rafmacalaba/datause-encoder-data huggingface dataset:
27

**Table 9: Simulated Poverty and Inequality for 1985, Using 1996 coefficients.**

||Mean|Inequalit|y|||Poverty||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||p/c|||||Z = R$30|/ month|_|Z= R$ 6|0 / month||
||Income|Gini|E(O)|E(l)|E(2)|P(0)|P(1)|P(2)|P(0)|P(l)|P(2)|
|1985 observed|243.152|0.575|0.588|0.654|1.432|0.0738|0.0307|0.0205|0.2258|0.0901|0.0514|
|1996 observed|276.460|0.591|0.586|0.694|1.523|_0.0922|0.0530|0.0434|0.2176|0.1029|0.0703|
|**_Income Generation_**||||||||||||
|**cX,**_,_for wage earners|221.944|0.563|0.557|0.631|1.403|0.0758|0.0306|0.0203|0.2353|0.0929|0.0524|
|cc,f3for self-employed<br>|241.405|0.572|0.58 1|0.647|1.392|0.0725|0.0299|0.0200|0.2236|0.0887|0.0504|
|a,1for both|220.421|0.560|0.549|0.625|1.380|0.0744|0.0299|0.0198|0.2332|0.0915|0.0514|
|_a_only, for both|265.972|0.569|0.575|0.636|1.343|0.0599|0.0262|0.0184|0.1936|0.0758|0.0434|
|All , (but no_a)_for both|170.654|0.582|0.597|0.698|1.754|0.1308|0.0494|0.0291|0.3484|0.1467|0.0838|
|Education**13**for both|199.652|0.562|0.552|0.637|1.473|0.0864|0.0343|0.0221|0.2659|0.1054|0.0592|
|Experience f for both|217.070|0.579|0.594|0.666|1.472|0.1049|0.0521|0.0388|0.2651|0.1189|0.0754|
|GenderPfor both|249.474|0.573|0.583|0.647|1.381|_ 0.0698|0.0290|0.0196|0.2160|0.0855|0.0487|
|**_Occupational Choice_**||||||||||||
|y for both sectors (and both<br>heads + others)|237.069|0.591|0.630|0.690|1.502|0.1048|0.0532|0.0398|0.2577|0.1176|0.0756|
|y for both sectors (only for<br>other members)<br>|241.081|0.580|0.603|0.663|1.422|0.0833|0.0344|0.0228|0.2391|0.0982|0.0568|
|y, a,ffor both sectors|217.070|0.579|0.594|0.666|1.472|0.1049|0.0521|0.0388|0.2651|0.1189|0.0754|
|**_Demographic Patterns_**||||||||||||
|,u only, for all|275.264|0.573|0.583|0.702|2.420|0.0368|0.0114|0.0063|0.1724|0.0558|0.0266|
|>, r,<br>a,d, for all|210.838|0.599|0.605|0.761|2.335|0.0997|0.0462|0.0339|0.2910|0.1215|0.0726|
|**_Education_**||||||||||||
|**_p.**only, for all|281.427|0.587|0.614|0.680|1.451|0.0648|0.0293|0.0209|_ 0.1985|0.0800|0.0469|
|_ , p[. for all|292.292|0.579|0.588|0.662|1.385|0.0498|0.0246|0.0188|0.1718|_0.0659|0.0386|
|p.t, .d,r,cx,**1P,**<br>for all|254.675|0.580|0.590|0.666|1.410|0.0774|0.0434|0.0348|0.2151|0.0937|0.0606|
|Source: Based**on**"Pesquisa Nacional**por**Amos|tra de Domicilio|s" (PNAD) of|1985 and 1996|.||**=**||||||
**<u>│</u> 23**

# 3.3. Inclusiveness proxies: Jobs, income, linkages, gender, and geographic spread

Inclusive investment is associated with foreign multinational activity that aims – by structure or purpose – to maximize benefits to the local economy and people. Whether it be providing jobs for the local workforce or opening up activities to local firms, inclusive investment is a fundamental element of what developing countries need to grow sustainably. How inclusive foreign multinationals are in their activities depends on a combination of four factors: what they do, how they do it, how much of it grows in the local economy, and what the local conditions are.

