rafmacalaba/data-use-mentions-extended huggingface dataset:
|**Principle**|**Indicators**|**How Measured**|
|---|---|---|
|Harmonization|**Ind 10a:**Joint missions to the field|Number of joint missions/total number of missions<br>(%)|
|Harmonization|**Ind 10b:**Joint country analytic work|Number of joint analytic work /total number of<br>analytic work(%)|

_Source: World Bank (2011, p.4), see Appendix 1_ While the results of the PD survey provide useful data sources to track progress against the commitments in the Paris Declaration, there are some limitations (World Bank, 2011b). The limitations include (i) the fact that the PD survey tracks only selected PD commitments and does not cover many commitments identified as important for aid effectiveness by the subsequent Accra Agenda for Action (AAA) in 2008; and (ii) methodological concerns regarding some indicators. For example, indicator 3 on ̳aid flows aligned to national priorities‘ is actually more of a measure of aid on budget then alignment per se. Moreover, not only does this not measure what was originally intended, but it even oversimplifies the concept on aid on budget. Therefore, while the advantages of the results of the PD survey still hold, the results of the analysis using the results of the PD survey – including the ones in this paper - need to be carefully interpreted.

The results of the 2011 survey are available for 77 partner countries and 33 selected development partners<sup>9</sup> . Table 2 and 3 summarize the profiles of partner countries and development partners.

**Table 2: Profile** **<u>of Partner Countries which Participated in the 2011 PD survey</u>**

||T|otal|o/w IDA<br>(incl.|eligible<br>blend)
production methods, which entail the use of more nonspecialized inputs and a greater reliance on variable factors of production. Mills and Schumann (1985) also argue that firm size and flexibility are inversely related within industries. According to these authors, small firms tend to have fewer decision-makers and a less complicated bureaucracy than large firms. These organizational characteristics mean that small firms can respond more quickly to changes in market conditions. Fiegenbaum and Karnani (1991) also highlight these and other organizational factors. Using data from Compustat on 3,000 companies from 83 industries, the authors find that smaller firms have greater output flexibility than large firms and that this greater output flexibility improves the performance of smaller firms. Interestingly, the authors predict that there is no significant relationship between output flexibility and the performance of the average firm, a positive relationship between the two for small firms, and a negative one for large firms. We find similar results below for the impact of market volatility on hotel efficiency. Zimmermann (1995) also develops a theoretical model and provides empirical evidence from German manufacturing industries that shows that smaller firms are more flexible. Hirsch et al. (2020) consider 2,186 firms in the EU dairy processing industry. They also find that larger firms have lower long-run average cost curves or static efficiency, while smaller firms are more flexible. Renner et al. (2014) use data on Polish agricultural farms to estimate the relationship between farm size and flexibility in production. As in the above studies, flexibility is captured by the slope of the average cost curve. This study also confirms a negative relationship between flexibility and farm size. For 58 manufacturing industries in Malaysia, Nor et al. (2007) find that variations in sales are larger for the relatively smaller firms. The authors argue that this is consistent with smaller firms responding better to unexpected demand fluctuations.

7
is to investigate the trade impact associated with a sample of 746 antidumping cases pooled across fourteen policy-imposing G20 member economies. We take information from the _Temporary Trade Barriers Database_ regarding when the import restriction was imposed, when it was removed, and the product codes associated with the TTB, and we match this to annual HS-06 trade data available from UN Comtrade. We require sufficient time coverage in the trade data so as to trace out ten

> 9 We also clean the data set of instances in which a policy-imposing country may have removed an antidumping duty over a particular product _h_ but left in place another TTB over the same product against the same trading partner.

11
.71|1.76|2.00|3.21|0.29|0.60|1.39|2.51|2.78|
|households,million VND)|(0.03)|(0.07)|(0.14)|(0.18)|(0.31)|(0.03)|(0.05)|(0.12)|(0.19)|(0.25)|

Note: The sample is limited to households residing in districts with a poverty rate exceeding 40% in 2006. Within this sample, households living in the 30A districts are referred to as the treatment group, while households residing in districts without the 30A Programs but having the 2006 poverty rate above 40% are considered the control group.

There are no data on loans from formal sources in VHLSSs 2010 and 2012 Standard errors of the means in parentheses.

52
HFPSs have been conducted by the World Bank, in collaboration with national statistical offices, in over 80 countries around the world and cover over 100 harmonized indicators. As such, they are designed to be nationally representative by using various weighting measures. Relative to the traditional in-personbased surveys, however, phone surveys (or other remote surveys such as internet surveys) have several limitations. For instance, the phone survey sample is mostly representative of the phone-owning population, which could mean that the surveys potentially underrepresent the poorest populations within a country. In addition, these surveys have been found to collect more information from respondents who are more likely to be heads of households, overstating such things as employment rates (Kugler et al. 2021). While the phone surveys are not as comprehensive as the traditional household surveys, they remain important modules for data collection when traditional surveys cannot be conducted – as was mostly the case for 2020.

