rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3: Probability of early-phase income loss, total income and by source**

||LOST INCOME|LOST INCOME|LOST FARM<br>INCOME|LOST NFE<br>INCOME|LOST WAGE<br>INCOME|LOST DOM.<br>REMITTANCES|LOST REMITT.<br>FROM ABROAD|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Female household head|0.0298|0.0174|0.0188|0.0710**|-0.0516|0.0370|0.0838|
||(0.0318)|(0.0325)|(0.0498)|(0.0358)|(0.0442)|(0.0485)|(0.102)|
|Age of household head|-0.00109***|-0.00120***|-5.38e-05|-0.00106*|-0.00229***|-0.00216**|-0.000112|
||(0.000366)|(0.000368)|(0.000559)|(0.000548)|(0.000788)|(0.000861)|(0.00198)|
|Primary education|-0.0192|-0.0208|-0.00854|0.00187|0.0257|0.0348|-0.0155|
||(0.0172)|(0.0174)|(0.0304)|(0.0189)|(0.0228)|(0.0469)|(0.0520)|
|Secondary education|-0.00387|0.000941|0.0404|0.0198|-0.0389|0.0320|0.0816|
||(0.0194)|(0.0194)|(0.0319)|(0.0217)|(0.0288)|(0.0513)|(0.0760)|
|Tertiary education|-0.0959***|-0.0590***|-0.00268|0.0292|-0.256***|0.00993|-0.0411|
||(0.0209)|(0.0194)|(0.0328)|(0.0276)|(0.0370)|(0.0548)|(0.0942)|
|Urban|0.0222*|0.0298**|-0.00484|0.0224|0.0283|-0.0607*|-0.105*|
||(0.0131)|(0.0135)|(0.0218)|(0.0143)|(0.0225)|(0.0346)|(0.0550)|
|Children 0-4 present|0.0331***|0.0233*|0.0131|0.0105|0.0325*|0.0383|0.0386|
||(0.0124)|(0.0128)|(0.0185)|(0.0170)|(0.0178)|(0.0389)|(0.0613)|
|Female hh head*Children 0-4|-0.0368|-0.0223|-0.0186|0.0123|0.0347|-0.120**|0.114|
||(0.0270)|(0.0245)|(0.0353)|(0.0251)|(0.0495)|(0.0492)|(0.0808)|
|Children 5-18 present|0.0560***|0.0321**|0.0126|0.0306|-0.0168|-0.000398|0.0618|
||(0.0163)|(0.0158)|(0.0242)|(0.0225)|(0.0301)|(0.0389)|(0.0854)|
|Female hh head*Children 5-18|0.00141|0.00701|-0.00474|-0.0443|0.0931*|0.0480|-0.101|
||(0.0370)|(0.0373)|(0.0533)|(0.0390)|(0.0538)|(0.0613)|(0.103)|
|Had farm income during previous||0.0354**||||||
|12 months||(0.0165)||||||
|Had non-farm enterprises income||0.190***||||||
|during previous 12 months||(0.0185)||||||
|Had wage income during previous||-0.0923***||||||
|12 months||(0.0114)||||||
|Had domestic remittances during||0.0446**||||||
|previous 12 months||(0.0175)||||||
|Had remittances from abroad<br>during previous 12 months||0.0378<br>(0.0257)||||||
|Constant|0.798***|0.683***|0.700***|0.817***|0.692***|0.809***|0.592***|
||(0.0288)|(0.0308)|(0.0422)|(0.0419)|(0.0577)|(0.0735)|(0.156)|
|Observations|6,314|6,314|3,866|2,718|2,520|997|319|
|R-squared|0.083|0.140|0.040|0.023|0.116|0.117|0.074|

Note: OLS estimation results, standard errors clustered at EA level in parentheses, *** p<0.01, ** p<0.05, * p<0.1. All estimations control for country fixed effects. Samples in col. 3-7 include only households receiving the relevant type of income before the Covid-19 outbreak. _Source_ : HFPS R1.

18
Figure 29: Variance in the task performed of temporary jobs. (Change from the late 90s) <!-- Start of picture text --> 0,8<br>0,6<br>0,4<br>0,2<br>0<br>‐0,2<br>‐0,4<br>‐0,6<br>‐0,8<br>‐1<br>‐1,2<br>Georgia Kyrgyzstan Armenia<br>NRCA NRCI RC RM NRM<br><!-- End of picture text --> Source: Own calculations based on Household surveys.

