rafmacalaba/datause-encoder-data huggingface dataset:
foreign owners do not alter the skill composition of labor, they are able to attract more experienced and better-motivated employees. They may also substitute expatriate staff for local managers and introduce pay scales linked to performance. This would be in line with the earlier observation that acquired plants hire a large number of new workers and increase the average wage. Finally, foreign owners may invest more in staff training, which is consistent with the evidence mentioned earlier. Another possibility is that the use of higher-quality inputs or more suitable parts and components translates into higher productivity, which is consistent with the observed increase in the use of imported inputs in the aftermath of foreign acquisition.<sup>10</sup> The productivity effects of foreign acquisitions are not limited to the manufacturing sector. A study by Arnold, Javorcik, and Mattoo (2011) finds that the foreign acquisitions of Czech service providers resulted in large changes in the labor productivity and sales of the acquired firms. These findings are consistent with foreign services providers bringing new technologies and know-how to the Czech Republic and providing services with greater appeal to Czech consumers.

The findings of econometric studies are consistent with the conclusions of the case study literature. In a survey of case studies from around the world, Moran (2007) provides many examples of knowledge, know-how, and technology transfer from parent companies to foreign affiliates. However, he also argues that in distorted environments where host governments impose local content, joint venture 16
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(2015). “Causes and Consequences of Income Inequality: A Global Perspective”. IMF Staff Discussion Note No. 15/13 Easterly, W. (2007). “Inequality Does Cause Underdevelopment: Insights from a New Instrument.” _Journal of Development Economics_ 84:755–776.

Fatas, A. and I. Mihov (2003). "The Case for Restricting Fiscal Policy Discretion." _Quarterly Journal of Economics_ 118: 1419-1447.

- Forbes, K.J. (2000). "A Reassessment of the Relationship between Inequality and Growth." _American Economic Review_ 90: 869–887.

- Galor, O. (2011). “Inequality, Human Capital Formation, and the Process of Development.” _Handbook of the Economics of Education_ , North Holland.

- Galor, O. and J. Zeira (1993). “Income Distribution and Macroeconomics.” _Review of Economic Studies_ 60: 35-52.

- Galor, O., O. Moav, and D. Vollrath (2009). “Inequality in Land Ownership, the Emergence of Human Capital Promoting Institutions, and the Great Divergence.” _Review of Economic Studies_ 76: 143-179

- Halter, D., M. Oechslin and J. Zweimüller (2014). “Inequality and Growth: The Neglected Time Dimension.” _Journal of Economic Growth_ 19: 81-104.

Hausman, J., W. Newey, W. Taylor (1987). “Efficient Estimation and Identification of Simultaneous Equation Models with Covariance Restrictions.” _Econometrica_ 55: 849-874.

- Heston, A., R. Summers and B. Aten (2012). "Penn World Table Version 7.1." Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania.

- Kraay, A. (2016). “Weak Instruments in Growth Regressions: Implications for Recent CrossCountry Evidence on Inequality and Growth.” World Bank Policy Research Working Paper 7494.

Maddala, G.S. and Wu, S. (1999). “A Comparative Study of Unit Root Tests With Panel Data and A 24
based on a measure of distance from the United States, which the author argues is the primary source of most imports. In keeping with most of this literature, he finds incomplete pass-through of the tariff changes to consumers in Mexico, with the extent of pass-through being smaller for agriculture than for manufactured goods. When coupled with a rapid erosion of pass-through with increasing distance from the border, this means that agricultural tariff cuts have little or no impact on the more remote regions of Mexico. Nicita argues that this low pass-through for agricultural products is due to the fact that transportation costs are high, and these products face more competition from domestic sources so that local production quickly becomes more profitable as one moves away from the border. Indeed, he notes: “domestic supply is likely to set the price of certain agricultural products regardless of border measures” (p. 23). This type of assessment sharply alters our view of the impact of trade liberalization on rural households, and much more work on the problem of price transmission is needed.

