rafmacalaba/datause-encoder-data huggingface dataset:
share of processing exports in total exports, and especially when destined for far away countries. These results are in line with Yi’s (2003) theory that intra-GPN trade is more sensitive to changes in trade costs than regular trade.

In Section 6, we use the processing trade data to analyze the impact of the recent Great Recession on intra-GPN trade. This analysis has become particularly relevant in light of the great collapse in trade during the crisis, which was significantly larger than the drop in world GDP. While there is a general consensus among trade economists that GPNs played an important role in the great trade collapse, there continues to be a heated debate through which channels. Analyzing China’s processing trade data, we find evidence for the existence of three channels: a compositional effect, a trade cost effect and a bullwhip effect. First, in line with the _compositional effect_ , we find that the sectors that contributed most to China’s exports collapse are those where processing trade is more prevalent. Second, we show that, within industries, processing exports consistently dropped more than non-processing exports during the Great Recession. This is consistent with the _trade cost effect,_ since intra-GPN trade should be more sensitive to trade costs than regular trade. Third, in line with the _bullwhip effect_ , we show that in virtually all industries, the drop in demand for China’s processing exports led to a magnified drop in processing imports.

# **2. Mapping Global Production Networks**

Empirical evidence on vertical specialization and the role of trade costs in GPNs is remarkably scant, largely because concrete evidence is difficult to obtain. To measure vertical specialization, one would like to know the number of countries involved in the production process of a specific good, the value added created in each country, and the sequential supply chain linkages between production activities. However, this information is hard to come by. Firms are generally not willing to provide data on the cost structure of their own supply chain activities, and often do not know the full range of value chain activities conducted by its suppliers.<sup>5</sup> Furthermore, national statistical

> 5 Dedrick, Kraemer and Linden (2010) is a rare study that has been able to capture the value added created by a lead firm and its most important component suppliers for specific electronics products. For this

5
Sergio Firpo and Alysson Portella. Decline in Wage Inequality in Brazil: A Survey

# **5 Conclusion and a Guide for the Future**

## **5.1 General conclusions**

The decrease in wage inequality in Latin America, and Brazil in particular, was a surprising event in a world that observed widespread increases in dispersion of wage distribution since the 1980s. Economist have discussed the causes behind the observed trend in Brazil since the early 2000s, offering possible explanations for this phenomenon that included large increases in the supply of more educated workers, which affected inequality both through price and composition effects (Barros et al., 2010); increases in the minimum wage and formalization, which led to a compression of the distribution of wages; trade liberalization that reduced the skill premium through general equilibrium effects; and decreases in wage gaps across regions, races, and genders. Recently, some studies revisited these causes and added new evidence on this issue. Here, we reviewed these papers, discussing to what extent they agree and disagree with each other, highlighting their weaknesses and strengths. Considering the overall literature, we conclude that the observed decrease in inequality that started in the mid-1990s and accelerated during the 2000s resulted from a combination of favorable economic outlook, which included macroeconomic stability and beneficial terms of trade, with structural and institutional changes in the economy, most notably a growth in the share of skilled labor and increasing real minimum wages. This situation allowed for overall wage increases, but at a faster rate in the bottom of the income distribution, thus reducing wage inequality.

It has been theorized that changes in international trade, which include the trade liberalization carried out in the early 1990s and the commodities boom set by the rise of China in the 2000s, have potential implications for the wage distribution within countries. Recent research on the effects of these events on the Brazilian economy confirms the existence of the traditional mechanism of relative factor prices (Dix-Carneiro and Kovak, 2015; Ad ̃ao, 2015), but also point to other mechanisms, such as changes in the firm-specific wage premium (Helpman et al., 2017), wealth and cost channels that shift demand and employment across sectors (Benguria et al., 2018), and reductions in regional, racial, and gender wage gaps (Dix-Carneiro and Kovak, 2017; Hirata and Soares, 2016; Morchio and Moser, 2019). In particular, these other mechanisms may help explain the compression in firm effects, an important factor behind the reduction in inequality that has been recently uncovered, as documented by Alvarez et al. (2018). However, the direct impact of trade liberalization and the commodity boom seem to play a rather limited direct role.

