rafmacalaba/data-use-mentions-extended huggingface dataset:
We can now compute the local derivative of _f_ with respect to _ρ_ : The second and third terms of the equation are always positive, since _f >_ 1 _._ And by virtue of (16), the first term<sup><u>(1</u></sup><sup>_−_</sup> _ρ_<sup>_<u>η</u>_</sup> _−_<sup><u>)</u></sup><sup>_<u>f</u>_</sup> 1<sup>_−_1</sup> _>_ 0 _._ We thus have Q.E.D.■

# **B Instrumentation Strategy**

This Appendix describes the steps necessary to build the instrument for the female content of exports. The construction of the instrument follows Do and Levchenko (2007), and exploits exogenous geographic characteristics of countries together with the empirically observed regularity that trade responds differentially to the standard gravity forces across sectors. For each industry _i_ , we estimate the Frankel and Romer (1999) gravity specification, which relates observed trade flows to exogenous geographic variables: where _LogXicd_ is the log of exports as a share of GDP in industry _i_ , from country _c_ to country _d_ . The right-hand side consists of the geographical variables. In particular, _ldistcd_ is the log of distance between the two countries, defined as distance between the major cities in the two countries, _lpopc_ is the log of population of country _c_ , _lareac_ log of land area, _landlockedcd_ takes the value of 0, 1, or 2 depending on whether none, one, or both of the trading countries are landlocked, and _bordercd_ is the dummy variable for common border. The right-hand side of the specification is identical to the one Frankel and Romer (1999) use. We use bilateral trade flows from the COMTRADE database, converted to
bank activity possible in nine Pacific Basin countries, and he provides some aggregate statistics on the size and scope of foreign banking activities.<sup>3</sup> Using aggregate accounting data, Terrell (1977, Table 20-2) further compares the banking markets of 14 developed countries (8 of which allow foreign bank entry) for 1976 and 1977. Interestingly, countries that allow foreign bank entry on average experience lower gross interest margins, lower pre-tax profits, and lower operating costs (all scaled by the volume of business). Terrell (1977), however, does not control for influences on domestic banking other than whether or not foreign banks are permitted to enter. This paper aims to provide a systematic study of how foreign bank presence has affected the domestic banking markets in 80 countries. To do this, we use bank-level accounting data and macroeconomic data for the 1988-1995 period.

2 See Aliber (1984) for an early survey of the literature on the internationalization of banking. _3_ Cho and Khatkhate (l 989) provide in-depth case studies of financial liberalization in five Asian countries, however with no particular emphasis on foreign bank entry. Liberalization, though, is shown to lead to faster growth of the financial system and to increased competitiveness of the banking system, even ifthere is no conclusive evidence that financial liberalization leads to lower intemediation margins. In their comparative study, Frankel and Montgomery (199 I) also bypass the issue of internationalization.

2
and Zhou, 2013). Using the RUMiC survey, we estimate that migrant households received a school fee reduction of approximately 20 percent, but not 100 percent, in 2008 (Appendix 3, Table 3.4).<sup>9</sup>

> 7 Unless stated otherwise, school fees are calculated from the sample of migrants with at least one child enrolled in a school in the city. One yuan was approximately equal to 0.14 US dollars in 2008 (World Bank, 2016). 8 This correlation is somewhat stronger at -0.44 for the mean school fees. We describe how we construct different measures of school fees in section 4.2.

> 9  Our estimates using the most recent household survey from China in 2012 (i.e., the China Family Panel Studies implemented by Peking University) also indicate that, four (six) years after the official abolition of school fees in urban (rural) areas, both urban and rural households still paid various school-related fees. See Figures 3.1a and 3.1b in Appendix 3 for more details.

6
2021). This may explain why a much larger share of households in LICs report to have lost income since the pandemic started. Employment recovery among LICs appears to stall in 2021, with gains driven only by new entrants and, unlike in MICs, the share of adults that had stopped working since the pandemic did not continue to fall between the second half of 2020 and 2021 (Table A6.2 and Table A6.3).