**_Data Reconciliation:_** The proper treatment of marketing margins is also important when one seeks to reconcile household survey data with the national accounts upon which CGE models are based.  Expenditures in the survey are reported at consumer prices, while most CGE models measure consumer demand at producer prices. In order to bridge the two, one needs a producer-consumer transition matrix (e.g., Ballard et al., 1985).  Of the studies referred to above, only the one by Hertel et al. (2004b) explicitly models this wholesale/retail/transport margin.<sup>13</sup> In most studies, the two data sets are either allowed to diverge, or they are forced to balance by making arbitrary adjustments on both sides. This can give rise to misleading inferences about the incidence of commodity price changes on the poor, in particular, for the same reasons mentioned above.

Data reconciliation between household surveys and national accounts is also required on the income side (Robilliard and Robinson, 2003). Indeed, due to under-sampling of wealthy households, and under-reporting of income, it is not uncommon for total income reported in a household survey to be less than half that reported in the national accounts. In addition, the under sampling/reporting problem tends to be more severe for capital, as opposed to labor, and for non-farm activities as opposed to agriculture (Ivanic, 2004). Thus a failure to correct for this problem can result in two very different looking economies, depending on whether one uses the survey or national accounts data. Consequently, estimates based on the household survey data will tend to be excessively intensive in

> 13 Chen and Ravallion take an exogenous margin into account when computing their consumer prices.

29
# A Map of the Poor or a Poor Map?

Paul Corral,<sup>∗</sup> Kristen Himelein,<sup>†</sup> Kevin McGee<sup>‡</sup> and Isabel Molina<sup>§¶</sup>

**Key words:** Small area estimation, ELL, Poverty mapping, Poverty map, Empirical best, Parametric bootstrap, nested error model, twofold nested error model

**JEL classification:** C55, C87, C15

> ∗The World Bank Group - Human Development Group Chief Economist’s Office (pcorralrodas@worldbank.org)

> †The World Bank Group - Poverty and Equity Global Practice

> ‡The World Bank Group - Development Economics Data Group

> §Universidad Carlos III de Madrid - Department of Statistics (imolina@est-econ.uc3m.es)

> ¶The authors acknowledge financial support from the World Bank. We thank Sandra Carolina Segovia for research assistance. We thank J.N.K. Rao for comments and suggestions. We also thank Roy van der Weide, Alexandru Cojocaru, David Newhouse, and Samuel Freije-Rodriguez for comments. Additionally, we thank Carolina Sanchez and Roberta Gatti for providing support and space to pursue this work. Finally, we thank the Global Solutions Group on Data for Policy, as well as all attendants of the Summer University courses on Small Area Estimation. Any error or omission is the authors’ responsibility alone.
45

**Table A.3. Uncertainties affecting** **<u>population risk and economic risk.</u>**

|**Uncertainty**|**Population**<br>**Risk**|**Economic**<br>**Risk**|
|---|---|---|
|Increase in Rainfall Intensity|X|X|
|Increase in Saigon River Levels|X|X|
|Total Ho Chi Minh CityPopulation|X||
|Geographic Distribution of Population|X||
|PovertyRate|X||
|Average Annual Economic Growth||X|
|Geographic Distribution of Economic<br>Growth||X|
|Population Vulnerability|X||
|Economic Vulnerability||X|

# **_Uncertainties Associated with Future Hazard_**

The first two factors relate to the future hazard: the increase in intensity of future 3- hour rainfalls and the increase in the mean level of the Saigon River.

