rafmacalaba/data-use-mentions-extended huggingface dataset:
# Appendix A. Data

## A.1.   Questionnaires

### A.1.1.   Questions on conflict

An extract of the conflict questions administered in the telephone survey conducted in spring 2017 for 717 households in the GHS panel is shown below. For each reported event, questions about the timing and the perpetrator are asked for each year starting with 2010 and ending in 2017, as demonstrated below with 2010 and 2011 only.

### 1. Since 2010, has your household been affected by...?

_Record YES/NO to each category:_

1. Any household member killed (not natural death)

2. Any member suffered physical aggression (with or without any type of weapon) 3. Any member injured/disabled (after direct attack) 4. Any member suffered sexual violence 5. Any member forced to work (for free) 6. Any member captured/kidnapped/abducted 7. Any member robbed (money or assets) 8. Any member made a refugee/internally displaced 9. Household dwelling suffered from robbery 10. Household dwelling burned down/destroyed/seriously damaged/occupied 11. Household land occupied/expropriated/made unproductive 12. Household assets intentionally destroyed/seriously damaged 13. Household members restricted from going to or attending school 14. Household members restricted from seeking care at PHCs/clinics/hospitals<sup>29</sup>

- 2a. Did EVENT occur in **2010** ?

- 2b. Who was the perpetrator of EVENT in **2010** ?

1. Military

2. Police 3. Paramilitary 4. Militants 5. Insurgency 6. Bandits/Criminals 7. Pastoralist/Nomad 8. Neighbor(S) 9. Household Member(S) 10. Foreigner 11. Stranger 12. Vigilantes

> 29 _PHC_ refers to Primary Healthcare Center.

i
Priority areas for biodiversity conservation that do not overlap with currently protected areas cover 28 percent of the Brazilian territory. The Brazilian government pipeline for implementing measures for the conservation, recovery, and sustainable use of ecosystems includes Mato Grosso as the state with the larger surface of priority areas (40 percent). The states of Tocantins and Bahia also have a significant priority area share, at 36 and 34 percent, respectively. Nonetheless, our definition of conservation gaps, a more conservative measure that considers the more stringent priority area level (extremely high), depicts a different picture. Conservation gaps are more relevant in Espiritu Santo, Rio Grande do Sul, and Rio de Janeiro, at around 10 percent in the three states.

About one-third of banks’ loan portfolio to corporates is allocated to firms in 10 municipalities out of the more than 5,600 country-wide (Figure 11). The Herfindahl-Hirschman Index (HHI), a quantitative measure of concentration in a certain market, shows a low concentration level at 233.<sup>31</sup> Aggregating at the state level, the concentration is still relatively low, with an HHI of 1,299. The geographical concentration of banks’ loan portfolios in Brazil is the lowest in the Latin America and the Caribbean region (Calice and Miguel 2021). The Southeast region concentrates 51 percent of the banking system assets, being the states of São Paulo and Rio de Janeiro the most relevant, with 30 and 11 percent of the system’s assets, respectively. In contrast, while the North Region is the most geographically extensive, encompassing 45 percent of the Brazilian territory, its 7 states receive 4 percent of the banking assets only.

We find that as of March 31, 2020, Brazilian banks had BRL 254 billion in credit exposure to establishments that could be operating in protected areas. This amount is equivalent to 15 percent of the corporates’ credit portfolio. The exposure could increase to BRL 437 billion (25 percent of the corporates credit portfolio) should conservation
Together, the results suggest an ambiguous impact of controlling for human capital, but in neither case do we find that physical productivity is positive.

# _Sensitivity to sample periods_

The patterns observed in our base results are also not an artifact of disruptive sample periods. In Colombia and Indonesia, we drop the years when the country was going through a crisis (2000-01 in Colombia and 2008-09 in Indonesia), while in Ethiopia, we drop the year 2005 when the census data collection was rather idiosyncratic. Table 2, panel e shows that in all cases, these estimates are quite close to our original sample period.

# _Sensitivity to quality in price indices_

As noted earlier, higher product prices in cities may capture higher quality products rather than costs and hence deflating revenues may lead effectively to downward bias in the TFPQ estimates. We explore the possible impact of this using the four different price indices constructed for Colombia by Eslava and Haltiwanger (2021) that account for product quality: the constant basket, Sato-Vartia, Feenstra, and CES Unified Price Index (CUPI).<sup>16</sup> Table 3 shows that the elasticities of the various adjusted indices are now higher than the .006 in the OLS estimates, ranging from .013 to .021, and are now uniformly significant suggesting, counterintuitively, that quality is actually lower as urbanization increases, perhaps reflecting that producing for the mass market leads to a lower average level of quality.<sup>17</sup> Correspondingly, the Foster et al. (2008) TFPQ estimates now move from insignificant -.004 to a range of -.018 to .021 with all but the constant basket coefficients entering significantly. The negative impact of agglomeration on productivity seems exacerbated in the Colombia case.

