rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Machinery<br>Office machines Metalworking Textile and leather Machines for special Telecommunications<br>machinery machinery industries apparatus<br>HHI 70‐71 HHI 80‐81 HHI 90‐91 HHI 00‐01 HHI 05‐06 HHI 07‐08<br>1,964<br>1,714<br>1,490 1,460 1,520 1,434 1,555 1,439<br>1,158 1,248 1,207 1,270 1,314 1,211 1,106 1,308 1,132 994 969 1,079 938 1,312 1,242 969 923 1,225<br>705 816 763 633<br><!-- End of picture text --> <!-- Start of picture text --> Transportation<br>Road motor vehicles Road vehicles other than Aircraft Ships and boats<br>motor vehicles<br>HHI 70‐71 HHI 80‐81 HHI 90‐91 HHI 00‐01 HHI 05‐06 HHI 07‐08<br>Furniture, Travel Goods, Clothing<br>Furniture Travel goods, handbags Clothing except fur Fur clothing and articles Footwear<br>and similar clothing of artifi<br>HHI 70‐71 HHI 80‐81 HHI 90‐91 HHI 00‐01 HHI 05‐06 HHI 07‐08<br>4,471<br>2,728 2,585<br>2,379<br>2,164 2,180<br>1,350 1,479 1,266 1,009 935 903 1,071 982 968 860 851 894 944 868<br>649 556 484 525<br>3,872 3,792<br>3,172<br>2,915<br>2,444 2,579 2,540<br>2,159 2,215<br>1,626 1,345 1,576 1,667 1,451<br>907 902 786 656 1,050 1,138 893 1,130 671 602 595 821 639 1,001 1,102 1,108<br><!-- End of picture text --> Source: Own calculations. Data: UN Comtrade. Retrieved from: World Integrated Trade Solution.

21
We also estimate the sensitivity of poverty to the incremental price increase experienced by the 1st decile relative to the overall inflation as measured by the CPI for the period 2011-2019. The results are summarized in Table 1. The values are high, ranging from 1.41 in 2012 to 3.28 in 2015, underscoring the high vulnerability of households to the negative effects of fast price increases on purchasing power, particularly for families right above the poverty line when inflation is pushed by price hikes of food, non-alcoholic beverages, housing, education and health. The average value of this sensitiveness check for the period analyzed is 1.98. In other words, the poverty rate increases nearly twice as fast as the price increases above the CPI that are experienced by the bottom decile and are not offset by income (labor and non-labor) growth.

Figure 6: Household income Gini coefficient (2011 - 2019) <!-- Start of picture text --> 44.0<br>43.41<br>43.5<br>43.0<br>42.42<br>42.5 43.02 42.20<br>41.98<br>41.78<br>42.0<br>41.48<br>42.06<br>41.5 41.88 41.94<br>41.01<br>41.52<br>41.0<br>41.26<br>40.42<br>40.5 40.92<br>40.77<br>40.22<br>40.0<br>2011 2012 2013 2014 2015 2016 2017 2018 2019<br>Nominal Real - Decile Specific Inflation<br><!-- End of picture text --> _Source_ : World Bank calculations using data from TUIK

||Table1:Pove<br>|rtyrate and s<br>|ensitivity of<br>|poverty to us<br>|ing the price <br>|index forthe1|st decile<br>|
|---|---|---|---|---|---|---|---|
||**$5.50 Poverty**<br>**Line (2011**<br>**PPP) and**<br>**Official CPI**<br>(1)|**Poverty**<br>**Rate with**<br>**$5.50 Line**<br>(2)|**Poverty**<br>**Line -**<br>**1st-decile**<br>**CPI**<br>(3)|**Poverty**<br>**Rate - 1st-**<br>**decile price**<br>**index**<br>(4)|**Sensitivity**<br>**of the**<br>**poverty**<br>**rate**<br>(5)|**Population**<br>(6)|**Additional**<br>**Individuals in**<br>**poverty with**<br>**1st- decile CPI**<br>(7)|
|2011|189.8|13.0|189.8|13.0|-|||
|2012|206.7|12.0|206.9|12.0|1.407|73,603,548|14,721|
|2013|222.2|10.8|223.1|10.8|1.791|74,456,551|58,076|
|2014|241.9|9.9|243.5|10.1|2.537|75,693,281|122,623|
|2015|260.4|10.7|263.7|11.1|3.282|76,368,972|342,133|
|2016|280.7|9.4|283.7|9.6|1.940|77,109,456|150,363|
|2017|311.9|8.6|315.2|8.7|1.491|78,862,192|106,464|
|2018|362.9|8.5|365.7|8.6|1.770|79,761,944|91,726|
|2019|418|10.2|424.3|10.5|1.689|80,708,979|209,036|

