rafmacalaba/data-use-mentions-extended huggingface dataset:
reform on employment growth varies by an exogenously constructed technological measure for the industry’s demand for external financing provides further evidence that a causal link between financing and subsequent economic growth exists. The relative magnitude of the effect appears much larger among MSMEs and SMEs than the large firms. A t-test for the null hypothesis that the interaction term for the MSME/SME subgroup is equal to the large subgroup is rejected at the 1 percent significance level.

# **V. Conclusion**

There is an extensive literature in corporate finance establishing the link between finance and growth at the macro level (country economic growth and industry growth) and micro level (firm sales growth). However little is known about the link between finance and labor markets. Firms may grow by increasing investment, productivity, and thus output from greater access to finance without ever increasing labor, a case of “jobless growth” that is of particular concern after the global financial crisis of 2008.

Our paper is the first to establish that increased access to finance results in increased job growth in developing countries. We use two complementary data sets – a cross-sectional microlevel data set covering over 50,000 firms across 70 developing countries and a panel micro-level data set covering fewer developing countries but over 4.3 million observations – that allow us to conduct an array of tests to address endogeneity concerns. We find that the link between access to finance and employment growth is robust to using firm fixed effects to control for unobservable firm-level heterogeneity, using the introduction of credit bureaus as an external exogenous shock to the supply of credit in the country, and using propensity score matching to better match <mark>the countries that undertake credit bureau reform and those that do not. T</mark> he effect 27
, and Antoneta Stavreska.** 2007. "Global Inventory of Interventions to Support Young Workers: Synthesis Report" _World Bank_ , Global Inventory of Interventions to Support Young Workers: Synthesis Report.

**Bryson, A., R. Dorsett, and S. Purdon.** 2002. "The use of Propensity Score Matching in the Evaluation of Labour Market Policies" , Working Paper No. 4, Department for Work and Pensions.

**Campbell, Donald T.** 1969. "Reforms as Experiments" _American Psychologist_ , 24: 407–429.

**Card, D., J. Kluve, and A. Weber.** 2009. "Active Labour Market Policy Evaluations: A Metaanalysis" , Austrian Center for Labour Economics and the Analysis of the Welfare State Working Paper, (0902).

**Card, David, Pablo Ibarrarán, Ferdinando Regalia, David Rosas, and Yuri Soares.** 2007. "The Labor Market Impacts of Youth Training in the Dominican Republic: Evidence from a Randomized Evaluation." , NBER Working paper 12883.

**Fella, G. and G. Gallipoli.** 2007. "Education and Crime Over the Life Cycle" , Rimini Center 15-07.

**Frese, Michael and Gisela Mohr.** 1987. "Prolonged Unemployment and Depression in Older Workers: A Longitudinal Study of Intervening Variables" _Social science & medicine_ , 25(2): 173-178.

23
|Share of Entrantst= TEV of Entrantstin t / TEV in t|||||
|**5**|Firm 2-year Survival Ratet= N. 2-year Survivorst/ N. Entrantst|||||
|**6**|Firm 3-year Survival Ratet= N. 3-year Survivorst/ N. Entrantst|||||
|**D**|**PRODUCT DYNAMICS**|||||

> 30 For such cells the number of exporters shows a value of 1 and most measures except those based on exiter firms in Table 1 are missing. The developed countries in our sample applied their own confidentiality rules (often stricter). Due to confidentiality reasons, the information in the CYH6.dta file is not provided for Brazil, Belgium, Egypt, New Zealand, Spain, Sweden, the information in the CYH4.dta files is not provided for Brazil, New Zealand and Sweden, the information in the CYH2.dta and the CYD.dta files is not provided for New Zealand. A file with the percentage of total exports corresponding to the hidden values by country and year, for the developing countries for which we have the raw exporter-level data is available at http://data.worldbank.org/data-catalog/exporter-dynamics-database.