## **Change in Rainfall Intensity**

The IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) provides an analysis of how future rainfall intensity may change. For Southeast Asia, global climate models suggest that the intensity of a daily precipitation event in the time period 2045-2065 could be similar to that in the period 1981-2000 or may increase by as much as 35%. We use this as the basis for developing the range of potential rainfall intensities for future 2, 5, and 10-year events. For each return period, we consider a wide range of intensities. To increase confidence that we include the full range of plausible futures, our range exceeds that of the IPCC SREX report. Intensities range from the historical baseline to a 60% increases in intensity, as shown in Table A.4

**Table A.4. Range of intensities (mm) for 3-hour rainfalls with 2, 5, and 10-year return periods.**

|**Return Period**|**Recent(mm)**|**60% Increase(mm)**|
|---|---|---|
|2-Year|83|133|
|5-Year|104.1|167|
|10-Year|118|189|
Table (D8) Impacts of the Program on COVID-19 Labor-Related Outcomes

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|Weekly adult hours worked (before frst lockdown)|1.73|(4.93)|[0.77]|29.99|(31.33)|1,604|
|Weekly adult hours worked (during frst lockdown)|0.46|(0.74)|[0.65]|1.33|(6.39)|1,610|
|Weekly adult hours worked (after frst lockdown)|0.55|(3.67)|[0.81]|24.11|(28.87)|1,606|
|Weekly adult income (before frst lockdown)|-21.60|(15.83)|[0.33]|117.16|(112.22)|1,606|
|Weekly adult income (during frst lockdown)|-20.07***|(7.19)|[0.02]|30.22|(66.86)|1,609|
|Weekly adult income (after frst lockdown)|-3.27|(12.47)|[0.77]|99.37|(100.70)|1,605|
|Assets sold [before lockdown] (=1)|0.10|(0.08)|[0.34]|0.25|(0.43)|1,600|
|Assets sold [during lockdown] (=1)|0.04|(0.07)|[0.65]|0.28|(0.45)|1,602|
|Assets sold [after lockdown] (=1)|-0.02|(0.04)|[0.69]|0.11|(0.32)|1,608|
|Days of work/housework/school missed|0.47|(1.55)|[0.77]|4.48|(11.67)|1,407|
|Lost job (=1)|0.00|(0.06)|[0.83]|0.66|(0.48)|1,606|

**_Notes_** :

- The table shows the regression results on pre-specified COVID-19-related outcomes using the phone survey midline data. Each row is its own dependent variable.

- The independent variable of interest is the TOT treatment indicator, which is the predicted value from a first-stage regression of treatment implementation on treatment assignment.

- The periods referred to in the tables are before lockdown: (January 15 - March 15, 2020); during lockdown: (March 15 - May 15, 2020); and after lockdown: (May 15 - interview date). Monetary values are in USD PPP and are winsorized at the top 1% of values in order to limit the influence of outliers.

- The regressions also have assessment month-by-year fixed effects, enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program, baseline number of children, baseline number of children plus adults, respondent gender, and respondent age).

- In the parentheses are robust standard errors clustered at the locality level. Regressions are weighted by the number of people interviewed in each household. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to Family 14 in the pre-analysis plan.

10
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20 make it easy to compare with the unweighted regional averages for the national case studies. In all but three of those twelve comparisons for global liberalization (agricultural, nonagricultural and all merchandise), the projected regional average poverty reductions from global liberalization are larger from the available sample of national case studies than from Hertel and Keeney’s 15-country sample.

As for the individual country results, poverty is reduced in all countries by both global agricultural and, with the exception of the Philippines, non-agricultural liberalization (Table 6(c)). When all merchandise trade is liberalized, the extent of reduction ranges from close to zero to about 3.5 percentage points, except for Pakistan where it is more than 6 points.<sup>8</sup> On average nearly two-thirds of the alleviation is due to non-farm trade reform, with the important exception of Brazil where agricultural reform is the major contributor to its large pro-poor outcome. The latter result is despite the presence of tariff protection for Brazil’s poor import-competing farmers, and is a consequence of the increase in demand for unskilled labor following liberalization, which evidently outweighs the poverty impact of removing farm tariffs. The contribution of own-country reforms to the fall in poverty appears to be equally as important as rest-of-world reform on average, although there is some considerable cross-country divergence in the extent of this for both farm and non-farm reform.

