rafmacalaba/datause-encoder-data huggingface dataset:
probability of becoming an exporter. In specification two and three, I find, as expected, a positive and significant association between firms’ productivity and their entry into exporting. Larger and more capital-intensive firms are more likely to become exporters, as are multi-plant firms and firms that pay higher wages.

In line with the theoretical model, specification four shows that being an exporter to a developing market in year _t_ increases the probability of becoming an exporter to the EU in year t+5. This relationship is significant at the 1% level. I also add an interaction of product standards harmonization with the _OR_ firm variable to check whether responses to harmonization vary across types of exporting firms. The sign is, as expected, negative: the probability of entry into the EU market is relatively higher for firms that export to a developing country in the face of product standards harmonization. Hereafter, I further decompose the impact at the extensive margin of trade between _OR_ firms and new exporters.

# **4.3 The Extensive Margin of Trade Composition**

Product standards heterogeneity across market destinations in conjunction with productivity heterogeneity across firms provides a natural hierarchy of firms entering exporting markets. Less productive firms serve less stringent markets ( _OR_ firms) while more productive firms also serve most stringent markets ( _AF_ firms). The theoretical model predicts that harmonization of product standards triggers entry mainly from _OR_ firms (hypothesis 2). To examine the potential impact of falling trade costs on the probability that _OR_ firms become exporters to the EU, I start by estimating equations 12 for these two groups of firms separately. Table 7 reports the results, the first three columns present the specifications for _OR_ firms whereas columns 4-6 show the estimations for non-exporting firms. Across all specifications, I confirm that harmonization increases the probability of observing a new exporter to the EU market. This effect is statistically significant across specification. As implied by the theory, this effect is more important for _OR_ firms than for non-exporting firms. Remarkably, harmonization positively affects entry decisions for non-exporting firms. Again, tariffs do not significantly affect the probability of entering the EU market for either type of firms.

Harmonization of product standards can also influence firms’ decision to star exporting a product to the EU. To check whether firms that export a product to a developing ocuntry are more likely to star exporting it to the EU in response to harmonization, I estimate a logistic regression at the firm-product level on my measure of changing trade costs, firm productivity, and other firm characteristics across Census years. I define _Ep,t_ +5 as a dummy variable equal to one if a firm exports product _p_ to a developing country but not to the EU in year _t_ and in year _t_ + 5 that product is exported to the EU. These regressions are given by: 16
as in the CASE study, we would obtain more than one percent additional welfare gain as a percent of GDP.

# (iii) **Standards costs** .

Due to the lack of a survey for Armenia, both the CASE study and our study start with data for Ukraine (Jakubiak et al., 2006). The Ukrainian survey revealed that, on average for agriculture, mining and manufacturing, the cost of compliance with EU standards and regulations was 13.9 percent of production costs. Due to the extremely poor status of the National Quality Infrastructure in Armenia, both the CASE authors and our study assume that these costs are higher in Armenia than in Ukraine by 50 percent (or 20.9 percent of production costs on average). The CASE study assumes that Armenian production costs on exports to the EU will fall by 50 percent as a result of a DCFTA with the EU. Our estimates are smaller for two reasons. First, although the EU is likely to assist in the development of the National Quality Infrastructure, it is not likely to invest as heavily as it did in the countries involved in the Eastern Expansion of the EU. Consequently, we assume only a 25 percent decline in production costs. Second, it is necessary for Armenians to invest in the National Quality Infrastructure to be able achieve harmonization. We further limit the production costs cuts by two percentage points in agriculture and manufacturing to reflect the adjustment costs. In summary, we estimate that production costs of exports to the EU will fall, on average, from 20.9 percent of costs to 17.7 percent, whereas CASE assumes that they will fall from 20.9 percent of costs to 10.5 percent of the costs of exporting to the EU.