The economic benefits of trade and the commodity boom may have derived mostly from their positive impact on economic growth and how it allowed for policies and structural changes to reflect in lower inequality. First, real increases in the value of the 27
system. Specifically, consider a mechanism activated at a certain _t_ that produces a pulse sequence _ζ_ is the magnitude of the value of the considered impact. If **e** is the vector with a unit in the positions where a shock occurs, the response by the system is represented by This system is inactive until _t_ = 0, after which it generates the sequence _{_ **Ψ** 0 **e** _,_ **Ψ** 1 **e** _, ...,_ **Ψ** _∞_ **e** _, }_ . The impulse travels through the entire SVARMA structure with speed depending on the spatial autoregressive and time autoregressive parameters. It is possible to trace all the routes by taking into account how the spatial autoregressive polynomial **H** ( _z_ ) is structured. Finally, confidence bands around the response can be obtained by repeating an experiment of identical impact, and drawing different parameters for the SVARMA structure randomly from their confidence bands. Trivially, the sequence eq. (24) converges to zero exponentially fast _a.s._ , for a stationary and ergodic model. Hence, even when the aggregate behavior of all parameters is not directly of interest, the IRF provides a useful tool to explore stability of the estimated model, which is important also for Granger-causal inference on the individual parameters.

# **4 Penalized Estimation**

To relax the Gaussian assumption that may not hold for data that exhibits extreme tail movement with high probability, often the case in the environmental-economic data, we discuss estimation in the context of the Students’ _t_ -estimation. In line with our discussion on uniqueness, we apply _L_<sup>2</sup> (Eucledian distance) penalties set on the moving average components that vanish with a weight of 1 _/√NT_ . Penalizing the _L_<sup>1</sup> norm (absolute sum), as in popularized LASSO estimations, encourages parameter vectors with many elements set to zero, which results in an unidentified problem for **b** . _L_<sup>2</sup> penalization, like in the ridge framework, encourages solutions where parameters are small, and in fact the penalty effect reduces in strength as parameters become close to zero. To reduce dimensionality, we suggest to evaluate the _AICc_ 17
|Dar es Salaam Water Supplyand Sanitation Project|RosemaryMukami Kariuki|
|---|---|
|Dar es Salaam Water Supplyand Sanitation Project|RosemaryMukami Kariuki|
|Dar es Salaam Water Supplyand Sanitation Project|RosemaryMukami Kariuki|
|Dar es Salaam Water Supplyand Sanitation Project|RosemaryMukami Kariuki|
|Dar es Salaam Water Supplyand Sanitation Project|RosemaryMukami Kariuki|
|AGRIC.RES & TRNG. II|Madhur Gautam|
|AGRIC.RES & TRNG. II|Madhur Gautam|
|AGRIC.RES & TRNG. II|Madhur Gautam|
|AGRIC.RES & TRNG. II|Madhur Gautam|
|AGRIC.RES & TRNG. II|Madhur Gautam|
|AGRIC.RES & TRNG. II|Madhur Gautam|
|AGRIC.RES & TRNG. II|Madhur Gautam|
|AGRIC.RES & TRNG. II|Madhur Gautam|
|AGRIC.RES & TRNG. II|Madhur Gautam|
|AGRIC.RES & TRNG. II|Madhur Gautam|
|AGRIC.RES & TRNG. II|Madhur Gautam|
|AGRIC.RES & TRNG. II|Madhur Gautam|
|AGRIC.RES & TRNG. II|Madhur Gautam|
|AGRIC.RES & TRNG. II|Madhur Gautam|
|AGRIC.RES & TRNG. II|Madhur Gautam|
|Second PovertyAlleviation Microfinance Project(Microfinance II)|Kiatchai Sophastienphong|
|Second PovertyAlleviation Microfinance Project(Microfinance II)<br>|Kiatchai Sophastienphong<br>|
|Second PovertyAlleviation Microfinance Project(Microfinance II)|Kiatchai Sophastienphong|
|Second PovertyAlleviation Microfinance Project(Microfinance II)|Kiatchai Sophastienphong|
|Second PovertyAlleviation Microfinance Project(Microfinance II)|Kiatchai Sophastienphong|
|Second PovertyAlleviation Microfinance Project(Microfinance II)|Kiatchai Sophastienphong|
|Second PovertyAlleviation Microfinance Project(Microfinance II)|Kiatchai Sophastienphong|
|Second PovertyAlleviation Microfinance Project(Microfinance II)|Kiatchai Sophastienphong|
|Second PovertyAlleviation Microfinance Project(Microfinance II)|Kiatchai Sophastienphong|
|Second PovertyAlleviation Microfinance Project(Microfinance II)|Kiatchai Sophastienphong|
|Second PovertyAlleviation Microfinance Project(Microfinance II)|Kiatchai Sophastienphong|
|Second PovertyAlleviation Microfinance Project(Microfinance II)|Kiatchai Sophastienphong|
|Second PovertyAlleviation Microfinance Project(Microfinance II)|Kiatchai Sophastienphong|
|Second PovertyAlleviation Microfinance Project(Microfinance II)|Kiatchai Sophastienphong|
|KARNATAKA STRUCTURAL ADJUSTMENT LOAN II|Paramita Dasgupta|
|KARNATAKA STRUCTURAL ADJUSTMENT LOAN II|Paramita Dasgupta|
|Introduction of Climate Friendlymeasures in Transport|Walter Vergara|
|Introduction of Climate Friendlymeasures in Transport|Walter Vergara|
|Introduction of Climate Friendlymeasures in Transport|Walter Vergara|
|Introduction of Climate Friendlymeasures in Transport|Walter Vergara|
|Introduction of Climate Friendlymeasures in Transport|Walter Vergara|
|Introduction of Climate Friendlymeasures in Transport|Walter Vergara|
|Introduction of Climate Friendlymeasures in Transport|Walter Vergara|
|Introduction of Climate Friendlymeasures in Transport|Walter Vergara|
|Introduction of Climate Friendlymeasures in Transport|Walter Vergara|
|Introduction of Climate Friendlymeasures in Transport|Walter Vergara|
|Nakivubo Channel Rehabilitation Project|Lance Morrell|
5 c1trol are _freqmntly_ appied in or arzu bman dwllings. nz*thermre, safety of ie cides used in piblic helth is of pwraunt iprtaro. Pesticide aopn{ds destined for use in piblic health progrwu mist be dmrIutzably safe for use both by the spraymen applyirn the oupcrds and for the inabitants and dcmeatic animals of the treated areas. 8. An example of suci a xqxiu is =,which despite pcpilar caxezi has an emllent safety record in t of its low aaute and ctmiic taxicity to man. mide mOr has had advere affects cn the re icn of sme bird and fish species when used as an agrialtural pesticide, there is ro envirct'enrtal crntarination when used as an ir_oor residual **sIiy** applied ty trained spray perscmael in malaria contral progrark. Nor is there any indication of any adverse aoate or dhrronic affect on the hundreds of miuicos of peole whose homes wre sprayed with mOr in the course of the glcbal malaria eradication program or the subsequent widespread malaria oam*rol prograns carried out by many national ministries of health. 1 Therefore, DDr remains the pesticide of dhoice for malaria control. The min reason for the reduction in DDT usage is widespread pesticide resistance in many malaria-vactor mosquito species exposed to the ccurqnd over many years.

