rafmacalaba/datause-encoder-data huggingface dataset:
how bilateral bargaining among supply chain partners alters the mapping from tariffs to prices, and therefore optimal trade policy. In contrast to their approach, we are agnostic about the nature of price determination within global supply chains; our results obtain even if prices are determined by market clearing conditions, as in conventional models. Our work also builds on Blanchard (2007, 2010), who shows that foreign direct investment and international ownership alter the standard mapping from prices to income, and thus optimal tariffs. Though similar in spirit, the mechanics and empirical implications of the model in this paper are different. Our theory links observable input trade patterns to bilateral tariffs, separate from ownership concerns.

Turning to the empirics, our first contribution is that we combine data on bilateral import protection and value-added contents to test key predictions of the theory. We focus our analysis on dimensions of policy over which governments have scope to implement discretionary levels of protection.<sup>2</sup> We first examine bilateral applied tariffs, where countries offer preferential tariffs to selected partners. We then examine the use of temporary trade barriers (antidumping, safeguards, and countervailing duties) in a separate, complementary set of exercises.

Our approach to analyzing bilateral tariffs is guided by both the theory and key institutional features that govern tariff setting in practice. Theory motivates the empirical specifications we adopt and our choice of controls. In a first specification, we focus on identifying the role of domestic value added in foreign production, using fixed effects to control for export supply elasticities, political economy, and foreign value-added effects. We then turn to a second theory-based specification to identify the role of foreign value added in domestic production. Throughout the analysis, we measure value-added contents using input-output methods and data from the World Input-Output Database.

Because institutional features of the multilateral trading system constrain policy, we are careful to incorporate them into our empirical strategy. While governments have discretion to offer preferential tariffs bilaterally via various trade preference programs (under the GATT’s Article XXIV or Enabling Clause), they are subject to several relevant constraints. The first is the most-favored-nation (MFN) rule under the GATT, which caps bilateral tariffs for many trading partners at levels below the unilaterally optimal tariff. The implication is that we can observe bilateral optimal tariffs up to, but not above, the MFN threshold. We use non-linear methods to address this partial non-observability, or censoring, problem in the estimation. A second constraint is that some bilateral tariffs are set via reciprocal trade agreements. As

> 2Our study is in the tradition of earlier work examining unconstrained dimensions of policy, including Trefler (1993), Goldberg and Maggi (1999), Gawande and Krishna (2003), Broda, Lim ̃ao and Weinstein (2008), Bown and Crowley (2013), and Blanchard and Matschke (2015), among others.

3
Refugees (UNHCR). Retrieved from <u>https://www.unhcr.org/research/working/3b7d24059/forgotten-solution-local-integrationrefugees-developing-countries-karen.html</u>

- Kasozi, J. (2017). The refugee crises and the situation in Sub-Saharan Africa. _ÖGFE Policy Brief, 16_ , 2017.

- Kreibaum, M. (2016). Their suffering, our burden? How Congolese refugees affect the Ugandan population. _World Development, 78_ , 262-287.

- Loschmann, C., Bilgili, Ö., & Siegel, M. (2019). Considering the benefits of hosting refugees: evidence of refugee camps influencing local labour market activity and economic welfare in Rwanda. _IZA Journal of Development and Migration, 9_ (1), 5.

- Mathys, E. (2016). Opportunities to Provide Refugees and Ugandans with Alternative Livelihood Activities in Uganda’s Kamwenge District. _Washington, DC: FHI, 360_ .

- Maystadt, J. F., Hirvonen, K., Mabiso, A., & Vandercasteelen, J. (2019). Impacts of Hosting Forced Migrants in Poor Countries. _Annual Review of Resource Economics, 2019_ (11), 439–59.

- Maystadt, J. F., & Verwimp, P. (2014). Winners and losers among a refugee-hosting population. _Economic development and cultural change_ , _62_ (4), 769-809.

- Meyer, S., Tappis, H., Weiss, W., Spiegel, P., & Vu, A. (2011). Refugee site health service utilization: more needs to be done. _American journal of disaster medicine, 6_ (4), 231-242.

- Omata, N., & Kaplan, J. D. (2013). _Refugee livelihoods in Kampala, Nakivale and Kyangwali refugee settlements: Patterns of engagement with the private sector_ . Refugee Studies Centre, Oxford Department of International Development, University of Oxford.

- Raleigh, C., Linke, A., Hegre, H., & Karlsen, J. (2010). Introducing ACLED: an armed conflict location and event dataset: special data feature. Journal of peace research, 47(5), 651-660.

- REACH Initiative (2018). Uganda Joint Multi-Sector Needs Assessment. Identifying humanitarian needs among refugee and host community populations in Uganda. August 2018.

- Ruiz, I., & Vargas-Silva, C. (2016). The labour market consequences of hosting refugees. _Journal of Economic Geography_ , 16(3), 667-694.

