rafmacalaba/data-use-mentions-extended huggingface dataset:
than agriculture. Finally, Panel (d) suggests, consistent with Figures 1 and 2 that wage income losses were severe, especially in the LAC region, followed by some SSA countries. To understand the information content and internal validity of our different measures of labor market disruption, in Figure 7 we plot the relationship between four measures of income loss (total, farming, non-farming, and wage income) and stopping work. It appears that there is a broad positive relationship between each of the income measures and stopping work, although in general a higher share of respondents report household income loss than work stoppage. Furthermore, the relationship between work stoppage and wage income is much stronger than work stoppage and total income. This is consistent with income loss applying to all members of the household, and also with the finding that beyond stopping work, workers experience other disruptions, including reduced or no pay for work performed and changing jobs. Overall, however, the results suggest internal consistency between the prevalence of declines in household wage income and work stoppage, despite the latter being measured using a non-representative sample.

# **4.4 Relationship with Macroeconomic Projections and ILO Employment Data**

The labor market measure “stopped working” from HFPS differs in many cases from macroeconomic projections. Figure 8 shows the relationship between the HFPS-based measure of the loss of work and the change in the International Monetary Fund’s (IMF) World Economic Outlook (WEO) GDP projection for each country. The change in the WEO projection is the difference between the October 2019 and the October 2020 projections of 2020 GDP growth. Panel (a) suggests that the relationship goes in the expected direction in the LAC region: countries where the WEO projection has worsened more show higher shares of stopping work, with some notable outliers. At the same time, Panel (b) suggests that there is no relationship between the WEO’s projection change for countries in the SSA region. An important caveat when comparing the stopping work measures in the LAC
in their households than women in the role model analysis sample. For example, 12% of women in the promotion analysis sample consider themselves the household head, compared with only 1% in the role model analysis sample. In addition, the former group has on average about two years of education more than the latter group. The households in the promotion analysis sample also report slightly higher household income, of about 23,500 Taka (roughly 280 USD at the time of the surveys), compared to about 21,000 Taka (roughly 260 USD) in the role model analysis sample.

These differences underline that the sample of selected and nominated women is certainly not representative of all women in the garment industry. I however posit that comparing selected

> 31All are defined in detail in Table C.4 in the Online Appendix.

> 32See the summary statistics in Table C.5 in the Online Appendix.

> 33Note that this is derived from the non-exposed operators in the role model analysis sample, since I don’t have comparable data from the promotion analysis factory baseline.

> 34Note that we only collected individual income data for the wife and husband in the promotion analysis baseline, but total household income during the household surveys. For the promotion analysis baseline, I therefore approximate total household income as the sum of income of both husband and wife. This will only differ from the total household income variable used in Panel B if household members other than the husband and wife also contribute income.

14
members, inquiring about their personal ownership of and rights to assets in the aforementioned asset classes - corresponding to Treatment 5 (“T5”) of MEXA and leveraging the contextualized and improved versions of the MEXA T5 questionnaire instrument.<sup>10</sup> Appendix I includes the protocol for administering the IHPS individual questionnaire. The individual interviews were capped at four per household and it was ensured that the head of household and his/her spouse (if one exists) were among the interviewed individuals.<sup>11</sup> Within-household

> 9 The data, questionnaires and basic information document for the IHS4 2016/17 can be accessed here: <u>https://microdata.worldbank.org/index.php/catalog/2936. The data, questionnaires and basic information document</u> for the IHPS 2016 can be accessed here: <u>https://microdata.worldbank.org/index.php/catalog/2939. Both the IHS4</u> 2016/17 and the IHPS 2016 were implemented with technical and financial assistance from the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA), using the World Bank Surveys Solutions Computer-Assisted Personal Interviewing (CAPI) platform. The implementation of the individual interviews as part of the IHPS 2016 was made possible by technical and financial assistance from the World Bank LSMS Plus (LSMS+) initiative, which aims to improve in IDA countries the availability and quality of individualdisaggregated survey data on asset ownership, work and employment and entrepreneurship. For more information on - the LSMS+, please visit: http://surveys.worldbank.org/lsms/programs/lsms <u>plus.</u>

> 10 The IHPS additionally collected detailed information on individuals’ ownership of mobile phones.