The poverty alleviation is sub-divided in parts (a) and (b) of Table 6 into rural and urban sources. A glance at the final column of that part of the table reveals that rural poverty is cut much more than urban poverty in every case. That is true for both farm and non-farm trade reform, and for own-country as well as rest-of-world reform. Since the rural poor are much poorer on average than the urban poor, this would lead one to expect trade reform to reduce inequality also.

> 8 The Pakistan results were generated assuming replacement of trade taxation with a rise in direct income taxes. Only urban, non-poor households pay direct taxes in Pakistan, so the removal of tariffs decreases the aftertax incomes of the urban non-poor and means the benefits of trade reform go mainly to the poor.
**Table 4. Effect of preschool availability on maternal employment depending on which child is preschool-aged eligible**

||||**Work par**<br>|**ticipation**<br>|||
|---|---|---|---|---|---|---|
|Eligible Child|A<br>|ny<br>|Old<br>|est<br>|You<br>|ngest<br>|
||(1)|(2)|(3)|(4)|(5)|(6)|
|**Panel A: Publi**|**c**||||||
|Preschool *|0.057***|0.074***|0.041*|0.075***|0.035|0.055**|
|Eligible|(0.017)|(0.016)|(0.025)|(0.020)|(0.025)|(0.023)|
|Preschool|-0.016|-0.015|-0.004|-0.003|-0.003|-0.000|
|density|(0.013)|(0.012)|(0.013)|(0.012)|(0.014)|(0.012)|
|Eligible child|-0.051***|-0.034***|-0.042***|-0.046***|-0.001|-0.004|
||(0.007)|(0.005)|(0.006)|(0.006)|(0.006)|(0.006)|
|**Panel B: Privat**|**e**||||||
|Preschool *|0.001|0.002|-0.000|0.002|-0.001|0.001|
|Eligible|(0.002)|(0.001)|(0.002)|(0.002)|(0.002)|(0.001)|
|Preschool|-0.003*|-0.001|-0.003|-0.001|-0.002|-0.001|
|density|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|
|Eligible child|-0.046***|-0.032***|-0.035***|-0.046***|0.008|-0.000|
||(0.011)|(0.008)|(0.010)|(0.009)|(0.010)|(0.009)|
|Method|DDD|DDD-FE|DDD|DDD-FE|DDD|DDD-FE|

Note: Sample includes all mothers with and without a preschool-aged eligible child (age 3-6). Comparison group includes non-mothers and mothers of children with the wrong ages. Definition of eligibility varies across columns. Column 1-2 define eligibility pertaining to any child, column 3-4 to the oldest child, and column 5-6 to the youngest child. All columns regress work participation on preschool density, preschool eligibility dummy, and their interaction. All regressions include district, year, and an urban residence dummy. Odd-numbered columns are estimated using DDD method. Even-numbered columns add individual fixed effects in DDD-FE method. Standard errors clustered at the district-level is shown in parentheses; *, **, and *** indicate statistical significance at 10, 5, and 1 percent, respectively. There are 226,400 observations in all columns.

49
indicator when there is a choice, while we use disposable income for the rest; the latter is more common in Latin America and the OECD, and LIS uses incomes. We use over 1,500 household surveys spanning 150 countries. Estimates are done at 3-yearly intervals up to 2011, then annual to 2013. Estimates for survey years are interpolated to line up with these reference years (Chen and Ravallion, 2004).

We find that the percentage of the world’s population living below both bounds has fallen over time (Figure 6(a)). The trend rate of decline for the upper bound is 0.7 percentage points per year (a regression coefficient on the year of -0.688; s.e.=0.028). The corresponding trend for the lower bound is one percentage point per year (-1.055; s.e.=0.043); if this is maintained then the poverty rate for the lower bound will reach zero by 2025, although it may well be the case that it is harder to maintain the same trend rate of decline as the poverty rate gets closer to zero.<sup>50</sup> So the gap between the poverty rates for the two bounds has risen over time, with a trend rate of increase of about 0.4 percentage points per year (0.367; s.e.=0.025).

