rafmacalaba/datause-encoder-data huggingface dataset:
also purchased some parcels, purchased parcels are on average located at a larger distance from the homestead (11 vs. 16 min.) and from other parcels than owned ones, with the mean within-parcel distance increasing slightly from 17 to 23 min.<sup>13</sup>

# **4. Econometric results**

There are three econometric models estimated. First, the risk diversification benefits from fragmentation are supported by a reduced-form regression of crop shocks. Second, a household-level yield function points towards greater fragmentation and crop diversity being associated with higher rather than lower levels of output per hectare with the least cost path that connects all the parcels to the homestead being either insignificant or in some cases even positive. Third, a stochastic frontier production function that accounts for traditional inputs and includes measures of fragmentation as inefficiency parameters, suggests that number of parcels and the Simpson index increase efficiency while distances between parcels have no significant impact, consistent with the notion that the incremental costs it imposes are relatively low, while higher levels of fragmentation reduce it.

# **4.1 Fragmentation and risk diversification**

Results from the reduced-form regression of the share of a household’s land subject to a crop shock are reported in table 3. Main independent variables of interest are the Simpson land fragmentation index (col. 1), the number of parcels (col. 2), the Shannon crop diversity measure (col. 3), and the number of crops (col. 4). Point estimates for the coefficients on all of these are negative, suggesting they help reduce the likelihood of crop shocks. At the same time, magnitude and significance levels are slightly larger for proper fragmentation measures compared to indicators of crop diversity with the number of corps grown being insignificant at conventional levels.

Regarding other variables, the coefficient of variation for parcel altitude remains insignificant, suggesting that differences in elevation may likely affect output more through crop choice rather than risk diversification. A higher number of adult members aged 15-35 and 35-60 reduces the likelihood of being affected by crop shocks, most likely by allowing greater attention to crop management. Female headed households are no more likely to be affected by crop shocks than male-headed ones. While the lack of a measure of the severity of the shocks or losses associated with it cautions against over-interpretation and implies that the relationships should be taken as indicative only, the magnitude of fragmentation-related risk reduction effects seems substantial; according to the estimates, adding an additional parcel would reduce the likelihood of shocks as much as an addition of more than 3 adults to the household.

> 13 Interestingly, use of survey responses would lead one to underestimate this difference, suggesting that ideally the impacts of fragmentation should be studied using of GIS/DEM-based information.

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Research Consortium Final Report 12/05E. Available at http://www.eerc.ru/paperinfo/328

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- Markusen, James R, Thomas Rutherford and David Tarr (2005), Trade and Direct Investment in Producer Services and the Domestic Market for Expertise,‖ _Canadian Journal of Economics_ , Vol. 38(3): 758-777..

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Marshall, J.N. (1988), _Services and Uneven Development_ , London: Oxford University Press.

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More importantly, FDI can push up wage levels for skilled workers and elevate the overall returns to education in local labor market. FDI also raises household income in host locations. Table 9 already demonstrated a strong income effect. As parents’ income rises, they are more likely to send their children to high school and college. Unfortunately, we are unable to test whether cities that received more manufacturing FDI inflows experienced higher increase in skill premium because we only have income data in 2005.

# 6. Heterogeneity

## 6.1 FDI shock by export intensity

A main channel for manufacturing FDI to increase labor demand is through export expansion. Better knowledge about foreign markets and established global production networks allow foreign-owned firms to account for a disproportionately high share of exports in many host locations. By supplying to the global market, export-oriented FDI raises labor demand much more than domestic market-seeking FDI does. How does the effect of FDI shock on structural transformation and human capital accumulation vary by export intensity of FDI? We first calculate foreign manufacturing firms’ export intensity across 3-digit industries using 1998-2005 ASIF dataset. Export intensity of FDI is calculated as below: The numerator is total exports of all foreign firms in industry i during 1998-2005. The denominator is total sales of all foreign firms in industry i during the same period. Essentially, the export intensity of FDI is the share of total foreign exports in total foreign sales in each industry. Ideally, we would use 1990-2005 firm data to calculate FDI export intensity, but ASIF data is only available from 1998. Foreign firms’ export intensity in each industry remains highly stable over time, so we calculate this time-invariant indicator and divide manufacturing industries into two groups: high export intensity industries (above median export intensity) and low export intensity industries (below median export industry). Using a similar Bartik approach, we define change in high and low export intensity FDI: ∆FDI_export_H and ∆FDI_export_L as below: ∆FDI_export_H is the interaction between initial industry employment share in city c, province p and predicted change in national foreign employment share for all high export intensity industries.

