rafmacalaba/datause-encoder-data huggingface dataset:
power in their respective countries at several points in the "heyday" of populism from the 1930's to the 1960's and less strongly in the 1970's and 1980's (Drake 1991).1 Table 3 presents some populists episodes in Latin America during this period.

Populist policies sought to control the economy by means of direc. intervention or regulations. Prominent among the latter were labor regulations. Cardoso and Hehwege call minimum wage policies "the redistributive centerpiece of populism" (p. 68). Rather extensive labor legislati6n codes were enacted to purportedly further workers' welfare. These regulations, by effectively raising labor compensation above its market-dictated level, diminish the incentives for the industrial sector to provide work to the growing numbers of city inhabitants. Cardoso and Helwege conclude that "As the cornerstone of populist redistribution, minimum-wage increases promoted the welfare of relatively small groups at the expense of larger groups" (p. 62). As result of the state-induced excess labor supply in the cities, an informal sector, which evaded labor regulations, rose to give work to the masses of people for whom unemployment was not a viable option: "... informality represents the irruption of 'real' market forces in an economy straijacketed by mercantilist regulation."<sup>2</sup> The ILO's Regional Employment Program for Latin America and the Caribbean (whose Spanish acronym is PREALC) provides estimates of the urban informal labor force for virtually every country in Latin America. Given that most informal activities, _per se,_ are not recorded in official statistics, measuring informal production and labor force is very difficult Table 4 presents PREALC's estimates for 14 Lafin American countries for the years 1960, 1970, 1980, 'One exception to the waning of populism from the 1970's is the Alan Garcia administration in Peru from 1985 to 1990, which almost -brought the country to an economic and political collapse.

2Portes and Schauffler, p. 40, paraphrasing Hemando de Soto's view of informality in his book _The Other Path._

-6-
<!-- Start of picture text --> ECA LAC EU ECA EU ECA EU<br>panel cross-section pooled cross-section/ts<br>loginvest lnyearsimp<br>8<br>6<br>4<br>2<br>0<br>Log Program Investment/Average years of implementation<br><!-- End of picture text --> Source: Own estimations Figure 3. Number of Citations and Between Paper Correlations (Publication Weight) <!-- Start of picture text --> 0 1 2 3 4 5<br>logcitations<br>.25<br>.2<br>.15<br>pcorr<br>.1<br>.05<br>0<br><!-- End of picture text --> Source: Own estimations If evidence of heterogeneity in the effect of treatment between studies is found, then metaregression can be used to analyze associations between treatment effect and study characteristics. This is one of the main reasons to incorporate characteristics such as peer reviewing, downloads, citations, pages, and so on. The descriptive graphs impose a benchmark on the process of summing up research findings. It also helps represent findings in a more differentiated and sophisticated manner than conventional statistics. For instance they can show depictions of relationships across studies that are obscured in summary statistics and protects against over-interpreting differences across studies. They can also handle a large numbers of studies (this would overwhelm traditional approaches to review). Figure 4 shows papers’ comparability fit given their precision and standard errors of their precision with a single criterion for all pooled papers. In a similar fashion figure A1 (Annex 1) shows precision and comparability of those studies with similar methods used to identify impacts.

20
basic model allowing for measurement error in the PPPs not based on price surveys can explain almost half the variance in the proportionate changes in price levels.

This degree of explanatory power certainly does not eliminate concerns about the comparability of PPPs between ICP rounds. The dynamic Penn effect alone still leaves almost 80% of the variance in the proportionate changes in the price level index between 1993 and 2005 unexplained, although this drops to 60% or less once one allows for the measurement errors associated with the need to estimate PPPs econometrically for the countries that did not participate in the 1993 ICP. However, the results of this paper do cast doubt on the extreme view of “PPP non-comparability” in which past ICP rounds are essentially ignored at each update.

The results of this paper do not suggest that China’s new PPP is as “weird” or “implausible” as some observers have claimed. Given China’s high growth rate, it is not too surprising that the country’s price level index rose appreciably between the 1993 and 2005. This paper’s calculations suggest that about two thirds of that increase is accountable to the dynamic Penn effect. The bulk of the remainder may well reflect an upward bias in China’s PPP due to the 2005 ICP’s weak coverage of China’s rural areas.

