rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 7: Convergence tes**<br> <br>|**t: Adjusting for bias**<br>|<br>|
|---|---|---|
|**Period**<br>**Rice**|**Wheat**|**Maize**|
|(1)<br>**Panel A: Adju**|(2)<br>**sting using 1961-198**|(3)<br>**0 estimate**|
|2005-2017<br>2.019|-0.128|-0.321***|
|(0.201)|(0.157)|(0.119)|
|2005-2011<br>3.396|0.611|-0.406***|
|(0.088)|(0.046)|(0.138)|
|2012-2017<br>-0.891***|-1.577***|-1.645***|
|(0.040)|(0.019)|(0.008)|
|**Panel B: Adju**|**sting using 1981-200**|**1 estimate**|
|2005-2017<br>1.903|-0.204*|-0.374***|
|(0.199)|(0.156)|(0.122)|
|2005-2011<br>3.186|0.547|-0.395***|
|(0.059)|(0.042)|(0.135)|
|2012-2017<br>-0.918***|-1.591***|-1.666***|
|(0.041)|(0.018)|(0.010)|

Standard errors in parentheses. *Rejecting convergence at 10 percent, **Rejecting convergence at 5 percent, ***Rejecting convergence at 1 percent.

35
Moreover, all of our countries exhibit weaker bureaucracies and all of our specifications control for initial bureaucratic quality.<sup>8</sup> Previous cross-country research on the causes and effects of bureaucratic quality (e.g., Keefer and Knack 1995, Rauch and Evans 2000) has relied on subjective and broad evaluations of bureaucratic quality, particularly measures of corruption and bureaucratic quality in the _International Country Risk Guide_ . Keefer (2011) shows that these are, in fact, associated with programmatic parties. However, since the focus of these data is on the _performance_ of the public sector, they provide no information on the characteristics of the bureaucratic systems that underlie performance: whether public sector financial management systems are capable of tracking spending, or whether personnel management systems are designed to attract and retain the best and brightest candidates for public sector work. With our data, we are able to focus on specific aspects of bureaucratic organization that politicians can directly influence, abstracting from historical legacies, the socioeconomic environment and other unobservables that are not under the direct control of politicians.

# **A measure of programmatic parties**

The main correlate of interest is the presence of programmatic parties in a country and is constructed from variables in the _Database of Political Institutions_ (DPI, Beck et al. 2001).

They identify whether each of the largest three government parties and the largest

> 8 Our interpretation is also consistent with historical and comparative evidence that even strong bureaucracies often do not constrain political strategies.  For example, despite the strong bureaucratic legacy of British colonial rule in India and Kenya, clientelist appeals are pervasive.  Van de Walle (2003) argues that in the 1990s, parties in African democracies switched to programmatic appeals not because strong bureaucracies pushed them away from clientelist strategies, but because politicians themselves found it difficult to manage clientelist handouts.

20
show that my sample of Social Security increases did not have countercyclical motivations. Stimulus payments were widely spread across states and had simple eligibility rules, making it dicult to target them at specic states.<sup>9</sup> My allocation of transfers across states ( _statesharei,t−_ 4 ) cannot be aected by contemporaneous shocks because they are based on last year's eligibility characteristics.<sup>10</sup> The lagging of eligibility criteria does not prevent an accurate allocation because stimulus payments actually used last year's income for eligibility and demographic characteristics are predetermined and slow-moving over the medium term. For example, the size distribution across states of the 1972Q4 increase in Social Security payments was almost exactly proportional to the 1970Q2 increase in Social Security payments (R-squared of 0.96) (blue circles on the left side of Appendix Figure 1.1).

The secondand perhaps more seriousconcern is omitted variable bias. The rst way to address this is to include state FEs ( _μi_ ) and quarter FEs ( _μt_ ) in all specications. State FEs control for all state-level trends (like the faster growth of Sun Belt states relative to Rust Belt states), and quarter FEs remove aggregate variation (such as the US business cycle, monetary policy, aggregate expectations, or international shocks). The inclusion of state and quarter FEs means that regressions only use variation in transfers and growth both across states and over time, and so any omitted variable would also have to vary across states and over time. Including both eects in all specications drastically reduces the number of potentially confounding variables.

To further reduce the risk of omitted variable bias, I use a research design that involves many transfer policy changes with dierent sizes and timings and over a long period. Through this design, any omitted variable would need to take an unlikely time-varying pattern in high- versus low-transfer states, which is a much higher hurdle for potential confounding variables than in a standard dierence-in-dierence study with a one-o permanent policy change. For example, Social Security stipends increased by 10% in June 1971 and 20%

> 9Voting records reveal that political support for one-o transfers was either mostly partisan (2001) or bipartisan (2008), with no evidence that legislators from slow-growing states were more likely to support the legislation (see Appendix 3.2).

