rafmacalaba/datause-encoder-data huggingface dataset:
In this sense, the impact of the Europe Agreements on bilateral trade flows cannot be directly extrapolated for an EU-Ukraine FTA. Nevertheless, a glance at the overall development since 1995 is useful because Ukraine and the CEECs share similar legacies in economic and agricultural structures, and because Ukraine has stated that it intends to harmonize key elements of its agricultural policy with that of the EU.<sup>2</sup> The natural candidate for comparison in terms of agriculture is Hungary (Poland’s agricultural structures being very different), so we present the development of total (Table 2-1) and agricultural trade (Table 2-2) between the EU and Hungary below.

**Table 2-1: Development of total EU trade with Hungary (1995-2007, in million €)**

||Imports by the<br>EU|Exports to<br>Hungary|Total trade|Annual change|Balance|
|---|---|---|---|---|---|
|1995|7610|8731|16341||1120|
|1996|8847|10028|18875|15.5%|1181|
|1997|11684|13596|25281|33.9%|1912|
|1998|14655|16863|31519|24.7%|2208|
|1999|17624|18442|36066|14.4%|817|
|2000|22046|23039|45086|25.0%|993|
|2001|24825|23878|48703|8.0%|-947|
|2002|25268|25030|50298|3.3%|-238|
|2003|26033|26228|52260|3.9%|195|
|2004|27938|27974|55912|7.0%|37|
|2005|30204|29714|59917|7.2%|-490|
|2006|33470|34268|67738|13.1%|798|
|2007|38285|36930|75215|11.0%|-1355|

_Source: Comext Database_ The Europe Agreements for Hungary were signed in 1991, and effective since 1994. They involved full tariff dismantling for about 80% of all industrial goods. This dismantling was asymmetric in that the speed of tariff reductions was higher for the EU than for Hungary. Trade with agricultural goods was either exempted from gradual liberalization, or preferential treatment was granted subject to quantitative restrictions. The agreements were substantially extended for agricultural goods in order to prepare the accession process and to align CEEC agricultural policy with the CAP. In 2000, the so-called _Double Zero_ agreements were introduced by implementing zero tariffs with zero quantitative restrictions for selected goods, most notably pork and poultry. In 2003, the _Double Profit_ agreements opened quotas with zero tariffs even for the most sensitive agricultural products, i.e., those with unlimited intervention in the EU such as wheat, coarse grains, beef and dairy. In effect, 95% of all Hungarian trade with the EU was liberalized by 2003.

Besides the gradual phasing in of trade preferences up to almost complete liberalization, the Europe Agreements involved also technical help and investment aids. In agriculture, Support for Pre-Accession Measures for Agriculture and Rural Development (SAPARD) was the

> 2 The economic program of the Government of Ukraine includes the following among its “priority tasks” for agricultural policy: “implementation of the European principles of state support of agricultural producers taking into account the size of cultivated land, the unit of products and the unit of exported products”.

11
Poverty maps show poverty is increasingly concentrated in the Northern Mountains (World Bank 2012), which is a region that could aptly be described as Vietnam’s Appalachia given its inaccessibility and topography. It is surprising to consider that such a region could have the highest cost of living in the whole nation (figure 2), especially with housing costs included in the comparison. Also surprising is the position of the rural Mekong Delta as having the second highest cost-of-living, given that this is Vietnam’s rice bowl, with rice moving out of this region to feed the rest of the country. The correlations between the benchmarks and the Engel curve estimates of the spatial price indexes for 2010 are -0.16 for WCPD-vw and -0.22 for WCPD-fw.

