rafmacalaba/data-use-mentions-extended huggingface dataset:
.005|0.020**|-0.005|0.018|
||(0.008)|(0.012)|(0.009)|(0.011)|(0.012)|
|0-1 quarters after|-0.000|-0.017**|-0.014**|0.022***|0.022**|
||(0.006)|(0.008)|(0.006)|(0.008)|(0.010)|
|2+ quarters after|0.001|0.004|0.003|-0.006|0.004|
||(0.003)|(0.004)|(0.003)|(0.004)|(0.004)|
|Average|0.775|0.318|0.117|0.548|0.324|

_Source_ : Author’s calculations from Cambodia SES 2007-2015.

_Note_ : The table presents the estimate of _α_ 0 _, α_ 1 _,_ and _α_ 2 from Equation (3). The sample is restricted to working age population, and those observations surveyed within 8 quarters of any of the minimum wage hikes. The sample is further restricted to women in the bottom panel. Each column in each panel is a separate regression estimate which controls for location (province _×_ rural/urban) fixed effects and allows linear time trend for each location, as well as individual characteristics (age, gender, education, marital status, household size, numbers of females, adult, and children in the household, community wealth index) and their interactions with gender, in addition to fixed effects for survey month and year. Each panel is a separate regression estimate. Standard errors, clustered by survey year _×_ psu shown in parentheses. **
alpha of their ratings is 0.81.<sup>3</sup> To create a single index, we employ a double standardization method. First, we standardize both ratings to have mean zero and standard deviation one. Second, we standardize the average of the two standardized ratings to have mean zero and standard deviation one. A higher index score means greater physical attractiveness.

# **2.2.2 Attitudes**

We construct a gender attitudes score by aggregating interviewers’ level of agreement with four statements: “ _Females are not as good as males in school_ ”; “ _Girls can make as good leaders as boys_ ”; “ _If a woman differs with her husband, she must accept his opinion_ ”; and “ _In some circumstances, it is justifiable for a man to beat his wife_ ”. Possible responses were on a scale from 1 (“ _Really agree_ ”) to 4 (“ _Really do not agree_ ”). We recode negative items such that higher values mean more gender equitable views. We standardize the score to have mean zero and standard deviation one.

We measure interviewer attitudes towards the SOS program by asking: “ _How big of a difference do you think a program, like the SOS program, which matches girls to mentors and creates girls’ groups, will make in the lives of the girls who participate in it?_ ”, with possible responses on a scale from 1 (“ _Least_ ”) to 5 (“ _Most_ ”). Among the respondents, 53% selected 5 (“Most”); 27%, 4; and 8%, 3. Given this skewed distribution, we define _mentorship‐enthusiastic_ enumerators as those who selected “Most”.

# **2.2.3 Ability**

To control for interviewer ability, we construct scores for “hard” and “soft” skills. The hard skills score adds two indicators: having prior survey experience (48% have) and a university degree (53
“other” SOEs that are neither owned by the central or local government.

Table 4: Distribution of Firm-Year Observations by Sector, 2011-2019

||Observat<br>|ions|||SOEs|||
|---|---|---|---|---|---|---|---|
|Type of sector|All firms|SOEs|% All|%<br>Directly<br>owned|%<br>Indirectly<br>owned|%<br>Centrally<br>owned|%<br>Locally<br>owned|
|Competitive|3,769,932|5,691|0.15|83.2|16.8|19.3|75.7|
|Partially Contestable|85,411|1,907|2.23|92.3|7.7|49.7|48.9|
|Natural Monopoly|20,991|2,167|10.32|94.8|5.2|12.6|86.2|
|Excluded|151,449|9|0.01|100.0|0.0|0.0|100.0|
|Total|4,027,783|9,774|0.24|87.6|12.4|23.7|72.8|

Source: World Bank staff analysis using Romania MoF firm-level data, 2011-2019. SOEs are defined as firms where national or subnational governments have ownership stake of at least 10%, while POEs are firms where national or subnational governments own 0-9.9%. The percentages for centrally owned or locally owned may not add up to 100 because there are “other” SOEs that are neither owned by the central or local government.