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timely basis. The latter assumption would be difficult to dispute after the current crisis. So according to theory, raising capital requirements – which is what countercyclical capital requirements would do during boom times – could lead to an increase or a decrease in the risk that banks undertake. The issue is the adjustments that banks can make when the minimum required capital ratio is above that which they judge to be optimal, which is when they might have the incentive to engage in riskier activities.

Nor is there evidence that higher capital requirements lead to greater banking system stability. Barth, Caprio, and Levine (2006) assemble a large cross-country database on regulation and supervision and, among their other findings, see no evidence that the stringency of capital requirements has any impact on the stability of the banking system. A number of researchers have investigated these data and have not overturned this conclusion. Moreover, Laeven and Levine (2009) find that bank risk taking varies directly with the concentration of ownership among shareholders – in those countries with more (less) concentrated ownership, higher capital requirements result in greater (less) risk taking by banks, which provides confirmation of the ambiguous result obtained by Barth, Caprio, and Levine, and of the stalemate in the theoretical literature. Although there have not been tests on cross-country data about the stringency of provisioning requirements (in Barth, Caprio and Levine, provisioning is treated as part of their index of supervisory powers), conceptually provisions are similar to capital and therefore it is expected that the same empirical result would govern; compelling banks to set aside more provisions than they deem optimal should lead to the same behavior as forcing them to hold capital in excess of their optimal levels.

To be sure, the aforementioned empirical work holds for the range of capital regulation seen around the world in recent years; if in adopting countercyclical capital requirements, countries would effectively raise capital ratios in booms times to levels well outside the range in the data, then it is possible that there could be some effect. Perhaps for example if in a boom minimum capital requirements were raised from 8% to 20% or 30%, it might be difficult to for banks to take sufficiently large risk to offset this change. On the other hand, experience in the recent crisis belies this point – after all, some banks were able to take leverage from its supposed limit of 12.5 to 1 to about 30 to 1. Moreover, substantial changes in capital requirements of this 19
Policy Research Working Paper 9337

# **Abstract**

This paper studies whether there is a connection between finance and growth at the firm level. It employs a new dataset of 150,165 equity and bond issuances around the world, matched with income and balance sheet data for 62,653 listed firms in 65 countries over 1990–2016. Three main patterns emerge from the analyses. First, firms that choose to issue in capital markets use the funds raised to grow by enhancing their productive capabilities, increasing their tangible and intangible capital and the number of employees.

Growth accelerates as firms raise funds. Second, the faster growth is more pronounced among firms that are more likely to face tighter financing constraints, namely, small, young, and high-R&D firms. Third, capital market issuances are associated with faster growth among firms located in countries with more developed capital markets relative to banks. Capital markets are also comparatively effective at allowing financially constrained firms to raise capital.

This paper is joint product of the Chief Economist Office, Equitable Growth, Finance and Institutions Vice Presidency, the Development Research Group, Development Economics; and the Office of the Global Director, Finance, Competitiveness and Innovation 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 tdidier@worldbank.org, rosslevine@berkeley.edu, rllovetmontanes@worldbank.org, and sschmukler@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 the World Bank or the governments they represent._ Produced by the Research Support Team
**Table 15. Standardized effects of the determinants of occupational attainment. Youths from 18-35 years of age of ENCEL 2017. Comparison by ethnic background.**