- Ruiz, I., & Vargas-Silva, C. (2017). The consequences of forced migration for host communities in Africa. _Revue D'economie Du Developpement, 25_ (3), 135-154.

- Tatah, L., Delbiso, T. D., Rodriguez-Llanes, J. M., Cuesta, J. G., & Guha-Sapir, D. (2016). Impact of refugees on local health systems: a difference-in-differences analysis in Cameroon. _PLOS one, 11_ (12), e0168820.

- Thomas, M. (2017). Turning the Comprehensive Refugee Response Framework into reality. _Forced Migration Review_ , (56).

- United Nations Development Program (2017). Uganda’s contribution to refugee protection and management. Retrieved from https://data2.unhcr.org/en/documents/download/64687.

- United Nations High Commissioner for Refugees. (2005). “Handbook for Self-Reliance” Reintegration and Local Settlement Section Division of Operational Support UNHCR, Geneva. Retrieved from: https://www.unhcr.org/44bf7b012.pdf

38
to higher error (e.g. the subject might mark the wrong option even after evaluating the options correctly due to the change in side). The randomization within binary choice may also make comprehension harder if the exact parallel organization of each decision helps subjects understand the structure of the experiment. We believe that these risks are small compared to the potential benefits. In fact, we conjecture that within-choice randomization may increase attention, since the choices look less repetitive and are less susceptible to “automation”. That said, future work could test the optimal presentation of binary choices to minimize a potential trade-off between detecting hidden, existing error and introducing additional error. Our MPL template survey instrument (described below) allows the user to implement any pattern of randomizing the order of options within binary choice, including no randomization and full randomization.<sup>17</sup>

# **3.2 Random effects latent utility estimation**

Even if the MPL design would allow it, existing estimation approaches do not account properly for either systematic or idiosyncratic error. Building on our framework from section 2.2, we propose to estimate WTP with a latent utility binary choice model that captures both individual-level variation and choice-specific error and can also estimate the size of the systematic bias using data from a randomized MPL. This estimation approach takes seriously the structure of the data as a series of choices between two options, and it deals in natural ways with MSB, NSB, and interval-identified observations. The basic idea appeared early on in the literature on estimating risk aversion parameters from MPL (Holt and Laury, 2002; Harrison and Rutström, 2008; Harrison, 2008), and it is partially reflected in the use of interval regression for MPL data as well as in randomutility approaches found in the wider literature on WTP elicitation (e.g. Berry et al., 2020).<sup>18</sup> However, binary choice models do not seem to be commonly used to estimate preferences from survey-based MPL experiments.

For simplicity, let us assume we have MPL data that compares only two options A

> 17In line with the view that full randomization is not desirable, the template does not support a fully random order of choices within MPL. This would require additional coding.

18Recently, (Apesteguia and Ballester, 2018) pointed out that the estimation of time and risk preferences from MPL data with a random-utility model may be affected by the fact that the (cardinal) utility difference between two options may not be monotonic in the risk or time preference parameter. As Conte and Hey (2018) note, this non-monotonicity is a property of the (systematic component of) the utility function; issues may arise here from pooling subjects and implicitly making comparisons across subjects. In our case, the monetary value serves a “normalizing” function. We essentially equate WTP with the (relative) subjective value of each option to the subject, and so the utility difference is, by definition, monotonic in WTP.

17
# **3. Empirical Strategy**

where _Firm performance_ of firm _i_ in sector _j_ and country _k_ is captured by one of four variables: percentage change in sales; percentage change in employment; decreased employment; and anticipate falling into arrears. All firm performance variables are measured in Round 3 of the ES COVID-19 Followup Surveys.

_Government Support_ is a vector of one or more indicator variables equal to one if the firm received any type of government support and equal to zero otherwise.<sup>10</sup> We first use an indicator for having received government support in Round 1 or 2 of the ES COVID-19 Follow-up Surveys. Here, the coefficient represents the difference in Round 3 performance between firms that received support in Round 1 or 2 11 and firms that did not receive support in either of these rounds. ββ Next, we aim to unpack the role of government support in Round 1 versus Round 2. In this regression, we include three mutually exclusive indictors for government support: (i) receiving support only in Round 1, (ii) receiving repeated support in Rounds 1 and 2, and (iii) receiving support only in Round 2. This regression thus compares Round 3 performance of firms in these three categories to firms that did not receive any support in Rounds 1 and 2.

In identifying the causal effect of government support on firm performance, we face two main issues. The first is reverse causality. That is, government support may be determined by firm performance, for example if worse performing firms are more likely to receive support. We mitigate reverse causality by looking at firm performance one round after receiving government support, so that support is not directly influenced by firm performance. That is, we use Round 3 firm performance and government support in Round 1 and 2.