Note: Sensitivity of poverty to the incremental price increase experienced by the 1st decile relative to the overall inflation as measured by the CPI calculated as the ratio between the percentage change in poverty [(4)-(2)] and the percentage change in the poverty line [(3)-(1)]. _Source_ : World Bank calculations using data from TUIK.

18
**-7-**

# IV. A MODEL OF HOUSING--INDUCED LABOR MARKET DISTORTIONS

The model we use to identify the linkages between the housing and labor markets in Poland contains three components: (1) housing market disequilibrium, (2) migration flow disequilibrium, and (3) labor market disequilibrium. In this section we will examine the evidence on distortions in each of these markets.

# A. Is There Housing Market Disequilibrium?

We argue that the housing market is shown to be in disequilibrium if (1) housing prices exceed costs, (2) housing investment is too low, and (3) housing investment does not respond to the housing shorcage. The first two aspects of disequilibrium are taken up here; the chird aspect is discussed with the other statistical analyses in section V.

# Do Prices Exceed Costs?

There is a vast discrepancy between what housing appears co be worth and what most people pay for it. The deficits in housing have produced a situation in which housing that becomes available on the free market often sells for prices well in excess of construction costs; at the same time, however, and in part because of the housing subsidy system, households living in the existing housing stock are spending unusually low fractions of income for housing relative to what households in similar non-socialist countries spend, and relative to the cost of new free-marKet housing within Poland.

Information on free-market housing prices is largely anecdotal, but available information indicates that prices of housing units that trade on the free market (potentially up to about 60 percent of the stock) are well in excess of costs of construction or replacement. For example, discussions in 1986 with officials of housing cooperatives and their national association indicated that relatively large units (80-100 square meters) in older coops (built in the late 1950s) in Warsaw had recently sold for prices of from 120,000-150,000 zlotys per square meter. Selling prices for newer units were said to depend on unit size, with smaller units selling for about 60,000 zlotys per square meter and larger units, which are reh7tively more scarce, selling for about 120,000 zlotys per square meter.- Independent

- 4/ Ciechocinska (1987) indicated that the price of flats in Warsaw averaged 100,000 zlotys per square meter in 1985, and that a "typical flat" for a family of three could be assumed to be about 5 million zloty-s.
**The EU CS3D regulation is set to affect all industries with significant environmental footprints, including textiles** . Bosnia and Herzegovina, Ethiopia, Albania, and Tunisia are the countries with textile sectors most exposed to this regulation, as over a quarter of their textile exports are bound (directly or indirectly) for the EU (Figure 8).

**Figure 8 Global value chain exposure to the EU: Textiles**

<!-- Start of picture text --> 40 Direct export to EU Indirect export to EU<br> 35<br> 30<br> 25<br> 20<br> 15<br> 10<br> 5<br> -<br>Bosnia and Ethiopia Albania Tunisia Burkina North Cape Verde Morocco Madagascar Moldova<br>Herzegovina Faso Macedonia<br>Percent<br><!-- End of picture text --> _Source:_ Arvis et al (forthcoming) and authors’ calculations. _Notes:_ GVC exposure to the EU is the sum of direct exports to EU and indirect exports to EU over domestic output.

