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38
**<u>Table 2: Basic farm characteristics by region and farm size group</u>**

||<br>**Total **|<br>**Center **|<br>**North **|<br>**South **|**East**|**West**|**<50**|**50-120**|**120-500**|**>500**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Household characteristics**|||||||||||
|Lives in village<sup>a</sup>|0.70|0.70|0.62|0.71|0.68|0.76|0.77|0.71|0.64|0.64|
|Hh qnr applied<sup>a</sup>|0.82|0.85|0.74|0.84|0.81|0.78|0.94|0.91|0.75|0.52|
|Male farm owner|0.75|0.76|0.85|0.64|0.82|0.77|0.61|0.82|0.80|0.83|
|Age of owner|48.97|49.21|47.65|50.31|49.24|46.92|49.14|49.02|48.09|50.11|
|Education < secondary|0.01|0.01|0.01|0.02|0.00|0.02|0.02|0.01|0.00|0.00|
|Secondary education|0.10|0.09|0.10|0.13|0.16|0.07|0.14|0.09|0.09|0.01|
|Education > secondary|0.89|0.90|0.89|0.85|0.84|0.91|0.83|0.90|0.91|0.99|
|Owner has bank account|0.96|0.95|0.96|0.95|0.98|0.97|0.92|0.97|0.98|0.99|
|Other member w. bank acct|0.72|0.71|0.71|0.77|0.68|0.69|0.71|0.73|0.78|0.60|
|Owner has FB account|0.67|0.66|0.65|0.69|0.64|0.71|0.64|0.68|0.70|0.66|
|Other member w. FB acct|0.64|0.64|0.63|0.65|0.65|0.63|0.63|0.67|0.69|0.46|
|**War impact & coping strategies**|||||||||||
|War damage to land<sup>a</sup>|0.08|0.03|0.21|0.10|0.26|0.03|0.03|0.06|0.13|0.18|
|War damage to structures<sup>a</sup>|0.04|0.01|0.11|0.05|0.15|0.01|0.02|0.02|0.06|0.09|
|<br>Member migrated out in oblast|0.08|0.08|0.10|0.06|0.09|0.10|0.06|0.09|0.07|0.07|
|Member migrated beyond oblast|0.09|0.08|0.07|0.11|0.18|0.08|0.10|0.09|0.08|0.08|
|Could get informal support|0.46|0.45|0.44|0.48|0.48|0.48|0.46|0.50|0.44|0.32|
|<br>if yes, amount (US$)|7,311|7,505|9,922|5,461|9,276|6,260|4,145|7,735|10,490|12,141|
|$ 200 lottery: Consumption|0.17|0.14|0.20|0.18|0.20|0.21|0.18|0.15|0.17|0.19|
|$ 200 lottery: Working capital|0.40|0.39|0.42|0.44|0.39|0.39|0.46|0.42|0.34|0.26|
|<br>$ 200 lottery: Investment/savings|0.15|0.17|0.14|0.13|0.15|0.15|0.15|0.13|0.17|0.23|
|$ 5,000 lottery: Consumption|0.10|0.09|0.09|0.11|0.14|0.08|0.15|0.08|0.07|0.06|
|$ 5,000 lottery: Working capital|0.65|0.64|0.66|0.69|0.57|0.61|0.67|0.69|0.63|0.43|
|$ 5,000 lottery: Investment/saving|0.19|0.20|0.17|0.17|0.23|0.22|0.19|0.16|0.20|0.35|
|No. of obs. (farms)|2,251|1,059|316|464|124|288|619|790|501|341|

_Source:_ Own computation from MAPF/WB/EU small producer survey. _Note:_ Answers marked by<sup>a</sup> were answered by all farms while the remainder is only for the 82% owner-operated farms that answered the household section of the questionnaire.

21
report statistically significant changes in monitoring. However, for the social accountability committee group, we find very large and significant increases in the monitoring activities they conduct.

In columns 3 to 7 we explore the impacts on reporting of issues in the community to officials at different levels of government. This measure comes from the household survey conducted six months after the project assessment. The results show significant increases in the number of reports at all levels of government. Reports to the lowest level of government, LC1 and sub-county officials, increase by approximately 10%. Reports to officials at a slightly higher level of local government, the district, increases by 14%. Most strikingly, reporting to the central government through the IG increases by 59%. In appendix Table A3 we find similar results when looking at the full list of project types and only the social accountability training treatment.

