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potentially more damaging policy, such as taxes to hire and fire workers, Hopenhayn and Rogerson [1993] find TFP gains of up to 2% from removing plausibly calibrated values of these taxes. The highest gains are obtained in the financial frictions literature, where TFP rises between 7% and 40% when improving access to credit, depending on the features of the model (Buera et al. 2011, Moll 2014, Midrigan and Xu 2014). We interpret these findings as suggesting that observed misallocation is the outcome of a collection of policies that combine mild distortions with more damaging ones, and that size-dependent tax enforcement and compliance appears to be part of the former group.

# **7 Conclusion**

This studies the and of tax enforcement paper prevalence consequences size-dependent and compliance in 140 countries. The identification strategy uses the relative ranking of industries’ in the US as an instrument for the relative of average industry-size ranking the same industries’ size in developing countries. We find a positive size gradient in tax inspection and tax compliance. This result is robust across specifications with flexible controls, variation in size within narrowly defined industries and variation in industries’ size over time within a country. Moreover, we find that size-based enforcement is concentrated at the top of the industry size distribution and prevalent in lower-income countries, while it does not appear to be used in rich countries. When quantified in an general equilibrium model of firm dynamics, size-dependence in tax enforcement generates a drop in TFP of up to 0.8% in lowest-income countries, which is driven by a decline in average firm size and a reduction in innovation intensity.

There are several limitations to our study. First, given that the WBES sample only covers registered firms, it is unclear to what extent our estimates of size-dependent tax policy would generalize to non-registered firms. The second limitation is that we proxy for tax enforcement using only in-person physical inspections by tax inspectors. Tax authorities 38
and financial markets. The low U.S. saving hypothesis should be seen therefore as unconvincing, not least because the national account data underestimates savings by excluding purchases of consumer durables and expenditure on education and research and development from the definition, and because the U.S. current account deficit started in the 1990s—precisely when the external account balance swung into surplus.5 While these two opposing views often rely on some well-constructed theoretical and empirical underpinnings, they suggest a dichotomy that may not allow grasping fully the issues at hand. This paper offers a more nuanced view and argues that a more complete understanding of global imbalances requires a multidimensional perspective that more fully takes into account events beyond traditional macroeconomic variables. Global imbalances are neither just a temporary aberration that can be addressed through economic policy actions in the U.S. and China as suggested by proponents of the first view, nor are they only the result of globalization as implied by proponents of the second view. The paper uses Hegel’s dialectics to analyze the opposing dynamics of the U.S. and China external balances. While agreeing with the need for the two most dominant global powers on the world economic stage (U.S. and China) to take corrective action, it also suggests that eliminating global imbalances will require structural changes—some of them well beyond the realm of economics—that may take a long time to materialize.

The remainder of the paper is organized as follows: Section 2 uses the basic accounting linkages to explain why traditional narratives of current account deficits and surpluses often lead to competing theories and conflicting results. Section 3 suggests a game-theoretic approach of the U.S.-China economic relationship based on Hegel’s analysis of lordship and bondage, which explains the current situation of mutual dependence. Section 4 offers some concluding thoughts.

# 2. Traditional Narratives of Global Imbalances

The debate over global imbalances has been controversial, and at times confusing. On one side of the spectrum, there are those who lament the persistence

> 5 See Cooper (2005). The low savings hypothesis also appears inconsistent with the still low nominal and real interest rates, even before the relaxation of monetary policy to combat the 2008-09 recession. Xafa (2007) notes that the U.S. national accounts data also exclude capital gains (e.g. on housing and financial investment) from the definition of savings, although they potentially raise future consumption.

5
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE The quality of Angola’s regional road corridors is also poor, making the country’s regional connectivity with the broader SADC economic area difficult. This situation both prevents Angola from developing regional trade with surrounding countries and limits surrounding countries from making greater use of the Port of Luanda (see figure 13).