What light do these findings throw on the substantial data revisions implied by the 2005 ICP, such as illustrated by Figure 1 for real GDP? The current methods used to update PPPs between ICP rounds do not allow for the dynamic Penn effect identified here (yet the extrapolations used to fill in missing PPPs in a given benchmark year are explicitly based on the static Penn effect). The results of this paper suggest an alternative approach in which the dynamic Penn effect would be brought explicitly into the inter-temporal extrapolations for the price-level index, using market exchange rates to back out the implied PPPs for non-benchmark years. This method yields more reliable estimates than the widely-used inflation-adjustment method for updating PPPs between ICP rounds.

The upshot of all this is that many of the large revisions to real GDP data in Figure 1 could have been avoided by exploiting some simple but neglected insights from the original Balassa-Samuelson model—ironically, the same model that had helped motivate the considerable international statistical effort since the 1960s to collect price data for measuring PPPs—so as to better understand how price levels evolve over time in developing countries.

19
Table 1: State and locally-managed schools in São Paulo (2009)

||||**Students**|||||**Schools**|||
|---|---|---|---|---|---|---|---|---|---|---|
||Total|Pre-K<br>**Sc**|Primary<br>Education<br>**hools shutd**|Lower<br>Secondary<br>**own, exten**|Upper<br>Secondary<br>**sion of the wi**|Total<br>**nter-break**|Pre-K|Primary<br>Education|Lower<br>Secondary|Upper<br>Secondary|
|State-managed schools|4,338,887|1,193<br>locat|852,116<br>ed in all the|1,870,373<br>645 municipa|1,615,205<br>lities in the sta|9,787<br>te of São Paulo|39|2,144|3,833|3,771|
|Locally-managed schools|1,202,386|454,499<br>located i|454,703<br>n 13 out of t|281,392<br>he 645 munic|11,792<br>ipalities sin the|3,170<br> state of São P|1,579<br>aulo|972|596|23|
|Total|5,541,273|455,692|1,306,819|2,151,765|1,626,997|12,957|1,618|3,116|4,429|3,794|
|||**No s**|**chool shutd**|**own, no ex**|**tension of the **|**winter break**|||||
|Locally-managed|2,458,858|727,745<br>located i|1,380,053<br>n 632 out of|326,645<br>the 645 muni|24,415<br>cipalities in the|12,192<br> state of São P|6,204<br>aulo|4,956|947|85|
||**% o**|**f students **|**afected by **|**the shutdo**|**wns**|**% of**|**school**|**s afected b**|**y the shutd**|**owns**|
||||69.2%|||||51.5%|||

Notes: Pre-K: Kindergarten and pre-school. Primary Education: first to fifth grades. Lower secondary: sixth to ninth grades. Upper secondary education: tenth to twelfth grades (high school). The locally-managed schools: schools managed by the local authorities of the 645 municipalities in São Paulo. The state-managed schools: schools managed by the state government of São Paulo. Source: Census of Education, 2009.
Agreement. Some developed country Members have proposed expansion of the category of prohibited subsidies.<sup>68</sup> In November 2007, the Chairman circulated a proposed text<sup>69</sup> and this became the focus of intensified negotiations. It is not possible to predict what changes to existing subsidy disciplines, if any, may result from these negotiations.

# **_3. Other WTO Provisions of Relevance for SEZs_**

## **3.1 Most favored nation treatment; GATT Article I**

A cornerstone of the WTO system is the most favored nation (MFN) principle. It is a legal requirement to accord equal treatment to all Member nations regarding covered trade measures. (WTO approved regional trade agreements are an exception. See GATT Article XXIV.) The MFN principle applies to trade in goods (GATT Article I), trade in services (GATS Article II) and the protection of intellectual property rights (TRIPS Article 4). The denial of MFN treatment has been an issue in a number of WTO disputes. See, e.g., EC – Bananas III, DS27 (1997) (export certificate requirements accorded an advantage to some Members only); Indonesia-Autos, DS54, DS55, DS59, DS64 (1998) (duty and sales exemptions accorded to Korean auto imports were not accorded unconditionally to like products from other Members).