> 11 This was an upper limit that only applied to 1 percent of the sampled household population that had more than four adults. If a sampled household had more than four
The data contain nutrition and health information for children under six, health and service use information for adults, and health spending for households. Children’s nutrition measures, such as underweight and stunting, are constructed using objective measures of weight and height. Health measures include self-reported disability and chronic diseases, illness, and accidents. Health care service information includes each child’s immunization, assisted child delivery, and regular health checkups with health professionals.<sup>14</sup>

# <u>Health and Health Care in Yemen</u>

The most serious health problem faced by Yemenis is women and children’s health, where the achievement of Millennium Development Goals (MDGs) proves challenging. Child malnutrition is strikingly prevalent in Yemen where more than half of children under five suffer from malnutrition.<sup>15</sup> This is among the world’s highest child malnutrition rate along with Iraq and Sudan. Progress towards reducing the child malnutrition rate by half has been slow. The maternal mortality rate, 365 deaths per 100,000 lives, is the highest among the Middle East and North Africa (MENA) region countries.<sup>16</sup> While health indicators in Yemen pose a serious problem, access to health services are low, and services for major treatments are prohibitively expensive. The government, Ministry of Public Health and Population (MOPHP), is the main health service provider in Yemen. They operate a threetier network of primary, secondary, and tertiary level facilities through governorate and district health offices. One of the major challenges of the Yemeni health system is low coverage among the population. Furthermore, large discrepancies exist in levels of coverage between urban and rural areas (Fairbank, 2009). Due to the limited delivery of health services, particularly in rural areas where

> 14 World Bank (2007) noted that the questionnaire of HBS 2005 did not clearly distinguish if the question meant to ask about delivery by a doctor, midwife or a medically
: World Bank GDF and WDI central database Sep 2009<br>Billions (constant 2000 USD)<br><!-- End of picture text --> In 2007, China’s incremental growth in real GDP actually exceeded its entire real GDP in 1979. In 2010, China outpaced Japan and become the world’s second-largest economy. China has indisputably become an important growth engine of the global economy and a leader in international trade and investment. Rapid growth in the past decades has helped lift more than 400 million people out of poverty. These results are truly impressive.

While China’s rapid rise has become a hot topic for development debate among policy makers, business people, and scholars all over the world, the 2
index (see Tables 5 and 6). This index is a calculation of the minimum proportion of females who would have to change occupations (industries) in order to make the distributions of males and females equal. Thus, higher values of the dissimilarity index suggest higher degrees of segregation. According to this index, Latvia is not very different from Estonia. As far as occupations are concerned, it is not very different from Poland or Hungary. Latvia has somewhat higher occupational segregation than Lithuania or the Czech Republic.

**Table 5.  Occupational and Sectoral Segregation of Full-Time Employees by Gender and Ethnicity in the Baltic Countries, 2000: Dissimilarity Index**<sup>**a**</sup>

|Segregation<br>|Estonia|Latvia|Lithuania|
|---|---|---|---|
|Genders by occupations<sup>b</sup>|0.41|0.38|0.35|
|Genders by economic activities<sup>c</sup><br>|0.31|0.31|0.29|
|Ethnic groups by occupations<sup>b</sup>|0.15|0.11|0.29|
|Ethnic groups by economic activities<sup>c</sup>|0.26|0.21|0.14|

_Notes:_ Dissimilarity index is a number between 0 and 1, with 0 indicating equal distribution of genders (or ethnic groups) among occupations, and 1 indicating complete segregation. It shows the minimal proportion of females which have to change occupations in order to make occupational distributions of females and males equal.<sup>b</sup> Nine major groups of occupations according to ISCO 88.<sup>c</sup> 15 sectors (A, B,...,O) according to NACE. _Sources:_ Calculation based on LFS data.

Can
officials selecting money as the main barrier to improving learning and officials agreeing with the statement that more money would not make an impact._ <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Did not select money as the main Selected money as the main barrier to<br>barrier improving learning<br>Strongly agrees Agrees Disagrees Strongly disagrees Does not know<br>impact learning<br>Share of officials agreeing with "More money will not<br><!-- End of picture text --> Source: CGD–World Bank survey 2022.

22
15% in the CIS-7 on average against an average of 6% for the other CIS countries and the non CIS countries alike (WDI).

_Banking efficiency_ . The BEEPS asked enterprises about the average number of days necessary to make a bank transfer locally. Bank transfers within countries are usually made within three working days in the CIS-7. This is a reasonable time and also a relatively better performance than in other CIS countries and also relative to non CIS countries. Armenia and Moldova are two countries where a larger share of firms declared that bank transfers take more than three days while Uzbekistan seems the country within the CIS-7 where bank transfers are within the three days limit according to almost all enterprises surveyed (94%).