# **3.2 Impacts through green transition sectors**

**_3.2.1 Critical minerals_**

**The critical mineral policies of the EU, US, and China will affect developing countries differently.** Mongolia, North Macedonia, and Mozambique have the most exposed sectors as their mining sectors are almost entirely directed to these regions (Figure 9). The economic exposure is greatest in countries like Guinea and North Macedonia as a quarter of their GDP is from mining exports to these regions.

**Figure 9 Top 10 developing countries according to economic exposure to impacts of EU, US, and China policies on exports of critical minerals**

<!-- Start of picture text --> Economic exposure (LHS) Sector exposure (RHS)<br> 30 100<br> 90<br> 25 80<br> 20 70<br> 60<br> 15 50<br> 40<br> 10 30<br> 5 20<br> 10<br> - -<br>Percent Percent<br><!-- End of picture text --> _Source:_ 2022 data from the World Bank’s WITS.

Note: Sector and economic exposure are export of critical minerals to three economies divided by exports of critical minerals to world and GDP, respectively.

18
Trade has fallen dramatically since the onset of the financial crisis in the fall. Figure 17 compares trade growth (month over same month the previous year) in this crisis and in the previous downturns, using monthly data in constant $US, for a balanced sample of 31 countries that report data from January 1960-March 2009.<sup>10</sup> While growth leading up to the crisis was a bit higher in this episode, it still looked quite similar to the previous downturns. What is most evident from the picture is that the trade drop over the last few months has been much steeper and more severe than other recent episodes.

This note has offered some background on why the trade drop has been so large. We argue that the elasticity of trade to income has been increasing over time and that during recessions trade is especially responsive to income. On the positive side, we note that trade tends to rebound sharply when growth picks up. This is similar to the result in Reinhart and Rogoff (2008) that output declines resulting from a financial crises last only two years, as compared with about 4 years for employment and equity drops.

We have also seen that downturns tend to moderate global imbalances. However, the moderation tends to be temporary unless the downturn alters investment attitudes and/or government policies. Given that the downturn will get the process started, we hope that governments can use the transition to install policies that will ensure that imbalances do not revert to pre-crisis trends. This will include policies to encourage saving in the United States and prevent an overvalued dollar, and policies to stimulate spending in China and other parts of Asia and prevent undervalued currencies.

> 10 Month zero is the minimum trade growth during previous downturns.  The series for the current period is matched to previous downturns using the maximum in trade growth.  Specifically, the maximum trade growth before this downturn began is superimposed over the maximum trade growth on average before the previous downturns (month -20).

13
# ANNEX 1 _ENEU_

This paper uses information from the National Urban Employment Survey (ENEU), which is also a micro-level data set collected by (National Institute of Statistics and Geography of Mexico) INEGI and contains quarterly wage and employment data over 1987-2002. According to INEGI’s methodology document on the ENEU, the data are representative of the 41 largest urban areas in Mexico, covering 61 percent of the population in urban areas with at least 2,500 inhabitants and 92 percent of the population living in metropolitan areas with 100,000 or more inhabitants. In 1985 the ENEU included 16 urban areas: Mexico City, Guadalajara, Monterrey, Puebla, León, San Luis Potosí, Tampico, Torreón, Chihuahua, Orizaba, Veracruz, Mérida, Ciudad Juárez, Tijuana, Nuevo Laredo, and Matamoros, covering 60 percent of the urban population for that year. In 1992, 18 more urban areas were included in the survey: Aguascalientes, Acapulco, Campeche, Coatzacoalcos, Cuernavaca, Culiacán, Durango, Hermosillo, Morelia, Oaxaca, Saltillo, Tepic, Toluca, Tuxtla Gutiérrez, Villahermosa, Zacatecas, Colima, and Manzanillo. In 1993 and 1994 Monclova, Querétaro, Celaya, Irapuato, and Tlaxcala entered the ENEU. Finally, Cancún and La Paz joined the survey in 1996. According to INEGI, the ENEU always has covered about 60 percent of the national urban population.