**The MFN principle might be contravened if a government imposes measures that discriminate in law or fact between goods (or services) based upon the country of origin.**

## **3.2 National treatment; GATT Article III**

A second cornerstone of the WTO system is the national treatment principle. National treatment imposes an obligation of non-discrimination between domestic and imported goods. It is a principle incorporated in GATT, Article III and GATS, Article XVII. The denial of national treatment has been a frequent issue in WTO disputes. See, e.g., JapanAlcoholic Beverages II, DS8, DS10, DS 11 (1996)(Japan taxed _shochu_ – an indigenous alcohol – at a lower rate than imported alcoholic beverages); Indonesia-Autos, DS54, DS55, DS59, DS64 (1998)(Indonesian cars taxed at a lower rate than imported cars). Prohibited subsidies involving the purchase of domestic over imported goods also may contravene GATT Article III. See, e.g., China-Certain Measures Granting Refunds, Reductions or Exemptions from Taxes and Other Payments, WT/DS/358/13 (2007). The Agreement on Trade-Related Investment Measures, discussed below, also prohibits measures inconsistent with national treatment.

**The national treatment principle might be contravened if a government imposes measures that discriminate in favor of domestic over foreign goods or services.**

> 68   See Subsidies Enforcement Annual Report to the Congress, Joint Report of the Office of the United States Trade Representative and the U.S. Department of Commerce, February 2008. 69   See TN/RL/W/213.

25
# **References**

- ACAPS. 2017. “Historical Review - Rohingya Influx since 1978.” The Assessment Capacities Project .

- **URL:** _https://reliefweb.int/report/bangladesh/historical-review-rohingya-influx-1978_

- Alix-Garcia, Jennifer and David Saah. 2010. “The Effect of Refugee Inflows on Host Communities: Evidence from Tanzania.” The World Bank Economic Review 24(1):148–170.

- Alix-Garcia, Jennifer, Sarah Walker, Anne Bartlett, Harun Onder and Apurva Sanghi. 2018. “Do Refugee Camps Help or Hurt Hosts? The Case of Kakuma, Kenya.” <u>Journal</u> of Development Economics 130:66–83.

- Amnesty. 2017. “Myanmar: Crimes Against Humanity Terrorize and Drive Rohingya Out.” Amnest International . <u>y</u>

- **URL:** _https://www.amnesty.org/en/latest/news/2017/10/myanmar-new-evidence-ofsystematic-campaign-to-terrorize-and-drive-rohingya-out/_

- Anti, Sebastian and Colette Salemi. 2021. “Hungry Hosts? Refugee Camps and Host Community Nutritional Outcomes in SubSaharan Africa.” Research in Agricultural and Applied Economics Working Paper .

- Baez, Javier E. 2011. “Civil Wars Beyond their Borders: The Human Capital and Health Consequences of Hosting Refugees.” <u>Journal</u> of Development Economics 96(2):391–408.

- Bilinski, Alyssa and Laura A. Hatfield. 2018. “Nothing to See Here? Non-inferiority Approaches to Parallel Trends and Other Model Assumptions.” ArXiv e-prints .

- Burbidge, John B., Lonnie Magee and A. Leslie Robb. 1988. “Alternative Transformations to Handle Extreme Values of the Dependent Variable.” <u>Journal</u> of the American Statistical Association 83(401):123–127.

- CFR. 2020. “The Rohingya Crisis.” Council on Foreign Relations . **URL:** _https://www.cfr.org/backgrounder/rohingya-crisis_

25
# 1. **Introduction**

International migration offers individuals the potential to experience immediate and large gains in their income, with these gains far exceeding those from any other development policy intervention (Clemens et al, 2009; McKenzie et al, 2010). These gains are typically shared with household members accompanying them, and through remittances, with family members (and others) remaining in their home country. Both the number of migrants and the remittances they have sent have grown rapidly over the past two decades. The number of people living outside their country of birth totaled 232 million in 2013, up from 154 million in 1990,<sup>1</sup> while recorded remittances sent to developing countries were US$321 billion in 2010, compared to only $49 billion in 1990.<sup>2</sup> In addition to the direct effects of higher incomes, research has found migration to provide a number of other positive benefits to the sending country, including facilitation of trade, technology transfer and foreign direct investment, providing incentives for greater investment in globally marketable skills, and transmission of democratic norms.

However, these positive impacts of migration are also accompanied by concerns over a number of potential costs. These include concerns about human trafficking and abuse of migrant rights; concerns about the abilities of households receiving large, temporary flows of remittances to save appropriately; concerns that the benefits of migration do not spread far beyond the immediate household; and concerns that developing countries are losing the positive externalities of high skilled workers.

