rafmacalaba/datause-encoder-data huggingface dataset:
# **Works Cited**

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- Deaton, Angus, and Valerie Kozel. 2005. “Data and Dogma: The Great Indian Poverty Debate.” _The World Bank Research Observer_ 20 (2): 177–99. doi:10.1093/wbro/lki009.

- Dhaliwal, Iqbal, Esther Duflo, Rachel Glennerster, and Caitlin Tulloch. 2013. “Comparative Cost-Effectiveness Analysis to Inform Policy in Developing Countries.” In _Education Policy in Developing Countries_ , edited by Paul Glewwe. University of Chicago Press.

- Duflo, Esther, Pascaline Dupas, and Michael Kremer. 2012. “School Governance, Teacher Incentives, and Pupil-Teacher Ratios: Experimental Evidence from Kenyan Primary Schools.”

- Evans, David K, and Arkadipta Ghosh. 2008. _Prioritizing Educational Investments in Children in the Developing World Prioritizing Educational Investments in Children in the Developing World_ .

- Evans, David, Michael Kremer, and Muthoni Ngatia. 2008. _The Impact of Distributing School Uniforms on Children’s Education in Kenya_ . Mimeo.

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combined to assign a probability to the likelihood that the policy-imposing country will comply with WTO rulings and implement a change in policy that results in market access.

In economic terms, the purpose of combining such economic, legal and political expertise is to provide the exporting firm with an _expected value_ of the payoff to pursuing a WTO dispute, so that the firm can weigh that expected benefit against the expected cost of engaging in WTO dispute settlement.<sup>19</sup> Only an exporter that is informed about the expected benefits and costs of engaging the WTO’s self-enforcement system can make rational decisions regarding whether to use it.

This section has presented a basic template by which experts in economics, law, and politics would band together to use data provided by monitoring initiatives such as the Global Antidumping Database and GTA to generate and better package information of use to exporters in developing countries. We have not yet addressed the reality of resource limitations and the need for public good-minded analysts to prioritise their efforts based on (i) the (demand side) needs of particular interest to developing countries, (ii) the types of trade barriers being imposed (supply side), and (iii) the largest gap of recession. Furthermore, consider an example like the US bailout of the automobile sector that we discuss in more detail below. In this case, there was an almost simultaneous demand shock (in 2008) induced by high petrol prices and increased recognition of the impact of climate change that may have changed the tastes and preferences of consumers towards different types (of more fuel-efficient) cars.

19 Of course an expected value is only one summary statistic – the exporter is also likely to be interested in the statistical _distribution_ of possible outcomes to a potential dispute. This includes its expected payoff under various forms of ‘partial’ compliance, whether that partiality in compliance is due to differences in the timing of implementation or the completeness with which the trade barrier is removed.

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Table 2: Diagnostic of merging process between survey and tax records

|20|+ population (survey)|Tax data observations|Share obs from tax data (%)|Merging point (L)|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|2003|3,165,874|3,169|0.10|595,000|
|2004|3,347,641|6,708|0.20|445,000|
|2005|3,489,479|6,992|0.20|505,000|
|2006|3,583,144|7,180|0.20|580,000|
|2007|3,715,919|7,446|0.20|670,000|
|2008|3,917,356|1,959|0.05|1,440,000|
|2009|4,037,917|4,041|0.10|755,000|
|2010|4,209,107|3,369|0.08|1,110,000|
|2011|4,377,335|13,171|0.30|520,000|
|2012|4,464,598|17,931|0.40|480,000|
|2013|4,588,901|41,667|0.90|320,000|
|2014|4,494,971|22,587|0.50|475,000|
|2015|4,671,707|28,198|0.60|475,000|
|2016|4,814,880|38,834|0.80|425,000|
|2017|4,942,283|44,886|0.90|395,000|
|2018|5,199,944|52,520|1.00|365,000|
|2019|5,389,481|32,532|0.60|465,000|

_Note:_ This table summarizes the merging process between household survey microdata and administrative tax recordes. Column (1) presents the number of adults (20+) represented in the survey data, our underlying population of interest. Column (2) presents the number of individual observations from tax data that are used in the final microdataset, i.e. that replace high-income individuals from the survey, while column (3) presents the same number as a share of the reference population. Finally, Column (4) presents the merging point between the two surveys - below that income level the final datasets includes only survey information, while above it includes only tax records.