Table 3 gives the corresponding counts of the numbers of people below each line. The number living below the lower bound is falling at 47 million per year (a regression coefficient on time of -47.437; s.e.=4.472) while the trend rate of decline in the number of people living below the upper bound is 11 million per year (-10.971; s.e.=3.109) (Figure 6(b)). The number of people between the two bounds is rising at a rate of 36 million per year (36.467; s.e.=2.107). As can be seen in Figure 7, the falling global count of the poor by the lower bound has come with a similar increase in the numbers of people in the developing world who are not poor by this measure but live below the upper bound. The regression coefficient of the number of people in developing countries living below the upper bound on the number living below the lower bound is 0.213 (s.e.=0.045; n=11), which is significantly positive but also significantly less than unity (prob.<0.00005). By interpretation, slightly less than 80% of those who rise above the absolute lower bound end up living between the bounds—no longer poor by the global absolute line but still poor by standards typical of the country they live in.

**_Regional differences:_** Looking first at the lower bound, the poverty rate has fallen sharply in East Asia over this period, but there have also been recent (since around 2000) reductions at a comparable pace in South Asia and Sub-Saharan Africa (SSA). In 1990, East

> 50 For further discussion on this point see Ravallion (2013).

28
Learning from Developing Country Power Market Experiences: The Case of Philippines <!-- Start of picture text --> 100% 4,000<br>90%<br> 3,500<br>80%<br> 3,000<br>70%<br> 2,500<br>60%<br>50% 2,000<br>40%<br> 1,500<br>30%<br> 1,000<br>20%<br> 500<br>10%<br>0% ‐<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>FGC NPC PSALM AP SMC Others HHI<br>HHI<br>Market share<br><!-- End of picture text --> Figure 5‐4 Market share and Herfindahl‐Hirschman Index, in terms of registered generation capacity.

Source: (PEMC, 2017b) Some Gencos have also raised concerns on regulatory uncertainty affecting their investment appetite in the Philippine power market. Spot prices have been previously capped by the ERC after‐the‐fact (Nov‐Dec 2013) and caps have been lowered (from 62 PhP/kWh to 32 PhP/kWh). Delays and the overall shortage of available Power Supply Agreements approved by the ERC has also been cited as an obstacle for investment.<sup>20</sup> Moreover, high capital requirements and restrictions on foreign ownership have probably resulted in only a number of major players.

# 5.2 Investment and Security of Supply

Electricity demand has outpaced installed generating capacity in Philippines over the past decade (see Table 5‐3). Demand for electricity has grown at a CAGR of 4.6% from 2005 to 2017 (from 45 TWh in 2005 to 78 TWh in 2017), whilst system peak power demand has grown at a CAGR of 4.0% (from 9 GW in 2005 to 14 GW in 2017). On the other hand, generation capacity has grown at a CAGR of 3.5% (from 14 GW in 2005 to 21 GW in 2017). Given the sluggish growth of generation capacity, the system reserve margin in the Luzon and Visayas grid dipped to 17% in 2017, down from 31% in 2006 (see Figure 5‐5).<sup>21</sup>

> 20 BPI, 2015, “Financing Energy Projects”. Available online: <u>https://www.doe.gov.ph/sites/default/files/pdf/e_ipo/04_bank_of_the_philippine_islands_financing_facility_for_ene rgy_projects.pdf</u> 21 The reserve margin was calculated as dependable generation capacity divided by peak demand plus reserves. A reserve requirement of 23.4% was considered. Luzon is considered over all the horizon, and Visayas is included since 2010 due to its integration to the WESM. Mindanao is excluded over all the horizon.

36