rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 4:** Distribution of yields-inputs regression coefficients in the test for 100 different sample splits. The red and green kernel density plots represent the distribution of the coefficients when the relationship is estimated with SR and CC yields, respectively, and the blue histogram bars represent the distribution of the comparable coefficient when the relationship is estimated with ML yields. The left panel shows the distribution when in the machine learning model, we include SR. The middle panel of plots show the results of the regressions of ML yields that are obtained without including SR yields among the covariates. The right panel shows the results from the alternative regressions of ML yields which are computed with ML-based imputed measures of CC harvest that are in turn standardized by plot area (as opposed to imputing ML yields directly)

<!-- Start of picture text --> GPS measured plot area GPS measured plot area<br>−0.8 −0.4 0.0 −0.8 −0.4 0.0<br>15 20<br>10<br>10<br>5<br>5<br>0 0<br><!-- End of picture text --> <!-- Start of picture text --> GPS measured plot area<br>−0.8 −0.4 0.0<br>8<br>6<br>4<br>2<br>0<br><!-- End of picture text --> <!-- Start of picture text --> Quantity of Seeds Quantity of Seeds<br>0.0 0.2 0.4 0.0 0.2 0.4<br>10<br>12<br>8<br>6 8<br>6<br>4<br>4<br>2 2<br>0 0<br><!-- End of picture text -->

**Quantity of Seeds**

<!-- Start of picture text --> 0.0 0.2 0.4<br>8<br>6<br>4<br>2<br>0<br><!-- End of picture text -->

**Days of household Labor**

**Days of household Labor**

**Days of household Labor**

<!-- Start of picture text --> −0.1 0.0 0.1 0.2 −0.1 0.0 0.1 0.2 −0.1 0.0 0.1 0.2<br>β β β<br>12<br>20 20<br>8<br>10 10 6<br>4<br>5 5<br>2<br>0 0 0<br><!-- End of picture text --> 25
of “bounded rationality, bounded willpower, and bounded self-interest” (Sunstein, Jolls and Thaler, 1998).

There is also empirical evidence on the effectiveness of informational interventions on health behavior change (Wakefield, Loken, and Hornik, 2010). Mass media campaigns have been associated with declines in smoking (National Cancer Institute, 2008) and increases in demand for cervical cancer screening (Comino et al., 2012; Shelley et al., 1991). Cohort studies of largescale communication interventions have also reported increased awareness of healthy behaviors and improvements in diet and physical activity. There are, however, mixed findings on the impact of these interventions on cardiovascular disease risk (Shea and Basch, 1990; Atienza and King, 2002). References to social norms in messaging interventions have also been shown to change individual behaviors, including charitable giving (Frey and Meier, 2004), voting (Gerber and Rogers, 2009), retirement savings (Beshears et al., 2015), water and energy consumption (Allcott 2011; Allcott and Rogers 2014; Ferraro and Price, 2013), and tax compliance (Hallsworth et al., 2017).

In this study, we tested the effectiveness of financial and informational demand-side interventions to promote screening uptake for hypertension and diabetes in Armenia, a middleincome country. The study uses an experiment randomized at the individual level (N=2,000) to evaluate four demand-side interventions, including personal invitations (intervention group 1), personal invitations mentioning that peers have tested (intervention group 2), a personal invitation accompanied by a labeled but unconditional cash-like incentive, in the form of a pharmacy voucher (intervention group 3) and a personal invitation accompanied by a conditional cash-like incentive (intervention group 4).

The personal invitation was common to all four intervention groups, and gave information about the importance of screening, the fact that it was free and details on how and where to go for screening. As designed, the personal invitations may have provided a nudge or external cue to action and increased the individuals’ sense of self-efficacy by clarifying how and where to screen. The invitations may also have led to re-evaluation of the perceived costs of screening, susceptibility to hypertension and diabetes, and the benefits of screening. By comparing the personal invitation (intervention group 1) and the personal invitation that mentions peers having been screened (intervention group 2), our experiment tested the impact of descriptive social norms on health prevention. In addition, by comparing the labeled, unconditional cash-like incentive (intervention group 3) and the conditional cash-like incentive (intervention group 4), we estimate the impact of conditionality on screening.

We show that compared to the control group in which only 3.5 percent of participants went for both screenings during the study period of approximately 6 months, intervention groups 1 to 3 led to a significant increase in the screening rate of about 15 percentage points among 4
# Glossary of Terms

1. Profit II or Pre-tax profit ("wynik finansowy brutto") = Sales + Other Income - Cost of Sales - Turnover tax + Balance of extraordinary gains and losses

2. Other Income = subsidies + income from sales and leasing of assets 3. Net Sales = Sales - Turnover tax.

4. Balance of extraordinary gains and losses ("net extraordinary gains") are mainly devaluation gains on dollar accounts (late 1989) and penal.y interest on interfirm credit arrears as well as arrears to banks and tax arrears to the budget.