**Romanian SOEs are larger relative to POEs.** The median employment of SOEs is
137 -

-

the Higher Institute for Teachers Training (ENS), and the National Instutute for the Training of Technical and Vocational Teachers (IPNETP) (Table A3.3), The CAFOP/ENI figures come from the budget of the Ministry of Primary Education. Since we were unable to obtain the number of technical assistants in these institutes, we assumed the same proportion of expatriates in the teaching staff as for the entire secondary VTE system. Amortization data come from the Third Education Project. The ENS and IPNETP are independent institutes and the figures are derived from their respective budgets. Housing allowances were added to salary figures and expatriates' salaries were based on the actual number of expatriate teachers. Amortization figures are based on ONFP calculations.
Somali regions interviews were conducted during Wave 2 after an incident and close to it (Table 7 in the Appendix).

Table 1: Number of households by group and Wave for each sample alternative

||Alternative|Group|Wave 1|Wave 2|
|---|---|---|---|---|
|(1)|Moadishu|Exposed|21|113|
||g|Control|664|775|
|(2)|Mogadishu with overlapping exposed|Exposed|21|78|
||households in Wave 1 and 2|Control|664|775|
|(3)|<sup>Mogadishu with overlapping districts</sup>|Exposed|21|113|
||in Wave 1 and 2|Control|519|775|
|(4)|All urban areas|Exposed|21|135|
|||Control|2,712|3,876|
|(5)|Urban areas with exposed and control|Exposed|21|135|
||households in Wave 1 and 2|Control|664|1,468|

Source: Authors’ calculations based on data from the SHFS and ACLED.

The main sample considered in the econometric analysis corresponds to Mogadishu. The capital of Somalia is one of the most fragile cities in the world (Pape and Karamba 2019). It concentrates 16 percent of Somali households and poverty is higher in Mogadishu than in other urban areas of Somalia. A few additional samples are used to provide further robustness to the results from the econometric analysis (Table 1). We consider a few variations within Mogadishu; one restricting the group of exposed households to overlapping Wave 1 and 2 areas (Figure 6 in the Appendix), and another option restricting the sample to overlapping Wave 1 and 2 districts. Then, we consider all urban households across Somali regions. This alternative includes exposed households from Mogadishu and South West urban, as well as
34.7%|17.2%|11.1%|
|**2007**|957|399|215|**2007**|100%|41.7%|22.5%|
|**2008**||1764|525|**2008**||100%|29.8%|
|**2009**|||1484|**2009**|||100%|

_Source:_ Authors‟ calculation based on Lao PDR customs data In order to have a more precise understanding of the learning curves related to export survival, we also present in Figure **8** the year-on-year survival rates for firms, products, destinations, firmproducts, firm-destinations, destination-products and firm-product-destination units. These _Exporting from a small landlocked economy_ _15_
Lesotho may affect a specific indicator comparison. Nevertheless, since the indicators presented here are widely accepted and fairly unambiguous representations of major dimensions of sector performance, we believe that the benchmarking and comparisons are sufficiently meaningful that could prove indicative in further analytical and policy-oriented studies of Lesotho’s infrastructure performance. Another caveat is that available data indicators for Lesotho are more limited than, for example, in the case of South Africa. As a result, the comparative exercise for Lesotho is inevitably more limited in scope, focusing more on _access_ . Indicator availability for Lesotho is as follows: two indicators for electricity (access and prices), two for water and sanitation (both on access), eight for ICT, and four on roads transport. Nevertheless, since access indicators that are critical for poverty reduction are available for all four sectors, some broad comparisons and conclusions can be reached with appropriate caveats.