The second threat to identification is omitted variable bias. That is, other factors may determine both government support and firm performance. We mitigate omitted variable bias by controlling for firm background characteristics, as well as sector and country fixed effects ( _S_ and _C_ , respectively). _Firm Characteristics_ is a vector of firm-level background variables measured before the COVID-19 crisis, as described in Section 2 and Table 3 (at time t = 0). These variables may determine firm performance for the following reasons.

More productive, larger, and older firms may weather a crisis better because they are more established and have more financial and organizational resources to do so. These firms may also have strong

> 9 We chose a linear probability model since it is simpler and tends to yield similar marginal effects as a nonlinear model (Angrist and Pischke 2009). Our results are similar if we use a probit model to estimate the regressions with binary outcome variables.

> 10 One caveat in this analysis is that we do not know how much government assistance firms receive, but the amount of assistance has been shown to correlate with firm outcomes (Dvoulety and others 2021).

6
these decentralized networks allow for consensus formation about the “state of the world”<sup>6</sup> in lowtrust environments without requiring a trusted third party and seek to imbue crypto-assets with certain characteristics such as scarcity, verifiability, and, more broadly, programmability (e.g., Nakamoto (2008) and Buterin (2013)).<sup>7</sup> In response to the volatility of crypto-assets, stablecoins have emerged as a new type of cryptoasset which aims to maintain a stable value relative to a specified asset (typically a fiat currency and most commonly the US dollar), or a pool or basket of assets. Stablecoins have grown explosively with a market capitalization of US$136 billion in November 2021, up from US$28 billion in January 2021, according to <u>The Block. The three largest stablecoins, Tether, USD Coin,</u> and Binance USD, account for about 85 percent of the total. Furthermore, Decentralized Finance (DeFi) has also grown swiftly, a “smart contract”<sup>8</sup> -based crypto-assets financial ecosystem that uses programmability features, spanning, among others, collateralized lending, borrowing, exchange, stablecoins, investment management, and derivatives services (e.g., Harvey et al (2021), JP Morgan (2021a), Schar (2021)). These services are interoperable and can be used as building blocks by users or developers allowing for complex ecosystems to emerge (i.e., “composability”). According to CoinMarketCap, in November 2021, a total of about US$190 billion was locked in DeFi projects, up from about US$20 billion in January 2021. An important driver of this explosive growth is the recent price surge in ether. Ethereum is the dominant DeFi platform, although its share has been falling as rival platforms gain momentum.

While data gaps remain significant (e.g., IMF (2021)), it appears that crypto-assets activity is a global phenomenon. Some industry estimates claim that over 200 million people around the world own or use crypto-assets in 2021 (Figure 1). While its representativeness is not fully clear, a global Statista household survey conducted in 2020 found that there are at least 20 countries where over 10 percent of the respondents own or use crypto-assets (Table 1). According to industry analysis, global crypto-assets activity has grown by over 2,300 percent since Q3 2019 and over 881 percent to Q3 2021 and estimates suggest that the countries with relatively high activity are Emerging Market and Developing Economies (EMDEs) (Chainalysis (2021) and Table 1).

> 6 For example, the ownership status of all bitcoins on the Bitcoin ledger or the status of all smart contracts on Ethereum.

> 7 The benefits of decentralization come at a cost, typically by posing trade-offs with throughput capacity and/or security.

> 8 A “smart contract” is a piece of software that directly controls crypto-assets (also see Szabo (1997)). A smart contract runs on a distributed ledger technology network and can be created by anyone.

3
Column 7 shows results for the U.S. data. Again we find that stocks in China and Hong Kong (China) grew faster than in most Latin American countries. In the manufacturing sector, the ratios are significantly smaller than in China, suggesting that most of the growth in the manufacturing stocks in China and Hong Kong (China) was due to FDI in mainland china gather than Hong Kong (China).

Finally, the last panel of Table 3 reports the results relative to India. Not one of the Latin American countries accumulated more stocks than India during the period. Indeed, Latin American total stocks relative to India fell even more than those relative to China. Unlike those of China, however, these ratios are significantly bigger when we repeat the exercise with the stocks from the OECD.

U.S. stocks in India also grew faster than in Latin American countries. Contrary to what happened with China, it is interesting that this growth was less pronounced in the manufacturing sector. The most remarkable example is that of Chile, where total stocks relative to India in 2003 were about one third of those in 1990 but were 37 percent bigger in the manufacturing sector.

It is interesting to estimate the share of these variations explained by GDP growth. We do this by normalizing the FCS by the GDP and value added in manufacturing before calculating the ratios of the relative stocks between 2003 and 1990. The results are reported in Table 4. The first panel of the table shows the results for China. FCS in China grew more than in Latin America even after controlling for GDP growth. Although the ratios are still less than one, they are significantly higher than those in Table 3, reflecting that GDP growth was faster in China than in Latin American countries. FCS in India also grew more than in Latin America, even after normalizing by the GDP growth.