# **II. Comparative Steady State Estimates--Improvement in the investment climate**

The CASE study simulates improvement in the long run investment climate from the DCFTA in a scenario (called deep FTA+). This is the scenario that gives the CASE study the large 8.0 percent estimated gains—otherwise gains are a maximum of 3.4 percent of GDP. The CASE study employs a modeling approach first introduced into international trade analysis by Glenn Harrison, Thomas Rutherford and David Tarr for analysis of the impact of the EU single market (Harrison, Rutherford and Tarr, 1996) and the Uruguay Round (Harrison, Rutherford and Tarr, 1997). Harrison, Rutherford and Tarr have made their software publicly available and the CASE study uses it and acknowledges the source.<sup>45</sup> The present study uses the same modeling approach for its long run scenario to assess improvements in the investment climate. The CASE study gets a much larger gain in the long run than the present study. The reasons are two-fold: (i) with this methodology, the larger gains in the comparative static scenario of the CASE study are compounded and magnified

> 45 See Maliszewska _et al_ ., 2008, p.145). The CASE authors also acknowledge Jensen and Tarr as the source of the Social Accounting Matrix for Armenia.

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**Figure 6 GVC exposure to the EU: Metal products**

<!-- Start of picture text --> 60<br>Direct export to EU Indirect export to EU<br> 50<br> 40<br> 30<br> 20<br> 10<br> -<br>Armenia North Bosnia and Jamaica Suriname Libya Albania Türkiye Tunisia<br>Macedonia Herzegovina<br><!-- End of picture text --> _Source:_ Arvis et al. (forthcoming) and authors’ calculations.

_Notes:_ GVC exposure to the EU is a sum of direct exports to EU and indirect exports to EU over domestic output of metal products.

# _3.1.3_ **_Agricultural goods_**

**The EU Deforestation Regulation (DR) will impact agricultural and forestry exports from many developing countries by imposing strict tracking and environmental standards.** These requisites will increase costs, and they could decrease exports of affected products to the EU, especially from smaller farmers, who may struggle with meeting the information requirements and the necessity to use geolocation technology for traceability. Consequently, some producers might redirect their exports to markets with less stringent regulations, potentially diminishing the regulation’s effectiveness.

**Figure 7 Top 10 developing countries according to their economic exposure to EUDR**

<!-- Start of picture text --> Economic exposure (LHS) Sector exposure (RHS)<br>6 100<br>5 80<br>4<br>60<br>3<br>40<br>2<br>1 20<br>0 0<br>Côte d’Ivoire Bosnia and Honduras Serbia Liberia São Tomé North Ukraine Papua New Cameroon<br>Herzegovina and Príncipe Macedonia Guinea<br>Percent Percent<br><!-- End of picture text --> _Source:_ Authors’ calculation based on 2022 data from the World Bank’s WITS.

_Note_ : Sector and economic exposure are export of EUDR affected products to three economies divided by exports of EUDR affected products to world and GDP, respectively.

**Sectors of affected products that are heavily reliant on the EU as a market are likely to be the most affected by this regulation** . São Tomé and Príncipe and Guinea-Bissau have the most exposed sectors. For São Tomé and Príncipe, 96 percent of its global exports of affected products goes to the EU, and 95 percent of Guinea-Bissau's corresponding exports head to the EU. The countries whose economies are most exposed are Côte d'Ivoire and Bosnia and Herzegovina as affected exports to the EU represent 5.2 percent of Côte d'Ivoire 's GDP and 4.3 percent for Bosnia and Herzegovina (Figure 7).

# **3.1.4 Textiles**

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combined with the baseline survey cover the period from January to October 2020. Hence, the study period falls squarely within the timeline of the first wave of pandemic spread and the associated stringent government response measures.

First, we use the phone surveys to document the large negative impacts of the crisis on income generation, business outcomes, and life satisfaction among the control group. This analysis shows that 78% of respondents report a negative change to their income within three months of the start of the pandemic (i.e., by June 2020). In addition, only 50% of businesses survive the early months of the pandemic, and new business startup rates drop sharply from over 50% pre-pandemic to an average of 10% in the March-October period. Among the businesses that existed before to the pandemic, sales and profits fall sharply by over 65% compared to prior performance, and business investment and employees decline correspondingly. At the personal level, our data show 40% of youth lack confidence in maintaining their living standards in the aftermath of the pandemic, and 79% are dissatisfied with life. Hence, from both business and individual well-being perspectives, the effects of the crisis were hard hitting and immediate.