16
# **II. Consumption measurement errors: a brief taxonomy**

The degree and nature of measurement error in consumption captured by household surveys depend partly on survey design features.<sup>1</sup> These vary along a large number of dimensions, such as the length of the recall period or the level of item-specific detail sought (Fiedler, Carletto, and Dupriez 2012; Smith et al. 2014). Moreover, because these features affect the estimates of household consumption, comparisons across countries, as well as within countries over time, are compromised when questionnaires change.<sup>2</sup> Reporting error occurs if the information relayed by the respondent to the interviewer is not accurate. This error can take various forms, including the following:

- _Recall error_ . A main concern is that respondents might forget the occurrence of a consumption event. This could result in recall error. Lower salience and longer recall periods make forgetfulness more likely among respondents (Sudman and Bradburn 1973). Several studies show that, all else equal, the longer the period of recall, the lower the reported consumption per standardized unit of time (Grosh et al. 1995; Scott and Amenuvegbe 1991).

- _Telescoping_ . The converse of recall error is telescoping whereby a household compresses consumption that occurred over a longer period of time into the reference period and thus reports consumption greater than the actual value.

- _Rule of thumb error_ . Respondents may not always recall and count events (Menon 1993). Particularly for longer recall periods that typically involve more transactions, respondents may cease trying to enumerate each and instead use rules of thumb to estimate them (Arthi et al. 2016; Blair and Burton 1987; de Nicola and Giné 2014; Gibson and Kim 2007). In this case, rule of thumb error depends on transaction frequency and regularity; less frequent items are likely reported with more error. Whereas recall error biases the consumption estimate downward, and telescoping creates upward bias, there is no obvious direction
58205|64294|52199|16959|7365|13771|12349|5773|

Source: IMD, The World Competitiveness Yearbook 2000, Lausanne, Switzerland, pp461-463.

**Table 6. Cereal Yields (Selected Countries)**

<u>(kilograms per hectare)</u>

|Year|China|United<br>States|France|Japan|India|S Korea|Malaysia|Brazil|
|---|---|---|---|---|---|---|---|---|
|1976–81|3,027|4,151|4,700|5,252|1,324|4,986|2,828|1,496|
|1999–2001|4,869|5,824|7,088|6,147|2,321|6,500|3,075|2,825|

Source: The World Bank, World Development Indicators 2003, pp. 128–131.

# **Table 7.  Rural Labor Density and Agricultural Machinery Comparison (1999/2000)**

> 25 Knight, John and L. Song, “Chinese Peasant Choices: Migration, Rural Industry or Farming”, _Oxford Development Studies_ , V31, No.2, June 2003.

> 26 Yao, Shujie, “Agriculture’s Role in Economic Development: The Case of China”, from Cook, Sarah, Shujie Yao and J. Zhuang, ed., _The Chinese Economy Under Transition,_ MacMillan (London) and St. Martin’s (New York), 2000. Also Yao, S., _Economic Reforms and Grain Production in China_ , MacMillan, London, 1994.

27 A household survey in Zouping County, Shandong Province, collected between 1990 and 1994, shows that, when controlling for household off-farm employment, the effect of per capita land on incomes increases by approximately 10
that This information basis has the key advantage that it can be used both on ordinal and cardinal data. Let w<sup>S</sup> = (w1<sup>S, w</sup> 2<sup>S)∈[0, 1]2beavectorof</sup> weights such that<sup>�</sup> j<sup>w</sup> j<sup>S=1.LetkS∈[0, 1]denotetheidentification</sup> threshold. A **status-based linear identification method** (status-LIM) is defined as a triplet (z, w<sup>S</sup> , k<sup>S</sup> ) that identifies i as poor when For instance, both the UNDP-OPHI’s global MPI and the World Bank’s MPM use status-LIMs, though they consider more than two dimensions. We illustrate this for the World Bank’s MPM in Section 7.1.

A comment is in order. We do not consider that the deprivation bundle z is exogenously-given. We rather consider z as a parameter of an LIM.

> 22 A dimension is cardinally measured when achievements in this dimension are ordered and the distances between achievements can be meaningfully compared.

> 23 Monetary poverty is typically measured using cardinal data. However, when exactly one dimension is cardinally measured and the other dimensions are ordinally measured, we are virtually in a case for which all dimensions are ordinally measured. Indeed, when exactly one dimension is cardinally measured, the identification method cannot take advantage of the cardinal nature of this dimension.

12
2020). This underscores the need for more carefully designed, streamlined and communicated economic assistance programs.