11
between relative prices and relative labor productivities between the two sectors, thus breaking the Balassa-Samuelson effect. Whether that actually happens depends on how the relative wage rate ( _W_ T/ _W_ N) is in fact determined. However, the key point is that with market imperfections it is an open question whether the PPP will start to approach the exchange rate in poor but growing economies, or whether it continues to lag. Given market frictions, one might also conjecture that the DPE only starts to emerge when the growth rate is sufficiently high.

There are also measurement errors in the PPPs. While these are largely unobservable, one observable clue is that not all of the countries that participated in the 2005 round had participated in 1993. Most of the PPPs for these non-benchmark countries were estimated econometrically by the ICP team using regressors observed for both sets of countries.<sup>13</sup> Any bias in those estimates will be reflected in the subsequent changes observed when the country participates properly in the ICP’s price surveys. This too could dull the effect of economic growth on price levels.

# **3. Modeling the changes in PPPs**

Let _PPPri_ denote the PPP rate for country _i_ in year _r_ using ICP round _r_ and let _Eri_ be the corresponding market exchange rate. By definition, the price level index is _Pri_  _PPPri / Eri_ (which is of course a relative price, interpretable as the inverse real exchange rate). Also let _Yri_ denote GDP per capita in $US at the market exchange rate, while _YriPPP_ is GDP per capita at PPP. Thus _Yri_  _GDPri_ / _Eri_ where _GDPri_ is GDP in local currency units and _YriPPP_  _GDPri_ / _PPPri_ . The basic empirical model for changes in the price level index is as follows: ln( _P_ 05 _i_ / _P_ 93 _i_ )     ln( _Y_ 05 _i_ / _Y_ 93 _i_ )   _i_ (1) A number of remarks will help motivate and interpret this model. First, equation (1) can be interpreted as the time-differenced version of the widely used double-log model in the crosscountry literature on the Penn effect, incorporating a year effect but common slope (though this will be relaxed later).<sup>14</sup> Second, unlike the cross-sectional specification, the parameter estimates

> 13 See World Bank (2008a). The log GDP per capita in 1993 PPP was regressed on log GDP per capita at market exchange rates and the log of the secondary school enrollment rate This is equivalent to regressing the log of the price level index on these same two variables (though with different parameters of course). (China’s PPP for 1993 was estimated by a different method, as noted later).

> 14 The most common specification for the Penn effect in the literature expresses the log of the price level index as a linear function of the log of GDP per capita at exchange rates. Note that this is equivalent

8
These examples allude to the fact that statistical capacity varies by country characteristics, particularly by country income levels. Specifically, using the World Bank’s poverty database, we plot the number of poverty estimates against a country’s income (consumption) level (as measured in household surveys) over the past 40 years in Figure 1. The fitted line for the regression of these data points on country income is positive and strongly statistically significant, suggesting that countries with higher incomes more frequently implement household surveys. Indeed, a 10 percent increase in a country’s average income is associated with almost two-thirds (i.e., 0.67) as many surveys. This gap of survey data is consistent with the observation that poorer countries, especially in Sub-Saharan Africa, tend to have weaker statistical capacity (Devarajan, 2013; Jerven, 2013; Sandefur and Glassman, 2015; <mark>Dargent</mark> _<mark>et al.</mark>_ <mark>, 2018)</mark> .

The international community has long placed much attention on assessing and improving country statistical capacity—particularly for (poorer) countries with weaker capacity—to better guide international support. The Statistical Capacity Index (SCI) was a tool developed by the World Bank in 2004 to assess global improvements in country statistical capacity (World Bank, 2020). Most recently, it was replaced by the Statistical Performance Indicators and Index (SPI), which offers a clearer conceptual framework and broader country coverage (Dang _et al._ , 2023). Other global assessment tools that have been employed for evaluating country statistical capacity include the Open Data Inventory index (ODIN) (Open Data Watch, 2022), the Global Data Barometer index (GDB) and its predecessor the Open Data Barometer index (ODB) (Global Data Barometer, 2022). Regional assessment tools (e.g., the Ibrahim Index of African Governance Statistical Capacity (IIAG)) and self-assessment tools were also employed by other international organizations for countries to self-report on their statistical capacity.

While these (assessment) tools offer policy makers different options to assess country statistical capacity, they typically follow different philosophical principles in generating their metrics as well as in collecting and processing data. It would be useful for various stakeholders— policy makers, national statistical offices, international development organizations, researchers, private investors, and others—to have a clear understanding of these differences, and to appreciate the relative strengths and weaknesses of the tools, for their most effective use. We thus offer the first critical assessment of these tools.

Using the SPI framework as the reference point, we review the tools’ guiding principles before discussing their coverage, groupings (dimensions), indicators, and data sources. To compare their 3