9. TW inportant elements in the the selection of pesticides are the bionmics of the target vector s and the eWideuio1ogy of the The cnxtrol method or material mist be irdividually selected for eadh vector species as there are wida biological variances amng species. As an example, anopgline mcisqito v cts of malaria which are active WIH, (1971), "The Place of DOD in Operations aanst Malaria and Other Vector-borne Diseases," Official Records, World Health organization, Document No. 190:176-182, Geneva.
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**<mark>Table A2: Distribution of refugees and hosts in the most affected clusters of Kasulu, Ngara and Karagwe between January 1995 and January 1996</mark>**

||**Kas**|**ulu**|**Nga**|**ra**|**Kar**|**agwe**|
|---|---|---|---|---|---|---|
||_Jan-95_|_Jan-96_|_Jan-95_|_Jan-96_|_Jan-95_|_Jan-96_|
|Tanzania|430,480|442,533|189,108|194,403|348,410|358,165|
|Refugees|16,423|17,162|486,300|508,183|159,610|166,792|
|**Proportion**|**3.82%**|**3.88%**|**257.15%**|**261.41%**|**45.81%**|**46.57%**|
|Total|446,903|459,695|675,408|702,586|508,020|524,957|
|Source:Mwakasege (199<br>**Figure A6: Distributi**<br>600000|5) and Oxfam Tanzania O<br>**on of refugees and hos**|ffice Study report.<br>**ts in the most affect**|<br>**ed clusters of Kasulu,**|**Ngara and Karagwe i**|**n January 1995 and**|<br>**January 1996**|
|300000<br>400000<br>500000|||||||
|0<br>100000<br>200000|||||||
|**Source:** Mwakasege (1995<br><br>Jan-|) and Oxfam Tanzania Offic<br>95<br>Ja<br>Kasulu|e Study report.<br>n-96|Jan-95<br>Ngara<br>Tanzania<br>Refuge|Jan-96<br>es|Jan-95<br>Kar|Jan-96<br>agwe|

40
discounts; (iii) whether the states offered females stamp duty discounts; and (iv) the feasibility and cost of data collection. Each aspect is discussed in detail below.