|Variables|Indig<br>Eff|enous<br>ects|Non-i<br>E|ndigenous<br>ffects|Groups<br>Comparison|
|---|---|---|---|---|---|
|Socia<br>Di ff|l origin on|occupatio|nal attainme|nt||
|rect eects|-||-|||
|Indirect effects|**0.25**|***|**0.24**|***|**ns**|
|Via education|0.10|*|0.10|**|**ns**|
|Via education and cognitive<br>abilities|0.02|ls|0.02|*|**ns**|
|Via education and first<br>employment|0.13|***|0.12|***|**ns**|
|Total effect (direct + indirect)|**0.25**|***|**0.24**|***|**ns**|
|<br>Provider’s co<br>|gnitive ab|ilities on o|ccupational a|ttainment||
|Direct effect|-||-|||
|Indirect effects|**0.01**|**ls**|**0.03**|*|*|
|Via youth’s cognitive abilities|0.01|ls|0.03|*|*|
|Total effect (direct + indirect)|**0.01**|**ls**|**0.03**|*|*|
|Youth’s cog<br>|nitive abil<br>|ities on oc<br>|cupational att<br>|ainment<br>||
|Direct effect|**0.06**|ls|**0.08**|*|**ns**|
|Indirect effects|-||-|||
|Total effect (direct + indirect)|**0.06**|ls|**0.08**|*|**ns**|
|Education on occupational<br>attainment||||||
|Direct effect|**0.17**|*|**0.18**|**|**ns**|
|Indirect effects|**0.25**|***|**0.25**|***|**ns**|
|Via youth’s cognitive abilities|0.04|ls|0.04|*|**ns**|
|Via first employment|0.21|***|0.22|***|**ns**|
|Total effect (direct + indirect)|**042**|***|**043**|***|**ns**|
|<br>First o|**.**<br>ccupation|<br>to occupati|**.**<br>onal attainm|<br>ent||
|Direct effect|**0.31**|***|**0.31**|***|**ns**|
|Indirect effects|-||-|||
|Total effect(direct + indirect)|**0.31**|***|**0.31**|***|**ns**|

Levels of significance *** p<0.001, ** p<0.01, * p<0.05. Source: Own elaboration from ENCEL 2017 (n=3,310).

The comparison of migratory statuses shows differences in five paths (  _1,_  _2,_  _5_ ,  _6_ and  _7_ ) (see figure 5). The direct effects of the youth’s education on the cognitive abilities and occupational attainment are greater for migrants than for those who remained in the localities of origin. On the contrary, for the non-migrants group, the direct effects of the provider’s cognitive abilities are greater on the youth’s cognitive abilities, and of these on occupational attainment and of education on youth’s first employment.

48
# **Appendix 1:  CRU and GCM Sources**

Historical Data: 1961-2000 CRU: UK Climatic Research Unit, University of East Anglia, UK <u>http://www.cru.uea.ac.uk/</u> GCM Data: 1961-2100 GFDL: Geophysical Fluid Dynamics Laboratory, NOAA, USA Model Name: CM2 <u>http://www.ipcc-data.org/ar4/model-GFDL-CM2-change.html http://www.ipcc-data.org/ar4/model-GFDL-CM2_1-change.html</u> CCCMA: Canadian Centre for Climate Modeling and Analysis: GCM3 Model Name: GCM3 <u>http://www.ipcc-data.org/ar4/model-CCCMA-CGCM3_1-T63-change.html http://www.ipcc-data.org/ar4/model-CCCMA-CGCM3_1-T47-change.html</u> GISSA: NASA Goddard Institute for Space Studies (NASA/GISS), USA Model name: AOM <u>http://www.ipcc-data.org/ar4/model-NASA-GISS-AOM-change.html</u> INM: Institute of Numerical Mathematics, Russian Academy of Science, Russia. Model name: CM3.0 <u>http://www.ipcc-data.org/ar4/model-INM-CM3-change.html</u> IPSL: Institut Pierre Simon Laplace, France Model Name: CM4 <u>http://www.ipcc-data.org/ar4/model-IPSL-CM4-change.html</u> MIROC: CCSR/NIES/FRCGC, Japan CCSR: Center for Climate System Research, University of Tokyo NIES: National Institute for Environmental Studies, Japan FRCGC: Frontier Research Center for Global Change, Japan Agency for Marine-Earth Science and Technology (JAMSTEC) Model Name: MIROC3.2 <u>http://www.ipcc-data.org/ar4/model-NIES-MIROC3_2-HI-change.html http://www.ipcc-data.org/ar4/model-NIES-MIROC3_2-MED-change.html</u> MIUBE: Meteorological Institute of the University of Bonn (Germany), Institute of KMA (Korea), and Model and Data Group. Model Name: ECHO-G <u>http://www.ipcc-data.org/ar4/model-CONS-ECHO-G-change.html</u> UKMOHAD: Hadley Centre for Climate Prediction and Research, Met Office, UK 20
Multiplier when it comes to evaluating the health benefits of water and sanitation investments;

