving from one targeted individual to two, then shifts to less than 22 minutes per any additional individual interview target. Tanzania’s increase in additional time per interview target is the largest relative to other countries, given the number of modules the survey administered (as high as 191 minutes and as low as 81 minutes).

Furthermore, the analysis combines the duration data with the information on the total survey costs to provide the cost estimate for a minute of face-to-face multi-topic survey data collection in each country. In Cambodia, the cost per minute is estimated at $0.87, while the comparable figures are $1.71 in Ethiopia, and $3.94 in Tanzania. These cost estimates can be used to construct budgets for hypothetical surveys in comparable country contexts, as a function of the expected total interview time per household, which would be mediated by the estimated duration of specific questionnaire modules (which can be informed by this paper) and the number of intra-household interview targets (in the case of individual-level data collection). We posit the unit costs reported in this paper to be more informative and comparable than the past cross-country cost estimates per interviewed household (see, for example, Kilic et al. 2017) since the countries differ significantly in terms of the approach to and scope of household survey data collection.

Subsequently, we turn to the cross-country analysis of interviewer effects on module duration. Interviewers play a large part in survey implementation, with potential effects on how respondents answer questions, non-response, measurement errors, and interview length (West and Blom 2017; Flores-Macias
# IV. Early warning systems

Ethiopia has a range of EWS that permit the early recognition of the onset of a drought. These tools include, inter alia, the LEAP and LIAS tools, a bottom up early warning system, hotspots analyses and the Integrated Food Security Phase Classification<sup>39</sup> . The following sections will provide an overview of the methodologies used in each of the tools, with a view to assessing how the existing EWS can be used within Ethiopia’s drought response framework.

## IV.1.  The LEAP early warning tool

### _IV.1.1  The LEAP tool methodology_

LEAP<sup>40</sup> was initiated during PSNP I in 2006 and was developed to estimate food aid beneficiary numbers, and to develop indices for early response in terms of food security. Under PSNP III and IV respectively, the LEAP tool was chosen as a trigger to release PSNP funds under the RFM and FCB. Based on LEAP data indicating a drought of sufficient strength to trigger contingency funds, the PSNP would respond to drought in a timely manner. By providing early warning information during the onset of a drought, the LEAP tool facilitates early action, thereby mitigating the adverse impacts of droughts on food security.

The objective of the LEAP tool is to provide estimates of the number of people in need of food assistance during each of the main harvest seasons _meher_ and _belg_ , at the _woreda_ , regional and national scale. To obtain estimated beneficiary numbers, the LEAP tool performs the following steps (Source: Hoefsloot et al. 2013):

- (i) **_Calculation of planting date:_** Based on crop and rainfall data, the LEAP tool computes the estimated planting date for a range of crops. Thereby, planting dates are computed for each crop and geographic region individually, depending on current rainfall data. Planting dates are computed as a sowing window start dekad<sup>41</sup> and a sowing window end dekad
, the four largest items are value-added tax (VAT), corporate income tax, personal income tax and consumption tax. Contributions to social insurance amount to 30 percent of total revenue, the majority of which are collected through the employee’s social insurance system and only 4 percent is collected through the resident’s system. The employee’s system is legally required for any type of formal employment while the resident’s system covers those not covered by the employee’s system.<sup>4</sup>

> 4 Non-tax revenue accounts for the remaining 10% of total revenue, and includes special program receipts, charge of administrative and institutional units, penalty receipts, operation income from government capital, income from use of state-owned assets/resources and other non-tax revenue. Source: Statistical Yearbook of China 2019, National Bureau of Statistics, 2019.

4
, with mortality risk higher at both the bottom and top ends of the scale (Calle, Thun et al. 1999). Account is taken below of the non-monotonic relationship between health and BMI. Beyond BMI, the VLSS contains very little by way of useful information on adult health. Respondents were asked whether they had been sick in the four weeks prior to the interview on both occasions, but the question was posed differently in the two waves, so comparisons across the waves are not possible. In any case, evidence from the work of Gertler and Gruber (2002) suggests that changes in a short-term and self-reported health measure such as illness in the previous four weeks is unlikely to be associated with income and consumption changes. Indeed, a key conclusion of their work is that any failure to smooth consumption in response to health shocks is likely to be in relation to objective long-term health measures, not short-term subjective measures. Their measure—an index of people’s ability to perform activities of daily living—is not available in the VLSS. BMI seems, however, a good substitute. Indeed, it may be potentially a good complement.