# **V. Conclusions**

This paper offers a resolution to what it calls the urban productivity puzzle surrounding the net benefits of agglomeration in developing countries: despite recent estimates of the elasticity of wages with respect to population density which suggest that agglomeration
model based on rational inattention does not capture these cross-domain effects in terms of available cognitive bandwidth is therefore less of a concern.<sup>20</sup> In sum, standard modeling approaches, e.g. based on learning or credit constraints, have difficulties explaining the combination of low levels of demand for extension and a (positive) reaction of demand for extension to non-agricultural shocks. On the other hand, our model with costly attention would be consistent with non-agricultural shocks explaining changes in demand for extension and we verify this prediction of the model below. Because it is conceivable that non-agricultural shocks are linked to demand for extension through other channels (e.g., due to non-separability), the empirical part below also discusses the evidence in light of some alternative theories.

# **4 Empirical evidence**

In this section, we first show that there is statistically strong and robust evidence that, in line with the model, non-agricultural shocks are indeed associated with increased demand for extension. In addition, we assess the plausibility of some alternative explanations which might also account for the observed link. Our model is based on the assumption that agricultural advice has positive effects on farmers’ production. To complement the existing evidence, cited in the introduction, we therefore also investigate the relationship between agricultural advice, farm decisions, and outcomes in our data (see Appendix D). While our data allow us to improve upon prior literature that also uses observational data to study the effects of extension services, we stress that the observational nature of the data limits our ability to make causal claims.

## **4.1 Data and descriptive statistics**

The empirical work is based on survey data from three countries in Sub-Saharan Africa (Malawi, Nigeria, and Uganda), which were collected as part of the World Bank’s Living Standard Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA).<sup>21</sup> In each country, households are selected in a nationally representative
level.

As seen in Web Appx. Table D.6, there are no significant correlations. However, point estimates suggest that the top cities in countries specialized in agricultural exports indeed grow slower relative top cities in industrialized countries. Yet, differences are not significant. We also do not find any significant correlation in a 10-year panel framework with one or two extra lags included (see Web Appx. Table D.7).

The results presented in this section suggest that even if some resources might be associated with urban primacy in some countries, it is not the case in general. While we do not discuss this above, we also find no correlation with deindustrialization. Thus, in resource-rich and deindustrializing countries, consumption cities are found along the urban hierarchy and, as shown in Fig. 4(a), the shares of urban tradables in their cities are lower than those in MFGSERV countries for _all_ population size categories of cities.

# **7. Concluding Discussion: Consumption Cities & Urban Human Capital**

Using census data for a large number of cities and countries, we established the following stylized fact: While some cities have high employment shares of urban tradables and can be characterized as _production cities_ , a relatively high number of cities have high shares of urban non-tradables and can be characterized as _consumption cities_ . This appears to be
## **Box 1  Measurement of employment: household vs. firm based surveys**

The magnitude of measured employment decline in transition economies depends on the statistical source. It tends to be much larger if data come from firm based Employment surveys, and relatively smaller if data comes from the household based Labor Force Survey (LFS). The discrepancy stems from the fact that firm based surveys cover only registered, formal sector firms, while the LFS in principle provides data on all employed individuals. Thus, the difference in employment between those two sources is an approximate measure of informal sector employment, employment in small private firms, which often are under-represented in employer based-surveys, and self-employment. This difference also reflects the changing nature of jobs in ECA. There has been a partial shift from regular full-time jobs toward casual jobs, often in the informal sector, as well as a partial shift form wage and salary employment towards self-employment. The firm based employment surveys do not account for irregular jobs and overestimate the actual job loses, however they show quite accurately the decline in the number of traditional, regular jobs.

# **Despite the substantial job loses the employment ratio is still relatively high**

The substantial job loses that have occurred during the transition are reflected in the lower degree of the utilization of labor resources. The employment ratio – which shows the percentage of the population of working age that is employed –is presently much lower than before the transition in virtually all transition economies. On average it dropped by close to 20 percentage points in CEE, around 15 percentage points in Belarus, Russia and Ukraine, and some 10 percentage points in other CIS (Figure 1). This means that 10 to 20 percent of the working age population which would have been employed under the old regime, is currently without work. Apparently, this is a large decline in the utilization of labor. However, despite this big decline, the employment ratio in most transition economies is still within the OECD range.