- (2) that there are large positive health externalities of improved sanitation (but not for improved water infrastructure), implying that the need for public interventions should be larger in the realm of sanitation than when it comes to providing access to improved water technologies;

- (3) that the access to water and sanitation infrastructure on health is considerably larger if only used by the household and not shared with other households, which certainly puts some extra pressure on public water and sanitation budgets;

- (4) that even simplest water and sanitation technologies (officially often classified as unimproved water and sanitation access) show a modest but significant positive impact on children’s health;

- (5) that the estimated impact of sanitation on diarrhea is significantly smaller than suggested by previous studies in the area but that the impact of water infrastructure on children’s health is still smaller and less significant than the impact of sanitation technologies.

This last point (5) certainly needs some further discussion. The first question is why we find lower effects of sanitation than the existing studies which found reductions in diarrhea that are double our estimates. The second question is why we find (but this time in line with other studies) that the impact of water technologies on children’s health is smaller than the impact of improved access to sanitation.

We have four explanations for the first question. First, previous larger studies use a smaller set of controls, which may have induced a positive omitted variable bias in some of the reported observational studies, which we may have - at least partially - removed in our more tightly specified model. Second, when it comes to comparing the results of large-scale observational studies to local controlled trials, context and sustainability clearly make a 27/36
We use next the residuals from the earnings regression, vit, to construct individual level “observations” of income variances vit<sup>2andcovariancesvitvi,t+1,19thataretobeusedonthe</sup> left hand side of equations (4’) and (6’) to estimate the income mobility parameters. The age profile of the constructed variance and covariance measures are indicated in Figures 1 and 2, which are generated by regressing the two variables respectively on a complete set of age and time dummies and then plotting the former against age (see Deaton and Paxson, 1994, for a similar exercise). Consistent with equations (4’) and (6’), the accumulation of persistent shocks, σǫ<sup>2,asageincreases,givesbothrelationshipsanupwardslope,albeitat</sup> rates differing by a factor of ρ.

Estimation results from the joint estimation of (4’) and (6’), as described in the previous section, yield the parameter estimates listed in Table 3. The first column presents the results using the full sample, while the second column provides results obtained using data from just those households that enter the sample in the first quarter of each year. Our estimates of the income mobility parameters are also in line with those obtained previously in the literature. The autoregressive component, ρ, is estimated to be 0.977, which suggests that persistent shocks to income experienced by any individual i will indeed last a long time. The estimated variance of transitory shocks to income ση<sup>2=0.202,issignificantlylargerthanthevariance</sup> of persistent shocks to income σǫ<sup>2=0.015.Thisisnotsurprisinggiventhatthetransitory</sup> shocks in our specification subsume measurement error in income, which we expect to be quite large in our data set.<sup>20</sup> Finally, the estimated variance in initial incomes, σω<sup>2</sup> 0<sup>= 0.104.</sup> As the results in the second column indicate, the estimates are not appreciably different with the restricted sample of households who enter the survey in just the first quarter of each year.

Given our estimates of the income parameters, we can use expressions (7) to analyze mobility patterns. In particular, we can compute how much the individual parameters contribute to overall mobility. Table 4 shows that mobility in residual income across 1 year is 0.67 and it increases as the span of measurement increases to 10 years (0.76), and 25 years (0.84). The reasons behind the surprisingly high 1 year mobility level, and relatively

> temporal variation, by allowing all parameters to vary in each time period, and separately by constraining even the constant to be invariant across periods (unlike in the specification reported on in Table 2, which includes year fixed effects). The results do not change appreciably.

> 19Note that vt+1 denotes individual i’s residual one year (four quarters) after t

> 20See Antman and McKenzie (2007) for a discussion of measurement error and mobility using this data.

11
to 2010 and 2012; and given the effort to expand coverage in the last two years the picture might be less worrying now.

Our understanding of vulnerability in Mexico would benefit from an incidence analysis of non-social programs as well. The ENIGH is attractive for our study because it is comparable across time and has detailed information on income and household characteristics. Yet, the survey could be further improved by embedding questions on the incidence of a broader set of public programs that may affect various economic groups, including labor market policies and financial inclusion. Likewise, many risk-related aspects addressed by our study could be better understood with a comprehensive module on risks and their attributes, including their incidence (not just their impact), severity, and length, where applicable, as well as the household and public responses available and implemented to cope with them.

30