Note: NRCA refers to Non-Routine Cognitive Analytical, NRCI is Non-Routine Cognitive Interpersonal, RC is Routine Cognitive, RM is Routine Manual and NRM is Non-Routine Manual.

# 4.2.3 Summary of results for Europe and Central Asia

The analysis performed for a set of 7 selected countries shows that the trends of non-standard employment in Europe and Central Asia do not show a homogeneous pattern in the last 10/15 years. This heterogeneous picture is observed in both the prevalence of part-time and temporary employment.

Regarding the profile of NSE, we find several changes in the last two decades. First, from the education perspective, an improvement of the profile of workers employed as NSE can be observed in the countries analyzed, in both part-time and temporary employment. From the point of view of wages, two important concluding changes are identified. On the one hand, a shift to the right of the wage distribution is observed in all the countries analyzed, indicating an increase in their average. Finally, examining the task content performed in jobs, we find a heterogeneous picture. In some countries, NSE evidences a movement towards non-routine cognitive tasks, together with a decreased importance of manual tasks in line with the previous results in the literature for total employment, but we have several exceptions.

Additionally, like in the region of Latin America and the Caribbean, in most of the countries analyzed, the changes in the profile of NSE workers cannot be considered a specific characteristic of non-standard employment, since they are also observed in standard wage employment and selfemployed workers at least in sign.

Page 39 of 51
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**In addition to critical socioeconomic information, the EU-SILC data provides valuable information on gross and net labor incomes. Still, it is important to acknowledge its limitations, particularly about non-response bias and recall errors.** The EU-SILC (Statistics on Income and Living Conditions) survey relies on voluntary participation, whereas anyone with taxable income must file a tax return. This means there may be an under-sampling of households with high incomes, leading to underestimating top incomes and inequality. Furthermore, self-reported incomes in the EU-SILC may be subject to recall errors, with respondents potentially misremembering their income sources over the past year (the reference year). Incomes in the EU-SILC are also subject to imputations by the National Statistical Office, which might introduce some inaccuracies.  Despite these concerns, the EU-SILC remains the only reliable source of household self-reported incomes, with sufficient sample size and detailed demographic information. As a result, we base our analysis on EU-SILC data to estimate true income.

**Another primary concern with the labor income data in the EU-SILC is the lack of information on monthly incomes; to overcome this, we perform the analysis with annual income estimates rather than monthly.** The income information is only obtainable every year and does not correspond to the same period as the labor market status and additional labor variables. Yearly income measures cannot be used as a substitute for monthly income measures because yearly income may accumulate in only a few months of employment. Therefore, to accurately assess monthly income, we must consider the duration spent in different employment statuses during the year. The retrospective primary economic status data provides some information that can help divide the income into monthly parts, but to do so, we need access to longitudinal data. As a result of the different periods, the income divided by 12 may differ from the monthly income of the job at the time of the interview in the previous or current year. This issue is particularly significant for individuals with unstable careers, including job changes and unemployment interruptions, as the varying periods can result in substantial biases. To address these limitations, we decided to analyze yearly incomes instead of monthly incomes by aggregating administrative tax data, which is available every month, to a yearly level.

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34 Indeed, the regional differences among China’s provinces (and large cities) are so large that China looks like a continent with a mixture of emerging industrial countries and some of the poorest “countries” in the world. It is also well known that per capita income differs drastically between urban and rural areas within provinces. For instance, official statistics suggest that the ratio of average income in urban areas is about three times (3.3) as high as in rural areas. However, this is a highly uncertain figure because it may not fully reflect differences in costs of living in rural and urban areas – a measurement defect that is usual in other countries as well. Although average income and consumption have increased rapidly in nearly all parts of the country during the reform period (Chow, 2006a; Ravallion and Chen, 2006), these geographical differences are an important background for widespread dissatisfaction with the _social_ situation in the country – an issue to which I now turn.
# Appendix Table 2 Data Appendix