# 4.1.1 City size

India is both very large geographically and very diverse culturally, socially, and economically. More than 4,000 ULBs have discretion to define their own policies on recurrent property taxes. Further, India does not have a centralized repository of all legislation relating to property taxes that might provide a comprehensive review identifying cities that offer property tax incentives to females. A further complication is India’s linguistic diversity: Many of its 4,000 ULBs use languages other than Hindi or English to write their legislation, thus requiring translations for study. In view of these issues, the sample of cities was limited to India’s 20 largest, based on total population as measured by the UN World Urbanization Prospects 2018, and the ease of obtaining relevant legal information. The selected cities are listed (by urban area population) in Appendix 4.

# 4.1.2 Recurrent property tax discounts to females

The review of legislation, media reports, and telephone conversations with government officials found that only 4 of the 20 cities selected provide females with recurrent discounts on property taxes: Pune, Pimpri-Chinchwad, Delhi, and Chennai.

# 4.1.3 Stamp duty discounts to females

Unlike cities, most states publicize their Stamp Acts electronically on dedicated websites, allowing legal review of the state-level Stamp Acts of all 28 states and Delhi. This review, and telephone conversations with government officials, identified nine states and Delhi that provided stamp duty and registration charge discounts to females: Assam, Bihar, Gujarat, Haryana, Maharashtra, Odisha, Punjab, Uttarakhand, and Uttar Pradesh.

# 4.1.4 Feasibility and cost of data collection

Due to the ongoing COVID-19 pandemic, it was not feasible to do in-person meetings in the chosen cities. The necessity of relying on contacts within the government to organize remote interviews with relevant officials was therefore a pertinent consideration in the final selection of cities. Similarly, because COVID limited the option of face-to-face meetings, we relied on local personnel to organize FGDs. Due to budget constraints, six cities could be chosen for FGDs, and eight cities could be chosen for government interviews.

# 4.1.5 City selection based on the four-tier approach

Based on the results of this approach, four city-state combinations were identified for this research, using the following four tiers.

- City provides recurrent property tax discount and the matching state also provides stamp duty or registration charge discounts to females.

14
- Pollitt, Christopher, and Geert Bouckaert. 2004. _Public Management Reform: A Comparative Analysis_ . Oxford: Oxford University Press.

Rasul, Imran, and Daniel Rogger. 2013. “Management of Bureaucrats and Public Service Delivery: Evidence from the Nigerian Civil Service.” Working paper, International Growth Centre, London School of Economics, June.

- Recanatini, Francesca, Alessandro Prati, and Guido Tabellini. 2005. “Why Are Some Public Agencies Less Corrupt Than Others? Lessons for Institutional Reform from Survey Data.” Paper Prepared for the Sixth IMF Jacques Polak Annual Research Conference on Reforms, International Monetary Fund, Washington, DC, November 3–4.

Schein, E. H. 1999. “Kurt Lewin’s Change Theory in the Field and in the Classroom: Notes toward a Model of Managed Learning.” Reflections 1, No. 1, Society for Organizational Learning, and the Massachusetts Institute for Technology.

- ———. 2002. “Models and Tools for Stability and Change in Human Systems.” _Reflections_ 4 (2). Society for Organizational Learning and the Massachusetts Institute for Technology.

- Schick, Allan. 1998. “Why Most Developing Countries Should Not Try New Zealand Reforms.” _World Bank Research Observer_ 13 (1): 123–31.

- Schneider, B. Ross, and Blanca Heredia, eds. 2003. _Reinventing Leviathan: The Politics of Administrative Reform in Developing Countries_ . Miami: North-South Center Press at the University of Miami.

- World Bank. 2004. _World Development Report 2004: Making Services Work for the Poor_ . Washington, DC: World Bank.

