rafmacalaba/data-use-mentions-extended huggingface dataset:
is confirmed when we add to the estimations firm _×_ product _×_ year.

In our dataset, we can classify NTMs in three categories: SPS, TBT and others, which include Pre-Shipment Inspection, Contingent Measures and Quality Controls. While NTMs and tariffs have starkly different effects on market structure, there are fewer reasons to expect different effects across NTMs categories (in terms of sign). Still, in columns (4) and (8) we examine whether the effect on firm PTM varies across types of NTMs by including separately an interaction term between our exchange rate and these three categories. We find stronger impact of SPS regulations in column (4), but the difference across types of NTMs is much less clear in column (8). In Section C.4 of the online appendix, we further explore the heterogeneity of the effect of NTMs across industries. Given the set of fixed effects used in our estimations, repeating the analysis by HS2digit categories is not feasible due to the limited degrees of freedom. Table A.11 reports the results for three HS sections with the largest number of non-missing NTM data in our sample (HS sections 2, 4 and 11). Our results suggest that fresh products are mostly affected by SPS standards, such as for example pests control or pesticide usage regulations, while processed food products are more affected by TBT standards, including for example regulations on residues limits or packaging requirements.

Table 4: Non-tariff measures and pricing-to-market: Baseline

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Dep. var.||||ln unit|values||||
|Sample Split||NTM|AVE|||NTM
Lastly, the program’s staggered rollout in municipalities could potentially be used as a source of quasiexogenous variation. The program was implemented initially in all municipalities more or less simultaneously without any clear selection criteria. By the time the Consorcio Colombia Mayor was established, pretty much all municipalities had some beneficiaries. Unfortunately, according to the data made available to us by the consorcio, the subsequent expansion of the program with the aim of enrolling the entire target population proceeded in a parallel fashion across municipalities (meaning that there was no clearly identifiable subgroup of municipalities or other criteria that determined the speed of the expansion). Given that no objective estimate of the number of individuals in the target population—that is, individuals who fulfill the age and Sisben requirements—is available at the municipal level, we calculated an estimate of coverage by dividing the number of beneficiaries reported by the consorcio by the number of people who are age eligible according to Colombia’s population projections.<sup>4</sup> We have complete yearly data on 1,101 municipalities in 2010–14. The first observation is that, of the 5,505 individual observations, 55 are larger than 1 and, in many cases, by a substantial amount (the largest value is over 12). This points to the unreasonably large number of beneficiaries in the consorcio data. (Keep in mind that the denominator also includes noneligible individuals of the appropriate age.) We have run a simple regression of the estimated coverage—the share of beneficiaries in the ageeligible population—on a set of year-specific dummy variables and a set of municipality-specific dummy variables. This yields an R<sup>2</sup> value of 0.7305. With almost three-quarters of the variation explained by municipality and year fixed effects, the remainder may be too weak to act as a statistically strong source of variation. This problem can be visualized in figure 5 for the 298 municipalities that are included in any of the ENCVs over 2010–13 and that
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# **EXECUTIVE SUMMARY**

Transport infrastructure is among the most important driving forces for economic growth. In Nepal, rural road accessibility remains a crucial challenge. The vast majority of rural residents had to spend more than 30 minutes to access to paved roads. Public procurement is an important policy instrument to use resources wisely and efficiently.

This paper addresses a series of policy challenges in public road procurement, from procurement planning to contract management and project quality assurance. Important, these issues are interrelated to each other. Therefore, the issues need to be addressed in concert. Using data collected from 155 rural road upgrading contracts in Nepal, five questions are addressed: How do bidders determine their bidding strategy, i.e., bid prices, in competitive bidding for rural road contracts? How do they decide whether to participate in the procurement market? What are the main determinants of efficiency in contracting road works? Why ex post contract amendments, such as cost overruns and delays, happen? Which factor is important to ensure the quality of roads delivered by the project?