_Tax level and regulations_ : When asked about what is the major obstacle to growth, enterprises in the BEEPS put high taxes and tax regulations and administration at the top of the list in virtually all transitional economies. In this respect, the CIS-7 are not different from other groups of transitional economies. Instead, tax rates are generally lower in the CIS-7 than elsewhere for both the corporate and personal income tax rates. Data on social security contributions are scattered but the CIS-7 pay evidently less than other transitional economies also in this respect while the cumulated collection of other forms of taxes seems to be higher among the CIS-7 than elsewhere in transition (WDI). Thus, the CIS-7 probably rely more on indirect taxation than on direct taxation, a typical feature of developing and largely informal economies.

# **Institutional barriers**

_Governance_ . CIS-7 firms tend to be relatively unhappy about government performance with few exceptions. The perception of governance is rather diverse across transitional economies and also among the CIS-7. Generally, the majority of firms rank the government performance as inefficient and CIS-7 countries firms tend to be more pessimistic with the exception of firms in Uzbekistan where the large majority think that the government is efficient. Firms in Azerbaijan are also relatively optimistic while in Armenia, Georgia and Moldova firms are mostly very negative about the
0.9737|0.1736|0.0535|2.7704|
|Percentage of Good Soils|4266|0.7904|0.3071|0.0000|1.0002|
|Rainfall - First Principal Component|4266|0.0000|2.2685|-4.2315|6.9861|
|Rainfall - Third Principal Component|4,266|0.0000|1.5749|-2.6324|9.2440|
|ln Wage in 1991|4267|8.0265|0.4963|6.6139|9.8121|
|Proportion of Natives in 1991|4267|0.6565|0.1790|0.0855|1.0134|
|Homicides per Capita in 1991|4265|0.0016|0.0011|0.0000|0.0090|
|Employment in Farming Activities in<br>1991|4,267|0.1777|0.0854|0.0000|0.5437|
|Bank Dummy (1991)|4,267|0.7492|0.4335|0.0000|1.0000|
||4267|9.5287|1.0888|6.6214|16.0821|
|Average Temperature|4266|22.3050|2.8451|13.8325|27.8109|
|Fertility Rate in 1991|4,267|3.6449|1.1885|1.7600|8.6800|
WPS3745

# **Earnings Mobility and Measurement Error:** ∗ **A Pseudo-Panel Approach**

Francisca Antman Department of Economics, Stanford University

## David J. Mckenzie†

Development Research Group, World Bank

### **Abstract**

The degree of mobility in incomes is often seen as an important measure of the equality of opportunity in a society and of the flexibility and freedom of its labor market. However, estimation of mobility using panel data is biased by the presence of measurement error and non-random attrition from the panel. This paper shows that dynamic pseudo-panel methods can be used to consistently estimate measures of absolute and conditional mobility in the presence of nonclassical measurement errors. These methods are applied to data on earnings from a Mexican quarterly rotating panel. Absolute mobility in earnings is found to be very low in Mexico, suggesting that the high level of inequality found in the cross-section will persist over time. However, the paper finds conditional mobility to be high, so that households are able to recover quickly from earnings shocks. These findings suggest a role for policies which address underlying inequalities in earnings opportunities.

JEL classification: O12, D31, C81 Keywords: income mobility, dynamic pseudo panel; measurement error.

World Bank Policy Research Working Paper 3745, October 2005 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> ∗ We thank the Stanford Center for International Development (SCID) for research funding; and Aart Kraay and participants
conditions in the village economies. Figure 2 shows how ex-combatants from our sample reported spending their grant. It summarizes the self-reported information provided by 22 demobilized ex-combatants and 9 GdP/MC/AA who were interviewed in 2010. Respondents could give a maximum of three figures.<sup>8</sup> In line with the literature, we observe that a large proportion of ex-combatants used a share of the money to buy consumption goods: 48% reported purchasing food and drinks, and 26% reported buying clothes. This consumption boom is expected to be short-lived, especially if the discount rate of ex-combatants is low and if saving opportunities are scarce. In the short run, the demand increase should induce a jump in prices. This change in prices should increase the sales of shopkeepers and farmers, who are therefore expected to benefit from the return of demobilized ex-rebels. Other households may suffer from inflation. However, the global effect on nonbeneficiaries is expected to be positive, as most households are involved in some kind of trading activity.

Figure 2: DDR Grants Spending by Ex-combatants <!-- Start of picture text --> Land Drink/eat Clothing Small shop Working Wedding Housing Health Cattle Repay Financial Education<br>equipment loan saving<br>0.5<br>0.4<br>0.3<br>% of respondents 0.2<br>0.1<br>0.0<br><!-- End of picture text --> _Source_ : Authors’ analysis based on data described in the text.