The spatial deflators affect estimates of the level and location of poverty and the gap between nominal and real inequality, while estimated cost-of-living changes affect assessment of overall progress in raising living standards and escaping poverty. Once again, the experience of inflation implied by the Engel curve deflator is unrelated to the record of inflation given by the WCPD benchmarks, with zero correlation between benchmarks and Engel estimates (figures 3a and 3b). The cost-of-living increase between the 2010 and 2012 surveys ranges from 15–25% with the WCPD-vw and 14–26% with the WCPD-fw, with the least increase in the urban Southeast and the most in the rural Central Highlands. A much more varied experience of inflation is shown by the Engel curve deflator, with cost-of-living increases ranging from -3% to 45%. Such changes appear unlikely because of the arbitrage opportunities that they imply. For example, the Engel curve has the cost of living in the rural Southeast rising by 45% which is three times faster than the rise for that region’s urban sector and also contrasts with an estimate of an unchanging price level in the neighboring rural Mekong Delta. Such big price rises in the rural Southeast would be expected to attract food out of the rural Mekong Delta and industrial goods out of Ho Chi Minh City in order to moderate the price increases in the rural Southeast.

26
weaker negative impact on its export growth. The other findings are comparable to the baseline effects reported in Table 2 in terms of sign, size and significance.

**Table 7: Supply, demand and third-country shocks and bilateral export growth, the role of sector characteristics, mirror data, OLS regression results**

|FRAMEWORK|VARIABLES|(1)<br>**_growth_**_ijkt_|(2)<br>**_growth_**_ijkt_|(3)<br>**_growth_**_ijkt_|(4)<br>**_growth_**_ijkt_|
|---|---|---|---|---|---|
|**Exporter supply shock***|**_work mobilityit*remoteik_**|**0.074**|||**0.044**|
|**sector characteristic**||(0.336)|||(0.353)|
||**_work mobilityit*gvcil_**|**1.398*****|||**1.647*****|
|||(0.510)|||(0.517)|
|**Partner demand**|**_retail mobilityjt*durablek_**||**0.414*****||**0.425*****|
|**sector characteristic**|||(0.099)||(0.103)|
||**_retail mobilityjt*gvc_**||**-0.186****||**-0.214*****|
||||(0.072)||(0.074)|
|**Third country**|**_competition shockijkt_**|||-0.012|0.203|
|**supply shock**||||(0.180)|(0.187)|
||**_upstream shockilt_**|||**1.835****|**1.673***|
|||||(0.865)|(0.870)|
||**_global outputmt_**|**0.765*****|**0.502*****|**0.729*****|**0.426****|
|||(0.160)|(0.172)|(0.171)|(0.179)|
||**Constant**|**0.748*****|**0.607*****|**0.802*****|**0.906*****|
|||(0.049)|(0.018)|(0.078)|(0.094)|
||Observations|323,998|323,998|323,998|323,998|
||R-squared|0.446|0.446|0.446|0.446|
||Exporter-Time FE|Yes|Yes|Yes|Yes|
||Importer-Time FE|Yes|Yes|Yes|Yes|
||Exporter-Partner-Sector FE|Yes|Yes|Yes|Yes|
||Sector-Time FE|Yes|Yes|Yes|Yes|
||Cluster|Exporter-Partner|Exporter-Partner|Exporter-Partner|Exporter-Partner|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Note: Sector-time fixed effects control for unobserved effects affecting aggregated sectors over time (see footnote 10). The variable _global outputmt_ additionally controls for global sector-time trends.

# **5. Conclusions**

Using monthly trade data for 28 exporting countries and multiple importers over the January-June 2020 period, this paper investigated how the Covid-19 pandemic impacted international trade. We find strong evidence that sectoral characteristics such as the feasibility of remote work, durability of goods, and integration into global value chains played a large role in mitigating or augmenting the trade effects of Covid-19 shocks. While more work is needed, particularly to include sectoral trade data from a larger set of developing countries, these findings provide relevant policy insights. First, they help identify the sectoral attributes that create vulnerability during a pandemic, providing guidance for sectoral policy 26
telecommunications sub-sector. Similarly, the development of workforce skills required to use ICT intensively should also be a priority in the growth agenda of ECA countries.