Across all regions, employment fell most in the early phase of the pandemic and was substantially lower in LAC compared to other regions. Employment levels recovered during the second half of 2020, most significantly in Europe and Central Asia (ECA), LAC and SSA, relative to prepandemic levels. The EAP region diverges from these patterns when considering results in a panel of five countries, where there is limited recovery from initial labor market losses throughout 2020 and into 2021. Results for a smaller but stable sample of countries also suggest little recovery in employment levels during 2021 in ECA and SSA (Table A6.1).

# **4.2 We’re all in this together? Employment gains across different population groups**

One of the main findings in studies of initial COVID-19 impacts described in Section 2 related to the uneven impacts of the pandemic on different population groups—those with lower levels of education, informal employment, and women have been impacted to a greater degree initially by job losses.

Table 3 reports the percentage change in employment across different population groups in 2020 and 2021, relative to each group’s prepandemic employment level (based on recall), both for the full set of countries and for the country panel. Our main interest is in comparing the magnitudes of relative employment changes across different population groups over time. To make comparisons across countries in terms of education level, we define low education as primary or less in LICs and LMICs, and secondary or less in UMICs and HICs.

The estimates from the expanded HFPS sample confirm that the initial impacts
varying factors at the household level, such as temperature and precipitation, factors that could directly affect our outcome variables (see the variables in table A.1). Finally, we also consider that there might be regional trends that correlate with conflict intensity, which is why we also control for region-time trends τr,w (region-wave with the wave-based panel and region-round in the round-based panel). We cluster our standard errors at the LGA level to account for spatial correlation.

Our analysis might be further confounded by the fact that a household might receive assistance from informal safety nets after a conflict event. To understand how receiving assistance might affect our findings, we run alternative specifications by removing households that reported any such assistance after the most recent event (from the telephone survey) as well as households that reported having received either cash or in-kind transfers in the GHS panel. A small subset (just 6 percent) of the households reported any assistance.

Even though our fixed effect estimators remove the time-invariant household characteristics, we are still interested in examining whether the targeting of households was systematically driven by household characteristics. We do so by investigating the differences across households that experienced any conflict during the survey period and those that did not by running a t-test with our outcome variables of interest and with a number of household and geographical characteristics related to the household’s location. We find that in 2010–11, households that were exposed to conflict at any time during the survey were indeed similar to those not exposed to conflict.

The results are reported in appendix C, table C.1. Panel a compares household characteristics, and panel b compares household location-specific geographical characteristics.<sup>20</sup> We can see from panel a that in the first round of the GHS, there are no statistically significant differences in the poverty status, level of consumption, and the CSI between households that had experienced conflict events between 2010 and 2017 and households that had not. We find only a couple of statistically significant differences
|455,692|1,306,819|2,151,765|1,626,997|12,957|1,618|3,116|4,429|3,794|
|||**No s**|**chool shutd**|**own, no ex**|**tension of the **|**winter break**|||||
|Locally-managed|2,458,858|727,745<br>located i|1,380,053<br>n 632 out of|326,645<br>the 645 muni|24,415<br>cipalities in the|12,192<br> state of São P|6,204<br>aulo|4,956|947|85|
||**% o**|**f students **|**afected by **|**the shutdo**|**wns**|**% of**|**school**|**s afected b**|**y the shutd**|**owns**|
||||69.2%|||||51.5%|||

Notes: Pre-K: Kindergarten and pre-school. Primary Education: first to fifth grades. Lower secondary: sixth to ninth grades. Upper secondary education: tenth to twelfth grades (high school). The locally-managed schools: schools managed by the local authorities of the 645 municipalities in São Paulo. The state-managed schools: schools managed by the state government of São Paulo. Source: Census of Education, 2009.
respectively report average annualized inflation, maximum draw-down, and average annual realized volatility in percentages. The final column reports the cross-validated confidence score that ranges from 0 to 1 for the final food price index using the calculations from the paper. Additional cross-validation statistics can be found on the World Bank Data Catalog page where a live version of the data base is maintained. _Source:_ The statistics have been prepared by the author for this paper based on end-of-August (2021) food price data from World Food Program.
sup>(0</sup><sup>_,_0),</sup> further assume that the intracluster correlation is constant across clusters. We then impute all these magnitudes based on our baseline data. The parameters of interest are the difference in means between treated or untreated units in each treated group and the pure control units, _βn_ ( _d, t_ ) for _d_ = 0 _,_ 1 and _t_ = 1 _,_ 2 _,_ 3.