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- Minaya, V. and Scott-Clayton, J. (2020). Labor Market Trajectories for Community College Graduates: How Returns to Certificates and Associate’s degrees Evolve Over Time. _Education Finance and Policy_ , pages 1–62.

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- Mountjoy, J. (2022). Community Colleges and Upward Mobility. _American Economic Review_ , Forthcoming.

- Rodríguez, J., Urzúa, S., and Reyes, L. (2016). Heterogeneous Economic Returns to PostSecondary Degrees: Evidence from Chile. _Journal of Human Resources_ , 51(2):416–460. Publisher: University of Wisconsin Press.

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- Słoczyński, T. (2021). When Should We (Not) Interpret Linear IV Estimands as LATE? _arXiv:2011.06695 [econ, stat]_ . arXiv: 2011.06695.

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**Table 7: Impact of Reform by Pre-Reform Occupation**

||De|pendent variable:||
|---|---|---|---|
||Low-risk|Low-risk|High-risk|
||registered business<br>owner dummy|wage earner<br>dummy|wage earner<br>dummy|
||(1)|(2)|(3)|
|Panel A: Impact on allgroups||||
|SARE*Past registered owner|0.0053|0.0104*|-0.0043|
||(0.0086)|(0.0057)|(0.0061)|
|SARE*Past non-registered owner|-0.0004|0.0031|-0.0062**|
||(0.0066)|(0.0089)|(0.0026)|
|SARE*Past wage earner|0.0021|0.0051|-0.0125**|
||(0.0014)|(0.0045)|(0.0049)|
|SARE*Past not employed|0.0000|0.0124***|-0.0022|
||(0.0017)|(0.0028)|(0.0028)|
|R-squared|0.347|0.198|0.252|
|No. of observations|1,051,274|1,051,274|1,051,274|
|Panel B: Impact on wage earners||||
|SARE*Past low-risk wage earner|0.0026|-0.0013|-0.0057*|
||(0.0020)|(0.0056)|(0.0031)|
|SARE*Past high-risk wage earner|0.0016|0.0067**|-0.0123**|
||(0.0015)|(0.0032)|(0.0059)|
|R-squared|0.018|0.400|0.595|
|No. of observations|520,804|520,804|520,804|

_Note:_ Standard errors in parentheses (clustered at municipality level). Past occupation variables are pre-reform occupations when the person was first observed. The past not employed include the unemployed, people who are not in the labor force, and the small fraction of people who say that they are not currently working, but have a job lined up that they are planning to start within one month. Regressions include quarter and municipality fixed effects, as well as dummies for occupation interacted with quarter and municipality fixed effects, and individual background variables. They also include 1999 municipality log GDP per capita, log number of economic establishments per 1000 capita, log fixed assets per capita, and log investment per capita, all interacted with a linear time trend. Significance levels: * 10%, ** 5%, *** 1% 41
Current Account of the Balance of Payments For our purpose we need to distinguish in the current account of the balance of payments total exports and imports, interest payments per sector, total profit remittances, foreign transfers to the private sector and the public sector and worker remittances. We also distinguish among imports or investment goods, consumption goods and intermediate goods. For this purpose additional information was obtained based on customs statistics.

To preserve the original consistency between the balance of payments data and the national accounts, we adopted the somewhat peculiar system of the Turkish authorities. They consider all current account transactions except interest payments, profit remittances and private and official workers remittances<sup>2</sup> as goods and non-factor services. Hence, the resource balance includes some transfer items that are assumed to be goods and non-factor services by Turkish officials. Private worker remittances of Table A1.3 correspond with worker remittances in Table Al.4, foreign transfers to the budget in Table A1.4 correspond with official worker remittances in Table A1.3 while total profit remittances and interest payments in Table A1.4 are the net of the corresponding debit and credit entries of Table A1.3. Transfers debit, private other transfers credit and public transfers credit from Table A1.3 are added to exports and imports, respectively, in Table A1.4. In 2 This category consists mainly of payments by Turkish nationals abroad in lieu of military service.