We also look at reported issues during the procurement and contracting process and the quality of interactions with bureaucrats in Tables A4 and A5 in the appendix. We do not find statistically significant impacts on whether communities reported issues with procurement or contracting, their satisfaction with suppliers, whether they hired a contractor, or their satisfaction with the contractor. We also do not find impacts on whether they made a payment to a district representative, their satisfaction with the local NUSAF2 coordinator, or their satisfaction with the local veterinarian officer.

In Table 11 we explore several additional outcomes and potential mechanisms. In columns 1 and 2 we test whether treatment affected individuals’ perceptions of the performance of the project leaders. In columns 3 and 4 we ask about general satisfaction with the quality of the project and management committee, respectively. We do not find meaningful changes in any of these measures. In columns 5 and 6 we look at two measures of the ability of communities to deliver public goods and find there does appear to be a significant, though small, effect on whether people believe members of the community can come together to solve issues in the community. In appendix Table A6 we look at the full sample and only social accountability training treatment status. Here we find statistically significant decreases in whether people report choosing the same project leadership, increases in public goods provision, and an increase in the perception of being able to make their community a better place to live. However, all of these effects are very small relative to control group means. These results suggest that the social 24
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<mark>Sheng, Y., J. Ding, and J. Huang. 2019. “The Relationship between Farm Size and Productivity in Agriculture: Evidence from Maize Production in Northern China.”</mark> _<mark>American Journal of Agricultural Economics</mark>_ <mark>101 (3): 790–806.</mark>

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- <mark>Udry, C. 1996. “Gender, Agricultural Production, and the Theory of the Household.”</mark> _<mark>Journal of</mark> Political Economy_ 104 (5): 1010–46.

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30
# **9. References**

Aina, C., Brunetti, I., Mussida, C., & Scicchitano, S. (2021). Who lost the most? Distributive effects of the Covid-19 pandemic (No. 65; INAPP WP). www.inapp.org

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- Berniell, I., Gasparini, L., Marchionni, M., & Viollaz, M. (2021). The Role of Work-from-Home in the Gender Asymmetries of COVID-19 (No. 9887; Policy Research Working Paper). <u>http://www.worldbank.org/prwp.</u>

- Blau, F. D., Brummund, P., & Liu, A. Y. H. (2013). Trends in occupational segregation by gender 1970–2009: Adjusting for the impact of changes in the occupational coding system. _Demography_ , _50_ (2), 471-492.

- Blau, F. D., & Winkler, A. E. (2017). _Women, work, and family_ (No. w23644). National Bureau of Economic Research.

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Bonacini, L., Gallo, G., & Scicchitano, S. (2021). Will it be a shecession? The unintended influence ofworking from home on the gender wage gap relatedto the COVID-19 pandemic (No. 771; GLO Discussion Paper). http://hdl.handle.net/10419/229149

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Africa, the more movement). This also suggests that, given wealth differentials among developing countries, there is a considerable potential for South-South trade under mode 4: in the case of Tunisia, Saudi Arabia is by far the main destination of the moving healthcare labor workforce, and the Gulf captures more than 80 percent of total flows.

**Table 3: African physicians practicing in the US and Canada**

|**Country of origin**|**Number of African-**<br>**trained physicians**<br>**in USA**|**Number of African-**<br>**trained physicians**<br>**in Canada**|**Number of**<br>**physicians**<br>**remaining in home**<br>**country**|**% of total African-**<br>**trained now in USA**<br>**or Canada**|
|---|---|---|---|---|
|Nigeria|2158|123|22894|9|
|South Africa|1943|1845|23844|14|
|Ghana|478|37|1210|30|
|Ethiopia|257|9|1564|15|
|Uganda|133|42|722|20|
|Kenya|93|19|4001|3|
|Zimbabwe|75|26|1694|6|
|Zambia|67|7|676|10|
|Liberia|47|8|72|43|
|Other 12 countries|83|35|12912|1|
|**Total/Average**|**5334 **|**2151 **|**69589**|**10**|