For poverty calculations, the phone surveys report whether (or not) a household has lost income or consumption because of the pandemic. However, the phone surveys by themselves cannot be used to quantify the welfare changes of households for at least two reasons: (i) no baseline household welfare measure is available in the phone surveys, and (ii) we do not know the size of the welfare changes in the phone surveys, only which households lost or did not lose income or consumption.<sup>8</sup> To quantify the impact of COVID-19 on poverty, we need to map the income or consumption changes from the phone surveys to a traditional household survey for that country.

Mahler et al. (2022) propose a methodology that can be used to map the income or consumption losses in phone surveys to an underlying welfare distribution (see also Narayan et al., 2022). First, they calculate the probabilities for each type of household defined by the household size, education and age of household head, and area of residence from phone surveys to estimate the income loss
1|0.0000|0.0000|297,352|506,961|
||2|0.0001|0.0003|476,101|811,713|
||3|0.0008|0.0022|684,948|1,167,779|
||4|0.0006|0.0019|970,770|1,655,084|
||5|0.0000|0.0021|1,806,516|3,079,962|
||**Total **|**0.0003**|**0.0014**|**4,235,687 **|**7,221,499**|
|**e=-0.6**|1|0.0000|0.0017|107,242|38,997|
||2|0.0003|0.0028|171,709|62,439|
||3|0.0022|0.0054|247,030|89,829|
||4|0.0019|0.0058|350,114|127,314|
||5|0.0021|0.0031|651,530|236,920|
||**Total **|**0.0014**|**0.0038**|**1,527,625**|**555,500**|

Source: Verme (2011). (*) Quintiles of electricity consumption.

15
# **Summary Findings**

Since the adoption of the _doi moi_ or renovation policy in 1986, Vietnam has been undergoing a transition from central planning to a socialist market oriented economy. This has translated into strong economic growth, led by the industrial sector which has expanded at more than 13% per annum over the period 1993-97. There is a concern, however, among policymakers in Vietnam that employment growth has lagged behind.

This paper examines this concern using new employment data from the _Vietnam Living Standard Survey_ (VLSS 2) which was completed in 1997-98. Data from this survey can be compared with information from the first household survey undertaken in Vietnam in 199293.

Part I of the paper shows that over the period 1993-97 industrial employment has grown at an average of about 4% per year, which is low compared to industrial GDP growth. This is mainly due to the capital-intensive and import-substituting nature of the State sector and of foreign investment, which dominate industry. The labor-intensive and export-oriented domestic private sector, although growing quickly, is still small.

In the future, however, growth promises to be more labor-intensive than in the past. Prior to the Asian crisis there were signs of an emerging export-oriented sector. Using previous statistical analysis by Wood and Mayer (1998) as well as factor content calculations, part II of the paper estimates that given Vietnam's endowment of natural and human resources there is scope for Vietnam to triple its manufacturing exports and create around 1.6 million manufacturing jobs in export sectors in the near future.

Part III of the paper looks at Vietnam's labor regulations. It concludes that there is no need for fundamental labor market reforms. At current levels, minimum wages and non-wage regulations (even if better enforced) are unlikely to inhibit the development of the private sector or hurt export competitiveness. A restrictive interpretation of the Labor Code's provisions on employment termination may nonetheless hurt foreign investment, reduce the speed of reforms in the State sector, and slow the reallocation of resources
Haiphong, Vinh City, Hue, Danang, Bienhoa, Can Tho, Hatay, Nha Trang and Vung Tau; and $35 in other places.

# **BENEFITS OF SCHOOLING**

Returns to investments in schooling are estimated here using the 1992-93 Vietnam Living Standards Survey (VLSS). The VLSS was carried out jointly by Vietnam's State Planning Comrnmittee (now the Ministry of Planning and Investment) and the General Statistics Office, from October 1992 to October 1993, with funding from the United Nations Development Programme and the Swedish International Development Authority. It was the first nationally representative, multipurpose household survey ever done in Vietnam covering a broad range of social and economic indicators. Information was collected from about 23,000 people living in 4,800 households representing urban and rural areas in each of Vietnam's seven geographic regions (for more details, see Vietnam 1994). However, since many of the labor market reforms did not come into effect until later (for example, salary reform did not take place until 1993), this analysis uses data covering a period relatively early in the reform process.