In the following, we examine the first two aspects: structural and behavioral differences of foreign multinational activity for which data are scarce in developing countries. In particular, we present indicators on job creation, geographical spread, gender empowerment, and linkages with domestic suppliers. The actual impact of foreign multinationals remains dependent on the volume of investment and local conditions, aspects about which we already know a fair amount.

# Job creation and wages

Foreign direct investment generates employment in two ways. First, foreign-owned firms are generally larger than domestic firms and have the capacity to employ more workers (Hijzen et al., 2013). Greenfield investments tend therefore to create more jobs than any domestic new ventures. Second, employment can increase through growth of existing ventures when a foreign takeover takes place, although the evidence on the direction of change in employment in that case is not clear cut. Most of the existing evidence suggests that firm-level employment levels remain unchanged or increase following foreign acquisition.<sup>3</sup> Variation in the impact of foreign investment on host employment could depend on the source country and its level of development: South-South FDI appears to generate more jobs than North-South FDI (Coniglio et al., 2015). Drilling down to specific countries and regions within South-South FDI, FDI from China seems to generate the largest employment increase, followed by FDI from South African firms and from the MENA region. Importantly, these differences are not driven by sectoral specialization of firms – FDI from other developing countries seems to target blue-collar, relatively less-skilled jobs, and generate relatively more of such employment.

The World Bank Enterprise Surveys confirm that growth in full-time employment in foreign multinationals occurs at a larger scale as a whole (see Fig. 8), yet not necessarily more frequently than employment contraction in several developing countries. As differences

> 3See, for example, Girma (2005) for the United Kingdom; Bandick and Karpaty (2007) for Sweden; Balsvik and Haller (2010) for Norway.
19 members ranges between 0 and 10, with the median situated around 2.4.<sup>23</sup> When expressed in terms of shares of vouchers received by elected officials and VVC members, it ranges from 16 percent for the 10<sup>th</sup> percentile village to 100 percent for the 75<sup>th</sup> percentile village.<sup>24</sup> The within village (land) Gini, which also varies considerably, is included to facilitate comparison with the literature, which has often used inequality to reflect power structures. Awareness is proxied through the proportion of households regularly using public media. This is relatively high, with in each of the sample villages more than half the village population (57 percent) consulting public media at least once a month.<sup>25</sup> Education levels, here proxied by illiteracy levels among household heads, are regularly considered in examining targeting performance (Galasso and Ravallion, 2005). While illiteracy is generally low (quasi non-existent in almost half the villages), it still reaches 42 percent in the most illiterate village. In a novel addition to the targeting literature, the level of trust households in a village have in others is further included as a correlate of social cohesion. The proportion of households in a village who (somewhat or strongly) agree that most people can be trusted (arguably a rather demanding measure of trust) ranged between zero and 33 percent with the median situated around 17 percent. Clearly, trust levels are higher in some communities than others.

An important program design feature that has been reported to affect performance is the scale of the program (Galasso and Ravallion, 2005; Bardhan and Mookherjee, 2005). This is proxied by the number of (attrition corrected sample) households receiving a voucher, which ranges between 1 and 15, with half of the villages receiving more than 4.7 vouchers

> 23  These have been corrected for potential attrition bias using the inverse of the probability of being selected in round 3 (appendix A1).

> 24 While the share could also have been used as proxy, the number of vouchers in a village received by elected officials and VVC members and the number of vouchers received in a village, which together define the share, are introduced separately in the regression, not to constrain their coefficients to be the same.

> 25 The degree of participation in meetings and associations was also considered as a proxy for awareness. It was not statistically significant in the regression analysis reported in Table 5 and given its correlation with the use of public media (ρ=0.26)  and the limited degrees of freedom  it was not retained.
to vessels built before 2016 to ensure that our results are driven by vessels that did not choose their characteristics with respect to fuel subsidy thresholds or with respect to other, simultaneously-introduced regulations that could confound our estimates.<sup>8</sup> Finally, subsidy days are the minimum of authorized fishing days and “vessel monitoring system (VMS) days”. As we explain below, subsidy days roughly correspond to the number of days vessels fish, and the fuel subsidy is linear in subsidy days, the fuel subsidy policy could increase fishing because it reduces the marginal cost of fishing.