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time and across trading partners, measurement error is much less of a concern for two of our main questions of interest addressed below: _intertemporal changes_ (i.e., whether the scope of imported products subject to a country’s use of TTBs is increasing or decreasing over time) and the _relative exporter incidence_ (i.e., whether certain exporters are relatively more or less frequently targeted than others by the stock of imposed TTBs).

Second, my approach concentrates entirely on the potential first‐order impact of TTBs on trade. There is a substantial theoretical and empirical literature from case studies that identifies potentially important second‐order effects of TTBs (especially antidumping) on trade flows. Some accentuate the potential negative trade effects beyond what I identify here, while others are offsetting and reduce the overall size of the trade effects.<sup>11</sup> Third, even trade‐weighting the incidence of TTBs does nothing to address heterogeneity in the size of the imposed trade barriers. Bown (2010c), for example, notes substantial heterogeneity in the size of duties imposed across both policy‐imposing economies and across targeted exporters by (within) a policy‐imposing country, especially with respect to barriers imposed on imports from China.

Fourth, I also do not address potential heterogeneity to the _form_ of the applied TTBs. For example, some economies apply antidumping as ad valorem duties, others may be more likely (or against certain trading partners or over certain imported products) to apply it as a specific duty or a ‘price undertaking’ in which the exporter voluntarily raises its price above some threshold under the threat of an imposed duty. Global safeguards, on the other hand, are frequently applied as quantitative restrictions such as tariff rate quotas.<sup>12</sup>

> 11 Excellent surveys of the antidumping literature that review these effects include Blonigen and Prusa (2003) and Nelson (2006). Examples of accentuating effects include downstream impacts, tariff‐jumping foreign direct investment, and retaliation, while examples of offsetting effects include trade diversion. For a recent discussion and a relatively comprehensive list of such effects, see Vandenbussche and Zanardi (2010).

> 12 I also do not address the issue of the likely import demand or export supply responses to the imposed TTBs, because I do not control for import demand or export supply elasticities as in the Overall Trade Restrictiveness Index (OTRI) approach of Kee, Nicita and Olarreaga (2008, 2009). For an application of the OTRI to the global economic crisis of 2008‐2009, see Kee, Neagu, and Nicita (2010).

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**Figure 7. CO2 intensity and fuel exports as indicators of exposure to transition risks**

<!-- Start of picture text --> A. CO2 intensity, 1995‐2014 B. CO2 Intensity by region<br>3.5<br>3.0<br>3.0<br>2.8<br>2.5<br>2.6<br>2.0<br>2.4<br>1.5<br>2.2<br>1.0<br>2.0<br>0.5<br>1.8<br>0.0<br>1.6<br>1.4<br>EAP ECA LAC MENA SA SSA<br>Note: Regional median per period in blue. 25 th and 75 th percentiles are<br>Note: Country median per year. 75th and 25th percentiles in dotted lines<br>bottom and top of box, respectively<br>C. Fuel exports (% of GDP), 1995‐2017 D. Fuel exports (% of GDP) by region<br>8 8 50<br>7 7 45<br>40<br>6<br>6 35<br>5 30<br>5<br>4 25<br>4 3 20<br>15<br>3 2<br>10<br>2 1 5<br>0 0<br>1<br>0<br>EAP ECA LAC MENA SA SSA<br>Note: Country median per year. 75th and 25th percentiles in dotted lines Note: Regional median per period in blue. 25 th and 75 th percentiles are bottom<br>and top of box, respectively<br>CO2 intensity (kg per kg oil equ. energy use)<br>CO2 intensity (kg per kg oil equ. energy use)<br>1995‐02 2003‐10 2011‐14 1995‐02 2003‐10 2011‐14 1995‐02 2003‐10 2011‐14 1995‐02 2003‐10 2011‐14 1995‐02 2003‐10 2011‐14 1995‐02 2003‐10<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>ports (% GDP)<br>el e<br>Fuel exports (% GDP)<br>Fuel exports (% GDP)<br>MENA F<br>1995‐02 2003‐10 2011‐17 1995‐02 2003‐10 2011‐17 1995‐02 2003‐10 2011‐17 1995‐02 2003‐10 2011‐17 1995‐02 2003‐10 2011‐17 1995‐02 2003‐10 2011‐17<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br><!-- End of picture text --> 32
a checkup every six months. Older children have about .1 visits. We examine curative care conditional on the child’s experiencing a symptom, including fever, diarrhea, digestive track problems, respiratory infection, malaria, and skin rash. About one in four children who experienced illness symptoms sought treatment.