> 10After removing trends and aggregate variation, state growth rates have little quarterly persistence, which removes the possibility of ex-ante targeting states in recession.

10
In Table 5 we summarize the information reported in Table 4. Without zeroing in 30 instances the dumping margin would have been lowered and in 42 instances the margin would have been eliminated, i.e., a zero margin. Said differently, more than half of the cases submitted to the WTO would have no dumping but for the practice of zeroing.

**Table 5 – WTO Disputes – Reported Impact** **<u>of Zeroing (summary)</u>**

|Dumping margin lower|30|
|---|---|
|Dumping margin eliminated|42|
|Dumping margin change “N/A”|2|
|Totalcases|74|

Source: Compiled from the information in Table 4.

One needs to be cautious extrapolating the statistics from the WTO AB cases to all U.S. AD activity. There are two reasons why we are concerned that there is a possible selection issue which might result in the WTO AB evidence overstating the impact of zeroing. First, the cases submitted to the WTO may have been selected precisely because they were particularly egregious examples of zeroing. While we have no evidence for this, it is nevertheless a concern given the complainants desire to submit the most compelling cases to the WTO.

Second, the cases chosen for WTO appeal might have lower margins and thus be more likely to have a zero margin if the practice of zeroing ceased. There is some evidence that this is the case. Using information from Bown (2010a), we compared the dumping margins for cases that were the basis for WTO zeroing complaints with all other U.S. AD cases. The average margin for cases not brought to the WTO is 62.6% – 40 –
<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>1985-1995 1995-2005 2005-2015<br>extension leapfrog infill<br>percentage<br><!-- End of picture text --> Figure 8: **Type of Built-Up Expansion in the Sahel, 1985-2015** . The figure displays the relative proportions of extension, leapfrog, and infill development in the Sahel between 1985 and 2015. Extension clearly dominates, followed by leapfrog development.

sion of the metric to account for the slow-onset nature of droughts. Our key finding is a robust relationship between drought and urban growth. Cities that are surrounded by drought-stricken regions subsequently experience precipitous growth in remotely sensed built-up pixels. Our approach highlights the importance of accounting for spatial dynamics because our results suggest that drought can impact neighboring urban areas. Our results exhibit notable heterogeneity. The relationship we identify is particularly pronounced in low- and middle-income countries. Within the developing world, reliance on rain-fed agriculture further amplifies the effect of drought on growing cities. That said, we find suggestive evidence for the notion that the poorest households in repeatedly drought-stricken areas might be trapped in their current location. Much of climate-induced migration might be driven by households who are bearing the brunt of extreme climate but retain the means to relocate. We our simultaneously supplement 26
13 As long as raw material or other input prices in the other FSU republics remain below world prices (including transaction costs), and infrastructure for trading with other countries is deficient, the Baltics may have an incentive to imnport from the East. A case in point is energy. For example, the switching of oil imports to non-FSU sources in Lithuania was first made difficult for the lack of necessary port infrastructure. The share of energy in total inter-republic imports was highest in Lithuania (35%) compared to Latvia (24%) and Estonia (17%) (see table 4). Dependence on Eastern raw-materials may also arise on technical grounds as standards with the West may be different. Estimates of initial terms of trade gains (losses, table 5) for the Baltics for maintaining trade at distorted prices show that largest "gains" would be in Lithuania

**Table 4: Structure of Baltic Trade with the FSU (1990)**

|**%**|**Estonia**|**Latvia**|**Lithuania**|
|---|---|---|---|
|**Imports**||||
|Energy|17|24|35|
|Capital goods|39|35|33|
|Intermediate goods|31|31|23|
|Consumergoods|-|8|7|
|Food|4|2|2|
|**Exports**||||
|Energy|9|4|20|
|Capital goods|28|37|43|
|Intermediate goods|27|31|20|
|Consumer goods|20|20|7|
|Food|16|8|10|