The municipality authorities requested that the total number of letters sent be set to _L_ = 25 _,_ 061. To incorporate this constraint into the choice of the saturation probabilities _qt_ , we set up a system of equations as follows. The expected number of treated units is _n_ 1 = _n_ (0 _._ 2 _q_ 1 + 0 _._ 5 _q_ 2 + 0 _._ 8 _q_ 3). Since the assignments _Tg_ = 1 and _Tg_ = 3 can be seen as symmetric, we set _q_ 1 = _q_ 3. Finally, we add an equation that ensures that the variance of the effect at 50% saturation is equal to the variance for the “small” cells (treated units in 20% clusters and untreated units in 80% clusters), so that V[ _β_<sup>ˆ</sup> ( _d,_ 2)] = V[ _β_<sup>ˆ</sup> (0 _,_ 3)] = V[ _β_<sup>ˆ</sup> (1 _,_ 1)]. This 10 For the full distribution, see Figure B.15.

21
, also for main<br>food and fiber commodities.|http://pure.iiasa.ac.at/id/eprint/13290|Fischer, G., F. O. Nachtergaele, S<br>Prieler, E. Teixeira, G. Toth, H. van<br>Velthuizen, L. Verelst, and D. Wiberg.<br>2012._Global Agro-Ecological Zones_<br>_(GAEZ v 3.0)—Model Documentation_.<br>Laxenburg, Austria: IIASA<br>(International Institute for Applied<br>Systems Analysis) and Rome, Italy:<br>FAO.|
|Livelihood zones|Map of farming systems in Africa in 2015. The<br>farming systems analysis integrates an extensive<br>range of spatial data, administrative statistics,<br>assessment reports, and expert knowledge in<br>order to update the African component of the<br>2001 FAO/World Bank farming systems analysis.<br>Data sources include GAEZ FAO/IIASA, FAOSTAT,<br>and HarvestChoice.|Personal copy of data from Christopher<br>M. Auricht(https://auricht.com/)|Dixon, John, Dennis P. Garrity, Jean-<br>Marc Boffa, Timothy O. Williams,<br>Tilahun Amede, Christopher Auricht,<br>Rosemary Lott, and George Mburathi,<br>eds. 2019._Farming Systems and Food_<br>_Security in Africa: Priorities for Science_<br>_and Policy Under Global Change._<br>Routledge.(See figure 2.2b.)|

_Source:_ World Bank compilation.

# Appendix B. Hazard Data

**Table B.1. Information on hazard data**

||**ERA5**|**CHIRPS**|**SPEI**|**WRSI**|**SWI10**|*
question by comparing the estimations without and with as a control variable. For a better interpretation of the results, this table reports elasticities. DDiiDDiiffvvrr We see that when digitalization is not controlled for, a 1% increase in Provision in MENA reduces Participation by 0.54%. However, when digitalization is included, the reduction of Participation reduces to 0.44%. Both elasticities are significant at the 1% level.

> 57 A few words of caution are warranted before drawing conclusions from the tempting comparison that the reader might rightly do between the 0.78 threshold of the U-shaped effect of Digitalization found in the OECD data and the 0.57 threshold of the inversely U-shaped effect found in the MENA data. Indeed, while in the OECD data, the minimum level of Digitalization is equal to 0.48, in the MENA data it is equal to only 0.03. Thus, the threshold levels represent 163% and 1900% of these minimum levels in respectively OECD and MENA. Hence, despite the fact that the threshold level beyond which digitalization becomes participation-beneficial in OECD is higher than the threshold level beyond which it becomes participation-detrimental in MENA, it takes much more time, effort, and investment for a MENA country than for an OECD country to reach the respective threshold. This suggests that digitalization has bright days ahead of it in terms of being an effective instrument to enhance participation in MENA.