5. Underlying Profitability = (profit II - other income - net extraordinary gains)/Net Sales.

6. Net income = profit II - corporate income tax.

7. Disposable cash = net income - dividends + depreciation.

# Other Notes

1. Excess wage tax is also called "popiwek" or by its acronym, PPWW.

2. Quarters are indicated by Roman numerals, e.g., 1990:IV is fourth quarter, 1990.

3. SOE is "state-owned enterprise".

(*) Brian Pinto is at the World Bank. Marek Belka and Stefan Kra,ewski are, Professors of Economics at Lodz University, Poland. A first version of this rport, which contains the findings of a quantitative study of 75 large Polish SOEs supplemented by manager interviews, was distributed under the title 'Microconomic response to the Economic Transformation Program: Evidence from the largest Polish SOEs" in September 1991. World Bank funding from the Europe and Central Asia Region and from Research Administration under RP0#67658 is gratefuly acknowledged. The inspirational guidance of Stan WcUliaz, the help and advice of Anna Krajewska and the excellent research assistance of Robert Sierhej were indispensable in producing the report. The support and guidance of [an Hume and comments from Luca Barbone, Tim Lane, Ulrich Thumm and Sweder van Wijnbergen are gratefuDy acknowledged.
into account the relevant exclusions and phase-in delays. Combining a PTA database developed in the first part of this section with data on trade flows at the national tariff line level, the second part of the section will estimate the share of world trade that, given the above considerations, can be meaningfully considered “preferential.” We will then use these data to test if the findings of a gravity model change depending on whether the dependent variable is defined as all trade or only “preferential” trade among the members of a PTA.

# **3.1 Documenting the rise in the number of PTAs**

The pursuit of PTAs has a long history, and their numbers have been rising particularly fast in recent years even as (and partly because) progress on the multilateral front had slowed. By 1990, 53 regional and bilateral agreements were in force. Through the first quarter of 2004, that number has grown to 229,<sup>17</sup> and the trend shows no signs of slowing down. Despite the growing importance of PTAs in the global trading system, there is no overarching body that monitors their progress or sets standards for compliance. Therefore, the task of collecting data on PTAs is a difficult one. The PTA database published by the WTO is the single consistent publicly available source of this information, but it includes only those agreements that have been notified to the WTO. Thus, agreements between non-WTO members are omitted from the data,<sup>18</sup> and even PTAs involving WTO members often go unreported since the organization does not enforce PTA reporting or compliance.<sup>19</sup> Although PTAs represent a fundamental departure from the core WTO principles of nondiscrimination and Most Favored Nation (MFN) status, the WTO affords its members a large degree of flexibility in entering new PTAs. Within the WTO framework, countries can join preferential agreements by meeting the requirements of General Agreement on Trade and Tariffs (GATT) Ar-

> 17This estimate counts the 25-member European Union as a single agreement. The main reason for this approach is that the EU negotiates all of its PTAs with outside countries as a single body, with new members becoming automatic signatories to earlier agreements. Since we are interested in the number of PTAs in force rather than their historical evolution, other methods of dealing with the EU, such as regarding each round of accession as a new agreement, would lead to double-counting.

> 18For example, Russia (the largest country in the world that is not a WTO member) participates in two regional PTAs and is actively negotiating several others.

> 19The WTO database does not include the SADC and SACU agreements in Africa, even though a WTO member (South Africa) is party to both. Similarly, the India-Nepal and India-Bhutan bilateral agreements in South Asia remain unreported despite India’s membership in the WTO. We will provide more detail on the unreported agreements, including the data sources used in their identification, later in this section.

11
; justified in advance, only 23 of more than 70,000 TRI reporting forms submitted in 1988 included trade secret claims. Quantitative estimates in pounds must be provided for the mass of the TRI chemical released (not the total volume of the waste stream containing the chemical) in each of a range of categories, including:

- ® fugitive or non-point air emissions; @® stack or point air emissions; @ discharges to streams or receiving water bodies; @® underground injection on-site; @ releases to land on-site; @ waste-water discharges to publicly-owned treatment works;

- @® transfers to off-site facilities for treatment, storage or disposal.

For the purposes of inter-media analysis these seven categories can be aggregated under the three standard headings of releases to air, land and water.