# **Energy – electricity**

In the electricity sector, the database contains only two indicators: one on access to the electricity network and another on the average end-user prices. The overall picture is very unfavorable (Table 1). On access, Lesotho’s performance is among the worst in the world, even compared with its own income group of countries (low-income), the Sub-Saharan African region, or SACU countries. On affordability, it also ranks very low with some of the highest prices in the world. The two indicators combined suggest that the electricity services in Lesotho serve only a small, privileged segment of the population and the economy that can afford the high service prices. The scope for improvements in electrification, access to the poor, competition, and affordability is substantial.

**_Access:_** Lesotho’s access to electricity network is one of the lowest in the word with only 6% of the population having access (the latest data for Lesotho and other groups are for 2002). This is extremely low, even in comparison to low-income (Lesotho’s income group)
# 6. Conclusions

This study has looked at a hitherto understudied aspect of health care pricing in the Latin America and Caribbean region: procurement policies. Our novel analytical focus was enabled by a unique, high-granularity item-level public procurement database covering 7 countries and 2 territories throughout the 2012-2018 period. We analyzed the impact of a host of policy-relevant predictors on unit prices for pharmaceuticals, equipment, and medical supplies, using a range of regression and machine learning methods. Overall, our models perform exceptionally well, accounting for over 80% of price variance in unseen data. These models enabled us to draw on a set of realistic policy changes to produce expected price savings in the health sector across the region. Overall, the identified small-scale interventions are predicted to lower unit prices by about 14%.

Our approach is novel, but it has a range of strengths and weaknesses which could be taken up by future research. The strength of the analytical framework rests in its rich empirical basis, government-wide administrative data of great detail; the strong links between well-established economic theories and the relationships sought; and the diversity of analytical methods, both quantitative and qualitative, leading to similar conclusions across the board. Moreover, our experiences with applying the framework in 9 LAC countries and territories suggest that the methods are replicable and largely standard across diverse contexts. Even though regulatory, data, and market differences require a great deal of care and adaptation of the methodology.

However, the analytical framework is not without its weaknesses, some of which can be addressed in future iterations or with better data. Most fundamentally, missing and incorrect data can invalidate findings which is, luckily, a relatively limited problem in transactional e-procurement systems. Nevertheless, highly technical and seemingly minor problems can pose substantial challenges. For example, lack of sufficiently standardized units of measurement or dosage information can render unit price comparisons flawed. While the analyzed products are highly standardized across the region, important quality differences may remain in the products themselves or in the delivery schedules (e.g.
e. the last month before the ban was implemented) and January 2022 to estimate districts’ income losses in that period.<sup>92</sup> Estimated income losses are displayed in Figure VIIIa. The decreases in district-level real income predicted by the counterfactual exercise are displayed in Figure VIIIb.<sup>93</sup> A visual comparison of the two figures suggests that the model performs well in terms of replicating the spatial pattern of real income losses during the post-ban period. For example, both maps show that the north and east of Sri Lanka were particularly severely affected relative to the southwest. Moreover, Figure VIIIc shows that these northern and eastern regions are also the ones most economically dependent on fertilizer, thus explaining why they were so affected by the ban.<sup>94</sup>

> 90Note that, regardless of the trade policy instrument, there are no rents to be captured in the extreme cases (such as Sri Lanka’s ban) in which imports are driven down to zero. That is why we perform the comparison exercise between the two instruments using a less restrictive (50%) reduction in the quantity of imported fertilizer.

> 91The mechanism by which welfare losses are attenuated in the QR case is related to the tax rate. In both cases, reduction in fertilizer imports leads to a reduction in fertilizer use in Sri Lanka, which scales down the fertilizer subsidy program, lowering the tax rate that is needed to fund such program. However, the tax rate drops by a greater magnitude in the QR case (0.77 percentage points) than in the iceberg cost case (0.4 percentage points).

> 92For details on our night lights data, see Appendix A.I.

> 93More precisely, Figure VIIIb displays the average EV across agent types (weighted by types’ population shares) in each district, as predicted by our model-generated counterfactual exercise. For details, see Appendix F.