Each year, vessel owners (firms) request a number of authorized fishing days, as well as the areas in which their vessels will fish, from the Ministry of Agriculture (MOA), which houses the Bureau of Fisheries. Authorized fishing days do not bind subsidy days because the MOA approves requests as long as the firm and its employees are not on the Ministry’s blacklist (Bureau of Fisheries, 2020; FAO, 2022a; Ministry of Agriculture, 2003).<sup>9</sup> VMS days refers to the number of days in which the MOA receives a minimum number of pings (messages) from vessels’ VMS transponders.<sup>10</sup> Each ping from a vessel contains its identifying information, location, speed, and direction of travel. Between 2015 and 2019, the minimum ping rate was 6 per day and at least one ping every four hours (Ministry of Agriculture, 2014a). In 2020, the MOA increased the minimum ping rate to 24 per day and at least one ping per hour (Bureau of Fisheries, 2019). VMS days roughly correspond to days of fishing because pings must originate from vessels’ authorized fishing areas in order to contribute toward vessels’ subsidies (Ministry of Agriculture, 2012).

We do not observe VMS pings because the MOA considers these data to be confidential. Instead, we define a subsidy day as a day in which we observe at least one ping from a vessel’s Automatic Identification System (AIS) transponder, which are publicly available

> 8These regulations are renovation and reconstruction subsidies and vessel standardization regulations (Sections 5.2.2 and 5.3). We also support the assumption that vessels in our regressions did not choose their characteristics with respect to subsidy thresholds by providing evidence against bunching just above subsidy thresholds (Section 5.2.2).

> 9China’s blacklisting policy began in 2017 (Zhang, 2018). We do not find evidence that this policy induced selection into fuel subsidies. While 26 vessels are blacklisted for at least one year of our data, none of them fall within the optimal bandwidth of our preferred specification (Column 1 of Table 3). The data on blacklisted companies and vessels are from Ministry of Agriculture and Rural Affairs (2018a, 2018b, 2019a, 2019b).

> 10In cases where the countries visited by fishing vessels forbade vessel location surveillance equipment, Chinese distant water fishing vessels are required to install Automatic Identification System (AIS) transponders (Ministry of Agriculture, 2014a).

8
- 11 -

a good statistical fit as measured by summary statistics such as the correlation coefficient (R<sup>2</sup> ). As long as a variable on the right hand side of the regression meets the three criteria presented above one can use it to predict y. For estimation one can simply use ordinary least squares (OLS) techniques, so that one estimates a parameter B under the assumption that g(xi) = **ox.** 12" These OLS esLimates of income can then be used both to determine whether a house should get any benefits and, if they should, what the level of benefits should be, by the solution of (9).