The national repository for TRI data submitted to the EPA is the TRI Reporting Center in Washington, D.C. The information is computer-accessible through the National Library of Medicine's TOXNET database. The National Technical Information Service of the US Government Printing Office is also able to provide the data on tape, disk, CD-ROM and microfiche.

# 2.1.2 Aerometric Information Retrieval System (AIRS)

AIRS is the management system of the US national database for ambient air quality, emissions, and compliance data. It is 6
|(0.02)|(0.01)|(0.02)|(0.02)|(0.02)|(0.0)|(0.0)|(0.0)|
|---|---|---|---|---|---|---|---|
|<br>0.33|0.37|0.31|0.37|0.34|0.1**|0.0|-0.0|
|Household owns a bicycle<br>(0.03)|(0.01)|(0.02)|(0.03)|(0.03)|(0.0)|(0.0)|(0.0)|
|Hhld bil h<br>0.89|0.88|0.87|0.90|0.89|0.0|0.0|-0.0|
|ouseo owns a moe pone<br>(0.01)|(0.01)|(0.02)|(0.02)|(0.02)|(0.0)|(0.0)|(0.0)|
|<br>0.18|0.14|0.12|0.13|0.12|0.0|-0.0|-0.0|
|Household owns a video/DVD<br>(0.02)|(0.01)|(0.02)|(0.02)|(0.02)|(0.0)|(0.0)|(0.0)|
|Hhld tlii<br>0.25|0.28|0.28|0.28|0.27|0.0|-0.0|-0.0|
|ouseo owns a eevson<br>(0.02)|(0.01)|(0.03)|(0.03)|(0.03)|(0.0)|(0.0)|(0.0)|
|Hhld t<br>0.02|0.03|0.03|0.03|0.04|-0.0|0.0|0.0|
|ouseo owns a compuer<br>(0.01)|(0.00)|(0.01)|(0.01)|(0.01)|(0.0)|(0.0)|(0.0)|
|Household owns a<br>0.08|0.09|0.08|0.10|0.09|0.0|0.0|-0.0|
|refrigerator/freezer<br>(0.01)|(0.01)|(0.02)|(0.02)|(0.01)|(0.0)|(0.0)|(0.0)|
|Hhld AC/f<br>0.07|0.07|0.07|0.07|0.07|-0.0|0.0|0.0|
|ouseo owns an an<br>(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.0)|(0.0)|(0.0)|
|<br>0.49|0.44|0.42|0.46|0.39|0.0|-0.0|-0.1**|
|Household owns a radio<br>(0.03)|(0.01)|(0.02)|(0.03)|(0.03)|(0.0)|(0.0)|(0.0)|
|Hhld i<br>0.85|0.85|0.83|0.86|0.86|0.0|0.0|-0.0|
|ouseo owns a mosquto net<br>(0.02)|(0.01)|(0.02)|(0.02)|(0.02)|(0.0)|(0.0)|(0.0)|
|L f it idtil<br>0.44|0.45|0.46|0.48|0.48|0.0*|0.0|-0.0|
|og o per capa resena area<br>(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.0)|(0.0)|(0.0)|
|Roof is made of concrete/metal<br>0.85|0.84|0.83|0.85|0.85|0.0|0.0|-0.0|
|sheets/tiles<br>(0.02)|(0.01)|(0.02)|(0.03)|(0.02)|(0.0)|(0.0)|(0.0)|
|Wall is made of burnt<br>0.62|0.55|0.60|0.56|0.56|-0.0|-0.0|-0.0|
|bricks/concrete<br>(0.03)|(0.02)|(0.03)|(0.04)|(0.03)|(0.0)|(0.0)|(0.0)|
|Floor is made of<br>0.51|0.52|0.48|0.47|0.50|-0.0|0.0|0.0|
|concrete/cement/tiles/timber<br>(0.03)|(0.01)|(0.03)|(0.03)|(0.03)|(0.0)|(0.0)|(0.0)|
|Source of drinking water: piped<br>0.41|0.44|0.40|0.38|0.34|-0.0|-0.1**|-0.0|
|water/truck<br>(0.04)|(0.02)|(0.04)|(0.04)|(0.04)|(0.0)|(0.0)|(0.0)|
|Tilt flh/VIP<br>0.33|0.35|0.36|0.35|0.34|-0.0|-0.0|-0.0|
|oe: us<br>(0.03)|(0.01)|(0.03)|(0.03)|(0.03)|(0.0)|(0.0)|(0.0)|
|<br>0.25|0.28|0.25|0.24|0.27|-0.0|0.0*|0.0**|
|Residence: Urban<br>(0.02)|(0.01)|(0.04)|(0.04)|(0.04)|(0.0)|(0.0)|(0.0)|
|Rid Rl<br>0.75|0.72|0.75|0.76|0.73|0.0|-0.0*|-0.0**|
|esence: ura<br>(0.02)<br> <br>|(0.01)<br>|(0.04)<br>|(0.04)<br>|(0.04)<br>|(0.0)<br>|(0.0)<br>|(0.0)<br>|
|**Number of observations**<br>**1179**<br>**Note:**The standard errors are in parentheses, a<br>population weights are applied. The consumpti|**4644**<br>nd the difference<br>on data in NPS 2|**711**<br>s are estimated<br>019/20 and in|**701**<br>considering the<br>NPS 2020/21 are|**698**<br>complex survey<br>deflated to 2022|**1412**<br>design. * p < 0.1,<br>prices using the a|**1409**<br>** p < 0.05, ***<br>nnual WB CPI.|**1399**<br>p < 0.01. The|