**Source:** <u>www.human-resources-health.com</u>

**Case study n°6**

**Exports under Mode 4 – The Case of Tunisia (year 2004)**

||Physicians|Technicians|Nurses|Total|Percent|
|---|---|---|---|---|---|
|Saudi Arabia|65|336|827|1,228|53%|
|Qatar|6|229|130|365|16%|
|UAE|9|107|163|279|12%|
|Kuwait|-|1|55|56|2%|
|Europe|1|2|278|281|12%|
|Others|21|18|81|120|4%|
|Total|102|693|1,534|2,329|100%|
|Number<br>remaining<br>at<br>home|13,330|10,799|28,537|52,666||

**Source:** Achouri and Achour (2005), based on Tunisian Agency of Technical Cooperation.

Here again, one should interpret these numbers with caution, since mode 4 covers only the temporary movement of persons, and in many cases the intent (with or without success) of the migrant it to permanently stay in the host country.

#  **The expected benefits from health services exports**

For all four modes of delivery, exports generate income that benefits the domestic economy and contributes to improving the balance of payments. The dynamic effects of this income will then vary depending on the mode of services delivery.

Exports under mode 1 increase the revenues of domestic health services providers, allow further investment, and potentially improve their profitability, competitiveness (e.g. through economies of scale), etc. They also create opportunities for healthcare providers at home (e.g. work on new technologies) and help preventing international brain drain.

Exports under mode 2 have similar effects. As previously observed (see potential risks of imports under mode 3 above), spill-over effects will depend on the legal/regulatory framework and

- 18 -
# **APPENDIX II: Methodology of the Surveys Cited in the Paper**

In section IV of the paper, I quoted three surveys on remittances by _Nikkeijin_ . Brief summary of methodology of the three surveys are as follows:

# **1. 1993 survey by Japan Institute of Labor**

- (1) Sample: _Nikkeijin_ living in Brazil

- (2) Number of questionnaires distributed: 500

(3) Number of interviews: 376 (of which 202 (53.7%) had an experience of working in Japan)

- (4) Date of interviews: September 1993

# **2. 1998 Survey by Japan Institute of Labor**

- (1) Sample: _Nikkeijin_ living in Brazil

- (2) Number of questionnaires distributed: 300

(3) Number of interviews: 205 (of which 106 (51.7%) had an experience of working in Japan)

- (4) Date of interviews: September 1998

# **3. 2005 Survey commissioned by the Inter-American Bank**

(1) Sample: Latin American immigrant adults living in Japan (2) Number of Interview: 1,070 interviews (3) Language of Interviews: Portuguese, Spanish and Japanese (4) Date of interviews: February 2005 51
Table 1. Descriptive Statistics, Children 5-13 in the LSS Rural Sample

|Variable|Mean|Standard<br>deviation|Observations|
|---|---|---|---|
|IRT cognitive score|-0.01|0.91|1411|
|Height for age (HAZ)|-2.38|2.62|1285|
|Moderately stunted (HAZ<-2)|<br>0.47|0.50|1285|
|Severely Stunted (z-score<-3)|0.32|0.45|1285|
|Parents are related|0.65|0.48|1411|
|Parents are 1st cousins|0.55|0.50|1411|
|Parents are 2nd cousins|0.10|0.30|1411|
|F-coefficient of inbreeding|0.07|0.06|1411|
|Grand-father owns/owned land|0.53|0.50|1411|
|Maternal grand-father died before 65|0.30|0.46|1411|
|Age|8.78|2.51|1411|
|Being male|0.52|0.50|1411|
|Household wealth index|-0.40|0.84|1411|
|Father's years of education|3.76|4.71|1411|
|Punjab province|0.66|0.47|1411|
|Sindh province|0.22|0.42|1411|
|Khyber Pakhtunkhwa(KPK)Province|0.12|0.32|1411|

20
components in (17) are calculated as: where the max and median values in (18) are taken over the asset classes rather than over the banks, and _LB_ and _UB_ the lower and upper bounds of each component, respectively.<sup>37</sup> We recommend to set _LB_ around 0.2. The three upper bounds _UB_<sup>_ξ_</sup> , _UB_<sup>_θ_</sup> and _UB_<sup>_ζ_</sup> need to be calibrated so that _Ri_<sup>_H_</sup> , _j_<sup>ranges between 0.3 and 0.5.This recommended range is based</sup> on anecdotal evidence from interviews with bank credit risk officers on the impact of the global financial crisis.