Before beginning the data analysis in the next section, three issues need to be discussed, the use of expenditure data instead of income data, the treatment of household size, and the choice of poverty index. In any study of poverty one is ultimately concerned with welfare levels, and income is often used as an indicator of them. Yet there are several reasons why it is better to use expenditure levels rather than income levels. First, for purely theoretical reasons, income only generates welfare if it is actually used to raise consumption levels. On the other hand expenditures are closely tied with consumption levels and are thus more appropriate from a theoretical level. The inaccuracy of income for measuring welfare levels is especially true of farmers and other persons whose income fluctuates from year to year, and such people are often found in developing countries. A more compelling reason to use expenditure rather than income data is that the latter is often under-reported by survey respondents who fear that the income data **will be** 31 Note that **xs** may include quadratic terms, interaction terms, etc. and thus **Oxi** can approximate any functional form as in a Taylor expansion.
||||||different impact of<br>different reform steps|
|---|---|---|---|---|---|
|ESMAP<br>(2011)|econometric|global|panel data; 20<br>countries with<br>different<br>system sizes|Fixed effects,<br>random<br>effects|vertical unbundling<br>reduced electricity<br>tariffs by 10 percent|
|Sen and<br>Jamasb<br>(2012)|econometric|India|panel data: 19<br>Indian states,<br>1991-2007|bias<br>corrected<br>fixed effects|political economy<br>factors giving rise to<br>adverse outcomes in<br>the initial stages of<br>reforms|
|Kundu and<br>Mishra<br>(2011)|econometric|Indian state<br>of Orissa|survey based<br>approach|partial least<br>squares|some consumers<br>group benefited<br>(such as industrial)<br>while some lost<br>(such as agricultural)|
|Estache and<br>Rossi (2004)|econometric|Latin<br>America|distribution<br>companies of<br>14 countries,|correlation|fall is prices in<br>general did not<br>match the<br>productivity gains|
|Balza,<br>Jimenez and<br>Mercado<br>(2013)|econometric|Latin<br>America|panel data: 18<br>countries,<br>1971-2010|generalised<br>least squares|no robust results in<br>terms of privatisation<br>and end-user-prices;<br>strong and robust<br>association between<br>regulatory quality<br>and electricity prices|
|Anaya<br>(2010)|cost-benefit<br>analysis|Peru|electricity<br>distribution<br>companies<br>(privatised and<br>non privatised)|single<br>country|privatisation<br>contributed to price<br>increase|
|Haselip and<br>Potter (2010)|case study|Argentina|power sector<br>indicators|single<br>country|reforms led to price<br>decline until<br>macroeconomic<br>crisis|
|Pollitt<br>(2004)|case study|Chile|power sector<br>indicators|single<br>country|average node prices<br>declined after<br>reforms|
|Karahan and<br>Toptas<br>(2013)|case study|Turkey|power sector<br>indicators|single<br>country|no reduction in retail<br>electricity prices<br>after reforms|

18
# CHAPTER **I.** INTRODUCTION: PURPOSE AND SCOPE OF REVIEW

1.1 The objectives of this Population, Health and Nutrition (PHN) Annual Sector Review (ASR) are: (a) to provide an overview of the FY92 work portfolio, including statistics on the volume and mix of lending and sector work and a brief discussion of salient features and trends; and (b) to address in some depth, as the special topic of this year's review, the contribution of PHN lending to the Bank's poverty alleviation objective.

This report did, however, take great care to review the lessons learned through implementation experience, as documented in the Seventeenth ARIS and in the Staff Appraisal Reports (SARs) and Projection Completion Reports (PCRs) produced for the PHN sector during the year. These lessons are compiled and discussed in a specially prepared annex to this report, and served to inform and inspire the approach to this review's special topic.

1.2 This report covers three components of the PHN work portfolio. First, the portfolio of FY92-approved _lending_ operations considered in this review comprises the sixteen projects officially categorized as PHN sector, including a number of multisectoral operations, aimed at poverty alleviation objectives that support, in some cases, relatively small PHN components. Second, the review provides statistics on _sector work_ and some analysis of sector reports' treatment of poverty and quality issues. Third, the coverage of the projects under _superviron,_ now numbering more than one hundred, is limited to some basic statistics on the size and composition of the supervision portfolio, on the proportion of problem projects, and on Bank staff resources devoted to supervision. The Eighteenth Annual Review of Implementation and Supervision (ARIS), provides a thorough review of supervision work.
# **References:**

Barajas, A., Steiner, R., Salazar, N., 1999. Interest Spreads in Banking in Colombia, 1974–96. _IMF Staff Papers,_ 46, 196–224.

——— 2000. The Impact of Liberalization and Foreign Investment in Colombia’s Financial Sector. _Journal of Development Economics,_ 63(1), 157–96.

Beck, T. A., Cull, R., Jerome, A., 2005. Bank privatization and performance: Empirical evidence from Nigeria. _Journal of Banking and Finance_ 29, 2355-2379.

Beck, T. A., Demirgüç-Kunt, A., R. Levine, R. 2000. A New Database on Financial Development and Structure. _World Bank Economic Review_ 14, 597-605.

Beck, T.A., Fuchs, M., 2004. Structural Issues in the Kenyan Financial System: Improving Competition and Access. _World Bank Policy Research Working Paper_ 3363.

Beck, T. A., Hesse, H., 2006. Bank Efficiency, Ownership and Market Structure. Why are Interest Spreads so High in Uganda? _World Bank Policy Research Working Paper_ 4027.