**Note:** The standard errors are in parentheses, and the differences are estimated considering the complex survey design. * p < 0.1, ** p < 0.05, *** p < 0.01. The population weights are applied. The consumption data in NPS 2019/20 and in NPS 2020/21 are deflated to 2022 prices using the annual WB CPI.

73
Figure 5: Life-cycle of informal and formal plants in the benchmark <!-- Start of picture text --> (a) Informal Plants (b) Formal Plants<br>1.8 2.50<br>1.6<br>2.00<br>1.4<br>1.50<br>1.2<br>1 1.00<br>Age Age<br>Model Data Model Data<br>(c) Total<br>2<br>1.8<br>1.6<br>1.4<br>1.2<br>1<br>Age<br>Model Data<br>Average cumulative growth rate Average cumulative growth rate<br>[0-4] [5-9] [10-14] [15-19] [20-24] [25-29] [30-34] [35-39] [0-4] [5-9] [10-14] [15-19] [20-24] [25-29] [30-34] [35-39]<br>Average cumulative growth rate<br>[0-4] [5-9] [10-14] [15-19] [20-24] [25-29] [30-34] [35-39]<br><!-- End of picture text --> Source: INEGI the enforcement levels, _b_ and _φ_<sup> ̄</sup> . Then we discuss the implications of revenue-neutral changes in enforcement where the tax rates are adjusted to keep tax revenues constant as enforcement levels change.

In this economy, the only burden of being formal is the output tax, _τ_ . Table 5 shows how the model behaves under different values of the tax rate. As the output tax increases, more plants choose to operate informally to escape from taxation. Doubling the output tax increases the share of informal plants by 13.1 percentage points, and decreases mean size and aggregate output by 36.1% and 37.6%, respectively. The overall plant growth rate in the economy rises as more productive formal plants in the benchmark decide to operate informally. A higher tax rate generates stronger incentives to avoid it, which can happen in this economy by investing less in entrepreneurial skill 22
their remoteness. However, the statistical backing of the significance of the 2 km measure seems to be absent. Moreover, definition of rural roads usually depends on the responsible agency for the road and not necessarily on the economic function of the road.

Literature on the impact of rural roads on agricultural growth and poverty has flourished. One of the most prominent was Fan el al. (2004) who estimates the effects of different government expenditures on agricultural growth and poverty in rural Uganda. The analysis was carried out at the national, regional and district levels using data collected from the national government and the aggregation of the Uganda National Household Surveys (UNHS) to the district level, from 1992, 1995, and 1999. The expansion on this paper is in response to some of the shortcomings of the article, mainly in reference to the data used and the missing components of the survey that do not allow for a thorough analysis.

Indeed, a common problem in this literature is that the distance to roads is only an estimate, which may suffer from inaccuracies or may misrepresent the situation.

Another frequent problem of the transport research is simultaneity bias/causality. When examining the relationship between income/consumption and road construction there is a possibility that they both may be influencing one another. Does road construction bring about higher incomes? Or do higher income areas demand new roads in their vicinity? A method used to overcome this problem is simultaneous equations which are used by Fan et al. (2004). However, the article was concerned with other variables and did not use road distance as both an independent and dependent variable. Therefore, there is no way to be sure that the causality flowed from the roads to the improved income and not the other way around.