Our main findings and policy implications are as follows:

# **_1. Competition needs to be enhanced particularly in government owned projects._**

Competition is the most important factor to bring down road procurement costs. The level of competition is clearly insufficient in some public tenders in Nepal. In particular in government owned projects, the average competition is about 4.5, unfavorably compared to an average of 7.3 in World Bank financed projects. Significant losses―possibly, $3 million―are estimated to be incurred by the lack of competition.

# **_2. Wider distribution of tender information at cheaper costs increases competition as well as transparency and accountability._**

To attract more interest and bidders, tender notices should be distributed widely and at low costs. This will bring down the cost of entry for firms. Public tenders can be advertized on national newspapers, rather than local ones. E-bidding or
likely to tighten up environmental policy in sectors that are facing strong import competition, then if this is not accounted for one might falsely conclude that weak environmental policy leads to reduced international competitiveness.

Much of this recent work has found evidence in support of the weak version of the pollution haven hypothesis – that is, in many recent studies more stringent environmental regulation in the manufacturing sector has a statistically significant negative effect on various measures of competitiveness in the affected sectors.<sup>6</sup> For example, Becker and Henderson (2000) and several other studies have found that increases in the stringency of environmental regulations induced by the US Clean Air Act tend to reduce the likelihood that a new plant will locate in the affected jurisdiction. Keller and Levinson (2002) found that high abatement costs have a negative effect on foreign investment inflows, while Hanna (2010) found evidence that the US Clean Air Act induced some multinationals to relocate production away from affected jurisdictions. Ederington and Minier (2003) and Levinson and Taylor (2008) found that higher abatement costs tend to have a positive effect on net imports (or equivalently, reduce net exports) in affected sectors.

There are a few studies that use data from newly industrializing countries, mostly focusing on incoming foreign investment. The results here have been mixed, with most studies finding little or no evidence that environmental policy affects the pattern of investment, but with some finding a negative effect and others a positive effect. Dean, Lovely, and Hwang (2009) used data on variations across provinces in China in charges for water pollution to determine whether the stringency of environmental policy affected incoming foreign investment. They found that more stringent regulation had a negative effect on investment from ethnically Chinese source countries (Hong Kong SAR, China; Macao; and Taiwan, China) in highly polluting sectors. However, environmental policy had no statistically significant effect on investment from other countries. Eskeland and

> 6 For reviews of this work, see Brunnermeier and Levinson (2004)
sales and value-added in Korea and Brazil, respectively. Furthermore, receiving procurement contract allows small and financiallyconstrained firms to acquire credit (di Giovanni et al. 2023), and increase investments (Hebous and Zimmermann 2021). The firm-level studies confirm how crucial the allocation of government contracts can be for productivity and economic efficiency.

> 2 One of the multilateral provisions with the strongest impact on cross-border procurement imposes the adoption of an e-procurement system. This evidence echoes the results of Lewis-Faupel et al. (2016), who find that regional governments with e-procurement systems in India and Indonesia are more likely to award contracts to firms from outside the region.

> 3 This standard definition of cross-border government procurement, which is consistent with the aggregated country-level approach, misses two other ways of exposure to foreign firms: (i) when the contract is awarded to the local subsidiary of a foreign firm; (ii) a foreign company indirectly participates in the execution of the contract by providing intermediate inputs to the domestic ‘winner’ (Cernat and Kutlina-Dimitrova 2016; 2020). Only (ii) could be measured in our “macro” approach using ICIO tables (see Riker (2013) for an application).

> 4 Taş et al. (2019) use contract-level data for members of the European Economic Area (EEA), Switzerland and Macedonia over the 2006-2016 to assess the role of WTO GPA. They find that the probability of awarding a contract to a foreign firm is significantly higher if the foreign firm is based in WTO GPA member country. They also provide suggestive evidence of a cost-reducing effect of the WTO GPA.