# **e. Child Health Outcomes**

We measure child health outcomes for two age groups: children 0-11 months and 24-47 months. Children in the 0-11 months range in the treatment group in the 2008 survey would have been exposed to P4P during the full prenatal period and during the full time after birth. Children between 24 and 47 months in the treatment group in the 2008 survey would have been exposed to P4P for 18-23 months during the early stages of life, but not during the prenatal period.

We consider two measures of health: (i) height-for-age z-score and (ii) weight-for-age z- score. A child’s height results from her genetic potential, adjusted for insufficient nutrient intake and inability to absorb nutrition because of illness. Therefore, height is a summary measure of health and nutrition since conception. By contrast, weight is an indicator of current nutrition and illness status and does not represent factors that accumulate over the lifetime. Better prenatal care, which includes nutritional advice to mothers and the diagnosis and treatment of maternal illness, could in principle increase both infant height and weight. Better child preventive care, which includes vaccination and growth monitoring, and child curative care, which limits the duration and severity of illness, also have the potential to affect height and weight.

We measured child height and weight using standard international procedures and portable scales and stadiometers, which were recalibrated on a twice-weekly basis in the field. As part of the quality control procedures, all children were measured twice during the visit. We first report height and weight in centimeters and kilograms respectively. Then, we standardize these measurements into height-for-age and weight-for-age z-scores in accordance with World Health Organization guidelines. The z-scores measure the number of standard deviations from age-sex standardized height of a healthy (U.S.) reference population. Finally, we classify children with a height-for-age or weight-for-age below -2 SD of the corresponding reference median (Z score < or = -2) are classified as stunted or underweight, respectively.

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4 by Kim and Henderson (2005), or the negative output and employment effects reported by Cordero (2007).

At the empirical front, Svensson (2007, p. 3) states that “there is no evidence that inflation targeting has been detrimental to growth productivity, employment, or other measures of economic performance”, a view supported by Dotsey (2006) in his comparison of 5 industrial countries that have been targeting inflation for at least 10 years and 6 non-IT industrial countries. Similarly, Ball and Sheridan (2005) find no meaningful effect of IT on price stability, exchange rate volatility, long-run interest rates or output growth when they employ a cross section difference-in-difference model to explore the effects of IT in a sample of 20 OECD countries.

Also, regarding the more fundamental empirical relation between inflation and economic growth, the existing body of evidence does not reach a consensus but does establish important differences between industrial and emerging economies. For instance, using data for 12 Latin American countries during the period 1950-1985, De Gregorio (1993) documents statistically significant and negative effects of average inflation on growth. Meanwhile, recent evidence by Pollin and Zhu (2005) for OECD countries shows that no clear pattern emerges at all between inflation and output growth.

Although the general issue of collateral benefits having an impact on GDP has been conjectured by Kose et al. (2006), we are not aware of empirical work focusing on any benefits of monetary policy _jointly_ with advances in the globalization of financial flows. The availability of the Lane and Milesi-Ferretti (2007) data and of recent theoretical constructions by Kose et al. (2006) and Evans and Hnatkovska (2007) make the proposed research route worth exploring. In this paper we examine the effects of IT
CONSUMPTION SUB-AGGREGATES 2 on other, more readily available data sources and consumption prediction methods, we explore whether less comprehensive consumption measures, or consumption sub-aggregates, that are also easier and less expensive to collect, might not suffice as an information base to track poverty over time. We draw on demand theory to establish the conditions under which this might be the case, and subsequently explore empirically whether these conditions hold in practice given the experience from a couple of different country settings.