# **
**Figure 2a. The proportion of beneficiary and non-beneficiary farmers growing at least one crop in the three cropping seasons**

<!-- Start of picture text --> 99% 99%<br>89%<br>65%<br>43%<br>28%<br>Kharif 2020 Summer 2020 Rabi 2021<br>Johar Beneficiary Non-Beneficiary<br><!-- End of picture text -->

**Figure 2b. The cropped area as a percentage of cultivable land in Rabi and Summer seasons**

<!-- Start of picture text --> 32%<br>22%<br>7%<br>6%<br>Summer 2020 Rabi 2021<br>Johar Beneficiary Non-Beneficiary<br><!-- End of picture text --> _Source: Created by authors using primary data collected from farmers_

# _5.4. Cultivation of vegetables_

Engaging in high-value agriculture is essential for smallholders to earn a decent income from agriculture. Therefore, the JOHAR project encourages and supports farmers to grow high-value crops (HVCs). Jharkhand has favorable agro-climatic conditions for horticultural crops. Our data shows that access to irrigation from JCLIS increases the likelihood of a farmer engaging in high value agriculture (HVA) on both extensive and intensive margins. The beneficiaries are more 19
a 59 percent average monthly coverage, while off-shore fishing covers over two-third of the marine pixels.<sup>15</sup> The respective boat-detection likelihoods in the control provinces are 40 (near-shore) and 55 (off-shore) percent. On average, there are approximately 5 boats detected per month in each of the 10-mile-square pixel in the treatment provinces, similar across both near-shore and offshore fishing grounds. It is also noted that these statistics on fishing intensity are consistent with the LFS labor-market information; recall in Panel I of the table that saltwater fishers in the central ( _Formosa_ -affected) provinces have a greater baseline average workload relative to those in the control provinces.

> 13 As subsequently discussed, our main empirical setting employs three full years of pre-treatment observations (2013-2015) to capture potential seasonality effects in fishing activities in different coastal regions. It is noticed that 2013 is the first year with available VIIRS data for all months.

> 14 According to Elvidge et al. (2018), the monthly temporal aggregation addresses each of the three criteria that could be potential concerns for higher-frequency intervals: lunar cycle effect, seasonal variation, and cloud cover. The monthly aggregation VBD mitigates lunar cycle effects and improve the cloud-free boat-detection capability. It should also be noted that monthly temporal aggregation is widely used in economic analyses to mitigate seasonal effects on economic and fisheries data (Burkhauser et al., 2000; Garza-Gil et al., 2006; Neidell, 2004).

> 15 We cap the off-shore marine space to be the pixels located between 20nm and 80 nm from the shoreline.

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each year).

The mean and median of most relevant variables" for the full sample are presented in columns 1 and 2 of Table 3. The considerable differences between the mean and the median of many variables indicate a skewed distribution. It turns out that there is a minority of very large firms (mainly (former) state owned enterprises) with many employees and a large capital stock. Columns 3-6 in Table 3 illustrate that firms with a

> 8 Ideally, one would like to measure investment directly. However, as in all related studies the micro-data are based on the balance sheet of the firms, which leaves no other option then to proxy for investment by taking the first differences of the capital stock (see Fazari et al., 1998 and Hubbard, 1998).

> 9 The use of an accelerator model of investment is open to the criticism that, as opposed to Tobin's Q, it does not deal sufficiently with expected profitability with the result that the liquidity variables in equation (1) mnight pick up this effect (see Hoshi et al, 1991).

IO We experimented with other proxies for the liquidity variable, notably we also looked at the _stock_ of liquidity, this proved immaterial to our results so in the remainder of the paper we stick to the use of cash flow as our proxy for the measurement of the impact of liquidity constraints on investment. l All the variables in this data set are averages for the period 1993 - 1995.

12
||||_Annual_<br>_Import_|_Gains from_<br>_Variety_|
|---|---|---|---|---|
|_Year_|_Bias_|(<br>1<br>_bias _<sup>_−_1)</sup> <sup>_×_ 100</sup>|_(% of GDP)_|_(% of GDP)_|
|2018|0.984|1.67|26%|0.42|
|2019|1.006|-0.55|26%|-0.14|
|2020|0.997|0.29|25%|0.07|
|2021|0.998|0.20|26%|0.05|
|**Average per-annum**|**0.983**|**3.69**|**21%**|**0.43**|
|**Total (1995-2021)**||**99.51**||**11.63**|

_Note:_ Authors’ calculation based on six-digit disaggregated data. See text for detailed explanation.