5
liabilities, such as property tax liabilities. Furthermore, we extend the literature by using rich administrative data to measure a variety of outcomes. Contrary to compliance crowd-out, we nd positive impacts on compliance in prior tax periods, ling of informative declarations, and a persistence of the main treatment eects in the medium term.

Finally, this paper contributes to the literature on rm formalization, as reviewed in Bruhn and McKenzie (2014) and De Andrade, Bruhn, and McKenzie (2014). This literature found that providing information, reducing registration costs or simplifying regulation is relatively ineective in encouraging rms to formalize. Only enforcement, e.g. in the form of inspection visits, or payment of about one month of prots were found to signicantly increase registration rates. We suggest that encouraging regular tax ling among rms that are tax-registered but do not le (regularly) is a more cost-eective way of casting the tax net wider. As non-lers are rms that have chosen to

> 11Hallsworth (2014) refers to three other papers considering ling behavior, but all are focused on individual taxpayers.

6
Table D3: Difference-in-Difference Regressions by Head-of-Household Gender [Dependent Variable: Food Insecurity] <!-- Start of picture text --> Fractional Index 3 Fractional Index 4<br>Ethiopia Malawi Ethiopia Malawi<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity<br>Nigeria Nigeria<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity Effect on Food Insecurity<br>Mild Moderate Severe Index Mild Moderate Severe Index<br>HHI 1 HHI 2<br>Ethiopia Malawi Ethiopia Malawi<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity<br>Nigeria Nigeria<br>-1 -.8 -.6 -
In order to assess the stationarity of the variables, unit root tests were performed using the Augmented Dicky-Fuller (ADF). The tests, summarized in Annex 2, show that all the variables are stationary (i.e. I(0)).

# 5. Results

In this section, the empirical results are presented and discussed. The long-term pass-through coefficients are estimated from the 2-step regression model, and the short-run dynamics are obtained from the VAR models. In general, the results show that pass-through is incomplete both in the long and short runs, which is in line with theory. However, the results on the size of the pass-through diverge between the long and short-term. In fact, the short-term impact of external food shocks is higher than in the long-run, suggesting that the effect of the shock tend to dissipate over time.

## 5.1 Long-term pass-through coefficients

The 2-step regression model shows that pass-through to domestic food inflation is low and lies between 13% and 16% (depending on the international price measure used) and the spill-over from domestic food inflation to core inflation is moderate, estimated at around 0.6%.

The first-step equation was estimated following the methodology shown in the previous section. Three measures of international food prices were used (IMF food index, FAO food index and FAO cereals subindex). Only statistically significant coefficients of the different lags, regardless of their signs, were used to compute the full long-term pass-through from international price inflation to domestic food inflation<sup>5</sup> . The results using the IMF food price index are not reported because it yielded negative pass through estimates. In general, the results from this step, reported in table 2, show that the pass through to domestic food inflation is low. A 1% increase in the international price inflation leads to a 0.16% increase in the domestic food price inflation when the FAO food index is used as a measure of international food prices, and to a 0.13%
(2019) argues that the labor market in Russia might be at risk of over-education, which leads to a reduction in educational premiums.

# **3. Data and Methodology**

In this paper we use the Russian Longitudinal Monitoring Survey (RLMS) – the only representative Russian household survey with a sizable panel component allowing for dynamic analysis (Kozyreva, Kosolapov and Popkin 2016). The data are notable for their reliability, diversity, and applicability to a variety of research questions. The RLMS collects information on people’s income and expenditures, educational and occupational behavior, and a range of other variables. RLMS sampling procedures have been thoroughly and extensively described elsewhere (Kozyreva et al. 2016). The present research uses all 23 waves (1994 - 2018) that were available as of June 1, 2020. Two years (1997 and 1999) are missing in the data because data were not collected in those years due to funding problems. The sub-sample selected for empirical investigation in this paper consists of working individuals aged 25-64 who are out of school and have positive labor market experience and income.

