rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. INTRODUCTION**

Of the 1.4 billion people living in extreme poverty, the majority reside in rural areas and rely on agriculture as a source of income and livelihood (Olinto et al. 2013). In Sub-Saharan Africa, nearly 75 percent of the extreme poor reside in rural areas, and over 90 percent participate in agriculture. Smallholder agriculture is the predominant form of farm organization, with 33 million small farms holding less than two hectares and representing 80 percent of all farms in Africa (FAO 2009). On these farms, agricultural practices are typically labor intensive, and the majority of the labor is provided by household members.

Accordingly, the labor of household members in agriculture is a key asset for poor households, and its accurate measurement is essential to the development of sound policy. Despite the importance of the agricultural sector in reducing poverty and food insecurity (Chen and Ravallion 2007; Irz et al. 2001; Ligon and Sadoulet 2007), serious weaknesses in agricultural statistics persist.<sup>2</sup> In this study, we examine one aspect, measures of family farm labor.

To assess the degree of recall bias in household farm labor, we conducted a survey experiment in Mara Region, Tanzania, over the long rainy season, January–June 2014. Smallholder farming households were randomly assigned to one of four survey designs: (a) households reporting agricultural labor in weekly face-to-face visits; (b) households reporting agricultural labor in weekly phone surveys; (c) households reporting agricultural labor in a single postharvest recall survey, the Tanzania National Panel Survey (NPS); and (d) households reporting agricultural labor in a shorter version of the Tanzania NPS postharvest recall survey. Household labor information collected in weekly visits—our resource-intensive golden standard—was then compared with data reported after the harvest. After establishing the magnitude of recall bias, we investigated the mechanisms by which it arose.

We found strong evidence of recall bias in the reporting of family farm
even after controlling for several firm-level and country-level variables. The relationship is quantitatively large. For instance, for the baseline specification, our most conservative estimate suggests that moving from a country of the size of Namibia (25<sup>th</sup> percentile value of population) to a country of the size of Morocco (75<sup>th</sup> percentile value of population) is associated with an increase in the level of overall corruption by 0.28 percentage point or about 23 percent of its mean value. This is an economically large increase.

The plan of the remaining sections is as follows. In section 2, we describe the data and the variables used in the regressions. Our empirical results for overall corruption and petty corruption are provided in section 3. We summarize the main findings in the concluding section and suggest scope for future work.

# **2. Data and main variables**

In this section, we discuss the data. The main data source we utilize is firm-level survey data collected by the World Bank’s Enterprise Surveys (ES). The ES are nationally representative surveys of the non-agricultural and non-financial private economy. A common sampling methodology – stratified random sampling – is followed in all the surveys along with a common questionnaire.<sup>4</sup> The sample for each country is stratified by industry, firm-size, and location within the country. Weights are provided in the survey and used in all our regressions so that the sample is representative of the target population.

Our main regression results are based on a sample of 47,952 firms in 135 countries.<sup>5</sup> Of these, 26,526 are manufacturing firms while the remaining 21,426 firms belong to the services

> 4 Details of the sampling methodology and other survey related information are available at <u>www.enterprisesurveys.org.</u>

> 5 The list of countries is provided in Table A1 in the Appendix. All
The gravity model of trade that relates bilateral trade flows between two countries to their economic size and variable trade costs is one of the most successful empirical models in economics (Anderson, 2011). Most studies focus on aggregate bilateral trade flows between countries but a few recent studies have begun to delve into the role of firms for the gravity equation but focusing on single countries only.<sup>47</sup> An exception is Mayer and Ottaviano (2008), who examine data from a handful of European countries. Using measures in the Database at the country-year-destination level, we provide estimates of a gravity equation for our large sample of countries (and their trading partners) that allow us to examine whether the effects of the classical determinants of bilateral trade –economic size and proxies for trade costs (distance and tariffs) along with the level of development– operate through firm export participation or through the average value exported per firm.