> 94To measure the economic importance of fertilizer for
Yang, Y., You, Z., & Xiao, C. (2021). Which gridded population data product is better? Evidences from mainland southeast Asia (MSEA). _ISPRS International Journal of GeoInformation_ , 10(10), 681. https://doi.org/10.3390/ijgi10100681

# – APPENDIX A1 CHECKING THE ASSUMPTION OF JOINT DISTRIBUTION

As we mention above, the main data source for poverty and education comes from the same surveys in the GMD, and the social protection data comes from the ASPIRE, which is also based on household surveys collected in various years. For a small set of 42 countries, we can identify the same household surveys used for the ASPIRE indicator and for poverty and education indicators. For these 42 countries, we construct the joint distribution of poverty and social protection at the household level and aggregate this joint estimate to the subnational level. The subnational joint estimates are compared with the above estimates resulting from the random assignment at the household level due to lack of having indicators in the same survey data.

Figure A1.1 shows the scatter plot at the subnational level of no access to social protection from survey estimates and ones from simulation. On average, the graph shows the correlation of 80%. Simple average from both sources is similar for 557 subnational areas in 42 countries: with 60% from the simulation and 56.4% from the surveys.

Figure A1.2 shows the scatter plot of joint distribution of poverty and no access to social protection. On average, both survey and simulated estimates show similar numbers, with 25% from the simulation and 21.3% from the surveys.

40
Policy Research Working Paper 7848

# **Abstract**

This paper studies how migration from a poor to a rich country affects key economic beliefs, preference parameters, and transnational household decision-making efficiency. The setting is the migration of Tongans to New Zealand through a migration lottery program. In a 10-year followup survey of individuals applying for this program, the study elicited risk and time preferences and pro-market beliefs. It also linked migrants and potential migrants to a partner household consisting of family members who would stay behind if the migrants moved. Survey participants played lab-in-the-field games designed to measure the degree of intra-family trust and the efficiency of intra-family decision-making. Migration provides a large and permanent positive shock to income, a large change in economic institutions, and a reduction in interactions with partner household members. Despite these changes, the study finds no significant impacts of migration on risk and time preferences, pro-market beliefs, or the decision-making efficiency of transnational households. This stability in the face of such a large and life-changing event lends credence to economic models of migration that treat these determinants of decision-making as time-invariant, and contrasts with recent evidence on preference changes after negative shocks.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at dmckenzie@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
A.1 shows a schematic of the Analytica model developed for the purpose of calculating population risk. The model developed for calculating economic risk uses a similar structure. First, data related to hazard (inundation depth from SWMM), exposure (GIS data and population and poverty data), and vulnerability (depth-damage curves) are input into the model (orange trapezoids on the left). From this data, we calculate the percent of the poor and non-poor affected in each subcatchment, the number of poor and non-poor in each sub-catchment, and, in combination, the risk to the poor and non-poor in each event (blue rectangles). As described next, risk is calculated for extreme rainfall events with 2, 5, and 10-year return periods and using different infrastructure operation rules. We assume infrastructure operators will apply the operating rules that best reduces risk in each event
Figure 3a: China’s imports of metals and minerals, 1996–2013 <!-- Start of picture text --> 14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>1996 1998 2000 2002 2004 2006 2008 2010 2012 2014<br>Year<br>Iron Ore Manganese Chromium Copper<br>Source: Authors' construction using data from Chinese National Bureau of Statistics<br>Figure 3b: China’s imports of metals and minerals, 1996–2013<br>4<br>3.5<br>3<br>2.5<br>2<br>1.5<br>1<br>.5<br>1996 1998 2000 2002 2004 2006 2008 2010 2012 2014<br>Year<br>Aluminium Steel Petroleum Crude Oil<br>Source: Authors' construction using data from Chinese National Bureau of Statistics<br>10000 metric tonnes<br>in constant 2000 levels<br>10000 metric tonnes<br>in constant 2000 levels<br><!-- End of picture text --> 27
**Table 2: Decomposition of changes in activity export shares, 2000-2018.**