The lending concentration term ( _LendConc_ ) can be approximated as either the share of the ten largest exposures or as the Herfindahl-Hirschman (HH) concentration index for each asset class and each bank. The _ξi_ component postulates that if a given asset class is relatively more prone to default in the TTC scenario compared to any other asset class, it will have a proportionately higher asset performance correlation during times of stress. For instance, we specify the Corporates asset class to have a floor correlation that is lower than Consumer Loans (QRE retail) in our empirical application.<sup>38</sup>

> 37In (19) and (20), the max and median values are again taken over the banks as before.

> 38In principle, the floor per asset class can differ also across the banks based on their business models if relevant supervisory information is available – e.g. a bank, on average (through the cycle), being more regionally focused than other banks, or servicing a more vulnerable population or firms than other banks.

45
WPS3484

# The Relative Richness of the Poor? Natural Resources, Human Capital and Economic Growth<sup>_∗_</sup>

Claudio Bravo-Ortega<sup>_†_</sup> Jos ́e De Gregorio<sup>_‡_</sup> World Bank Banco Central de Chile and Department of Economics Universidad de Chile Comments welcome Comments welcome

## **Abstract**

Are natural resources a blessing or a curse? In this paper we present a model in which natural resources have a positive effect on level of income and a negative effect on its growth rate. The positive and permanent effect on income implies a welfare gain. There is a growth effect stemming from a composition effect. However, we show that this effect can be offset by having a large level of human capital. We test our model using panel data for the period 1970-1990. We extend the usual specifications for economic growth regressions by incorporating an interaction term between human capital and natural resources, showing that high levels of human capital may outweigh the negative effects of the natural resource abundance on growth. We also review the historical experience of Scandinavian countries, which in contrast to Latin America, another region well endowed with natural resources, shows how it is possible to grow fast based on natural resources.

## JEL Classification: J24, O41, O57, Q00.

> _∗_ We would to thank specially to Maurice Obstfeld for his comments that helped greatly improve our empirical work. We also would like to thank Pranab Bardhan, A. De Michaelis, Julian di Giovanni, Andr ́es G ́omez-Lobo, Botond K ̋oszegi, Patricio Meller, Ted Miguel, David Romer, J.P. Stijns and the participants of the Seminars at the Universidad de Chile, University of California at Berkeley, and at the World Bank and Central Bank of Chile joint conference on Natural Resources and Growth for their helpful comments. Claudio Bravo-Ortega would also like to thank CIEPLAN and the Ministry of the Economy for their support and hospitality at different stages of this research.

> _†_ Address: The World Bank, MSN I-8-801 1818 H Street, N.W. Washington, DC 20433. E-mail: `cbravoortega@worldbank.org`

> _‡_ E-mail: `jdegrego@bcentral.cl`
Figure D.17. Plotting the Effects of Guerrilla Control on Homicide Rates <!-- Start of picture text --> (a) Homicides (2017)<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Distance to border (Km)<br>Mean residual per bin Linear prediction 95% CI<br>.3<br>.2<br>.1<br>0<br>Estimate magnitude<br>-.1<br>-.2<br><!-- End of picture text --> _Notes:_ This figure shows the results obtained from the estimation of equation (1). The estimates shown include up to 400 break fixed effects. There are no effects of guerrilla control on homicide rates in 2017.

Figure D.18. Effects of Guerrilla Control on Homicide Rates under Different Bandwidths <!-- Start of picture text --> (a) Homicides (2017)<br>.1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4<br>Bandwidth (Km)<br>Mean of Outcome: .308<br>1.5<br>1<br>.5<br>0<br>Coefficient magnitude<br>-.5<br>-1<br><!-- End of picture text --> _Notes:_ This figure shows the results obtained from the estimation of equation (1). The figure illustrates the coefficients for 40 individual estimations, one for each of the different bandwidths around the discontinuity. The estimates shown include up to 400 break fixed effects. The gray coloring illustrates 95% confidence intervals.

76