Ex-combatants also invested part of their allowances in productive assets. More than 50% of ex-combatants reported investing part of the grant in a plot of land, 23% of them invested in a small shop, 19% in working equipment, and one ex-combatant reported buying a cow. The economic situation of ex-combatants who invested in productive assets is expected to improve in the short run and in the long run following their enhanced production capacity
more staggering and widespread across countries and products, both in terms of values and quantities. Figure 1 presents the distribution of trade discrepancies for a sample of more than 40 importing countries.<sup>2</sup> At the sample mean, the trade discrepancy is about 85 percent –suggesting that most import records are much larger than the export records, for both quantity and value.

What could cause such discrepancies in the o¢ cial trade statistics? There are several contributing factors. The discrepancies could be at least in part due to the di¤erent valuation methods applied by customs agencies, which normally include freight and insurance costs in import values but not in export values.<sup>3</sup> Another contributing factor is that the transshipment and re-export of products going through third countries cause a mismatch in the country of origin.<sup>4</sup> The discrepancies could also result from trade fraud in the form of undervaluation in imports in order to evade import tari¤s,<sup>5</sup> or the undervaluation in exports in order to evade export taxes or VAT.<sup>6</sup> The analysis of trade discrepancies has also been increasingly used to detect tari¤ evasion, especially by the customs authorities of developing countries.<sup>7</sup> However, even taken together, these contributing factors cannot fully explain the large discrepancies in the data, which tend to vary across products and trading partners, particularly with the recorded import quantity larger than the export quantity.

The objective of this paper is to assess whether part of the large discrepancies in the o¢ cial statistics is actually trade fraud due to the avoidance of costs associated with the non-tari¤ measures (NTMs) of the importing countries.<sup>8</sup> Such trade fraud may not only

> 2In this paper, trade discrepancies are de...ned according to Equation (8) ; which measures the percentage di¤erence
Policy Research Working Paper 5931

# **Abstract**

This paper examines two sources of global knowledge spillovers: foreign direct investments and trade. Empirical evidence demonstrates that foreign direct investment and trade can contribute to overall domestic productivity growth only when the technology gap between domestic and foreign firms is not too large and when a sufficient absorptive capacity is available in domestic firms. The paper proposes the terms research and development and labor quality to capture the innovative and absorptive capacity of the country. The spillover effects in productivity are analyzed using a stochastic frontier approach. This productivity (in terms of total factor productivity) is decomposed using a generalized Malmquist output oriented index, in order to evaluate the specific effect in technical change, technical efficiency change, and scale efficiency change. Using country-level data for 16 Latin American countries for 1996–2006, the empirical analysis shows positive productivity spillovers from foreign direct investment and trade only when the country has absorptive capacity in terms of research and development. Foreign direct investment and trade spillovers are found to be positive and significant for scale efficiency change and total productivity factor change.

This paper is a product of the Finance & Private Sector, Latin America and Caribbean 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 author may be contacted at jguasch@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 the International Bank for Reconstruction and Development/World Bank
Cash Transfers at Improving Nutritional Status?" _World Development_ 48: 133-155.

Ngure, Francis, Brianna Reid, Jean Humphrey, Mduduzi Mbuya, Gretel Pelto and Rebecca Stoltzfus. 2014. “Water, Sanitation and Hygiene (WASH), Environmental Enteropathy, Nutrition, and Early Child Development: Making the Links,”" _Annals of the New York Academy of Sciences_ 1308(1): 118-128.

O’Donnell, Owen, Eddy van Doorslaer, Adam Wagstaff and Magnus Lindelow. 2008. Analyzing Health Equity Using Household Survey Data, World Bank.

26
**Table** **<u>S3. Change in behavior since COVID-19 outbreak</u>**

||Handwashed<br>with soap more<br>often|Avoided<br>physical<br>greetings|Avoided crowds|
|---|---|---|---|
|Ethiopia|0.079***|0.238***|0.231***|
||(0.014)|(0.019)|(0.024)|
|Nigeria|0.050***|0.196***|0.329***|
||(0.016)|(0.022)|(0.023)|
|Uganda|0.061***|0.232***|0.301***|
||(0.014)|(0.019)|(0.023)|
|Ethiopia-Nigeria|0.001|0.001|0.000|
|Ethiopia-Uganda|0.005|0.485|0.000|
|Nigeria-Uganda|0.235|0.004|0.037|
|Observations|9,138|9,138|9,131|
|R-squared<br>_Note_: Each column re|0.011<br>ports results from a sin|0.045<br>gle regression of a|0.064<br>binary variable equal|

_Note_ : Each column reports results from a single regression of a binary variable equal to 1if the respondent changed their behavior and 0 otherwise. The binary variable is then regressed on indicators for each country, with Malawi as the base case. The second panel reports p-values for Wald tests of the simple linear hypothesis that the coefficient on one country indicator equals the coefficient on an indicator for a different country. Data are only from