Returns to schooling are estimated here using a variety of methods. This is necessary given the limited sample size of the wage earning population in Vietnam. One method used is the human capital earnings function (Mincer 1974): where Si is the number of years of schooling of individual i, and _ETP,_ and _EKPj_ are years of experience and its square. Hours worked per week is added as a compensatory factor. In this _5_
those surveyed. Through a longitudinal household survey, Gray and Mueller (2012a) studied the consequences of drought on population mobility in Ethiopia’s rural highlands, providing evidence that drought increases long-distance and work-related relocation of men, especially in land-poor households. However, severe drought reduces women’s short-distance and predominantly marriage-related mobility.

Another study by Karanja Ng’ang’a et al. (2016) shows that climate change influences the livelihoods of shepherds in arid and semi-arid lands in Kenya. By analyzing data from a survey of 500 rural households in northern Kenya that relates adaptive family behavior to family migration, their analysis suggests that migration and local innovation are complementary mechanisms to ensure resilience to adverse shocks. In addition, families with at least one migrant member can employ high-cost agricultural innovations through remittances, thus improving their self-protection against weather shocks. Mueller, Gray and Hopping (2020) use census data on the migrations of 4 million individuals over 22 years to estimate the climate effects on migration in Botswana, Kenya, and Zambia. Their results for Kenya show that temperature had limited effects on migration, whereas a one standard deviation increase in precipitation caused a 10% reduction in migration. In Botswana, mobility decreases by 19% with a one standard deviation increase in temperature, and an equivalent change in rainfall causes an 11% decrease in migration. The effects of temperature appear more severe among poorly educated individuals. Rainfall shocks increase mobility in Zambia, while an increase in temperature does not affect mobility in the region. Decreases in inactivity and unemployment coincide with increases in migration, which suggest that the perspective of new job opportunities may act as a driver of climate-induced migration.

Mastrorillo et al. (2016) also argue that agriculture may function as a primary channel through which adverse weather conditions influence migration. They combine South African census data with climate data on spatiotemporal weather variability to examine South African bilateral inter-district migration flow patterns and determinants during the periods 1997-2001 and 2007-2011. The results reveal that precipitation scarcity and higher temperatures act as push
sub-sample|0.38|0.53|0.85|0.80|0.55|0.55|0.53|0.54|0.43|
|_<br>Rsquared in testing sub-sample|0.40|0.54|0.86|0.77|0.56|0.55|0.54|0.55|0.44|
|_<br>Rsquared|0.40|0.54|0.85|0.79|0.56|0.55|0.54|0.55|0.44|
|_<br>MSE in training sub-sample|0.33|0.25|0.08|0.11|0.24|0.24|0.25|0.24|0.31|
|<br>MSE in testing sub-sample|0.33|0.25|0.08|0.12|0.24|0.24|0.25|0.25|0.31|
|MSE|0.33|0.25|0.08|0.11|0.24|0.24|0.25|0.24|0.30|
|N|4644|4644|4644|4644|4644|4644|4644|4644|4644|

Note: Elastic net linear model is used. The model is trained using Tanzania 2020/21 round. The training data is split into two subsets: 50% used for training (training sub-sample) and 50% used for validation (testing sub-sample).

66
Learning from Developing Country Power Market Experiences: The Case of Philippines

- Regularly collect and process market monitoring data and indices; and

- Prepare periodic assessment reports on overall market performance and competitiveness.

The MAG issues monthly and annual reports assessing the market. The market assessment reports comprise figures and analyses of outages, spot prices and traded quantities, concentration and structural indices (such as HHI, Price‐Setting Frequency, Pivotal Supply and Residual Supply Index), among others. However, the reports fall short of analyzing bidding behavior (beyond identifying generation offer patterns) and assessing market outcomes, particularly whether or not these outcomes are deemed to be competitive.<sup>11</sup> The Market Surveillance Committee does analyze events of unusual spot prices detected by the MAG, but again their annual reports fall short of more in‐depth and comprehensive analyses of market outcomes.

# 4.1.3 Oversight, market intervention and dispute resolution

## _4.1.3.1 Rule‐based market intervention_

There have been interventions of the market during natural calamities or international / national security emergencies, based on explicit provisions laid out in market rules. There are no publicly known instances of distortion of the system or market operation by political pressures or other criteria unrelated to reliability standards and economic efficiency. Market rules establish the conditions for market intervention and suspension, during which market‐based prices are replaced by an administered price cap set by the Market Operator, to be used as basis for settlements.

## _4.1.3.2 Regulatory oversight_

The regulator (Energy Regulatory Commission, ERC) is responsible for the oversight of the Philippine wholesale market. The ERC has the primary jurisdiction to impose the rules and regulations of the electricity spot market and to investigate and act against any participant or player in the electricity industry for violations of any law, rule or regulation governing the same, including the rules on cross‐ ownership, anti‐competitive behavior, abuse of market positions, must‐offer rule of
(i) physical violence,<sup>11</sup> (ii) sexual violence,<sup>12</sup> (iii) emotional violence<sup>13</sup> and (iv) economic violence.<sup>14</sup> The second classification is based on survivor’s relationship with the perpetrator, namely (i) intimate partner violence (IPV) or violence perpetrated by husband/partner, (ii) non-partner domestic violence (NPDV) or violence perpetrated by other household members,<sup>15</sup> and (iii) non-domestic violence (NDV) or violence perpetrated by non-household members.<sup>16</sup> For IPV, we collect data on lifetime incidence and frequency of exposure in the last 12 months preceding the survey.<sup>17</sup> For NPDV and NDV, we only administer questions about incidence in the last 12 months.