Next, we turn to the causal impacts of business grants and BDS. The study design has multiple arms, with one group randomly assigned to receive business grants of Ksh. 40,000 (about US$ 360); a second group assigned to BDS in the form of classroom, digital business training, and follow-up counseling visits; a third group assigned both grants and BDS; and a final group assigned as control. The business grant is equivalent to approximately two months of baseline average sales and five months of average profits. Therefore, it represents a sizable monetary boost to entrepreneurs in our sample. While the grant disbursement was not conditioned on business outputs or outcomes, youth were subject to monitoring via a mix of phone follow-up and spot checks.<sup>2</sup> Our results show that youth who were assigned business grants or a combination of business grants and BDS are twice as likely to have an operational business in the follow-up rounds compared to the control group, with statistically significant impacts on both business survival and new business startup. These youth are also 90% more likely to report business as their main source of income; 15% less like to report a negative change to their income; and 11% more likely to be the main household income provider, compared to the control group.

On business performance, we find significant treatment effects for both surviving and new businesses, with up to 47% higher sales as well as profits for the former group. Relative to the control group, these effect sizes correspond to 0.28 standard deviation (SD) and 0.26 SD,

> 2 The grant disbursement was divided into two tranches with attendance at an initial orientation session mandatory to receive the first tranche of funds. A second orientation session was originally planned but subsequently cancelled due to COVID-19 social distancing measures, and funds were disbursed without a meeting.

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an analysis instrument, which was in turn used to gather and organize information on the selected components. Data were drawn primarily from Bank Appraisal ancl Completion Reports, and sector studies, augmented by interviews with Bank project staff for selected countries.

1.32 Limitations in the data inevitably led to selectivity in applying the elements of the conceptual model. In particular, detailed data on the operation of labor markets and employing enterprises was absent from the documentation. Information on the components of training systems (especially on student and teacher characteristics) was rarely available. Thus the analysis that follows, while broadly structured around the elements of the systems model, does not seek to address all elements. However, the variables that are addressed were developed from this conceptual basis.

1.33 Finally, the study has informed by a comprehensive review of the literature on vocational education and training (Dougherty, 1988 forthcoming), providing a broader context in which the implications of World Bank experience can be interpreted.
mated caloric intake using the 7-day recall period. The alternative estimates of caloric intake in column 3 (using the 30-day recall) are also lower than TEE in the later period, but display a similar decline between 2005-2012, such that TEE could “explain” about half of the decline in caloric intake estimates during this period. In column 7, average TEE for adults is reported to once again underscore that, were it not for demographic change, changes in estimated TEE for adults would be enough to “explain” a decrease in caloric intake of 34 calories per day between 1983-2005 (about 20% of the 150 calorie decline in the official estimates) or 111 calorie per day between 1983-2012 (over 100% of the 102 calorie decline in the official estimates).

Further evidence that changes in TEE may have contributed to lower caloric intake between 2005-2012 but are unlikely to have done so for the 1983-2005 period comes from an analysis of state-level changes. Figure 2 plots the change in state caloric intake against the change in state TEE for the 1983-2005 period and the 2005-2012 period. In the earlier period, although there are some states that appear to line up along the 45 degree line, most lie well below, indicating that measured caloric intake relative to TEE declined. The association is also very weak and not statistically significant. In the later period, the relationship is considerably stronger, with most states lining up along the 45 degree line and a statistically significant slope of 0.666 (with associated _R_<sup>2</sup> of 0.08). Using the full state panel, TEE estimates are significantly correlated with changes in caloric intake within states over time.<sup>8</sup> Given that the finding that population TEE rose during the 1983-2005 period of falling caloric intake considered by (Deaton and Dr`eze 2009), it is worth considering the best estimates of the potential effect of the disease environment on caloric intake from (Duh and Spears 2017). They use a district panel to show that within-district decreases in infant mortality between 1988 and 2005 are associated with within-district decreases in NSS caloric intake, with a slope coefficient of about 1.7. They find smaller but still highly significant cross-sectional associations between village/block infant mortality and caloric intake using a different survey, with slope coefficients ranging from 0.426 (when including household and demographic controls) to 1. They also report results based on open defecation and latrine ownership, but infant mortality has data available for the entire period and plausibly capture changes in the disease environment related to diarrhea, parasites, and in-

> 8See Appendix Table S.6 for these results.