Furthermore, because the survey does not delve into services or costs, Fan et al. are limited to only investigating the impact of government expenditure on road construction. They were unable to examine the supply side constraints of transport that exist in Uganda. As noted above, though a community may be in close proximity to a road, a lack of transportation services will limit the community’s potential growth. This is especially the case when costs of these services are prohibitively high resulting from poor road conditions, a mismatch in supply and demand, or high vehicle costs. In the end, Fan et al. use of secondary data forced their analysis in a certain direction as they used the 8
Late school entry may be a concern because of the way I define crisis exposure. There is some evidence from Ghana that parents delay schooling entry if their children are malnourished (Glewwe and Jacoby 1995). If the 1988-92 crisis in Peru led to a deterioration in nutritional status, then young children surveyed in 1997 whom I have defined as "unexposed" to the crisis (because they were not of scliool-age during the crisis) may have started school late, and therefore have completed fewer years of schooling for their age than they would have in the absence of the crisis.'<sup>5</sup> There is no completely satisfactory way of resolving this problem, but I do not believe that this is the explanation for thie patterns I observe. First, enrollment rates for young children are very high throughout, suggesting that late entry is not much of an issue in urban Peru. Second, in separate results (not reported, but available from the author upon request), I show that school attainment is higher in 1991 thaan in either 1985/86 or 1997. Of course, it is possible that the improvement in attainment between 1985/86 and 1991 is explained by an upward underlying time trend, while the deterioration in attainment between 1991 and 1997 is explained by patterns of late entry for young children exposed to the crisis, but this seems unlikely to me. Moreover, a decree which was passed between the 1995 and 1996 school years in Peru whereby all children who completed first grade were automatically promoted to second grade could _downward-bias_ the estimates of the crisis on school attainment I report. By construction, all first-graders aged 8 or younger in 1995, who stood to benefit from the automatic promotion policy, are defined as unexposed to the crisis because they would be 10 or younger in 1997.

An increase in public expenditures on education during a crisis could reduce the marginal cost of education (for example, if the increase in expenditures takes the form of scholarships) or increase the marginal benefits (for example, if the increase in expenditures improves the quality of education). The Garcia governiment put no program in place to attempt to encourage schooling attainment during the crisis. Indeed, as Figure 6 shows, public expenditures on education followed roughly the same pattern as per capita GDP-rising sharply between 1985/86 and 1987, dropping equally sharply between 1987 and 1991, and steadily increasing thereafter. If anything, the constant attendance patterns and the improvements in age-specific grade completion during the crisis took place _in spite_ oftlhe evolution of public expenditures on education. Moreover, it is unlikely that households compensated for changes in public expenditures by increasing private expenditures on education between 1985/86 and 1991, and decreasing them thereafter: At best,

> i5 I thank John Maluccio for this comment. In future research, I intend to look at the impact of the 1988-92 crisis on nutrition outcomes in Peru.

_15_
(differentiated). The homogeneous goods category includes products that are traded in an exchange and those that are reference priced.<sup>11</sup> The interaction of the homogeneous good dummy with the the budget of the EPA identifies the asymmetric information problem that EPAs can help resolve.

# **4 Results**

The result from the estimation of (1) and (2) are shown in Table 1 for the whole sample and for developing countries using OLS and a Heckman-selection correction. Across all specifications the EPA budget has a positive and statistically significant effect on exports. The coefficient is not statistically different across estimators or samples, which suggests that there is little heterogeneity in the impact of EPAs on exports per capita.<sup>12</sup> Note however, that the selection parameter _λ_ is statistically different from zero in both samples suggestions that sample selection is an issued to be addressed in both samples.

The elasticity is around 12 percent which is slightly higher than the range of estimates by Rose (2007), which suggest that the presence of a consulate or embassy engaged on export promotion leads to a 6 to 10 percent increase in exports. Also, note that this is not a welfare calculation, and such “returns” may be consistent with a welfare loss associated with EPA’s activities, as discussed earlier. Moreover, a point estimate of 0.12 suggests that there are strong diminishing returns to scale. Consequently, large expansions of EPAs budgets may not be desirable.

Regarding the other explanatory variables, GDP per capita (ln _GDP/pop_ ) has a positive and statistically significant sign in all specifications suggesting that richer countries, with stronger and better institutions –including trade institutions– export more. The restrictive11 Rauch provides a liberal and a conservative classification of homogeneous goods and we will be reporting results for both in the next section. The conservative classification has 495 goods classified as homogeneous out of 1189 SITC rev. 2 4 digit goods, and the liberal classification includes 533 goods.

> 12We also estimate the equation in the first column using a Poisson estimator and a robust regression estimator to correct for Jensen-inequality biases in the coefficients (Santos-Silva and Tenreyro, 2007), outliers or other forms of heteroscedasticity. Results are qualitatively the same and not statistically different from the ones reported here. They are available upon request.

12