We decompose exports from country _i_ to partner country _j_ for any given year (ignoring the year subscript) into the product of nij the number of country _i_ firms exporting to country _j_ (extensive margin) and sij = Xij⁄nij the average exports per firm for firms that export from country _i_ to country _j_ (intensive margin): Xij = nij ∗(Xij⁄nij). The measures corresponding to the three elements in this decomposition are available in the Database at the country-yeardestination level (CYD.dta) and are used in turn as dependent variables in the gravity equations whose estimates are shown in Table 9. Data on bilateral distances is taken from CEPII described in Mayer and Zignago (2011), and data on bilateral tariffs from Kee, Nicita, and Olarreaga (2009).<sup>48</sup> Since OLS estimation is used in Table 9, the coefficient on an independent variable in

> 47 For example Bernard, Jensen, Redding, and Schott (2007) and Lawless (2010) examine bilateral U.S. exports, Bastos and
-46-

Table 3.10 shows value added (US census concept) as a percentage of gross value of output. These percentages were applied to the gross output figures for Brazil and the USA in table 3.7 and for Mexico and the USA in table **3.8.** The results for the two countries are presented, respectively, in table 3.11 and 3.12.

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__------

**TABLE 3. 10**

|**Value Added (US Census Conce**|**pt) as a per**|**centage**<br>**of**Gr|oss**Value of**|
|---|---|---|---|
|Output,<br>1975,|**in national**|**currencies**||
||**...-**<br>|**------------------------**<br>|**-------------------------------__----------------**<br>|
||Brazil|Mexico|USA|
|Total Manufacturing|39.21|45.79|42.57|
|Grain Mill Products<br>|25.83
-- End of picture text --> _Source:_ World Bank, including the Information and Communications for Development database. _Note:_ ECOWAS = Economic Community of West African States.

Prices for telephony remain modest, particularly via the Internet; end-user prices are below those of countries both with and without access to the submarine cable (table 16). Such prices are a result of the advanced roaming arrangements for mobile services that exist in the Economic Community of West African States (ECOWAS) area. The West African region, by comparison with the rest of Africa, has made significant progress in promoting preferential intraoperator roaming arrangements. Subscribers who belong to one of these networks can use their mobile handsets in other countries, where they do not pay for incoming calls and are charged local rates for outgoing calls. Prepaid users can also recharge their phones in the country that they are roaming. Two factors explain the relatively advanced state of ECOWAS’s regional integration with regard to mobile roaming arrangements. One is the existence of a proactive regional regulatory association for ICT—the West Africa Telecommunications Regulators Association (WATRA)—the other, the existence of many large mobile operators with a presence across multiple ECOWAS countries. Seven large mobile groups with a multicountry presence dominate the regional telecommunications market.<sup>13</sup> These multicountry networks have provided the basis for the regional roaming arrangements that essentially collapse into three roaming areas: Orange Zone, Zain One, and One World (table 17). Within these areas, roaming charges are very modest.

> 13 Etisalat, France Telecom, Maroc Telecom, Millicom, MTC (Zain), MTN, and Comium.

33
- Timilsina, Govinda R. and Florian Landis (2014). Economics of Transiting to Renewable Energy in Morocco: A General Equilibrium Analysis, World Bank Policy Research Working Paper, WPS 6940, World Bank, Washington, DC.

- Timilsina, Govinda R. and Kalim Shah (2020). Economics of Renewable Energy. Oxford Encyclopedia. Forthcoming.

- Timilsina, Govinda R. and Mike Toman (2016). Potential Gains from Expanding Regional Electricity Trade in South Asia. Energy Economics, Vol. 60, pp. 6-14.

- Timilsina, Govinda R. and Ilka F. D. Curiel (2020). Power System Implications of Subsidy Removal, Regional Electricity Trade, and Carbon Constraints in MENA Economies. World Bank Policy Research Working Paper, Forthcoming, World Bank, Washington, DC.

- World Bank (2015a). Guidelines for Economic Analysis of Power Sector Projects. Technical Notes. World Bank. Washington, DC.

World Bank (2015b). Bringing Variable renewable energy up to scale Options for Grid Integration Using Natural Gas and Energy Storage. Technical Report 006/15, Energy Sector Management Assistant Program. World Bank. Washington, DC.

World Bank (2020). World Bank commodity prices database. <u>https://www.worldbank.org/en/research/commodity-markets.</u> Yuan, J., S. Sun, W. Zhang and M. Xiong (2014). The economy of distributed PV in China. Energy 78: 939-949.