There is increasing interest among development institutions and developing country governments in identifying policies that can enhance the development impacts of international migration, by facilitating more of the benefits and mitigating some of these potential costs. This has led to the deployment of a wide range of migration policies that occur throughout the different stages of the migration process (pre-departure, while the migrant is abroad, and upon possible return), and which cover a wide range of different areas (financial sector, social protection, employment policies, etc.).

> 1 <u>http://esa.un.org/unmigration/documents/The_number_of_international_migrants.pdf</u> [accessed May 5, 2014] 2 Amounts are expressed in constant 2011 US dollars, and sourced from the World Development Indicators, as reported in Clemens and McKenzie (2014). Clemens and McKenzie (2014) note that much of this increase in remittances likely reflects changes in measurement, rather than genuine growth.

2
**<u>Table 9: Impact of reservation on willingness to contribute money to public good provision</u>**

||**Total contribution**<br>**willing to make**|**Water, schools,**<br>**health, sanitation**|**Roads, electricity,**<br>**lights, irrigation**|**Credit, schemes,**<br>**NRM, communic.**|
|---|---|---|---|---|
|Reserved now|0.222***|0.0172|0.0693***|0.135***|
||(6.378)|(0.912)|(4.233)|(7.420)|
|Reserved now|-0.0755***|0.0301|-0.0465***|-0.0590***|
|* female|(-2.749)|(1.564)|(-2.836)|(-3.598)|
|Reserved lag1|0.359***|0.0604***|0.161***|0.138***|
||(8.092)|(2.860)|(8.199)|(6.413)|
|Reserved lag1|<br>-0.0740**|<br>0.0513***|<br>-0.0676***|<br>-0.0578***|
|* female|(-2.556)|(2.619)|(-3.979)|(-3.470)|
|Observations|24,071|24,071|24,071|24,071|
|R-squared|0.659|0.508|0.514|0.419|
|Tests|21.46***|6.16**|1.96|23.77***|
||47.29***|29.53***|24.74***|18.15***|
|**_Model 2_**|||||
|Reserved now|0.305***|0.0142|0.113***|0.178***|
||(7.162)|(0.666)|(5.820)|(8.060)|
|Reserved now|<br>-0.0707**|<br>0.0393*|<br>-0.0565***|<br>-0.0535***|
|* female|(-2.138)|(1.769)|(-2.972)|(-2.707)|
|Reserved lag1|0.453***|0.0793***|0.201***|0.173***|
||(8.352)|(3.203)|(8.687)|(6.662)|
|Reserved lag1|<br>-0.0641*|<br>0.0676***|<br>-0.0768***|<br>-0.0549***|
|<br>* female|(-1.822)|(2.980)|(-3.905)|(-2.743)|
|Reserved lag2|-0.108**|-0.123***|-0.00486|0.0195|
||(-2.163)|(-5.024)|(-0.224)|(0.818)|
|Reserved lag2|<br>0.0261|<br>0.0469**|<br>-0.0434**|<br>0.0226|
|<br>* female|(0.802)|(2.129)|(-2.202)|(1.208)|
|Observations|<br>18,476|<br>18,476|<br>18,476|<br>18,476|
|R-squared|<br>0.634|<br>0.479|<br>0.509|<br>0.417|
|Tests|37.89***|6.04**|8.85***|42.90***|
||58.69***|36.90***|31.39***|26.21***|
||3.62*|10.17***|5.61**|4.21*|

Note: In each column, the dependent variable is the total amount of voluntary contributions (in Rs. 100 increments) the respondent was willing to make for the category in question.

Regressions are clustered at household level and information on education, age, gender, marriage status, household size as well as dummies for cast, religion, land ownership, and overall wealth are included in all regressions but not reported.

28
WPS 3343

# Linking Representative Household Models with Household Surveys for Poverty Analysis A Comparison of Alternative Methodologies

Pierre-Richard Agénor<sup>∗</sup> , Derek H. C. Chen,<sup>∗</sup> and Michael Grimm<sup>∗∗</sup>

## Abstract

We compare three approaches to linking representative-household macro models with micro household income data in terms of their implications for measuring the poverty and distributional effects of policy shocks. These approaches are a simple micro-accounting method, an extension of that method to account for changes in employment structure, and the Beta distribution approach. Even though in our simulation exercises the three methods do not lead to fundamentally different results in absolute terms, we show that potential differences in the measurement of distributional and poverty effects of policy shocks can be very large.