The data are from household surveys, which fully describe family composition, human capital acquisition, and experience in the labor market (the variables contain information about social household characteristics, activity condition, position in occupation, unemployment, main occupation, hours worked, earnings, benefits, secondary occupation, and search for another job). As with the National Household and Income Survey (ENIGH), the sampling design was stratified in several stages (where the final selection unit was the household) and with proportional probability to size.<sup>3</sup> This statistical construction allowed us to make comparisons among different years. Moreover, this survey is structured to generate a panel data set that conforms with a rotator or rotating panel (a fifth of the total sample goes out and a new one comes in every quarter). Hence, the panel data follow the same household throughout five quarters.

> 3 For this it was necessary to use weights or expansion factors.

36
Both national targeting coefficients are decomposed into (i) inter-district, (ii) intra-district intercommunity, and (iii) intra-district intra-community (i.e. CBT) components in a way that identifies the relative contributions of beneficiary targeting at each level. While the three-tier decomposition expands on the two-tier decomposition pioneered by Galasso and Ravallion (2005) and represents a contribution to the literature, its use is driven by the way that the FISP coupons are allocated across the country and within localities. Further, given the lack of clarity on the definition of “resource poverty” and the concerns that have been raised regarding the spatial inconsistencies in the identification of program beneficiaries, our analysis utilizes multiple plausible indicators of eligibility drawing on the rich household survey data at our disposal. To infer the poverty targeting nature of the FISP, the paper also models the householdand community-level characteristics influencing household program participation.

Our analysis shows that the FISP is not poverty targeted in that it does not exclusively target the poor or the rich at any level of the program administration, in line with the insights from previous studies based on sub-national data. The program reaches all socioeconomic strata of rural Malawi. If there is any targeting, it is in the middle of the welfare distribution as such the share of the non-eligible population receiving FISP benefits ranges from 52 to 57 percent depending on the eligibility indicator used. The MoAFS, the districts, and the communities are nearly uniform in their failure to target the poor, with any minimal targeting (or mis-targeting) that does take place overwhelmingly occurring at the community-level. These findings, which are robust to the choice of the eligibility indicator and the decomposition method, cast doubt on accurate beneficiary identification under the current FISP approach to decentralized targeting that relies heavily on CBT. The multivariate analysis of household program participation reinforces these findings and reveals that the relatively well-off, rather than the poor or the wealthiest, have a higher likelihood of program participation and, on average, receive a greater number of input coupons.

The rest of the paper is structured as follows. Section 2 describes the conceptual foundation for the empirical analysis. Section 3 offers a brief country background and a description of the program of interest. Section 4 describes the data used in the analysis. Section 5 articulates the empirical framework used to evaluate the targeting performance of the program. Section 6 presents a discussion of the results. Section 7 summarizes and concludes with a discussion of potential revisions to the program design in the future.

# **2 CONCEPTUAL FRAMEWORK**

There has been a marked movement toward decentralization of decisions regarding the provision of government services in developing countries (Bardhan, 2002). In comparison to central 4
poverty considerably; absolute poverty increases by 2.4 pp for women and 2 pp for men—more women than men became poorer due to consumption taxes (Table 4). And both women and men close to defined poverty lines consume both food and non-food items that are subject to indirect taxes. Moreover, they pay more in indirect than direct taxes.

In-kind indirect transfers constitute the largest part of government spending (Annex 1). Moving from consumable income to final income, we see that these transfers substantially reduce poverty (Table 4). Using the absolute line, the poverty reduction effect of the transfers is higher for women than men. However, the transfers allow more men than women to escape relative poverty. Though the headcount and poverty gap go down, poverty severity increases when there is a move from consumable to final income, and poverty appears to be more severe among men.

In-kind transfers on education and health services reduced absolute poverty by 4.7 pp for women and 3.6 pp for men, though they increased the severity of poverty mainly among men. At the relative poverty line, the poverty-reducing effects are 4.4 pp for women and 4.5 pp for men. This suggests that the poverty impact of the fiscal system is weaker for women in terms of their position relative to the absolute poverty line.