The idea is inspired by Lanjouw and Lanjouw (2001) who imagine predicting poverty with food consumption only. Provided that Engel’s law holds (so that the food share is larger for the poor than for the nonpoor) and given a particular method of adjusting the poverty line when moving between expenditure aggregates,<sup>2</sup> the Lanjouws demonstrate that the Foster-Greer-Thorbecke (FGT) family of poverty measures will yield weakly smaller measured poverty when one uses only food expenditures rather than all expenditures.

This raises the question: Under what circumstances can one construct consistent poverty measures using different expenditure aggregates?<sup>3</sup> We extend the Lanjouws’ result, and show that so long as household welfare can be summarized by total non-durable expenditures, then one can construct consistent welfare measures using smaller aggregates provided that the Engel curve (quantities vs. total expenditures) for the sub-aggregate is linear.

Accordingly, we search for sub-aggregate expenditures that are linear in total expenditure. However, if relative prices within the subaggregate change, then the resulting sub-aggregate expenditures are only guaranteed to _remain_ linear if all of the constituent goods in the aggregate also have linear Engel curves. Thus, the key to constructing a robust proxy for total expenditures that can be used to track changes in poverty over time when prices are changing is to identify particular goods that have linear Engel curves that are fairly insensitive to changes in prices.

We use LSMS data from Rwanda, Tanzania, and Uganda and devise a simple way to identify goods with (nearly) linear Engel curves using data in an initial period. We form a linear sub-aggregate of such goods, and then ask whether this sub-aggregate can be used to accurately measure poverty in a subsequent period. Theory suggests that this will not work if there are significant changes in relative prices. Unfortunately, 2. The “upper bound” method described by Ravallion (1994).

3. In this paper, the tracking of monetary poverty is based on the commonly used “Foster-Greer-Thorbecke” (FGT) family of poverty measures described by Foster, Greer, and Thorbecke (1984).
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# **Figure 2: A Subnational DSA Model – SPVs**<sup>**35**</sup>

<!-- Start of picture text --> SPVs REVENUE AND OPERATING EXPENDITURE SPVs EARNINGS<br>Subnational AND NET INCOME<br>GDP growth<br>rate Sales<br>Initial sales Sales<br>growth rate<br>Elasticity sales<br>w.r.t. GSDP Revenue<br>Other<br>Initial other income Other income Earnings before<br>income growth rate interests, taxes,<br>and depreciation<br>allowance<br>Initial wage Wage Wage (EBITDA)<br>expenditure expenditure expenditure Operating<br>growth rate expenditure<br>(excluding<br>Other deprec.allow.)<br>Initial other Other<br>operating<br>operating operating<br>costs costs growth costs<br>rate<br>SPVs CAPITAL EXPENDITURE (INFRASTRUCTURE)<br>Initial capital Capital Capital<br>Depreciation<br>expenditure expenditure expenditure allowance<br>(infrastructure) growth rate (infrastructure)<br>Net income<br>before taxes<br>SPVs DEBT AND FINANCING NEEDS and resource<br>transfer<br>Interest<br>Debt stock payments<br>on debt Taxes<br>Amortizations<br>of initial debt<br>Interest rate<br>on debt<br>Amortizations Resource transfer<br>Initial debt New debt of new debt LG-SPVs<br>Net income<br>Gross financing after taxes and<br>needs - new debt resource<br>transfer<br><!-- End of picture text --> 35 Exogenous parameters, initial conditions, and exogenous projections are represented by orange objects. Functions and public-finance identities are indicated with arrows. Endogenous variables are represented by blue objects, with debt instruments indicated in yellow. The resource transfer between the local government’s general budget and SPVs is the green object.