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# **2. Data**

We gather information from several sources. This section presents the essential features of the data, focusing on our novel NTM dataset. Summary statistics of all variables used in the analysis are presented in Table 1. Details on all sources and variables’ construction are presented in Appendix B.

# _2.1. NTMs in Indonesia: Institutional Background_

The overall responsibility to determine rules over products sold in Indonesia lies with the Ministry of Trade (MoT). However, different government institutions are also responsible to determine NTMs for specific products. For instance, agricultural products are usually subject to NTMs applied by the Ministry of Agriculture and the Ministry of Health, including the food and drug agency (BPOM). These NTMs typically aim to protect consumer safety from risks such as plant and animal diseases through sanitary and phyto-sanitary (SPS) measures. Similarly, the Ministry of Industry regulates NTMs aiming to ensure consumer protection from risks linked to the use of manufacturing goods through technical barriers to trade (TBT).

Overall, 13 government institutions (ministries and agencies) have issued regulations on NTMs in Indonesia during our period of analysis (2008-15). As a result many products are subject to multiple NTMs. In our data, an average of 2.6 NTMs are applied to the universe of HS-10 digit products in 2008-15; and 5.4 for products with at least 1 NTM applied. These span a wide range of NTMs. Using the most refined NTM classification of UNCTAD at the 3-digit level, Indonesia applied 56 individual NTM types to the universe of its products in the 2008-15 period.<sup>5</sup> The Indonesian government has been increasing the use of NTMs over the period of analysis through relatively frequent changes in their application across HS-10 digit products. To the extent that NTMs mainly fulfill the objective of addressing market failures in trade, such changes should be less subject to the kind of regulatory capture typical of import tariffs (Grossman and Helpman, 1992). This would
Figure O9: Comparison of Nielsen price indices with CPI, grocery stores <!-- Start of picture text --> NEW YORK NY<br>1.3<br>1.2<br>1.1<br>1<br>2006m1 2008m1 2010m1 2012m1 2014m1 2016m1<br>Year-month<br>F, Chained F, Alternative weights<br>F, Tornqvist F, One-stage<br>F, Geometric F, Fixed<br>F, Fixed posted F, County base<br>CPI Food and Beverages CPI Food at home<br>Index value<br><!-- End of picture text --> Notes: This figure plots city-level price indices from 2006 to 2015 constructed using Nielsen retail scanner data with alternative methods against those used by the BLS to construct the CPI. F correspond to Nielsen price indices for drug stores, grocery stores, and mass merchandise stores respectively. Nielsen price indices are first constructed at the store level, and aggregated to the city level by taking a sales-weighted average.

91
To assess the quality of the data, we compare the total values obtained from aggregating the customs data at the country level with the total values obtained at the country level from Comtrade. We dropped the years in which the total values obtained from the customs data represented less than 70 percent or more than 130 percent of the total values obtained from Comtrade aggregates. Table 1 has the full list of countries and periods included in the final sample used in this paper and the complete list of countries and years for which data are excluded can be found in Table A1 in the Appendix.

Table 1 also shows the summary statistics with annual averages for the years available from 2004 to 2008. There is significant variation in the number and size of exporters across countries and also within countries. For example, Cambodia (KHM) has a relatively small number of exporters, yet they are relatively large on average. In contrast, Bulgaria (BGR) has a relatively large number of exporters that are relatively smaller. The correlation between number and size is 0.36. Within countries, we also observe a large difference between the median and the mean values per exporter—the mean values are, on average, 51 times larger than the median values per exporter. This reflects the incredibly skewed distribution of firm size.

Figure 1 shows the distribution of the 200 largest firms by size on average across countries in the sample. Figure 1.i shows firm rank against cross-country average exporter size on a log-log scale for year 2008.<sup>7</sup> We find that the relationship is nearly linear–the Pareto exponent (the slope of the regression of log(rank) on log(export size)) is 1.05, which is consistent with Zipf’s law.<sup>8</sup> Figure 1.ii highlights the skewed distribution in simpler terms. It records the average share per exporter by rank in total exports. The top firm across countries accounts for

> 7 We tried estimations
Yes|Yes|Yes|Yes|
|Firm age|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector 2d|Yes|Yes|Yes|Yes|Yes|Yes|
|Year|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|10762276|10762276|10762052|9827417|9827417|9827251|
|R-squared|0.00618|0.00620|0.00620|0.00643|0.00645|0.00645|

_Note:_ The table presents the results of firm-level regressions of yearly employment growth rates on ownership status. All specifications include fixed effects to control for initial firm size, initial firm age, 2-digit industry and year. Data from RAIS, from 2016 to 2020. Robust standard errors in parentheses. Significance levels: * 10%, ** 5%, *** 1%.