**Figure 7: SMEs rely significantly more on government grants and equity**

<!-- Start of picture text --> Percent of firms using equity finance as the main source<br>Percent of firms using government grants as the main source<br>Percent of firms using loans from commercial banks as the<br>main source<br>Percent of firms delaying payments to suppliers or workers<br>-15 -10 -5 0 5 10<br><!-- End of picture text -->

## Source: World Bank Enterprise Follow-Up Surveys

Note: Chart illustrates the average difference between SMEs and large firms within a country-sector pair with their standard errors. Country-sector fixed effects are included. Stratification weights adjusted for the followup enterprise surveys according to median eligibility were used to produce the estimates. The weights account for non-response, assuming that businesses that could not be re-contacted (unobtainable) have exited the market Finally, SMEs resort to payment delays to suppliers or workers as much as other competitors in the same location and sector.<sup>21</sup> This is despite their low bargaining power and a greater need for SMEs to retain their existing relationships with suppliers and workers. Likely reasons for this include the knock-on effects of late payments from large and other businesses and a lack of penalty, which could incentivize SMEs to delay payments from a practical standpoint. The former remains a long-standing issue for many SMEs. In Europe, for instance, about a third of SMEs in Poland, Italy and Bosnia and Herzegovina claim that late payments threaten the survival of their business (Intrum, 2020). The COVID-19 crisis exacerbates this issue and the liquidity problems faced by SMEs.

# 4. DFIs are better positioned to help SMEs in the recovery rather than the crisis

The COVID-19 crisis is expected to plunge many countries across the world into recession in 2020. Notwithstanding unprecedented
- 67 -

Central Government employment figures are drawn from Modernization of the State Technical Assistance Project of 21 November, 1994. In Ecuador, Education is considered one of the primary responsibilities of the Central Government. The sector is highly centralized. However, post-secondary education is managed by an autonomous agency (CONUEP) and Vocational education under (SECAP). The Health data comes from the Second Social Development Project Staff Appraisal Report of June 29, 1992, which indicates that the total staff of the Ministry of Health is 30,000, of whom 8,000 are doctors.

Consolidated Central Government wages and salaries are for 1994 and are taken from IMF Government Finance Statistics, 1995. Data on wages in manufacturing are from the International Labor Office's Yearbook of Labor Statistics 1995 and refer to 1994.

El **Salvador** Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1992. Unemployment data are taken from the ILO's Yearbook of Labor Statistics 1995 and refer to 1992 and source l: _Labor force sample surveys_ and _General household sample surveys._ Central Government and Local Government employment is taken from document provided by Country Team for El Salvador (Jorge Shepherd- (LADCN) and relate to 1995. Central Government employment includes personnel of ministries (except for education and health--see notes below) plus personne of Procuraduria General, Banco Central de Reserva, Cortes de Cuentas, Fiscalia General, Fondo de Inversion Social (in total 2,865 employees). Education employment is taken from the same document, which indicates that Ministry of Education staff is 38677, of which 32570 are teachers. According to our definition we only included teachers in the education category. The remaining other personnel was included in the central government category. Health employment data is taken from the same document. However, it indicates that 21,455 people work in the Ministry of Health, of which only 6,244 are doctors and paramedics. Since this proportion
(2000) ‘Does public infrastructure affect economic activity?: Evidence from the rural interstate highway system’, _Regional Science and Urban Economics_ , 30(4), pp. 457–490. doi: https://doi.org/10.1016/S0166‐0462(00)00040‐5.

- Cole, M. A. _et al._ (2018) ‘Power outages and firm performance in Sub‐Saharan Africa’, _Journal of Development Economics_ , 134, pp. 150–159. doi: https://doi.org/10.1016/j.jdeveco.2018.05.003.

- Czernich, N. _et al._ (2012) ‘Broadband Infrastructure and Economic Growth*’, _The Economic Journal_ , 121(552), pp. 505–532. doi: 10.1111/j.1468‐0297.2011.02420.x.

- Damania, R. _et al._ (2017) _Uncharted Waters: The New Economics of Water Scarcity and Variability_ . Washington DC. doi: 10.1596/978‐1‐4648‐1179‐1.

- Datta, S. (2012) ‘The impact of improved highways on Indian firms’, _Journal of Development Economics_ , 99(1), pp. 46–57. doi: 10.1016/j.jdeveco.2011.08.005.

- Debaere, P. (2014) ‘The Global Economics of Water: Is Water a Source of Comparative Advantage?’, _American Economic Journal: Applied Economics_ , 6(2), pp. 32–48. doi: 10.1257/app.6.2.32.