||Adv<br>|anced econom<br>|ies<br>|Deve<br>|loping econo<br>|mies<br>|
|---|---|---|---|---|---|---|
|Occupational group|Within<br>industry|Between<br>industry|Total|Within<br>industry|Between<br>industry|Total|
|Managerial|104.4|-4.4|100|138.2|-38.2|100|
|Support services|84.0|16.0|100|47.3|52.7|100|
|Production|57.0|43.0|100|22.0|78.0|100|
|Other occupations|41.8|58.2|100|-10.3|110.3|100|
|Engineering|32.6|67.4|100|39.4|60.6|100|

_Notes_ : The change in the share of a broad occupation group in export incomes is decomposed into between-industry and within-industry effects according to equation (2) for five broad aggregations of occupational groupings. Results are standardized by the total change within each broad aggregation and ordered by the within-industry component for advanced economies. Simple country averages are given for advanced and developing economies (listed in Appendix Table C1).

# **4. Development patterns of activity specializations**

We make use of the full detail in our data set to probe particular patterns in the development of new export activities. To this end we make use of the concept of ‘proximity’ introduced by Hidalgo et al. (2007) and adapt this from a product to an activity perspective.

# _4.1 Measuring activity specialization_

14
--|---|
|Poverty headcount|0.167|0.125|0.428|0.450|
|Mean poverty gap|0.042|0.027|0.143|0.130|
|Meansquared poverty gap|0.016|0.009|0.064|0.052|

Source: « Pauvreté et Structure Familiale » (PSF) and « Enquête de Suivi de la Pauvreté au Sénégal » (ESPS) surveys, authors' calculations based on 13988 and 123486 individual observations for PSF and ESPS surveys respectively Note : Poverty measures obtained with the two surveys.

> 6 We repeated the same exercise on the ESPS sample. The resulting basic needs thresholds are at bit lower than those of PSF (507, 592 and 780 CFA per adult equivalent and per day for rural areas, other towns and Dakar respectively).

14
%) level. The dependent variable in column 1 is a dummy variable (DV) equal to 1 if the respondent’s household ever receives any food transfers from other households The dependent variable in Panel A, column 1 is a dummy variable (DV) equal to 1 if the respondent’s household ever receives any food transfers; in column 2 it is the number of households (capped at 3) that the respondent’s household receives food transfers from; in column 3 it is a DV equal to 1 if the respondent’s household ever gives food transfers to other households; in column 4 it is the number of households (capped at 3) that the respondent’s households gives food transfers to. All dependent variables in Panel B (C) are identical to those in Panel A, except they refer to within (outside) village food transfers.

13
Policy Research Working Paper 6359

# **Abstract**

This paper analyzes the reliability and consistency of subjective well-being measures. Using the Life in Transition Survey, which was administered in 34 countries of Europe and Central Asia in 2006 and 2010, the paper evaluates subjective well-being measures (satisfaction with life and subjective relative income position) against objective measures of welfare based on consumption and assets. It uses the different formulations of life satisfaction in the survey to test robustness to alternative framing and scaling. It also explores within-household differences in subjective wellbeing assessments. The analysis finds that subjective relative income is weakly correlated with household relative welfare position as measured by consumption or assets. Life satisfaction, by contrast, is highly correlated with objective and subjective measures of household welfare. It generally reflects cross-country differences in average consumption, assets, or per capita gross domestic product, although Central Asian countries report much higher life satisfaction levels than their incomes would suggest. Two alternative measures of life satisfaction are highly correlated and the correspondence between verbal and numeric scales is strong within a country or groupings of similar countries. Within households, subjective assessments of relative income are roughly consistent but measurement error is correlated with individual characteristics (gender and age of respondents), which could cause systematic biases in the analysis.

This paper is a product of the Poverty Reduction and Economic Management Unit, Europe and Central Asia Region. 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://econ.worldbank. org. The authors may be contacted at acojocaru@worldbank.org or mfdiagne@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