- ———. 2005. _Harmonized Evaluation Criteria for ICR and OED Evaluations._ Washington, DC: World Bank.

- ———. 2008. _Public Sector Reform: What Works and Why? An IEG Evaluation of World Bank Support._ Washington, DC: World Bank.

- ———. 2012a. _Beyond the Annual Budget: Global Experience with Medium Term Expenditure Frameworks._ Washington, DC: World Bank.

- ———. 2012b. _World Bank’s Approach to Public Sector Management 2011_ − _2020: “Better Results from Public Sector Institutions.”_ Washington, DC: World Bank.

44
**<u>Table A.33. The district fixed-effect RDD-DD regressions of households’ income without control variables</u>**

||||D<br>|ependent variables<br>|<br>|||
|---|---|---|---|---|---|---|---|
|Explanatory variables|Log of per<br>capita income|Log of per<br>capita income<br>from wages|Log of per<br>capita income<br>from nonfarm<br>production|Log of per<br>capita income<br>from farm<br>production|Log of per<br>capita<br>remittances|Log of per<br>capita public<br>cash transfers|Log of per<br>capita income<br>from other<br>sources|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Program *Post-program period|-0.0209|0.4515|0.8195**|-0.3693*|0.5160|0.8267***|0.9735**|
||(0.0968)|(0.5037)|(0.3844)|(0.1956)|(0.5730)|(0.3156)|(0.4203)|
|(Poverty rate – 50) *Post-program|0.0062|-0.0778*|-0.0639|0.0063|-0.0270|-0.0542*|-0.0120|
|period|(0.0101)|(0.0438)|(0.0418)|(0.0234)|(0.0552)|(0.0325)|(0.0420)|
|Program * (Poverty rate – 50) *Post-|-0.0107|0.0323|0.0488|-0.0008|-0.0115|0.0403|0.0167|
|program period|(0.0107)|(0.0498)|(0.0453)|(0.0242)|(0.0605)|(0.0384)|(0.0468)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|8.8557***|3.3849***|1.6412***|8.0901***|3.5404***|0.7814***|1.1349***|
||(0.0246)|(0.1341)|(0.1007)|(0.0648)|(0.1239)|(0.0873)|(0.0998)|
|Observations|40,468|40,468|40,468|40,468|40,468|40,468|40,468|
|R-squared|0.287|0.155|0.069|0.087|0.209|0.104|0.075|

Note: This table reports district fixed-effect RDD-DD regressions of income and poverty of households using household-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

83
The decompositions by age-group presented in Table 5 do not necessarily compare different cohorts of women. Given that the age-intervals are longer than the time span between the two surveys, each of the three age group-specific comparisons in Table 5 contains both a “panel” and a “crosssection” element. For instance, the decomposition for the youngest age-group compares the 1986-1990 cohort in 2005 (at ages 15-19) with the same cohort in 2010 (at ages 20-24), which is the (pseudo) panel element, and compares the 1986-1990 cohort with the 1991-1995 cohort, the cross-section element. To explain the cohort effects observed in Figure 1 and Table 1, we repeat the decompositions for each of the 7 five-year age-groups, comparing each five-year cohort with the previous one. The results are summarized in Appendix Table A2. The first (15 to 19-years-old) and the third (25 to 29 years-old) agegroup are the exceptional ones. For the youngest age-group, we would, based on the changes in the covariates, expect a 47.2 percent decrease in fertility between 2005 and 2010. Instead, we observe a 41.7 percent increase. This increase is left fully unexplained. While all key covariates changed in the direction to drive down fertility (more years of education, higher score on the asset index and a higher employment share outside agriculture) fertility increased nonetheless. Future DHS rounds will show whether this increase is genuine/structural or due to chance.<sup>15</sup> The third age-group is an exception in the opposite direction. While the evolution of covariates would predict a slight and statistically insignificant increase in fertility, observed fertility dropped by 20 percent. This age-group observed in 2010 (the 1981-1985 cohort) forms the core of the genocide-cohort for whom education was interrupted by the events. As such, the deterioration in education outcomes for this age group between 2005 and 2010 would predict higher fertility levels in contrast to the actually observed ones.

For the other age groups improvements in education and household living standards from one five-year cohort to the next emerge as the main drivers of the decrease in age-specific cumulative fertility. The increase in female educational attainment accounts for 25 percent of the decrease in

> 15 Note however that in absolute terms the increase is small: 0.015 children per woman.

23