- Dethier, J.‐J., Hirn, M. and Straub, S. (2011) ‘Explaining enterprise performance in developing countries with business climate survey data’, _The World Bank Research Observer_ , 26(2), pp. 258–309.

-
CDF)|16881.9|21799.1|12852.4|8946.6***|
|Daily Turnover (average in CDF)|181992.|265110.6|113880.0|151230.6***|
|Daily Volume purchace (average in CDF)|123843.8|167462.8|88099.5|80309.8***|
|Monthly spendings (average in CDF)|414270.1|479050.0|361185.1|121805.1***|
|Monthly per<br>~~c~~apita spendings (average in CDF)|70674.5|78512.5|64224.3|14446.9***|
|Monthly income (average in CDF)|471532.0|602929.7|363855.9|250018.5***|
|Monthly per<br>~~c~~apita income (average in CDF)|87898.4|115477.9|65201.8|52127.2***|
|Headcount ratio (%)|82.55|77.68|86.55||
|Poverty gap ratio (%)|56.74|51.99|60.65||
|Extreme Poverty Headcount ratio (%)|49.69|45.64|53.02||

Source: Our own computation based on data collected ***,**,* Denote significance at 1%, 5% and 10% levels.

While household income indicates the ability of the household to purchase its basic needs, per capita expenditure reflects the effective consumption of households and there13
help policy makers design coherent domestic labor market and migration policies to maximize development gains.

A large and growing body of literature documents numerous push and pull factors affecting the likelihood of international migration—from economic gains to social networks and to prevailing social norms in sending and destination countries. Nevertheless, many empirical papers are often constrained by the scarcity of detailed data on migration and potential drivers for migration. A large body of the empirical literature instead evaluates specific channels such as education (e.g. Docquier et al., 2020) or social networks (Cairn and Smyth, 2011). In the rare instances in which rich data is available in developing countries, researchers may face arbitrary choices of which variables to include and how to sequence their inclusion in the analysis. For example, in estimating the black-white wage gap in the United States, Gelbach (2016) demonstrates that the choice of covariates to include first led to different interpretations of what factors matter in explaining the wage differential and by how much.

To overcome this issue and systematically compare drivers of migration for women and men, we leverage rich household data on international migration from Indonesia and implement a data-driven approach to identify and rank the predictors of migration, separately for women and men. We analyze 301 potential determinants of migration with 5,131 men and 8,187 women in our sample. We use two different supervised machine-learning (ML) algorithms to identify the determinants of migration: (i) first, using a Random Forest algorithm to identify the top predictors based on the importance indicator, and (ii) second, using LASSO Stability Selection to identify the predictors with the greatest impacts on the variance by computing the rate of appearance of these top predictors in multiple iterations of LASSO. These methods have two key advantages in comparison to theory-driven empirical models: (1) they select variables that have important predictive powers; and (2) they do not require that we specify a prediction model beforehand which would require subjective choices—the models are indeed generated in
Caselli, F., and G. Michaels (2013). ‘Do Oil Windfalls Improve Living Standards? Evidence from Brazil’. _American Economic Journal: Economic Policy_ , 5(1): 208–38.

Chen, X., and W.D. Nordhaus (2011). ‘Using Luminosity Data as a Proxy for Economic Statistics’. _Proceedings of the National Academy of Sciences_ , 108(21): 8589–94.

Chong, A. (2004). ‘Inequality, Democracy, and Persistence: Is There a Political Kuznets Curve?’ _Economics and Politics_ , 16: 189–212.

Chuhan-Pole, P., A.L. Dabalen, B.C. Land, A. Sanoh, G. Smith, A.K. Tolonen, F. Aragaon, C.M.E. Andersson, O.D. Hall, K.A. Kotsadam, M. Lewin, and N. Olaon (2015). _Socioeconomic Impact of Mining on Local Communities in Africa_ . Washington, DC: World Bank.

Collier, P., and A. Hoeffler (1998). ‘On Causes of Civil War’. _Oxford Economic Papers,_ 50: 563–73.

Collier, P., and A. Hoeffler (2002). ‘On the Incidence of Civil War’. _Journal of Conflict Resolution_ , 46: 13–28.