In column 1 of Tables 4 to 6, the ratio of the aggregate exact price index including variety and the aggregate conventional price index is reported as the yearly _bias_ measure as in equation (10). It is worth explaining the intuition behind this bias. If this fraction is lower than one, it means that the changing set of imported varieties has _lowered_ the import price index. In that case, the consumers benefit from lower unit costs of imports. Thus, these lower costs are the source of the welfare gains. On the other hand, if the bias is larger than one, this means that the import price index is _increased_ by the changing variety set. Thus, the disappearing varieties
However, the OECD effort relied heavily on the local SOE definitions, which can vary substantially across jurisdiction. The IMF also collected firm-level data for about 10,000 SOEs leveraging the ORBIS database from _Bureau van Dijk_ for the period 2014-2016 although this one excludes SOEs operating in natural monopolies or sectors where private firms are barely present (IMF, 2019), and omits countries with fewer than 30 SOEs or restricts the sample to the largest companies implementing some thresholds in assets.<sup>55</sup> The original exercise covered 20 countries in Central, Eastern and Southeastern Europe (IMF, 2019) and in 2021, it was expanded to the Middle East, North Africa, and Central Asia region (IMF, 2021). The IADB led an exercise to collect financial performance information of non-financial SOEs for 16 countries in the LAC region covering the period 2010-2016 (Musaccio & Pineda, 2019). Other efforts include an analysis of majority-owned Chinese SOEs (Freund & Sidhu, 2017) (Harrison, Meyer, Wang, Zhao , & Zhao, 2019); the IMF Fiscal Monitor, which included firms with 20 percent of state participation and above (IMF, 2020); and the EBRD cross-country study on SOEs with about 12,000 companies with at least 25% participation of the state (Borkovic & Tabak, 2020). Table A.1 summarizes existing comparable SOE databases.

A global SOE database cannot be simply compiled by combining the various existing databases due to the lack of a uniform definition of SOEs. In absence of a commonly accepted definition of SOEs,<sup>56</sup> regional and sectoral exercises cannot be merged to create a single repository. As shown in Table A.1, each one of the aforementioned databases employs a different definition of SOEs with varying degrees of state ownership thresholds, and some of them follow countries’ official SOE definition, which varies significantly and complicates comparability even further. For example, the survey-based exercise led by the OECD quoted above collects information on
the distribution of "true" incomes with the distribution of "reported" incomes.** Still, several challenges arise, and selecting and matching the appropriate datasets is necessary. Following the literature, we assume that true income is revealed in income or labor surveys such as the European Survey on Income and Living Conditions (EU-SILC), the Household Budget Survey (HBS), the Labor Force Survey (LFS), while "reported income" is the income reported to the tax authority. However, the main challenge with this approach is that there is no single data set that includes both the true and the reported income of individuals. Official tax return data only capture reported income and do not provide undeclared or tax-exempt income information, meaning it cannot accurately measure "true" income. Conversely, survey data does not provide reliable information on reported income to tax authorities. Therefore, it is necessary to match datasets to obtain a

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Policy Research Working Paper 9432

# **Abstract**

Using firm-level data for Georgia, the paper estimates the quasi-elasticity of employment and wages with respect to the share of exports in total sales, to explore whether changes in the structure of sales (exporting versus selling to the domestic market) matter for labor market outcomes. The methodology uses exogenous fluctuations in exchange rates combined with firms’ initial exposure to various markets as instrumental variables to identify a causal effect. The results differentiate employment levels and average wages by gender and consider whether export destination or the competiveness of economies matters for the magnitude of this elasticity. The data are from the National Statistics Office of Georgia Statistics Survey of Enterprises merged with customs data for 2006–17. The instrumental variables regression results show that the act of exporting improves female employment but reduces overall average wages and female wages. Increasing exports to the European Union as well as high-income countries drives this positive result for female employment, whereas exporting to upper-middle-income countries is found to have a negative relationship with female employment.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at chollweg@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of
accounting year-end.

As an extension, rather than a binary treatment variable, we also calculate a continuous measure reflecting the number of pounds the firm is below the AIA ceiling. The intuition being that firms with more unspent allowance, have a greater incentive to increase their investment. We use this continuous measure of the AIA allowance available to firms as a robustness test below. That the magnitude of the response to the AIA policy is likely to increase with the gap between a firms historical capital investments and the AIA threshold also helps to provide an argument for why the DID approach adopted in the paper is preferable to, for example, a regression discontinuity (RD) design. An RD design would measure the effects of the AIA for firms around the threshold, thereby ignoring those for which the change in the AIA threshold is most significant.<sup>27</sup> We note some differences in the specification when using the capital investment or worker data and that on cloud, big data and AI. When studying the effects of AIA on capital investment or demand for data workers this data is available on an annual basis and therefore the treatment status of the firm is determined by these annual changes in the marginal incentive to invest by year. The presence of firm/worker and year fixed effects in Equations 1 and 2 along with the changes to the AIA policy across years means the regression belongs to the two-way fixed effects models with staggered treatment design that has received much recent criticism about ‘forbidden comparisons’, i.e. comparing treated firms to previously treated firms as well as those not treated (Goodman-Bacon, 2021; Borusyak et al., 2021; Callaway and Sant’Anna, 2021). As our preferred method, we use the approach of Callaway and Sant’Anna (2021), which accounts for staggered treatment designs and heterogeneous treatment effects under semi-balanced panel data. The approach presents event study estimates of investment of treated firms (for each cohort of AIA changes), by comparing against those firms