JEL Codes: C68, D31, D58, I32, O11.

Keywords: Applied General Equilibrium Models, Poverty, Income Distribution, Policy Evaluation.

> ∗The World Bank; and ∗∗Department of Economics, University of Göttingen, and DIAL, Paris. We are grateful to various colleagues and seminar participants for comments and discussions. The views expressed in this paper do not necessarily represent those of the World Bank.

1
Table 5 shows considerable variation in the prevalence of child undernutrition by region of Ethiopia. In 2013/14 stunting ranged from 32% in Oromia to 55% in Amhara, while wasting was lowest in SNNP (8%) and highest in other regions (20%). There are also substantial differences across regions in the change in undernutrition rates between 2013/14 and 2015/16. For example, stunting declined in Tigray and Amhara but increased in Oromia and SNNP.<sup>21</sup> While stunting declined in Tigray, wasting increased.

**Table 5: Nutritional status by area and region, 2013/14 and 2015/16 (% below -2 S.D) for children aged 6 to 59 months old**

||**Stun**|**ting**|**Was**|**ting**|
|---|---|---|---|---|
|**Description**|**2013/14**|**2015/16**|**2013/14**|**2015/16**|
|**_Area_**|||||
|Rural|41.5|43.8|11.3|11.1|
|Small town|24.6|37.2|7.8|9.9|
|**_Region_**|||||
|Tigray|49.9|46.7|11.4|18.6|
|SNNP|40.7|44.1|7.8|9.7|
|Amhara|55.1|45.2|10|9.8|
|Oromia|31.5|40.1|11.8|9.2|
|Other regions|34.8|37.0|19.4|24.4|

# **5.2 Results from the Panel Data Fixed Effects Analysis**

We now turn to the panel data fixed effects<sup>22</sup> model to examine if changes in crop prices are associated with changes in child nutritional outcomes. Table 6 presents the results of changes in crop prices on height for age.

**Table 6: Fixed effects regression results of crop prices on height for age (stunting)**

|**VARIABLES**|**(1)**<br>**Other**<br>**Covariates (Prob**<br>**> F= 0.0000)**|**(2)**<br>**Teff Prices**<br>**(Prob > F**<br>**0.0000)**|**(3)**<br>**Wheat Prices**<br>**(Prob > F =**<br>**0.0000)**|**(4)**<br> <br>**Maize Prices**<br>**(Prob > F =**<br>**0.0000)**|
|---|---|---|---|---|
|Age|-0.0318**|-0.0578***|-0.0383**|-0.0390**|
||(0.0150)|(0.0163)|(0.0157)|(0.0152)|
|Age squared|0.000372*|0.000332|0.000432*|0.000398*|

21 There was a drought in some regions when the 2015/16 survey was undertaken. This could have contributed to the increase in child undernutrition rates in most of the regions between the two waves. 22 A Hausman test was used to determine whether a fixed effects or a random effects model was relevant. Our p- value was less than 0.05 for all the specifications, rejecting the null hypothesis that our fixed effects are not consistent and efficient.

11
and long-term bond mutual funds, pension funds, and insurance companies) based on detailed asset-level time-series holdings between 1996 and 2005. We focus most of the analysis on Chilean asset-management institutions (mutual and pension funds). We compare their maturity structure to each other, to that of US mutual funds (a developed country benchmark), and to that of Chilean insurance companies (an asset-liability management benchmark).

The empirical approach followed in this paper has important advantages for understanding how much emerging market investors demand long-term instruments, and what factors affect their maturity structure decisions. First, the comparison across different types of institutional investors is particularly insightful since it is difficult to identify on theoretical grounds the optimal maturity structure for a given investor; it depends on factors such as the investor’s goals, preferences, and the markets in which it participates (Campbell and Viceira, 2002). Comparing the maturity structure of different investors allows us to have benchmarks and test whether one type of investor is significantly more tilted toward the short term. It also allows us to control for specific sources of variation in the maturity structure across investors and asset classes. In particular, the comparison between institutional investors in Chile and the US sheds light on how much further emerging economies might be able to extend the maturity of their fixed-income assets by promoting the growth of domestic institutional investors. The Chilean institutional investors we analyze are relatively developed and sophisticated, managing professionally a large pool of assets. So they should be able to take on risk and invest long term. In addition, the within-country comparison across investor types helps us shed additional light on the role that different factors play in shaping the maturity 4