Transportation represents an important bottleneck for Angola’s economy. Poor road infrastructure and transport logistics slow Angola’s overall economic development. Angola is one of the worst performers in the world according to the 2010 Logistics Performance Index (LPI). Its manufacturing sector depends on imports for as much as 40 percent of inputs, which can be difficult and expensive to procure given the poor quality of the roads. Moreover, around 2.1 percent of the production of manufacturing firms is lost in transit, more than in any other African country (World Bank 2007). It takes 49–53<sup>1</sup> days to export or import goods, among the longest times in Africa. Road access is particularly problematic for firms outside Luanda.

For all of these reasons, Angola urgently needs to establish an operational road fund. The country’s current extraordinary spending on the road sector is entirely budget-financed. Such high levels of public funding are not sustainable in the long term, and there is the risk that today’s heavy investments may not be adequately maintained in the future. It is therefore critical for Angola to work toward the establishment of a road fund resourced from a fuel levy and other road-user charges. The optimal fuel levy needed for long-term road-network sustainability in Angola would be on the order of $0.15 per liter of fuel, which is toward the middle of the range for African countries (figure 12). But currently, gasoline and diesel prices are heavily subsidized by the state and there is no mechanism for fuel levy collection.

**Figure 12. Angola’s public contribution exceeds the maintenance and rehabilitation needs of its road network**

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> 1 www.doingbusiness.org/data/exploretopics/trading-across-borders.

21
Mira (2007). Specically, a woman makes annual decisions of whether to become pregnant beginning at the age of her marriage and ending when she becomes infecund at a xed age _F_ (assumed to be 45). A woman gives birth in the period following the one in which she became pregnant. If never infected, a woman survives with certainty to age _T_ (assumed to be 60). An infected woman might die prior to reaching the terminal model period. HIV infection of a mother at time of birth also increases mortality probabilities of children of ages zero to three. Given that HIV infection is asymptomatic for the majority of the infection duration, women cannot observe their HIV status.

Women are heterogeneous with respect to a group of characteristics that are treated as exogenous and constant determinants of their choices. These characteristics include region of residence, completed schooling level, the size of the household's land plot, age of marriage, and whether a woman is married to a polygamous husband. Women are also of dierent discrete unobserved types, which are incorporated in the model to account for unobservable permanent factors which might aect preferences as well as exposure to HIV.

# 2.2 Preferences

Each period, a woman receives a utility ow from household consumption ( _C_ ), her number of children ( _N_ ), pregnancy status ( _p_ ), and a time-varying preference shock ( _εp_ ) which is iid across time and women. The per-period utility function is given by The utility function exhibits constant relative risk aversion (CRRA) in consumption and includes an interaction term between household consumption and the number of children to reect consumption being divided among more individuals as the household size increases. The utility is quadratic in the number of children. The parameters related to preference for children, _λ_ 2 and _λ_ 3 , are allowed to vary with region of residence ( _r_ ), schooling level ( _e_ ), 8
in the gains from trade across households is negatively correlated with the log of per capita GDP (Figure 9). This is because, in the HIT data, poorer countries tend to have more heterogeneous income and consumption household portfolios.<sup>9</sup> Pooling all countries in our database, we plot the household-level gains from agricultural trade against the initial level of per capita household expenditure using a kernel non-parametric regression. Figure 10 shows the result of this exercise. In line with the stylized facts above, we find that the kernel slope is positive and steeps upward until the top percentiles of the developing world distribution of income and then becomes negative. Still, richer households tend to gain more from liberalization than poorer households. But it is the upper-middle class that stands to gain the most from agricultural tariff liberalization.

To quantify the extent to which the effects of trade vary for the poor vis-`a-vis the rich, we calculate the pro-poor bias in agricultural trade policy. This is the difference between the average welfare effect for the poor (defined as the bottom 20% of the income distribution) and the average welfare effect for the rich (the top 20%).<sup>10</sup> On aggregate, agricultural tariff liberalization would be slightly pro-rich, with the richest households gaining 2.64 percent and the poor 2.20 percent (see columns 4-6 in Table 2). There are 29 of the 54 countries in which tariff liberalization would have a pro-rich bias. Using data from six countries which are also included in HIT data set, Nicita, Olarreaga and Porto (2014) show that tariff protection is pro-poor. In this study, however, we find that their results do not generalize to all developing countries. In 24 countries in the HIT data set tariff protection is pro-rich (and liberalization, pro-poor).