Finally, we close this section with three important remarks. First, FDI inflows would be an important variable to consider in order to explain the divergence in performance across sub-sectors and countries. Collecting disaggregated and time-varying FDI data for services sub-sectors should be a priority for statistical agencies in ECA countries, to allow a thorough assessment of the effects of FDI. Second, regulatory reform in sub-sectors such as wholesale and retail trade which have generated substantial improvements in productivity in OECD countries (OECD, 2005) are not covered by the EBRD index. Assessing the effect of reforms in those sub-sectors in ECA countries should be a priority for future research. Finally, the liberalization of services can influence average performance in a sub-sector through several margins: by increasing average productivity for incumbent firms but also by allowing new entry of firms, likely to be more innovative and successful in meeting consumer demand, and by encouraging the exit of less productive firms. Analyzing these margins is possible only through the use of firm-level similar to manufacturing census data. Statistical agencies in ECA countries should be encouraged to collect such data to enable policy-makers to understand the dynamics of productivity and growth in the services sector.

# **5. The Effect of Services Liberalization on Manufacturing**

The efficiency of services sub-sectors is important per se as these sectors increasingly contribute to the economies of the ECA region but also because services are important inputs for downstream sectors. Hence, liberalization-related improvements of the performance of the services sector can be crucial for growth promotion. Indeed, Eschenback and Hoekman (2006) show that progress in services policy reforms helps explain differences in economic growth across ECA countries since 1990. Arnold et al. (2006a) find a significant positive effect of services liberalization and FDI in the services sector on manufacturing firms’ total factor productivity (TFP) in the Czech Republic. Similarly, Arnold et al. (2006b) find a significant positive effect of regional availability of communications, electricity, and financial services on TFP for a large cross-section of manufacturing firms across several African countries.

34
# **_Annex I: Summary Tables and Integration Tests for Money Demand Model_**

## **Table (1): Data Description for Money Demand Model:**

|**Acronym**|**Description**|**Source**|**Adjusted**|**Date Accessed**|
|---|---|---|---|---|
|rm2|**Monetary Aggregate:**<br>Log of Real Broad Money<br>(including money in<br>circulation, demand and<br>time saving deposits)|IMF<br>International<br>Financial<br>Statistics|Yes<br>(log of levels less)|January 25<sup>th</sup>2015|
|rgdp|**Scale Variable (1):**<br>Log of real Gross<br>Domestic Product; proxy<br>for wealth|IMF<br>International<br>Financial<br>Statistics|Yes<br>(log of levels deflated<br>by CPI: base year<br>2010)|January 25<sup>th</sup>2015|
|INFL|**Price Variable:**(Inflation<br>Rate: annual change in<br>CPI)|IMF<br>International<br>Financial<br>Statistics|No|January 25<sup>th</sup>2015|
|Interestrt|**Opportunity Cost for**<br>**Holding Money (1):**<br>Three months deposit rate|IMF<br>International<br>Financial<br>Statistics|No|January 25<sup>th</sup>2015|
|Disc|**Opportunity Cost for**<br>**Holding Money (2):**The<br>discount rate|IMF<br>International<br>Financial<br>Statistics|Yes<br>(log household<br>consumption)|January 25<sup>th</sup>2015|
|rcons|**Scale Variable (2):**<br>Log of real household<br>consumption|IMF<br>International<br>Financial<br>Statistics|No<br>(log of levels deflated<br>by CPI: base year<br>2010)|January 25<sup>th</sup>2015|
|Dexrtend|**Expected Exchange Rate**<br>**Depreciation:**of<br>differenced log of official<br>nominal exchange rate|IMF<br>International<br>Financial<br>Statistics|Yes<br>(differenced log of<br>levels)|January 25<sup>th</sup>2015|
|FFUNDSRT|**Foreign Interest Rate:**<br>US Federal Funds Rate|IMF<br>International<br>Financial<br>Statistics|No|January 25<sup>th</sup>2015|

40
# **1 Introduction**

Innovation is paramount for firm growth and productivity. Well-functioning deep financial markets promote innovation by efficiently allocating capital to enterprises with promising projects (Levine 2005). Firms with new and better ideas are provided with the means to finance their projects, gaining competitive advantages through productivity increases (Beck, Levine, and Loayza 2000). Enterprises that fail to innovate are at increased risk of losing their competitive edge and may exit the market, allowing for an efficient allocation of (often scarce) financial resources in the economy. In turn, this efficient allocation leads to higher productivity and, ultimately, growth.