|Variable|Variable Description (unit)|Source|
|---|---|---|
|Policy rate|Nominal monetary policy interest rates by central banks<br>(in percent). In some specifications, euro area policy<br>rates before 1997 are GDP-weighted averages of policy<br>rates in member countries.|BIS, Haver Analytics, OECD|
|Shadow rate|Shadow policy rate point estimates (in percent) as<br>explained in Krippner (2013). Data are available for<br>seven economies (Australia, Canada, euro area, Japan,<br>New Zealand, United Kingdom, United States) over<br>1995-2024.|LJK Limited; Krippner (2013)|
|Headline CPI|Headline Consumer Price Index. Inflation rates are<br>calculated on a month-over-month basis or relative to 12<br>months earlier based on the analysis.|OECD, Haver Analytics,<br>Ha, Kose, Ohnsorge (2023)|
|Core CPI|Core Consumer Price Index, following OECD<br>classification: Headline CPI excluding food and energy<br>components. Inflation rates are calculated on a month-<br>over-month basis or relative to 12 months earlier based<br>on the analysis.|OECD,<br>Ha, Kose, Ohnsorge (2023)|
|Output|Real gross domestic product index based on chain-linked<br>volume (in local currencies). The reference year is 2015.<br>Real GDP growth rates are calculated on a quarter-<br>over-quarter basis or over the past 12 months based on<br>the analysis.|<br>OECD, Haver Analytics|
|Industrial Production|The industrial production index covers the volume of<br>production in sectors such as mining, manufacturing,<br>electricity, gas, steam, and air conditioning. The<br>reference year is 2015 (OECD) unless specified<br>otherwise. Growth rates of production are calculated on<br>a month-over-month basis or relative to the past 12<br>months based on the analysis.|OECD, Haver Analytics|
|Unemployment rate|Unemployment rate (in percent):<br>unemployment/labor market participation|OECD|
|Employment|Number of persons employed with age 15 and over (in<br>thousands)|OECD, IFS, ILO, FRED,|
|Oil price|Nominal oil prices (average of Dubai, WTI, and Brent<br>oil prices). Oil price growth rates are calculated on a<br>month-over-month basis.|World Bank (Pink sheet<br>database)|

Notes: Sample period is from 1970m1 to 2024m3, except policy rate data is through 2024m5. Economies included are Australia, Austria, Belgium, Canada, Switzerland, Germany, Denmark, Spain, Finland, France, the United Kingdom, Greece, Ireland, Israel, Italy, Japan, the Republic of Korea, Netherlands, Norway, New Zealand, Portugal, Sweden, the United States, and the euro area.

83
livia, Paraguay, and Peru, the distance between the median and the 10th percentile of their respective distributions declined considerably during the period (Messina and Silva 2018).

Although the precise contribution of the minimum wage to the decline of wage inequality remains to be established, there is increasing evidence suggesting that it played an important role. Maurizio and Velazquez (2015) build counterfactual wage distributions using semiparametric methods to analyze the impact of the minimum wage on several inequality measures in Argentina, Brazil, Chile, and Uruguay. They find that, except for Chile where the growth of the minimum wage was more moderate, the minimum wage was a significant driver of wage compression in the bottom half of the distribution. Ferreira, Firpo, and Messina (2017) find that in Brazil increases in the minimum wage significantly helped reduce inequality during the boom years (2002-2012), but not during the preceding period of slow growth (1995-2002). Engbom and Moser (2017) focus on the formal sector in Brazil and find that, once spillover effects are accounted for, the minimum wage had large compressing effects on the distribution of wages.

# **9. Conclusions**

After wage inequality increased in the majority of the countries in Latin America between 1995 and 2002, it fell by 26 percent between 2002 and 2015, measured by the 90/10 log hourly earnings ratio. This paper characterized the evolution of inequality in the region between 1995 and 2015. It used harmonized household survey data for 16 Latin American countries and linked employer-employee data for Brazil, Costa Rica, and Ecuador. It characterized the main stylized facts associated with changes in inequality, discussed the potential driving forces and highlighted promising avenues for future research.