Questions on physical, emotional, and sexual violence were developed closely following the Demographic and Health Survey (DHS) instruments. Considering that economic violence is less frequently measured by secondary surveys such as DHS, we follow guidelines provided under the Anti-Violence Against Women and Their Children Act of 2004, issued by the Government of the Philippines, to develop questions on economic abuse. Notably, the resulting survey questions were similar to scales used in past studies; for instance, the checklist of controlling behaviors (CCB) - developed and tested by Lehmann et al. (2012). The full list of violent actions included in our survey instrument has been provided in Table A9.

# 4.2 Intermediary Outcomes

Alongside, we record data on a few intermediary outcomes to examine changes in channels or mechanisms through which cash transfers could impact exposure to GBV. We estimate the impact of CCTs on four indices – one corresponding to each mechanism. These indices are constructed using average z- scores based on Katz, Kling and Liebman (2007). Please refer to Table 2
to increase by approximately 1.08 percent, or around $US1.12 trillion (based on the estimated global GDP of $US103.86 trillion in 2022). Although this estimated figure only amounts to 2 percent of the investment cost of $US50 trillion, our analysis does not consider the positive externalities of poverty elimination on other welfare outcomes, such as improved health and subjective well-being, or the beneficial impacts of poverty reduction on growth in the longer term (Thorbecke and Ouyang, 2022).

To further our investigation, we estimate the allocation of costs for each country according to their respective contribution to warmer temperature. To provide a back-of-the-envelope estimate of this allocation, we use the share of CO2 emissions across countries from 1975 to 2022 (Friedlingstein et al., 2022). Table A14 (Appendix A) indicates a wide range of contribution shares for countries: while low-income countries should contribute less than 1 percent of the total costs, this figure increases to 10 percent, 29.7 percent, and 59.5 percent for lower-middle income, upper-middle income, and high-income countries, respectively. We plot in Figure A11 (Appendix A) more detailed estimates for each country.

# **6. Conclusions**

While there is growing evidence of harmful effects of climate change on macro-economic outcomes, little evidence exists regarding the impacts of warmer temperature on poverty and inequality on a global scale. A notable challenge is the absence of data at a disaggregated level that can allow for more accurate analysis of this relationship both within and across countries. Analyzing a new global panel dataset representative of subnational areas in 134 countries that we constructed, we find both hotter and colder temperature to result in higher poverty rate and inequality. We find stronger effects at the subnational level, which implies that country-level analysis does not reveal the true estimate of the global warming consequences. We also find 20
our sample. However, Gini coefficients from the top-income adjusted data, suggest that there is an upwards sloping segment and firmly re-establish a Kuznets-like relationship between economic development and observed inequality, as seen in Figure 7. The clearly inverted U-shaped curve results from the lowess regression of the adjusted Ginis. A quadric form regression of the Gini estimates from the adjustment on GDP per capita explains more than twice as much variation as the regression of the unadjusted measures.

> 34 We measure global interpersonal inequality, capturing inequality of individual incomes (or consumption), referred to as ‘concept 3’ inequality by Milanovic (2005). We estimate a global Gini coefficient giving each individual equal weight, regardless of where they live, using the same weights as used for estimating global poverty. The global distribution is generated using the reference year distributions for global poverty measurement in PovcalNet, using the method described in Section 2.

> 35 Lakner and Milanovic (2016) conduct most of their analysis using 2005 PPPs, but provide estimates based on 2011 PPPs as well.

26
Estimating Poverty in a Fragile Context – The High Frequency Survey in South Sudan

**Utz Pape and Luca Parisotto**<sup>1</sup>

Keywords: Consumption Measurement, Poverty, Questionnaire Design JEL: C83, D63, I32

> 1 Authors in alphabetically order. Corresponding author: Utz Pape <u>(upape@worldbank.org). The findings, interpretations and</u> conclusions expressed in this paper are entirely those of the authors, and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments of the countries they represent. The authors would like to thank Kristen Himelein, Syedah Iqbal and Ambika Sharma for discussions. In addition, the authors thank Véronique Lefebvre, Sarchil Qadar, Amy Nineman and Tom Bird from Flowminder and WorldPop for modelling and imputing poverty from spatial data, in collaboration with the authors.