Table 1 shows descriptive statistics for the key variables under focus and sample sizes by years. The mean of years of potential experience is relatively stable over time and the mean of years of education is observed to increase over time. The increase in mean years of education is matched by the increasing proportion of those graduating from higher education, shown in the last column. Average years of schooling increased from 12.4 to 13.3 years between 1994 and 2018, but the proportion of the labor force with higher education increased 26 to 41 percent, or by 59 percent.

6
# **1. Introduction**

One of the instruments many governments resorted to in responding to the COVID-19 pandemic was trade policy. Barriers to the importation of medical products and supplies and agricultural and food products were lowered, and restrictions imposed on exports of such goods. The mix of import facilitation and export controls was driven by the objective of maximizing the availability of critical supplies in the domestic market. Real-time tracking of the use of trade policy measures is difficult as governments only report changes in trade policy to the WTO with a lag and may not notify the WTO at all. Timely information on trade policy measures matters for businesses seeking to expand production and needing to source inputs to do so, to public authorities responsible for procuring critical supplies, and for policy analysts interested in assessing the effects and effectiveness of policy responses to the pandemic.

This paper introduces a new data set on trade policy interventions – both to control exports and facilitate imports of food, medical supplies, and personal protective equipment (PPE). The data set is the product of an ongoing open data initiative conducted by the Global Trade Alert (GTA) in partnership with the European University Institute and the World Bank to collect information on changes in trade policy towards exports and imports of medical and food products starting in January 2020.<sup>1</sup> Until midOctober 2020 the data were collected and reported on a weekly basis, providing the ability to track the imposition and removal of measures across a wide range of countries. The high frequency of the data is a unique feature of the exercise. The project is ongoing. The data used in this paper span the period from January 2, 2020 to October 9, 2020.

Our aim in this paper is to provide a descriptive assessment of the trade policy measures implemented in response to the COVID-19 pandemic. This paper and the accompanying database complement a growing body of policy oriented economic research studying international trade during the COVID-19 pandemic. Early in the pandemic many analysts and organizations stressed the importance
_established democracies_ (hereafter “democracies” or DEM) if they have an average Polity IV score >7.5 (Marshall et al. 2016), and as _autocracies or transition countries_ (hereafter “autocracies” or AUT) otherwise. The purpose of the high polity cutoff is to exclude countries that transition in and out of democracy, since countries are categorized once as one regime or the other.<sup>30</sup> Combined, there are around 125 countries, 7,000 observations, and 1,100 leaders for which we have growth, leader, and polity data. Of these, 23% are established democracies. (See the Online Appendix for further details and descriptive statistics.) For our results involving individual leaders’ growth estimates, we restricted the sample to leaders with tenure ≥ 3 years, and growth data available for their whole tenure, leaving a sample of approximately 650 leaders and 5,400 observations.

There are six regions (for region-by-year fixed effects), which are based on the World Bank classification, with three modifications: (i) European offshoots (the United States, Canada, Australia, and New Zealand) are added to Europe, as is common in the literature; (ii) South Asia and East Asia & Pacific are combined, due to the small number of countries in the former; and (iii) Communist transition countries geographically close to the former USSR form their own region, as they experienced a distinct set of common shocks, especially in the early 1990s.<sup>31</sup>

**Outliers.** Per capita GDP growth rates are often very volatile—for example, around wars— and a small number of extreme observations can have a large effect on estimated results. Accordingly, we drop outliers, which we define as | ⋆|>40%. This is quite conservative and only tt gg

30 AUT includes autocracies as well as democratic-transition countries that spent much of the sample as autocracies or anocracies,
# **Figure 3.1. Sovereign risk by country groups, 2000-21 averages**

<!-- Start of picture text --> A. 5-year CDS spreads (log) B. Sovereign ratings C. Sovereign ratings in EMDEs, by<br>income groups<br><!-- End of picture text --> _Sources:_ Bloomberg; Kose et al. (2017).