Birungi, P., 2005. Determinants of Interest Rate Spreads in Uganda. University of Pretoria, South Africa, Mimeo.

Brock, P. L., Rojas-Suarez, L., 2000. Understanding the Behaviour of Bank Spreads in Latin America. _Journal of Development Economics_ 63(1), 113–34.

Brownbridge, M., Harvey, C., 1998. _Banking in Africa: The impact of Financial Sector Reform since Independence,_ James Currey: London.

Caprio, G., Klingebiel, D., 2003. Episodes of Systemic and Borderline Financial Crises. World Bank, Mimeo.

Chirwa, E.W., Mlachila, M., 2004. Financial Reforms and Interest Rate Spreads in the Commercial Banking System in Malawi. _IMF Staff Papers_ 51(1), 96-122.

Cihak, M., Podpiera, R., 2005. Bank Behavior in Developing Countries: Evidence from East Africa. IMF Working Paper 05/129.

Claessens, S., Demirgüç-Kunt, A., Huizinga, H., 2001. How Does Foreign Entry Affect Domestic Banking Markets? _Journal of Banking and Finance_ Vol. 25, pp. 891–911.

Claessens, S., Laeven, L., 2004. What Drives Bank Competition? Some International Evidence. _Journal of Money, Credit and Banking_ 36 (3) Part 2, 563-583.

35
> FIGURE 1.2

regions less than 1 percent. There has been rapid growth in offerings of mobile money accounts around the world in the past three years—the GSMA MMU database reports 259 deployments in 89 countries at the beginning of 2015, up from only 100 deployments three years earlier. But most of these offerings remain relatively new, and mobile money accounts may have yet to take off.

Account penetration in countries with mobile money account penetration of 10 percent or more Adults with an account (%), 2014 <!-- Start of picture text --> Botswana 52<br>Côte d’Ivoire 34<br>Ghana 41<br>Kenya 75<br>Mali 20<br>Namibia 59<br>Rwanda 42<br>Somalia 39<br>South Africa 70<br>Tanzania 40<br>Uganda 44<br>Zambia 36<br>Zimbabwe 32<br>Mobile money account only<br>Financial institution and mobile money account<br>Financial institution account only<br>Source: Global Findex database.<br><!-- End of picture text -->

# How has account ownership changed over time?

The first round of Global Findex data was collected in 2011, and the second round three years later. How do the 2014 data on account ownership compare with the earlier data? Globally, the share of adults with an account increased by 11 percentage points, from 51 percent in 2011 to 62 percent in 2014. And the number of adults without an account—the unbanked—fell from 2.5 billion to 2 billion.<sup>6</sup> Yet while the number of unbanked adults fell by 500 million, the number of adults who became account holders over this period is actually larger—700 million. The difference between these numbers is due to population growth. In 2011 the world’s adult population was 5 billion, with 2.5 billion adults having an account and 2.5 billion being unbanked. By 2014 the world’s adult population had increased to 5.2 billion, with 3.2 billion adults having an account and 2 billion being unbanked.

Account ownership increased in every region. But the growth was particularly strong in East Asia and the Pacific, South Asia, and Latin America and the Caribbean, each of which saw an increase in account penetration of more than 10 percentage points. The increase was concentrated in financial institution accounts everywhere except Sub-Saharan Africa, where mobile money accounts drove the growth in overall account penetration from 24 percent in 2011 to 34 percent in 2014 (figure 1.3). In East Africa, where mobile money accounts are most common, these accounts increased overall account penetration by 9 percentage points to 35 percent while the share of adults with an account at a financial institution remained steady at 26 percent.

> FIGURE 1.3

<!-- Start of picture text --> Account penetration, 2011 and 2014<br>Adults with an account (%)<br>20 40 60 80 100<br>East Asia & Pacific 2011 2014<br>Europe & Central Asia<br>High-income OECD economies<br>Latin America & Caribbean<br>Financial institution<br>Middle East account only<br>Financial institution<br>and mobile money account<br>South Asia<br>Mobile money account only<br>Sub-Saharan Africa<br>Source: Global Findex database.<br><!-- End of picture text --> 13 THE GLOBAL FINDEX DATABASE