Income is total per capita household income, broken down into earned income (wage income as well as income from agriculture and any family business) and unearned income (gifts, remittances, pensions, and the like). Medical spending is per capita household medical outlays, excluding health insurance contributions, based on a 12-month reference period. Non-medical consumption is broken down into food consumption and non-food consumption, the latter including the use value of durables.<sup>3</sup>

> 3  Full details are available in the VLSS basic information document available at <u>http://www.worldbank.org/lsms/country/vn98/vn98bif.pdf.</u>

4
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We use an intent-to-treat (ITT) analysis in all of our main specifications. There are several reasons why actual treatment and assigned treatment might differ. First, some individuals left the factory before being treated. While our policy was to only announce treatment status one month prior to the actual treatment date, we conduct our analysis on the full set of workers present in the factory before the first treatment phase was announced. Second, a small number of individuals opted out of receiving the direct deposit component of the payroll account. Third, the bank and mobile money partners rejected a number of account applications due to insufficient documentation.<sup>27</sup> Finally, in some cases, account opening took longer than one month, thus delaying the actual treatment date. In our panel analysis of the follow-up surveys, we therefore use intended rather than actual treatment date in our main regression specifications. We present the first-stage relationship between assigned and actual treatment in Appendix Table D.3. On average, 92% of individuals assigned to the bank or mobile payroll treatment received the treatment and 90% of individuals assigned to the account only treatment eventually received an account. The difference is not = statistically significant (p-value 0.407).

# **3 Data and Descriptive Statistics**

To analyze how exposure to payroll accounts affects behaviors and real outcomes, we combine multiple rounds of survey data with administrative records that capture the universe of financial transactions made through mobile and bank accounts assigned as part of our study. These data cover a time period of approximately two years, during which our field experiment was conducted.<sup>28</sup>

## **3.1 Survey Data**

Our main data source consists of a comprehensive baseline and endline survey, as well as five rounds of shorter midline surveys, administered over a period of approximately two years. Before being assigned to one of the experimental treatments, each worker completed a baseline survey that included modules on demographics, savings, consumption
for a subset of the Indian population. Eligibility for anthropometric measurement within sampled households varies by year – in 1998, children under 3 and ever-married women aged 15-49 were measured, while in 2005 and 2015 all children under 5, all women aged 15-49, and random subsample of men aged 15-54 were measured. The results of applying the (Henry 2005) equation are reported in the first panel of Table 1.<sup>4</sup> As discussed in Appendix A, data generated by the National Nutritional Monitoring Bureau (reported in (Indian Council of Medical Research 2009)) provides additional measurement of height and weight by age for rural areas of 16 Indian states in 2000-2002 to extrapolate outside of the age range in the NFHS. The NFHS estimates shown in Table 1 highlight three facts that will be important later: (1) adult men have higher REE than adult women who have higher REE than children, (2) REE is higher in urban than rural areas as urban individuals are slightly taller and substantially heavier for a given age/sex, and (3) REE has been increasing over time for a given age and sex, mostly due to increasing weight (see Appendix Table S.1 in the supplementary appendix).

# **2.2 Activity Levels (AL)**

Activity levels are estimated by aggregating up from the most detailed activity data available. With data on the share of time allocated across _J_ different activities, an estimate of individual _i_ ’s activity level (AL) is as follows: where _Shareij_ is the fraction of individual _i_ ’s time spent on activity _j_ . _AFj_ is an activity factor for activity _j_ that measures the intensity of activity _j_ relative to rest (equal to one). Individual _AL_ are thus weighted averages of activity factors where the weights vary across individuals based on their time-use.

The most detailed activity data for India comes from the Time-Use Survey (TUS) conducted in 1998-1999 by the National Sample Survey Organization for six states (Gujarat,
**Figure 2: Cumulative Number of FDI projects from HICs to LMICs, by sector, 2004-2015 (natural logarithm scale)**

<!-- Start of picture text --> 12.00<br>Electrical/electronics<br>11.00<br>Motor vehicles and other transport<br>equipment<br>pharmaceutical and other chemical<br>10.00 products n.e.c.<br>Industrial machinery<br>Textiles, apparel, and leather products<br>9.00<br>Food and beverages<br>8.00 All other manufacturing branches<br>Rubber and plastics products, and other<br>non‐metallic mineral products<br>7.00 Metals and metal products<br>Wood and furniture<br>6.00<br>coke and refined petroleum products<br>5.00<br>4.00<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> Source: Calculations based on fDi Markets Database

# b) <u>Use of robots</u>

The data on industrial robots are taken from the International Federation of Robotics (IFR) database. It is the single source of data on the use of robots, which is comparable across countries and industries, and has been used by several studies in the literature that estimates the effect of automation on labor markets and other economic outcomes. High-income countries were the largest users of industrial robots in the manufacturing sector between 1995 and 2015. However, China is projected to have more than 400,000 industrial robots in operational stock in the manufacturing sector by 2018, more than doubling the number in 2015 (Hallward-Driemeier and Nayyar 2017). This would give China the distinction of having the highest number of installed industrial robots in the world, accounting for about one-fourth of total industrial robots projected to be installed globally. While
participation. In addition, Treisman (2007) finds that perceptions data often do not correlate well with experienced-based measures of corruption. Thus, results from the CPI should be interpreted with caution. More recently, experience-based measures have come to replace perceptions-based measures in the literature.