Using harmonized household data representing about 90 percent of the Latin American population, the study uncovered six empirical regularities behind the wage inequality reduction of the 2000s. First, the reduction in wage inequality was a regionwide phenomenon (Costa Rica was the only country where inequality increased). Second, wage inequality fell more in countries in South America relative to Central America and Mexico. Third, wage inequality fell at the upper and lower ends of each country’s wage distribution, but the reduction was pushed by faster wage growth of hourly wages in the bottom percentiles. Fourth, the premiums associated with labor market experience and education fell in all countries but Costa Rica. Fifth, two-thirds of the inequality decline took place across workers with similar skills. Sixth, changes in the composition of employment across sectors, occupations, and formal-informal status contributed to the decline, but residual (within education-experience35
**Table** **<u>C1: Estimated Elasticities of Substitution for Varieties in Tanzanian Imperfectly Competitive Goods Sectors</u>**

|**Sector in our Model**|**Matching HS‐3 Code from Broda et al estimates weighte**|**d elasticity of substitution**|
|---|---|---|
|Processedfood|110,150,151,160,170,180190,200,210|10|
|Beverages & tobacco products|220,240|2|
|Textile & leather products|510‐630, 650, 420|4|
|Manufacture of basic & industrial chemicals|280‐391|3|
|Manufacture of fertilizers & pesticides|310|2|
|Petroleum refineries|271|4|
|Glass & cement|680‐702|5|
|Iron steel & metal products|720‐831|7|
|Mining & quarrying|270, 271|4|
|Grain milling<br>Source: Authors calculations based on estim|110<br>ates from Broda, Greenfield and Weinstein (2006).|3|

# **Appendix D: Engineering Services in Tanzania - Restrictiveness Index**<sup>**31**</sup>

Table 1 below is a table taken from Nguyen-Hong (2000) for Vietnam in which the components of the engineering restrictiveness index as well as the scoring options are presented. The actual scores in table 1 are for Vietnam. The scores for Tanzania and the reasons for the scoring follow below the table. The Tanzania scores are based on the results of the World Bank Regulatory Survey in East Africa<sup>32</sup> and the World Bank Survey on Applied Policies in Services<sup>33</sup> .

# **Barriers to establishment**

# **Form of establishment Score 0.5**

All forms of legal entities (sole proprietorship, limited liability partnerships, general partnership, individual enterprise, limited liability companies and public limited companies) are permitted for both domestic and foreign firms.

However, according to the Engineering Registration Act no. 63 Para 12: “1) No person or body of persons not citizen of the United Republic shall be registered as a local consultant or consulting firm unless–(a) in the case of natural person, he is a citizen of the United Republic; (b) in the case of a company, it is incorporated in Tanzania and the majority of its shares are owned by the citizens of the

> 31 The work on the estimation of the ad valorem equivalents of the barriers in engineering services was done by Josaphat Kweka with the guidance of Nora Dihel.

> 32 The regulatory surveys were conducted by local consultants who interviewed the professional associations in the examined East African countries in 2009.

> 33 The policy surveys were conducted by The World Bank Research Department in 2008-2009.

112
(Ehiri and Prowse 1999). Malnutrition in Peruvian children is caused by food deprivation and infectious diseases, which, in turn, are related to poverty, lack of access to markets, deficient transportation, inadequate health and sanitary infrastructure, and low levels of education (Valdivia 2004; Escobal et al. 2005; Aguiar et al. 2007).

Public goods and services may directly affect a child’s nutrition by providing access to health facilities, better sanitary infrastructure, or nutritional programs (McGinnis et al. 2002). In addition, building roads lowers transportation costs, thereby allowing food to reach previously disconnected areas and potentially diversifying or enhancing a child’s diet. Building a dam may improve the agricultural productivity of a region and can consequently increase the food supply and ensure food availability. Furthermore, government investments in research and infrastructure may stimulate growth that may lead to greater income-earning opportunities (Fan et al. 2000). Additionally, it is important to measure the impact of total public expenditure because bundled public services have been shown to affect welfare more strongly than separate interventions (Escobal and Torero 2002; Chong et al. 2003). Hence, this paper examines the impact of total public expenditure rather than only the effect of increases in public health spending.

The impact of public spending on child nutrition does not depend only on the amount of money spent. The quality of the goods and services provided is crucial to their effectiveness. In Peru, the amounts spent and the quality of expenditure differs considerably between urban and rural areas. Finally, public expenditures will not be effective if households are not able or willing to access public goods and services due to child frailty, language barriers, or lack of economic resources. These concerns are addressed in this study.