Table 5 presents income inequality by gender at different CEQ income concepts. Within group (within all men or within all women) inequality is higher for men than for women at all income concepts. Almost all (99%) of this reduction in inequality is the within group inequality. The between group inequality (between men and women) is so small that it is not affected by the fiscal system.

# **Table** **<u>5: Inequality by CEQ Income Concepts and Gender</u>**

||**Market**<br>**Income**|**Gross**<br>**Income**|**Net**<br>**Market**<br>**Income**|**Disposable**<br>**Income**|**Consumable**<br>**Income**|**Final**<br>**Income**|
|---|---|---|---|---|---|---|
|Men|0.386|0.381|0.376|0.370|0.372|0.358|
|Women|0.344|0.337|0.338|0.331|0.332|0.321|
|Within-group component|0.366|0.360|0.358|0.352|0.354|0.341|
|Between-group component|0.004|0.004|0.004|0.004|0.004|0.004|
|Total|0.371|0.364|0.362|0.356|0.357|0.344|

_Source_ : Authors’ calculations; ESS 2018/19 data.

Indirect taxes and subsidies have little effect on inequality for both men and women; the Theil index remains the same for both disposable and consumable incomes (Table 5). However, direct taxes and transfers reduce within group inequality for both groups; when we move from market to disposable income, the Theil index declines from 0.385 to 0.370 for men, and from 0.344 to 0.331 for women (Table 4).

However, in-kind indirect transfers help reduce inequality. The movement from consumable to final income is equalizing for women and men. Monetized in-kind transfers for education and health services reduce the Theil index from 0.332 to 0.321 for women and from 0.372 to 0.358 for men. Overall, comparing within-group inequalities for market and final incomes, the fiscal system 20
Table 3: Factor Analysis - Bonding and Bridging (Step 4)

|**Factor**|**Eigenvalue**|**Difference**|**Proportion**|**Cumulative**||
|---|---|---|---|---|---|
|Factor1|1.10566|0.54352|0.965|0.965||
|Factor2|0.56214|0.43651|0.4906|1.4556||
|Factor3|0.12563|0.0482|0.1096|1.5652||
|Factor4|0.07743|0.12878|0.0676|1.6328||
|Factor5|-0.05135|0.03205|-0.0448|1.588||
|Factor6|-0.0834|0.00515|-0.0728|1.5152||
|Factor7|-0.08855|0.14675|-0.0773|1.4379||
|Factor8|-0.2353|0.03116|-0.2054|1.2326||
|Factor9|-0.26646|.|-0.2326|1||
|**Variable**|**Factor1**|**Factor2**|**Factor3**|**Factor4**|**Uniqueness**|
|Importance of family > importance of friends (a)|-0.245|0.2356|0.2034|0.0231|0.8426|
|Importance of family < importance of friends (a)|0.1951|-0.13|-0.2386|-0.0161|0.8879|
|Importance of work > importance of leisure (b)|-0.3329|0.4101|-0.1049|0.0433|0.7082|
|Importance of work < importance of leisure (b)|0.3181|-0.3733|0.1216|-0.0285|0.7438|
|Importance of religion > importance of politics (c)|-0.5551|-0.2788|-0.0124|0.0759|0.6083|
|Importance of religion < importance of politics (c)|0.5239|0.3167|0.0197|-0.0746|0.6192|
|Child independence Vs. Child obedience|0.2769|0.0645|0.0005|0.1329|0.9015|
|Trust in people Vs. trust in institutions|0.2087|0.0065|-0.0153|0.1526|0.9329|
|Justified homosexuality|0.3046|-0.0051|0.0283|0.1471|0.8848|