The above is not to belittle CPI-based research, which has produced many important insights into the correlates of corruption. Among the many studies in this area are those showing a relationship between corruption and judicial effectiveness (Treisman 2000), fairness in the rule of law (Uslaner 2005), political institutions (Lederman et al. 2005), economic liberalization (Goldsmith 1999), education (Shleifer and Vishny 1993), economic growth and openness to trade (Svensson 2005; Mauro 1995; Ades and Di Tella 1999), inequality (Jong-Sung and Khagram 2005).

On close examination, the term „corruption‟ is somewhat nebulous, in part because it incorporates a variety of actions at different levels of public office. The extent to which corruption is pervasive varies by both the level of government at which it occurs, and the type of corruption which occurs. Furthermore, measures of corruption are flawed in that they contain systematic biases, and do not disentangle the different forms that corrupt acts can take.

# **Culture: Theory and Empirics**

Defining culture is a challenge in its own right.<sup>9</sup> Economists narrow the definition down to shared values and beliefs governing interaction among individuals (Huntington 2000; Greif 2004; Fernandez 2008; Barr and Serra 2010). In all definitions, culture is “shared” among group members, and indeed, the group may be defined by these shared values. We can further distinguish two channels through which culture operates: social norms and formal institutions (Elster 1989). Social norms are informal rules, driven by values and beliefs that govern interaction, and are both shared and sustained by group members.<sup>10</sup> Institutions are formal rules

>
Georgia are calculated from the GEOSTAT Statistics Survey of Enterprises for 2006-2017.

**Nominal effective exchange rate:** A measure of the value of a currency against a trade-weighted average of several foreign currencies. The nominal effective exchange rate for Georgia for each destination market was calculated from the World Bank World Development Indicators using trade weights calculated from UNCOMTRADE for 2006-2012.<sup>8</sup>

> 8 We experiment with an alternative instrument that is also interacted with the country’s deposit dollarization rate, but the results do not change.

6
domestic violence in households in which the woman has more than primary education but increases domestic violence in households where the woman has less than primary education, if she has more than her partner.

Other research which has focused on the impact of microfinance on domestic violence has typically not found statistically significant average effects, though as in the studies of cash transfers there are important heterogenous effects among subgroups. Kim et al. (2009) found that a randomly allocated microfinance intervention in South Africa improved women’s economic standing but did not decrease domestic violence; although a treatment that received combined with cur- group microfinance gender/HIV training riculum did suffer less domestic violence. Ferrari and Iyengar (2010) found that a microfinance program in Burundi increased women’s reported empowerment but did not affect domestic violence.

Several studies have examined the relationship between domestic violence and microfinance using observational data from Bangladesh. Two studies find a negative correlation between microfinance participation and domestic violence (Bates et al. 2004; Schuler et al. 1996); another finds an overall zero correlation (Ahmed, 2011). An interesting pattern of heterogeneity with respect to time in the program yields the zero average effect in Ahmed (2011). He argues that empowerment programs increase domestic violence in the short run as the household adjusts to the newly empowered women, but decrease domestic violence in the long run as women are able to use their newly improved economic status to demand less violence within the household. Koenig et al. (2003) also find that the effect of participation in a credit group increased the chance of her facing domestic violence in conservative villages but decreased her risk in relatively more liberal villages.

# **2.3 Work versus Transfers**

While these results contribute to an understanding of the relationship between intrahousehold bargaining and domestic violence, there are several reasons why a woman’s labor force participation may impact domestic violence differently from money received in a cash transfer or microfinance program. First, the sample of women choose to participate in
(R2)<br>Urban<br>(U2)|Rural<br>(R1+R2)|Urban<br>(U1+U2)|
|Panel I: Young Cohort (15-24)||||||
|1993-94|42.0|18.3|10.7<br>31.0|52.7|49.2|
|2004-05|38.2|19.7|17.8<br>35.2|56.1|54.9|
|2011-12|22.7|14.5|32.8<br>45.7|55.5|60.2|
|Panel II: All (15 & above)<br>1993-94|49.0|23.7|3.3<br>9.7|52.3|33.4|
|2004-05|49.4|24.4|4.9<br>9.9|54.3|34.3|
|2011-12|35.8|20.5|8.5<br>12.0|44.4|32.6|

Source: Authors’ own estimate based on NSSO data.

> 3 Several recent studies have provided evidence for this hypothesis. Rangarajan et al. (2011) estimated that 44 percent of the share of women who withdrew from the labor force during 2004-05 to 2009-10 were those who chose to engage in education. Neff et al. (2012) also argue that the decline in FLFP in rural areas during the period of 2004-05 to 200910 is partly due to the effect of education, wherein nearly a quarter of the working age group, consisting of young females (15 to 24 years) have moved into higher education. Kannan and Raveendran (2012) estimated that within the total drop in FLFP during the same period, about 27 percent of this drop may be accounted for by additional enrollment