> 58 These countries are Belgium, Chile, Colombia, Costa Rica, Czech Republic, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Mexico, Poland, Portugal, Slovak Republic, Slovenia, Spain, and Türkiye.

32
Unlike the indicators discussed above, multiple models perform better than average for the USAD poverty gap. Models 8 and 3 have the highest imputation accuracy of 65 percent, followed by Models 1, 2, 7 and 9 (62 percent) and Model 5 (58 percent).

We further plot in Figure 2 the imputation accuracy for mean consumption. Model 3, again, is the best performer that achieves an imputation accuracy rate of 65 percent, followed by Model 9 with an imputation accuracy rate of 46 percent. Adding soil quality and distance to facilities to Model 9 increase imputation accuracy for this model to 50 percent and adding nightlight to Model 9 increases imputation accuracy to 100 percent for Viet Nam.

# _Machine learning as alternative_

We consider machine learning (ML) as an alternative imputation method.<sup>17</sup> The standard ML procedures split a data sample into a training sample (to estimate the imputation model) and an estimation sample (to obtain out-of-sample predictions). In our context, the base survey and the target survey respectively correspond to the training sample and the estimation sample. Employing three common ML techniques, LASSO, Elastic Net, and Random Forest, we show the estimation results in Appendix B, Tables B.1 and B.2 for Tanzania and Viet Nam, respectively. The ML poverty estimates do not work for both countries, except for the estimates of consumption mean that are within one standard error of the true mean for LASSO and Elastic Net in Malawi.<sup>18</sup> These

> 17 See <mark>Mullainathan and Spiess (2017) and Athey and Imbens (2019) for recent reviews of ML in economics.</mark> 18 Lasso linear model and Elastic net linear model are trained in the first round and tested against the second round. Lambda in LASSO is selected by 10-fold cross-validation for out-of-sample prediction. Alpha and lambda in Elastic Net are selected by 10-fold cross-validation for out-of-sample prediction. The final selected variables and prediction models with
## Source: King & Golledge (1978)

The URCA approach exploits this intuition by using travel time to locations as a proxy for cost and adopting an urban hierarchy based on city size to classify rural locations as gravitating around a specific urban center, as advocated by Partridge et al. (2007; 2008). This allows one to endogenously determine catchment areas of urban centers and do so in an exhaustive manner. Once these spatial urban–rural catchment areas (URCAs) are identified, the approach classifies the global population, allocating rural populations around differently-sized cities. Results can be mapped globally and consistently and then aggregated as administrative unit statistics to define an urban–rural continuum at any specified level of aggregation.

# _Potential applications in developing countries_

The three approaches presented above have been developed to assess, from different perspectives, the degree of interconnection between locations. The availability of two global data sets –providing estimates for FEAs (Moreno-Monroy, Schiavina, & Veneri, 2020) and URCAs (Cattaneo, Nelson, & McMenomy, 2021)– is an opportunity for development practitioners. Comparing the two approaches, one finds that FEAs tend to be more limited in their geographical extent to be able to capture sufficient economic spillovers to justify shared rural-urban governance. Researchers use FEAs if they desire a division into areas that are strongly affected by urban agglomeration economies versus those that are “rural” or weakly influenced by urban effects. The latter are excluded from the FEA, and therefore, in most situations, the approach does not provide an exhaustive partition of areas and population _._ Another feature of FEAs that affects how areas are partitioned is that a minimum threshold, usually of 50,000 people, is needed for there to be sufficient agglomeration economies. Therefore, FEAs are not focused on the rural-urban continuum _per se_ ; FEAs truncate the urban–rural continuum at the point where agglomeration economies become too weak and tend to exclude smaller urban centers.

URCAs, on the other hand, are focused on the full rural–
p<0.1. Survey weights are used in all regressions.