Collier, P., and A. Hoeffler (2005). ‘Resource Rents, Governance, and Conflict’. _Journal of Conflict Resolution_ , 49(4): 625–33.

Damgaard, C., and J. Weiner (2000). ‘Describing Inequality in Plant Size or Fecundity’. _Ecology_ , 81(4): 1139–42.

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Ellman, M. (1981).
# **2.2.1 Uncontrolled development in flood plains**

**A major cause of overexposure to flood is uncontrolled development of flood plains.** Development on flood plains is often seen as necessary, but if it is done in an unplanned fashion, it may lead to devastation. When development is not guided, planned or controlled, the land exposed to flood hazard is liable to be more highly developed due to its lower cost. According to the WMO (2007), choices of development are often made without proper awareness of the risk in the prevailing area. Despite increases in knowledge of where and how frequently floods may occur, resulting in predictive flood maps, the maps fail to be used for development control as other priorities take precedence. Equally, in the developing world the proportion of structures subject to planning or building controls is low. The lack of controls implies that the most sensitive receptors may well be increasingly placed at risk. Uncontrolled development is likely to be denser, especially in informal settlements, and is typically characterized by substandard accommodation with no view towards minimizing risk to others. Uncontrolled development behind existing flood defenses will lead to a larger impact if defenses are breached; outside these defenses it may lead to totally unprotected settlements. The continued expansion of Buenos Aires, for example, has rendered obsolete the old drainage system.

# **2.2.2 Development outside defenses**

**Allied to the lack of control of developments is the increased development of settlements which are outside the protection of any flood defenses.** The pressure of population growth leading to increased need for residential and other buildings leads to development in previously unoccupied areas. If an urban area is subject to flood hazard then the most likely areas to be defended against flood risk are traditional settlements. Newer development is more likely to be in undefended neighborhoods. However, it is also possible that traditional settlements are likely to be closer to the source of flooding than newer buildings and so these may be
# [Figure 4 about here]

Figure 5 shows that more than half of all estimates focus on the interest rate pass-through to corporate lending rates (541 estimates). The greater focus on corporate lending rates may originate from greater allocations of bank loan books to this market segment.<sup>3</sup> If we compare the amount of loans outstanding in individual segments across countries, we find that corporate loans usually occupy a leading position among credit segments, followed by mortgage and consumer loans. Researchers also focus on the pass-through to the average lending rate across all categories. Other lending rates include rates on loans such as business, debt consolidation, and education.

# [Figure 5 about here]

As for the estimation methodology, the typical paper estimates a single-equation model in an error correction form (ECM) for a single country using aggregate data in monthly frequency (see figure 6). Specifically, the approach for estimating the interest rate pass-through is primarily based on a singleequation model (948 estimates). Simple reduced-form models are thus most popular for examining the relationship between the reference rate and bank lending rates. These models are mostly based on the error correction form (790 estimates) using estimation methodologies such as autoregressive distributed lag (ARDL), mean group (MG), and pooled mean group (PMG) estimators.

As for the data, the studies use mostly aggregate (macro level) data for the whole banking system in a single country. More recent studies have been increasingly using micro (bank level) data for better identification. For instance, Holmes et al. (2015) examine the interest rate pass-through in Colombia using data for 15 banking institutions. Holton and d’Acri (2015) analyze the pass-through for 188 Euro Area banks, and Havránek et al. (2016) test the pass-through based on data from 52 Czech banks. Typically, the studies use data for a single country (967 estimates). Only a few studies estimate the passthrough using cross-country panel data (
(node labeled as “Pakistan”) and a set of firms linked to local governments (nodes labeled as “Government of Khyber Pakhtunkhwa” and “Punjab Provincial Government”).<sup>20</sup>

> 17 There are cases in the ORBIS ‘links’ file, where the direct shareholding percentage is not reported, but a total shareholding is provided. For the latter, however, ORBIS does not report the full ownership path for obtaining the total share and therefore it cannot be used for the purpose of our methodology.

> 18 A more detailed description of how the algorithm works is included in section 4.

> 19 This involved the processing of more than 1.9 billion observations of ownership links and several terabytes of processing capacity to retrieve the trees.

> 20 The algorithm allows to identify the unique code of the companies operating across borders and the country of location. For instance, the ownership tree reveals that SONANGOL, a large oil and conglomerate group has more than 200 subsidiaries operating in countries such as Brazil,

8