18
on the existing datasets.<sup>15</sup> We pursue this service ladder approach and apply our ordinal poverty measures to study the improvement in sanitation deprivation in Bangladesh. We classify households’ _access to sanitation_ in the five ordered categories presented in Table 2. The five categories are ordered as ‘open defecation’ _≻D_ ‘unimproved’ _≻D_ ‘limited’ _≻D_ ‘basic unsafe’ _≻D_ ‘improved’. We consider all persons living in a household deprived in access to sanitation if the household experiences any category other than ‘improved’.

Table 3: Change in population distribution across sanitation categories in Bangladesh

||B|anglade|sh||Dhaka||R|ajshah|i||Sylhet||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||2007|2011|2014|2007|2011|2014|2007|2011|2014|2007|2011|2014|
|Open defecation|7.5|4.2|3.3|7.5|4.0|2.2|13.8|3.9|3.2|2.1|12.5|9.4|
||(0.8)|(0.3)|(0.5)|(1.4)|(0.7)|(0.8)|(2.3)|(0.8)|(0.9)|(0.4)|(1.5)|(1.3)|
|Unimproved|47.1|38.3|25.7|44.3|35.9|22.6|45.3|36
the interviews.<sup>14</sup> Between January 4, 2015 and February 3, 2015, we interviewed 3,319 eligible potential migrants. Though the study was conducted in the DoP in Kathmandu, it appears to be representative of the population of current migrants in the country (Appendix Table A.2). The average potential migrant in the study sample is 27.6 years of age and has 7.5 years of schooling, quite similar to the age and schooling of current migrants in the 2011 census (top panel, columns 1 and 2). It is important to note that the study sample is predominantly low-skilled. Only 15 percent of the sample had completed more than 10 years of

> 12The estimates of the number of Nepali leaving the country hovers around 1,000 to 2,000 per day, many of whom may have old passports.

> 13Due to the large volume of people submitting their applications, the enumerators could not systematically keep a record of how many people they approached in a day. Though the office accepted applications from 8:00 AM until 4:00 PM, most eligible applicants chose the morning hours. On most days, the eligible applicants stopped coming in by 2:00 PM. 14The DoP made an exception for this study by letting the enumerators inside the premises and allowing them to conduct the interviews.

7
loan from a formal bank|0.036***<br>_(0.006)_|
|Proportion of borrowers who are women|-0.046***<br>_(0.010)_|
|Expenditure per capita|-0.002**<br>_(0.001)_|
|Loan terms and conditions||
|VF borrows to on-lend to members|0.017***<br>_(0.003)_|
|Memos||
|Number of observations|2,237|
|R2|0.189|
|Source: Village Fund Survey 2010, and SES 2009. Robust sta<br>parentheses. Stepwise regression uses p=0.2 cutoff for drop<br>* p<0.05, ** p<0.01, *** p<0.001.|ndard errors in<br>ping variables.|

These results are interesting in that they hint at a tradeoff between equity and efficiency. Village Funds that have computerized their accounts – which we interpret as effective management – are relatively less likely to lend to the poor, other things being equal. Lending to the poor is relatively lower in large villages; one might imagine VFs in such areas funding that there is plenty of demand by low-risk non-poor households, so they are less obliged to serve the poor. Where farmers are common, lending to the poor is more widespread; this 37
using NSS data have relied on an indicator variable for the phase-wise implementation of NREGA. However, controlling for SGRY in the form of an indicator is not possible after acknowledging (in section 2 above) that expenditure under SGRY and NREGA was perfectly negatively correlated with each other. In other words, if _NREGAd,t_ takes the value one (zero) for a district _d_ when NREGA was present (absent) in year _t_ , then an analogous indicator for SGRY is _SGRYd,t_ = 1 _− NREGAd,t_ . Therefore, in the NSS regressions, the key SGRY control is the per capita program expenditure in a district under SGRY during 2004, 2005, and 2007. I match this expenditure data with data from NSS rounds 61 and 64. For human capital regressions, SGRY expenditure is zero for round 66 (2009-2010).