Although the existing literature highlights the importance of financing innovation, less is known about the importance of the firm financial structure in supporting innovation. In this paper, we fill this void by investigating the role of a firm financial structure for innovation. In principle, firms can use either internal funds, cash flows from retained profits or new capital injections, or external funding, credit or risk capital. Each source of finance has different characteristics that in turn help in lessening market frictions that may result in the underinvestment in innovation projects. Thus, an important contribution of the paper is to show how the heterogeneity in funding sources is associated with greater firm innovation.

We also provide evidence on the relationship between a firm’s financial structure and innovation, defined both as invention and adoption of new technologies, products, and processes (henceforth adoption for ease of exposition). Invention and adoption have different project timelines and cashflow patterns. Moreover, firms face multiple competing investment decisions -e.g., capital investment, debt payments, dividends- to be financed through internal sources or external sources, such as bank loans or other debt or equity contracts. A firm production process may therefore be influenced by both the availability and type of finance. Besides, external funding can assume different forms and it may bring ancillary benefits (e.g., managerial skills) that may propel a firm’s innovation process.

Using survey data on more than 17,000 firms in 104 countries, we investigate the cross-country variation in the relationship between financial structure and innovation distinguishing between the development of invention and adoption and diffusion of new technologies, products, and services. We find that bank funding of fixed assets is strongly and positively associated with both the decision to innovate and the intensity of firm-level innovation. The relationship is stronger for invention. Moreover, funding by non-bank financial institutions (NBFI) is strongly associated with the overall extent of innovation and adoption, especially in countries with more developed financial markets and institutions.

2
7 been used in a number of articles (e.g., Knack and Keefer 1995) and that, uniquely, has ample country and year coverage, is the corruption indicator from Political Risk Service’s _International Country Risk Guide_ . This is a subjective measure of the extent to which bribes are a significant determinant of government decision making. The measure has broad country coverage, and is available since 1986. All of the Political Risk Service variables are scaled so that higher values are “better”. Hence, higher values of the corruption variable signify _reductions_ in corruption.

# **_Measuring public or non-targeted good provision:  The rule of law and the quality of bureaucracy_**

The rule of law is easily seen as a consequence of government incentives to respond to broad or narrow interests in society.<sup>4</sup> When the rule of law prevails, the umbrella of secure property, contractual and other rights extends over all citizens. The benefits of the rule of law – faster growth, for example – similarly extend to all citizens. However, when special interests (including politicians themselves) can use the power of government to abrogate the government’s obligations to average citizens – for example, to protect the property or contractual rights of average citizens – the rule of law is weak.<sup>5</sup> The costs of weak rule of law are borne by citizens in general. Government protection of property and contract rights can therefore be seen as a policy that has public good characteristics and is not easily targeted to particular groups of citizens. The rule of law measure from Political Risk Service’s _International Country Risk Guide_ is a common measure of the extent of

> 4 The literature (North and Weingast 1989, Acemoglu, et al. 2002, Clague, et al. 1996) more typically argues that secure property and contractual rights are the product of political checks and balances that create institutional road blocks to government efforts to expropriate.  Empirically, however, political checks and balances are weak predictors of the property rights measures used here.  The divergent incentives of politicians to pursue broad public interests, the core problem in the analysis here, explains why checks and balances may not always be a strong guarantee of the security of property rights.