Source: World Bank (1992a). Includes intra-Baltic trade.
Table 2: Pre-decree summary statistics of civil and commercial caseload

||N|Mean|StD|Median|Min|Max|
|---|---|---|---|---|---|---|
|PANEL A: Case-level characteristics and outcomes|||||||
|Duration of pre-trial hearings (in days)|2665|156.941|146.025|126.000|0.000|980.000|
|Likelihood of pre-trial completion in 4 months|2671|0.487|0.500|0.000|0.000|1.000|
|Duration of decision stage (in days)|2380|63.119|82.701|29.000|14.000|761.000|
|Likelihood of decision completion in 1 month|2405|0.499|0.500|0.000|0.000|1.000|
|Final outcome: Judgment|2639|0.884|0.320|1.000|0.000|1.000|
|Final outcome: Settlement|2639|0.035|0.184|0.000|0.000|1.000|
|Final outcome: Other|2639|0.080|0.272|0.000|0.000|1.000|
|Case fast-tracked to decision stage|2671|0.140|0.347|0.000|0.000|1.000|
|Judge more strict (share)|2287|0.123|0.151|0.063|0.000|1.000|
|Number of pretrial hearings|2671|8.259|6.468|7.000|0.000|42.000|
|Number of decision stage hearings|2405|2.599|3.399|1.000|1.000|36.000|
|Pre-trial likelihood of being heard|2287|0.854|0.149|0.875|0.167|1.000|
|Decision stage likelihood of being heard|2405|0.774|0.246|0.875|0.167|1.000|
|Pre-trial insufcient|2405|0.118|0.323|0.000|0.000|1.000|
|Decision postponed|2405|0.055|0.228|0.000|0.000|1.000|
|Claim amount (in million FCFA)|1675|71.542|339.338|8.000|0.075|7,400.000|
|Number of plaintifs|2541|1.232|1.542|1.000|0.000|38.000|
|Number of plaintifs which are frms|2541|0.541|0.515|1.000|0.000|3.000|
|Number of plaintifs which are private individuals|2541|0.685|1.682|0.000|0.000|38.000|
|Number of defendants|2541|1.318|1.057|1.000|0.000|22.000|
|Number of defendants which are frms|2541|0.579|0.634|1.000|0.000|11.000|
|Number of defendants which are private individuals|2541|0.650|1.072|1.000|0.000|21.000|
|More than one party on either side|2541|0.253|0.435|0.000|0.000|1.000|
|PANEL B: Party-level characteristics|||||||
|2012 revenues (in billion FCFA)|1992|21.806|81.054|2.516|0.000|720.057|
|2012 revenues (IHS transformation)|1992|20.443|6.349|22.339|0.000|27.996|

<u>Notes:</u> Baseline summary statistics, for cases entering between 38 and 4 hearings before decree application. 2671 baseline observations for pre-trial and overall outcomes, except for rows 1, 5-7 (censoring), 9 (only for cases that have any adjournments), 12 (only for cases that have more than one hearing). 2405 baseline observations for decision stage outcomes, except for row 3 (censoring). Fewer observations for case characteristics 16-22 (not available for all cases). IHS transformation: Inverse hyperbolic sine transformation.

41
- Dynarski, S., B. Jacob, and D. Kreisman. 2016. “The Fixed-Effects Model in Returns to Schooling and Its Application to Community Colleges: A Methodological Note.” New York, NY: Center for Analysis of Postsecondary Education and Formal Employment.

- Falch, T., J.M.V. Iversen, O.H. Nyhus, and B. Strøm, 2022. Quality Measures in Higher Education: Norwegian Evidence. _Economics of Education Review_ , _87_ , p.102235.

- <mark>Ferreyra, M. M., L. Dinarte Diaz, S. Urzua, and M. Bassi. 2021. “T</mark> he Fast Track to New Skills: Short-Cycle Higher Education Programs in LAC.” _Directions in Development._ Washington, DC: World Bank. Forthcoming.

- Garcia, L., and M. Ospina. 2019. _Valor Agregado y Educación Técnica y Tecnológica en Colombia._ Unpublished manuscript.

- <mark>Hastings, J. S., C. A. Neilson, and S. D. Zimmerman. 2013. “Are Some Degrees Worth More Than Others? Evidence from College Admission Cutoffs in Chile.” (No. w19241). National Bureau of Economic Research.</mark>

- <mark>Hastings, J. S., C. A. Neilson, A. Ramirez, and S. D. Zimmerman. 2016. “(Un)informed College and Major Choice: Evidence from Linked Survey and Administrative Data.”</mark> _<mark>Economics of Education Review</mark>_ <mark>51:136–51.</mark>

- <mark>Hoekstra, M. 2009. “The Effect of Attending the Flagship State University on Earnings: A Discontinuity-Based Approach.”</mark> _<mark>The Review of Economics and Statistics</mark>_ <mark>91(4):717–24.</mark>

- Hoxby, C. 2015. “Computing the Value-Added of American Post-Secondary Institutions.” Washington, DC: Internal Revenue Service.