One simple way to assess how much we can gain by exploiting the heterogeneity in the household surveys is to compute the gains from trade using aggregate price changes and aggregate income and budget shares. To do this, we aggregate the staple agriculture income and budget shares from the 4-digit classification up to staple and non-staple agriculture. We then compute the welfare gains from the elimination of aggregate tariffs, as before. The

> 9Note that the HIT data have limited heterogeneity in expenditures on manufacturing and services. This may also play a role in our results.

> 10In the online Appendix we show that our results are very robust to using alternative cutoffs. In addition, we show that both average gains and the pro-poor bias are correlated with GDP per capita. The pro-poor bias is also larger in countries with higher levels of poverty.

10
## **2. MIGRATION AND WELFARE: LINES OF INQUIRY**

# **2.1. Gauging the importance of migration**

Is migration, in a given country, important in the first place? The relevant indicator of the importance of migration would be the share of households having a migrant or a share of remittances in total income – as obtained from a household survey. In the case of the latter, when the survey is not available upfront one may consult the balance of payment statistics (BOP). As a rule of thumb, remittances obtained from the BOP equal 1.5 times those obtained from the household survey, with a correlation of .90 (Acosta, Calderon Fajnzylber and Lopez, 2006a). Nevertheless, a given country may be an outlier, so one may check also with other sources, for example a comprehensive estimate of bilateral stocks of migrants for 226 countries and territories compiled by Global Trade Analysis Project (GTAP, 2005), the OECD’s Database on Immigrants and Expatriates (OECD, 2005) containing detailed information on the foreign-born population for almost all member countries of the OECD, the Docquier and Marfouk (2004) as well as Beine, Docquier and Rapoport (2006) databases on migration by education (and age of entry) from developing countries to OECD countries or Bhargava and Docquier (2006) Database on the International Migration of Physicians. These figures should provide a first glance at the extent of migration.

# **2.2. Identifying channels of impact and relevant questions**

The transmission channels through which migration and remittances impact various living standards and human capital outcomes are numerous. The most obvious is the income channel, namely that remittances directly contribute to total income. Remittances should lead to increased consumption and investment, implying a positive effect on poverty reduction and human capital. The less obvious channels include interactions with government policies, consequences of parental absence, consequences of removing entrepreneurial individuals from the community. As a result of these various effects, the direction of the impact of migration on poverty, inequality and human capital is not predetermined and becomes an empirical question.

Accordingly, the following types of questions are common in operational research on migration-welfare links<sup>1</sup> :

- Who is receiving remittances (the poor or the rich)?

- Do remittances/migration alleviate poverty? And if so, how?

- Do remittances help smooth consumption and alleviate a credit constraint, thus preventing risky coping strategies of disinvestment?

- Do they provide income diversification and insurance, thus encouraging higherrisk-higher-return activities?

- Are remittances spent on consumption or investment?

> 1 These research questions, extracted in an ad hoc review of selected literature (see references section) are presented in no particular order.

3
Table 10 also shows poverty rates among households that exhibit different characteristics traditionally associated with economic disadvantage. The results are mixed, and it is not possible to conclude that the alternative poverty lines better identify households with characteristics associated with economic disadvantage. The alternative poverty measures do find a larger average number of dependents among poor households. However, the official measure finds a higher poverty rate among households whose head has low education. The official measure also finds a higher poverty rate among households whose head is currently not working, but Table 2 suggests that these households may in fact be, on average, better off than households whose heads are currently working. Finally, the official poverty line seems to better identify as poor households that do not have welfare-relevant assets. In this regard, the purely relative line performs worse, particularly in terms of washing machine ownership. Overall, we cannot conclude that the official poverty lines perform worse than the alternative poverty lines, which implies that a change in the methodology could only be defended on grounds of simplifying monitoring and interpretation.