Observations: 256629; Correlation method: Principal factors rotation; Number of parameters: 30 (a) Base category: Importance of family = importance of friends (b) Base category: Importance of work = importance of leisure (c) Base category: Importance of religion = importance of politics 28
<mark>Gruber, Jonathan. "The Incidence of Payroll Taxation: Evidence from Chile."</mark> _<mark>Journal of Labor Economics</mark>_ <mark>15.S3 (1997): 72-101.</mark> <mark>Huttunen, Kristiina, Jukka Pirttilä, and Roope Uusitalo. "The employment effects of low-wage subsidies." Journal of Public Economics 97 (2013): 49-60.</mark> <mark>Korkeamäki, Ossi, and Roope Uusitalo. "Employment and wage effects of a payroll-tax cut— evidence from a regional experiment." International Tax and Public Finance 16.6 (2009): 753772.</mark> <mark>Korkeamäki, Ossi. "The Finnish payroll tax cut experiment revisited." Government Institute for Economic Research Working Papers 22 (2011).</mark> <mark>Kramarz, Francis, and Thomas Philippon. "The impact of differential payroll tax subsidies on minimum wage employment." Journal of Public Economics 82.1 (2001): 115-146.</mark> Murphy, Kevin J. "The impact of unemployment insurance taxes on wages." Labour Economics 14.3 (2007): 457-484.

Månsson, Jonas, and AMM Shahiduzzaman Quoreshi. "Evaluating regional cuts in the payroll tax from a firm perspective." The Annals of Regional Science (2012): 1-25.

Scarpetta,S., A. Sonnet and T. Manfredi (2010), "Rising Youth Unemployment During The Crisis: How to Prevent Negative Long-term Consequences on a Generation?", OECD Social, Employment and Migration Working Papers, No. 106, OECD Publishing, Paris.

Skedinger, Per. “Effects of payroll tax cuts for young workers”. Nordic Economic Policy (2013): 125.

Statistics Sweden. “Labour Force Surveys (LFS)” (2015) Swedish Government, “Government budget statement for 2015” (2014), p. 238 Yellen, J. (1984) "Efficiency Wage Models of Unemployment," American Economic 16
<!-- Start of picture text --> Figure 4: Male Unemployment Rates by Urban and Rural location<br>wo 57 5.8<br>43<br>gos<br>=o<br>ee<br>35=>=2— a7<br>3<br>sy)<br>ro) Urban Rural<br>PO coe «= 2018<br>Source: ELMPS 2012, ELMPS 2018<br><!-- End of picture text --> <!-- Start of picture text --> Figure 5: Female Unemployment Rates by Urban and Rural location<br>25.9<br>a 22.9<br>21.5<br>3 20.0<br>o<br>le<br>=@<br>pee<br>o<br>e=250—<br>= -<br>Ww<br>fa) Urban Rural<br>Source: ELMPS 2012, ELMPS 2018<br><!-- End of picture text -->
# **Figure A.4. NIRP announcements: changes in EMDE financial variables up to one month**

# **A. Bond spreads**

<!-- Start of picture text --> Percent<br>40 Median<br>30<br>20<br>10<br>0<br>-10<br>-20<br>-30<br>0 1 2 3 4<br>Weeks after<br>announcement<br><!-- End of picture text -->

# **B. Nominal effective exchange rates**

<!-- Start of picture text --> Percent<br>3<br>Median<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>-4<br>1 2 3 4<br>Weeks after<br>announcement<br><!-- End of picture text -->

# **C. Equities**

<!-- Start of picture text --> Percent<br>6 Median<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>-10<br>0 1 2 3 4<br>Weeks after<br>announcement<br><!-- End of picture text --> Sources: J.P. Morgan, Bloomberg, Haver Analytics, World Bank.

Notes: Basis points or percent change from closing values on the day before the NIRP announcement. Line indicates median across 6 NIRP announcements. Grey area indicates 10<sup>th</sup> and 90<sup>th</sup> percentile range. Each event is extended out for 30 calendar days.

A. Bond spreads are the EMBI Index spread, calculated as the average premium paid over a U.S. government bond with comparable 10 maturity.

B. Effective exchange rate is the J.P. Morgan EM Foreign Exchange Index.

C. Equities are the MSCI Emerging and Frontier index.

55