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contrast, test for incentive effects associated with the introduction of the U.S. Millennium Challenge Account (MCA), which makes recipients eligible for its aid only if they are below a certain income threshold and above specified thresholds on a set of policy measures. Ohler,<sup> ̈</sup> Nunnenkamp, and Dreher (2012) focus on one key measure – an index of corruption – and report improvements in scores for countries and periods in a plausible treatment group relative to a plausible control group. This finding is rather surprising, however, given the short period of time covered by their analysis and the small amounts of aid commitments and disbursements by the MCA. Here, we use the World Bank’s “Country Policy and Institutional Assessment” (CPIA) index as our policy measure, which includes corruption as one sub-component. In addition, we show that policy-selectivity of aid started to increase quite substantially in the early 1990s, which corresponds to the end of the sample period used in Alesina and Weder (2002).

Our theoretical model is closely related to Annen and Knack (2018), who analyze a 2-donor-2recipient setup. Here, we look at a 1-donor- _n_ -recipient setup to focus more on the incentive effects the number of recipients has on the competition for aid. Also, we extend the framework by introducing an incentive alignment parameter between the donor and recipients, to see how it affects the incentive effect of policy-selective aid.

The remainder of the paper is structured as follows: Section 2 documents the policy selectivity of donors since 1985 as the key fact our paper builds on. Section 3 introduces the theoretical model. Section 4 uses the insights generated by the model to empirically test the impact of policy-selectivity on policies. Section 5 concludes.

# **2 Policy Selectivity of Foreign Aid**

The World Bank’s CPIA assesses the quality of countries’ present policies and institutions on a yearly basis for IDA and IBRD-eligible countries. Ratings for IDA but not IBRD countries are publicly available. The CPIA index comprises four broad dimensions or policy “clusters”: 1) macroeconomic management including monetary, fiscal and debt policy, 2) structural policies such as trade and financial sector policies, and the regulatory environment for businesses, 3) policies for social inclusion and equity, including policies related to gender equality, education, health, social protection and labor, and the environment, and 4) 4
It can be seen in Figure 2 that since around 1981, the basket of CPI's measure starts diverging significantly from the index of traded goods prices for the U.S..The question is therefore which measure should be used to construct the U.S. Real Exchange Rate. Figure 3 shows the alternative measures, using the U.S. CPI as the deflator. The CPI's Basket measure is represented by the series constructed by the IMF Information Notice System , and is denoted as the INS Real Exchange Rate. The Real Exchange Rate using the export and import unit values is denoted by PT/PN .

Since it is obvious that both measures of the RER move very differently, the question of which one would be relevant for policy purposes requires additional analysis in order to be answered.

In the particular case of the U.S. there is an additional problem relating the usefulness of the Real Exchange Rate measure for policy purposes. This arises because the U.S. is an important price setter in the market for traded goods. Therefore, we should not expect an immediate response form traded goods prices measured in dollars derived from a change in the value of the dollar in terms of other currencies. In fact, we may not even expect PPP to prevail as strongly as it would in the case of a smaller country. The 1986-87 experience shows that the substantial dollar devaluation failed in increasing the dollar prices of U.S.traded goods. Worse than that, the dollar devaluation apparently also failed in reducing the foreign currency prices of the goods traded by the U.S.. In consequence, PPP must not have held during this period. There is evidence, however, that prices are slowly converging to PPP, but the problem remains of whether the U.S., by nominal devaluations can affect the level of the relevant real exchange rate within a time frame useful for policy purposes.

For most other small open economies, the U.S. problem does not arise as changes in the value of the domestic currency in terms of foreign exchange are quickly reflected in the domestic currency price of traded goods.

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