- Hoxby, C. M.  and G. B. Bulman. 2015. The effects of the tax deduction for postsecondary tuition: Implications for structuring tax-based aid. _Economics of Education Review_ 51:2360.

- Hoxby, C. 2020. The Productivity of US Postsecondary Institutions. In Hoxby, C. and K. Stange (Eds.) _Productivity in Higher Education_ (pp. 31-66). University of Chicago Press.

- Hoxby, C. and K. Stange. 2020. “Introduction to Productivity in Higher Education.” In Hoxby, C. and K. Stange (Eds.) _Productivity in Higher Education_ (pp. 31-66). University of Chicago Press.

- Huettner. F. and M. Sunder. 2012. “Axiomatic arguments for decomposing goodness of fit according to Shapley and Owen values.” _Electronic Journal of Statistics,_ 6:1239 – 1250.

- Kurlaender, M., Carrell, S. and Jackson J. 2020. “The Promises and Pitfalls of Measuring Community College Quality”. _RSF: The Russell Sage Foundation Journal of the Social Sciences_ , 2(1):174-190.

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35
<!-- Start of picture text --> Figure 21: Log per capita GDP and Expenditure on Power, 2000<br>PHL<br>CHN<br>KHM<br>CHL<br>LAO<br>PER<br>ARG<br>COL<br>THA<br>BRA<br>IND<br>MEX<br>IDN<br>5 6 7 8 9<br>Log(GDP per capita)<br>Source: Appedix A, Connecting East Asia a New Framework for Infrastructure, Calderon and Serven, 2008<br>and authors’ calculations<br>Figure 22: Log per capita GDP and Expenditure on Water and Sanitation 2000<br>LAO<br>VNM<br>COL<br>THA CHL<br>BRA<br>KHM IND IDNCHNPHL PER MEX ARG<br>5 6 7 8 9<br>Log(GDP per capita)<br>2.5<br>2<br>1.5<br>1<br>.5<br>Expenditure on power (% of GDP)<br>0<br>8<br>6<br>4<br>2<br>0<br>Expenditure on water and sanitation (% of GDP)<br><!-- End of picture text --> _Source_ : Appedix A, Connecting East Asia a New Framework for Infrastructure, Calderon and Serven, 2008 and authors’ calculations.

36
# Table 11. Selected potential effects of trade normalization in South Caucasus

# _(U.S. Dollars Million)_

||_Armenia_|_Azerbaijan_|_Georgia_|
|---|---|---|---|
|Transport savings|6.4-8.4|0.7-1.8||
|Savings from using||||
|lower-cost energy|45|||
|Missing exports|268.9-342.4|103.2-104.6||
|Including:<br>gravity model|38.9-72.4|3.2-4.6||
|natural resources/<br>energy|255-296|100||
|Total effect|320.3-395.8|103.9 - 106.4||
|Complementary||||
|imports|80-100|||
|Total effect minus||||
|complementary||||
|imports|220.3-315.8|103.9- 106.4||
|As percent of 1999||||
|trade deficit|38-54|25 - 26||
|Loss of transit||||
|revenues|||7.4|
|As percent of 1999||||
|trade deficit|||1.4|
|_Source: Author's calcula_|_tions._|||

from Azerbaijan to Armenia. The sale of Azerbaijani oil products to Armenia using the same terms as they used with other countries, would lead to a cost reduction of 25 percent,'<sup>0</sup> or over US$20 million. These savings result from more than merely straitening supply routes to Armenia, meaning that Azerbaijani exports will probably replace those of other countries. However, Azerbaijan has production constraints on the volume of its petroleum exports and, in any case, would be able to export all it wants. Therefore, it cannot be stated that there is a potential for increasing Azerbaijani export volumes thanks to the opening of the Armenian fuel market.

# **5. Effects on the BOP**

This chapter sums up the potential benefits of trade normalization developed in the previous chapters.

Ibid.

34
survey data, ranging from issues of sampling design to issues of non-response bias. We note however the distinction with expert assessments, which by definition are based on the views of a very small number of respondents and so are less likely to be representative of the population of firms or households.<sup>14</sup> While these generic issues are important for all surveys, we focus here on difficulties specific to measuring governance using survey data.