_Note:_ The figure shows boxplot charts for 2000-21 averages of sovereign risk variables. AE = advanced economies; EMDE = emerging market and developing economies; HIC = high-income EMDEs; LIC = low-income EMDEs; MIC = middle-income EMDEs.

**Figure 3.2. Sovereign ratings by EMDE regions and groups**

<!-- Start of picture text --> A. Sovereign ratings by EMDE groups, simple<br>averages<br>Index<br>2009-2013 2017-2021<br>17<br>14<br>11<br>8<br>Commodity Commodity EMDEs without LICs<br>exporters importers LICs<br>C. Sovereign ratings by EMDE regions, simple<br>averages<br>Index<br>2009-2013 2017-2021<br>17<br>15<br>13<br>11<br>9<br>7<br>5<br>3<br>EAP ECA LAC MNA SAR SSA<br><!-- End of picture text --> <!-- Start of picture text --> B. Sovereign ratings by EMDE groups, GDP-<br>weighted averages<br>Index<br>18 2009-2013 2017-2021<br>15<br>12<br>9<br>6<br>Commodity Commodity EMDEs without LICs<br>exporters importers LICs<br>D. Sovereign ratings by EMDE regions, GDP-<br>weighted averages<br>Index 2009-2013 2017-2021<br>17<br>15<br
---|---|---|---|
|Energy|2.2|0.6|2.8|
|Transport|1.3|1.3|2.6|
|Water|1.0|0.8|1.8|
|Total|4.5|2.7|5.4|

Source: Derived from Rozenberg and Fay, 2019 However, evidence on how much countries are _actually_ spending on infrastructure has been sparse. This is due to limitations in the ability of traditional public finance sources – such as the IMF Government Finance Statistics – to provide sufficiently disaggregated measures to isolate infrastructure expenditure. A preliminary attempt to estimate aggregate financing flows to infrastructure in the developing world, leveraging a variety of different data sources, provided a central estimate of 4.0 percent of GDP for public investment in infrastructure by developing countries in 2011 (Fay et al., 2019). While this central estimate of spending is not far short of the projected investment needs above, the aggregate finding belies significant variation in the size of financing gaps across sectors and regions, with significant spending shortfalls estimated for Africa, Middle East, Europe and Central Asia, even as financing exceeds the minimum requirement for East Asia and Pacific.

Subsequent research has shed further light on the sectoral composition of capital spending on infrastructure projects and the extent to which it is delivered through public budgets, state-owned enterprises, or publicprivate partnerships (World Bank, 2020). This evidence, currently available for 2017 alone, suggests that 80 percent of infrastructure investment is publicly financed and two-thirds of that is channeled through

> 2 https://datatopics.worldbank.org/sdgs/sdg-goals-targets.html

2
because better students sort into better performing schools, but it rules out that students systematically sort based on subject-specific abilities into schools with subject-specific teacher knowledge. That is, the assumption would be violated, if, for example, students in lower primary with relatively higher motivation for mathematics systematically sort into schools (or classrooms) with relatively more knowledgeable mathematics teachers.

Assumption 3A also places some restrictions on what parents and schools do. For example, while our identifying assumption does allow for parents (or schools) to respond to their children’s low mathematics aptitude by providing additional teaching (or hiring a private tutor), they cannot do this to compensate for insufficient teacher mathematics knowledge.<sup>22</sup>

> 20 This assumption is also frequently made in the teacher value-added literature (see Kane and Staiger, 2008). Chetty et al. (2008) show that estimates of teacher value added with and without an assumption of timeinvariance give very similar results.

> 21 As shown in Todd and Wolpin (2003), assumption 1 is also required in order to derive the lagged-score valueadded model from a linearized cumulative student achievement function.

> 22 Using data from kindergarten students in Ecuador, Araujo et al. (2016) find that while parents recognize better teachers, they do not change their behaviors to take account of differences in teacher quality. Note that our

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