## Rainfall Data

I use remotely sensed rainfall data from the Tropical Rainfall Measuring Mission (TRMM) satellite. See Fetzer (2014) for a detailed discussion on the consistency and the quality of TRMM data over any other remote sensed or ground-based data.<sup>10</sup>

# **4. Empirical Model and Results**

This section discusses the impact of NREGA after controlling for SGRY on a variety of outcome variables. First, I use AWI data and estimate the effect of public workfares on agricultural wages. Here, I define treatment as a continuous measure using program expenditure. In this specification, I show how the impact of workfares on agricultural wages is underestimated by approximately 43% if we ignore spending under SGRY. Second, I use NSS data and estimate NREGA’s impact on casual earnings. Analogous to agricultural wages, daily casual earnings are underestimated by 34-40% if we do not control for expenditure under SGRY. Third, I estimate NREGA’s impact on employment outcomes: private sector work and public works. The effect on both employment outcomes is again underestimated by 25-34% without SGRY. Finally, I reassess child labor and human capital
|Other|6.1|4.5|5.8|
|---|---|---|---|
|**Employment categories**||||
|Private sector employee|34.7|12.8|13.7|
|Self-employed, agriculture|30.2|8.7|10.3|
|Self-employed, non-agriculture|9.6|56.5|50.8|
|Employer, agriculture|18.5|0.7|12.9|
|Employer, non-agriculture|4.1|3.4|2.5|
|Public sector employee|2.0|2.9|4.7|
|Unpaid worker|0.7|12.3|1.3|
|Domestic worker|0.3|2.7|3.7|
|**Main reason why not looking for a job (last month)**||||
|Very advanced age|39.8|1.3|12.7|
|Household tasks/chores|23.0|**97.2**|**81.6**|
|Retired|11.2|0.1|1.2|
|Disability|10.1|0.2|2.3|
|Temporary illness|7.3|0.6|1.6|
|Other reason|8.6|0.6|0.6|

_Source_ : Authors’ tabulations using the Encuesta de Hogares de Propósitos Múltiples 2015 (EHPM).

Furthermore, inactivity rates for women are higher if their spouse reports being either self-employed or an employer in agriculture – i.e. families with heavy reliance on their own agricultural production (Table 2). This is exactly the type of household one might expect women to have higher rates of employment if they are involved in supporting agricultural
be at play. For example, the very idea that internal conflicts are linked to rational individualism is sometimes questioned given the importance of other social and historical conditions and constraints (see, e.g., Cramer, 2002). Since the 2010s, the use of conflict location data and satellite imagery has offered new avenues for understanding in which local contexts violence develops. Empirical frameworks based on small spatial units (especially grid-cells) and georeferenced data introduce key sources of heterogeneity at the local scale, like the location of mineral deposits (Maystadt et al., 2014; Berman et al., 2017) or the volume of production/exports in each area (Dube and Vargas, 2013; Berman and Couttenier, 2015; McGuirk and Burke, 2020). These variations can then be exploited using quasi-experimental frameworks to isolate and test the validity of one mechanism among others (Blattman and Miguel, 2010; Couttenier and Soubeyran, 2015; Laville, 2019). For example, using grid-cells and georeferenced data on conflicts in Sub-Saharan Africa, Berman and Couttenier (2015) conclude that positive income shocks - proxied by shocks in global demand for the main good exported by the cell - decrease conflict probability in the cell,<sup>3</sup> and von Uexkull (2014) find that negative income shocks - proxied by the exposure of rainfed croplands to drought - significantly and substantially increase the risk of conflict. Although these two studies reach similar results, they present key methodological differences in the sign and proxies used to define the shock. This makes it difficult to establish income as a key local factor of conflict.

Figure 1 presents the distribution of the _t_ -students from 1.391 point estimates from 61 studies on income shocks and conflicts.<sup>4</sup> Figure 1a shows that the literature associates almost twice as often income changes with adverse rather than positive effects on conflict, suggesting a possible asymmetric research focus on the link between income and violence. In Figure 1b, we