> 5 This definition of rule of law is not the only one.  However, the specifics of the definition fit squarely in any definition, and are tracked by the Political Risk Services rule of law indicator.
concerned about the integrity of the index. This potential bias, however, does not seriously affect the outcome of our methodology for two important reasons. First, we use the index to translate original scores; since the same index is used for all original scores, each score is transformed equally. Second, it is unlikely that tests changed from year to year in a way that differentially affected certain countries, thus eliminating the concern of a potential bias in our index. For example, even if TIMSS 2007 was made more challenging as a result of 2006 SERCE test scores, which is highly unlikely to begin with, this change should not impact Colombia more than Bolivia. Thus, the index we produce can be a powerful and unbiased tool to link international achievement tests with regional tests.

Finally, we multiply our index by the regional test scores for those countries who did not participate in any test with a US comparison: Thus a test score from a regional achievement test has been converted to a score that is comparable to an international test result with US participation. These test scores allow the inclusion of developing countries, which participate only in regional assessments, to be included in our international achievement data set.

Next, to compare international assessments across countries we adjust achievement test scores in relation to the US. To this end, we construct a similar index to the one above. We create a ratio between US scores on the NAEP and on international achievement tests per subject in the most adjacent year. We multiply by a factor of ten for scaling purposes.

We then multiply all raw and doubloon country test scores from equation (4) by our new index to obtain test scores that are linked to the US and can be compared over time.

where _z_ is an internationally comparable test score across tests, across countries and over time.

9"
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Borjas, G.J. (1988), ‘Self-selection and the earnings of immigrants’, National Bureau of Economic Research, Inc. Retrieved from http://ideas.repec.org/p/nbr/nberwo/2248.html.

Borjas, G.J. (1989), ‘Economic theory and international migration’, _International_ _Migration Review_ , **23** (3), 457–85.

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Borjas, G. J. (2003), ‘The labor demand curve is downward sloping: 47
1011 to the treatment group. In order to avoid John Henry and Hawthorne effects, job candidates were not informed as to which group they were assigned.<sup>3</sup> Table 1 summarizes some basic characteristics of the job candidates by treatment status. The average participant is 23.5 years old, and graduated three-quarters of a year before participating in our program. 59 percent are female, 81 percent are university graduates and 19 percent community college graduates. The most common majors are accounting and business, engineering, and computing and information technology. The last column shows randomization has succeeded in generating balance on background characteristics, on the tawjihi score (the end of high school exam), and on the different test measures to be described below. The only variable which shows imbalance is whether they are a university versus a community college graduate. Since this is one variable out of nineteen in the Table, it appears likely to be due to chance.

The _firm sample_ consists of a primary, experimental sample, and a secondary, booster, sample. The experimental sample consists of 2,279 small and medium firms, who were selected via a listing survey that screened firms according to whether they planned on hiring a worker in the next six months, and whether they would consider young workers and female workers for these positions. Appendix 1 describes in more detail this listing process. Firms of this size typically do not have human resource departments, and have less experience hiring workers than large companies. As such, we might expect search and matching costs to be higher for such firms. Indeed, in our survey of these firms, 60 percent say they have difficulty distinguishing between good and bad candidates.

We stratified these firms by sector (commercial or industrial), whether or not the firm had recently hired a worker, whether or not the firm had previously hired a female fresh graduate, and by the number of female employees in the firm (0, 1 to 10, 10 or more). Within these 24 strata, we then randomly assigned half the firms to treatment, which would be attempted to be matched to job candidates, and half to control, which would not. This gave a treatment group of 1,137 firms and control group of 1,142 firms, with mean size of 17 workers, and a mean annual hiring rate of 1.6 workers per firm (appendix Table 1). After no hires occurred in a first phase, the control group was also offered the treatment, and a secondary booster sample of 175 larger

> 3 An additional 213 youth who joined the program in December were automatically assigned to receive the intervention but only had December in which to be matched. We do not include this group in our analysis.

9