One disadvantage is that some survey questions on governance can be especially vague and open to interpretation, although as we discuss below, many have improved. An interesting example of this comes from innovative recent work by Razafindrakoto and Roubaud (2006). They use specially-designed surveys in eight African countries to contrast corruption perceptions based on household surveys with those based on expert assessments. The unique feature of this exercise is that the experts were asked to predict the country-level average responses from the household survey. In this sample of eight countries it turns out that the experts' ratings were essentially uncorrelated with the household survey responses. The authors conclude that the household surveys capture the "objective reality" of petty corruption and that the experts are just plain wrong. While this is a creative effort, we disagree with their interpretation that there is measurement error only in the expert assessment and not in the household survey. Households were asked whether they had been a "victim of corruption". There are a variety of reasons why households might think they were victimized by corruption when in fact it was not actually present. For example, a patient waiting in the queue to see a state-provided doctor might think (incorrectly) that people at the head of the queue had bribed someone to get there. Conversely households might well have paid a bribe, received the associated benefit, and found themselves quite satisfied and not at all "victimized" by the transaction. Our rather more modest interpretation of their finding is that there likely is measurement error in both the household survey, and in the matching expert assessments. And moreover, as we

> 14 This is not to say that all of the surveys used to measure governance are necessarily representative in any strict sense of the term.  In fact, one general critique we note is that several of the large cross-country surveys of firms that provide data on governance are not very clear about their sample frame and sampling methodology.  The Executive Opinion Survey of the World Economic Forum for example states that they seek to ensure that their sample of respondents is representative of the sectoral and size distribution of firms (World Economic Forum, 2006, p. 127).  But at the same time they report that they "carefully select companies whose size and scope of activities guarantee that their executives benefit from international exposure" (World Economic Forum, 2006, p. 133).  It is not clear from their documentation how these two conflicting objectives are reconciled.

21
that it is likely to select more of the provisions with a causal impact than the plug-in lasso. Moreover, it performed reasonably well as a predictive method in our simulations. However, as is apparent from column 4 of Table 9, in this application, predictions based on iceberg lasso lead to some unrealistic estimates. Intuitively, the provisions selected by the iceberg lasso will, by design, include multiple regressors that are highly collinear with one another. Therefore, although it may be possible to estimate the joint effect of these variables with reasonable precision, the same is unlikely to be the case for each individual effect. This implies that the iceberg lasso is likely to be a good predictor of the effect of PTAs that include all these variables, but it may lead to unreliable results for PTAs that only include a subset of the highly collinear provisions.

Table 9: Summarizing Estimates of Heterogeneous PTA Effects

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||All variables|CV|Plug-in|Iceberg|Bootstrap|
|Descriptive statistics||||||
|Min|−81.2%|−50.4%|0.0%|−62.8%|0.0%|
|Max|>1e6%|387.0%|144.4%|284.9%|101.0%|
|Mean|328774.6%|32.1%|13.8%|17.2%|12.5%|
|Median|26.4%|14.4%|9.3%|6.7%|7.2%|
|Stdev.|300514.7pp|63.0pp|20.7pp|42.4pp|15.3pp|
|Correlations||||||
|PPML|1|0.146|−0.054|0.233|0.041|
|CV|0.146|1|0.391|0.550|0.513|
|Plug-in|−0.054|0.391|1|0.507|0.925|
|Iceberg|0.233|0.550|0.507|1|0.679|
|Bootstrap|0.041|0.513|0.925|0.679|1|
|Estimated partial efects|for selected|PTAs||||
|EU|104.9%|105.4%|87.1%|101.6%|64.2%|
|EEA|80.4%|90.5%|9.3%|94.4%|18.3%|
|Eurasian Econ. Union|−21.8%|71.8%|144.4%|38.5%|101.0%|
|NAFTA|77.9%|77.5%|79.9%|81.5%|52.9%|
|MERCOSUR|145.5%|115.9%|42.1%|76.2%|39.6%|
|ECOWAS|469.6%|379.2%|9.3%|23.3%|19.4%|
|ASEAN|1.8%|−9.4%|0.0%|0.0%|3.3%|

This table summarizes estimated partial effects for individual PTAs produced by the different methods we consider. The column labelled “All variables”refers to an unpenalized PPML regression with all 305 provision variables. The other columns refer to variants of the lasso discussed in Section 3.

The predictions based on the bootstrap lasso performed well in our simulations, and this approach also shows promise here. As shown in column 5 of Table 9, the PTA estimates produced by bootstrap lasso lead to less extreme predictions and have the lowest dispersion of any the methods we consider, consistent with what would be expected for a method based on bootstrap aggregation. Though they are highly correlated with 33