_Table 10: Comparison of three poverty measurement methodologies in 2015_

|HH t||Official|Absolute|Relative|
|---|---|---|---|---|
|caegor||2015|2015|2015|
||National|24.8%|24.8%|24.7%|
||Metropolitan|11%|13%|28%|
|Prt|Lower Urban|10%|10%|24%|
|ovey<br>rate|Lower Rural|17%|17%|24%|
||Upper Urban|24%|23%|25%|
||Upper Rural|55%|52%|24%|
||Borders|15%|18%|22%|
||HHs without dependents|13%|6%|6%|
||HHs with only 1 adult and more than 1 dependent|15%|22%|17%|
||HHs with 2 adults and 1 or 2 dependents|10%|11%|14%|
||HHs with 3 or more adults and 1 or 2 dependents|26%|16%|19%|
|Fraction of|HHs with 2 adults and 3 dependents|18%|25%|28%|
|poor|HHs with 2 adults and 4 or more dependents|42%|55%|49%|
|individuals|HHs with 3 or more adults and 3 or more dependents|48%|47%|42%|
|among|Other HHs|6%|6%|5%|
||Children|29%|33%|32%|
||Elderly|17%|15%|15%|
||Men|26%|24%|24%|
||Women|24%|25%|25%|
|Average HH|size among poor HHs|5.6|5.7|5.6|
|Average nu|mber of dependents among poor HHs|2.7|3.1|3.0|
||Whose head has low education|95%|93%|91%|
||Whose head is not currently working|25%|22%|23%|
|Fraction of|That have some welfare-relevant assets|33%|35%|43%|
|poor HHs|That have a private car/motorcycle or bike|15%|16%|14%|
||That have a washing machine|16%|19%|31%|
||That have a smartphone|8%|7%|8%|

**Source** : Authors’ calculations based on HIECS 2015.

16
# 1. Introduction

At the start of the COVID-19 pandemic, the international community established a new initiative—the Access to COVID-19 Health Tools Accelerator (ACT-A)—for the purpose of securing vaccines, tests, treatments, and personal protective equipment (PPE) for low- and middle-income countries. The pillar of the ACT-A that received the most funding was the COVAX (COVID-19 Vaccines Global Access) facility. Under the facility, the COVAX AMC (Advance Market Commitment) purchased vaccines on behalf of low- and lower-middle-income countries using funds raised from donors.<sup>2</sup> The facility was empowered to make these purchases ‘at risk’, defined as in advance of regulatory emergency use authorization.<sup>3</sup> In principle, this capability should have allowed the COVAX AMC to move as quickly as high-income countries did to secure low- and lower-middle-income countries a place in line for vaccines. However, a lack of timely financing limited its ability to do so at scale. In December 2020, when high-income countries had already placed many purchase orders, the COVAX AMC had raised only $2 billion of the more than $10 billion it would eventually deploy.

This paper argues that in a future pandemic on the scale of COVID-19, the delay in deliveries of vaccines and other health products to low- and middle-income countries could be reduced substantially, without any greater commitments from donors than those they eventually made in the COVID-19 pandemic. Section 2 develops this argument based on a novel analysis of COVID-19 vaccine advance purchase agreements between governments and manufacturers. The results confirm that low- and middle-income countries did experience delays in vaccine deliveries regardless of which vaccine they ordered or when—potentially due to export restrictions favoring high-income countries as a group. Nonetheless, the majority (60-75 percent) of the delay in vaccine deliveries to low- and middle-income countries is attributable to their signing purchase

> 2 The COVAX AMC was implemented as a partnership between many organizations, including CEPI, a foundation that finances research to develop vaccines against emerging infectious diseases; GAVI, a public–private partnership that supports routine immunization campaigns in low- and middle-income countries; UNICEF, a United Nations agency responsible for vaccine procurement on behalf of low- and middle-income countries; and the World Health Organization.

> 3 At-risk purchases are implemented through advance purchase agreements, which typically provide the manufacturer some prepayment and a legally binding commitment to procure the vaccine conditional on regulatory authorization. Such contracts can secure a buyer a place in line, provide manufacturers the certainty needed to procure inputs and other production capacity, and help manufacturers secure loans from banks. In the event the vaccine is not authorized by the regulator, the buyer potentially loses the prepayment.

2