It is possible that some of the individual management practices are likely to result in higher levels of productivity in larger establishments only. For example, there may be diminishing returns to the number of performance targets monitored in smaller enterprises; similarly, in smaller enterprises the question regarding the basis of performance bonuses may be less relevant. As such, the relationship between management and productivity is estimated separately for larger establishments (those with 50 or more employees). The results are displayed in Table 8 (which presents an abridged version of the results; the full results tables can be found in Appendix 2); Panel A of Table 8 shows that the relationship between the average management score and labor productivity is stronger for larger establishments (the coefficient on the average management score increases from 0.537 for all firms to 0.735 for large firms, although the difference is not statistically significant at 95 percent confidence level). Looking at the effect of the individual management practices on labor productivity, columns (2) through (6) of Table 5 show no significant effects, although the lack of statistical significance is likely to be due, in part, to a reduced sample size.

Turning to the total factor productivity results for establishments with 50 or more employees, Panel B of Table 8 shows that the effect of the average management score on total factor productivity results is of much greater magnitude for these establishments—the coefficient increases from 0.21 to 0.87 and becomes statistically significant. Furthermore, for this subset of larger establishments, there is a link between total factor productivity and the component of management that measures establishments’ response to problems in production, and the component that represents the basis of managers’ 19
# **Figure 11: Direct and indirect job content per $1 million of gross exports across macro sectors, South Africa**

<!-- Start of picture text --> Other commercial services<br>Construction<br>Trade, sales and accomodation<br>Agriculture<br>Public services<br>Electricity, gas and water<br>Manufacturing<br>Financial services<br>Transport and communication<br>Mining and Energy<br>Real estate, renting<br>0 20 40 60 80<br>Jobs per $1 mil. exports<br>Direct JOBX Indirect JOBX<br><!-- End of picture text --> _Source:_ Authors’ elaboration based on LACEX.

One drawback of the classification is that manufacturing is a very broad and heterogeneous category. In order to explore this heterogeneity further, we use sectoral employment data from South African labor force surveys instead of ILO, shown in Figure 12. Machinery, other metal products and ferrous metal products becomes the largest sector in terms of the number of jobs contained in its exports (which was all contained within the broad manufacturing sector in Figure 10).<sup>15</sup> It also witnessed the largest increase in jobs contained in exports across all sectors between 2001 and 2011, from 837,000 to 992,000 jobs. This growth was entirely explained by jobs in inputproviding sectors; in 2011, indirect jobs represented three quarters of the total jobs contained in machinery, metal products and ferrous metal exports. Minerals also experienced an important growth in the number of jobs contained in exports, jumping from 273,000 jobs in 2001 to 400,000 jobs in 2011. The indirect content grew as well, though at a much slower rate than that experienced by machinery and metals. But for each of these sectors, the growth in jobs was substantially lower than the growth in gross exports, resulting in a sharp decline in the number of jobs per
contrasting negative and positive effects from the own-cell and second ring respectively. Table 11 below, with more precise ring employment data, will also suggest that there are negative competition effects of employment in the own cell and positive spillovers beyond that.

We then looked at these effects by sub-industry. In general we found no effects of population or Landscan measures on any outcomes for any sub-industry, including no effects of nearby population on retail trade or personal services. The same comment applies to employment measures with one set of exceptions. These are industries which can be characterized as producing traded goods: manufacturing and business services. We expect that such firms are likely to benefit differently from density compared to other industries. Manufacturing, in particular, as well as business services are archetypal industries that are not only exportable in scope, but also input and/or employee intensive. Hence, such firms may not need to be close to local population and consumers, but they need good connectivity to transport infrastructure and employment. Moreover, such firms are likely to benefit more positively from knowledge spillovers and labor sharing, meaning there is a greater advantage of these firms to cluster together.

In Table 11, we focus on this sub-sample of firms. To maintain sample size, we pooled these two sets of industries. Table 11 in the left panel shows that manufacturing and business services firms in our sample see no benefit from greater population or Landscan density. However, they experience greater positive effects from employment and ownemployment density in the second and third rings, which compete against negative effects from greater local density in the own-cell. We think the own ring is dominated by negative competition effects whether in input markets (poaching of employees and suppliers) or output markets (shopping externalities with loss of customers to competitors and negative price effects). Those effects appear to apply to overall employment even more than own industry. Beyond the first two rings, positive spillovers dominate and have very strong elasticities, especially at ring 2. In the right panel of Table 11 we