> 3 A child is stunted if he/she falls two standard deviations below the height-for-age z-score. The z-score is calculated using the World Health Organization Child Growth Standards (WHO 2006).

5
# **Figure VI-5: Medium-term Credit Cycle and GDP Growth**

## **A- Oil exporters**

<!-- Start of picture text --> Kuwait: Medium-term Credit Cycle and GDP Growth<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term credit cycle GDP Growth<br>40<br>20<br>0<br>-20<br><!-- End of picture text --> <!-- Start of picture text --> Algeria: Medium-term Credit Cycle and GDP Growth<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term credit cycle GDP Growth<br>30<br>20<br>10<br>0<br>-10<br>-20<br><!-- End of picture text --> <!-- Start of picture text --> UAE: Medium-term Credit Cycle and GDP Growth<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term credit cycle GDP Growth<br>20<br>10<br>0<br>-10<br>-20<br><!-- End of picture text --> <!-- Start of picture text --> Iran: Medium-term Credit Cycle and GDP Growth<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term credit cycle GDP Growth<br>Bahrain: Medium-term Credit Cycle and GDP Growth<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term credit cycle GDP Growth<br>Oman: Medium-term Credit Cycle and GDP Growth<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term credit cycle GDP Growth<br>40<br>20<br>0<br>-20<br>-40<br>20<br>10<br>0<br>-10<br>-20<br>100<br>50<br>0<br>-50<br><!-- End of picture text --> 40
these three cases, Brazil ranks in the bottom third of the Doing Business 2018 ranking (176th, 170th and 131th, respectively). With regards to operational costs, the performance is even worse for paying taxes, where Brazil occupies the 184th position out of 190 countries (Figure 20).

# **<u>Figure 20. Doing Business in Brazil: rank and distance to frontier</u>**

|**Topics**|**DB 2018 Rank**|**DB 2017 Distance**<br>**to Frontier (%**<br>**points)**|**Change in**<br>**Distance to**<br>**Frontier 2018-**<br>**2017 (% points)**|
|---|---|---|---|
|Overall|125|56.45|0.38|
|Starting a Business|176|65.05|0.01|
|Dealing with Construction Permits|170|49.83|0.04|
|Getting Electricity|45|82.46|1.23|
|Registering Property|131|52.6|0.02|
|Getting Credit|105|45|..|
|Protecting Minority Investors|43|63.33|..|
|Paying Taxes|184|32.97|..|
|Trading across Borders|139|59.78|4.21|
|Enforcing Contracts|47|66|..|
|Resolving Insolvency|80|47.46|1.69|

Source: World Bank Doing Business 2018 dataset.

Note: An economy’s distance to frontier score is indicated on a scale from 0 to 100, where 0 represents the worst performance and 100 the frontier

## **Box 1. Tax structure and resource allocation in Brazil**

Aggregate productivity performance at country level is particularly impaired by a complex tax structure that hampers resource allocation across sectors and states. As highlighted by Apy (2017), the Brazilian tax structure with regards to goods and services is extremely complex, with four main taxes (ICMS, IPI, ISS and PIS-Cofins) that have distinct incidence coverage (cumulative, non-cumulative and mixed). More importantly, individual products are subject to different regimes depending on the industry, the way the production process is structured (vertical integration versus fragmentation) and the locality where the production process takes place. Thus, relative prices are distorted with impacts on resource allocation and productivity.

As a result, firms are often induced to integrate their activities in a vertical way, even when the production of goods and services would be cheaper if outsourced. Second, the fact that ICMS is cumulative – as it follows the origin principle and has restrictions on input tax credits – impairs exports of goods and services. Third, tax wars and regional tax breaks lead to regional capital misallocation. More specifically, Brazilian states (and also municipalities) have been engaging over the years in tax competition, known as “fiscal war.” Benefiting from full administrative autonomy in setting their ICMS rates, states have used the ICMS as an industrial policy instrument by granting tax exemptions to attract investment, not only in manufacturing but also services (distribution centers). Therefore, firms are often encouraged to locate their activities in regions/states where taxes are lower, even if the production is less efficient.

16