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- T. Hale, N. Angrist, E. Cameron-Blake, L. Hallas, B. Kira, S. Majumdar, A. Petherick, T. Phillips, H. Tatlow, and S. Webster. Oxford covid-19 government response tracker. Technical report, Blavatnik School of Government, 2020.

- R. E. Hall. Employment efficiency and sticky wages: Evidence from flows in the labor market. _The Review of Economics and Statistics_ , 87(3):397–407, 2005.

- M. Hallward-Driemeier and B. Rijkers. Do crises catalyze creative destruction? firm-level evidence from indonesia. _The Review of Economics and Statistics_ , pages 1788–1810, 2013.

- T. A. Hassan, S. Hollander, L. van Lent, and A. Tahoun. Firm-level exposure to epidemic diseases: Covid-19, sars, and h1n1. Working Paper 26971, National Bureau of Economic Research, 2020.

- J. E. Humphries, C. Neilson, and G. Ulyssea. The evolving impacts of covid-19 on small businesses since the cares act. 2020a.

- J. E. Humphries, C. A. Neilson, and G. Ulyssea. Information frictions and access to the paycheck protection program. _Journal of public economics_ , 190:104244, 2020b.

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- NYFED. 2020.

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- WBG. Covid-19 outbreak: Implications on corporate and individual insolvency. Technical report, Equitable growth, Finance and Institutions, COVID-19 Notes, Finance Series, World Bank, Washington DC, 2020.

- X. Zhang. The Impact of COVID-19 on SMEs in China: Evidence from Two-Waves of Phone Surveys. Technical report, 2020. URL https://www.slideshare.net/ifpri/ the-impact-of-covid19-on-smes-in-china.

16
<!-- Start of picture text --> Figure 7 ‐ Average Bureaucracy Quality<br>(index between 0‐4, higher index higher quality)<br>3.1<br>2.9<br>2.7<br>2.5<br>2.3<br>2.1<br>1.9<br>1.7<br>1.5<br>Fast growing countries Comparison group<br>1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text --> 49
# Figure 1: Female population, % (deviations from **50%)**

<!-- Start of picture text --> 2<br>...........<br>..........<br>I....]....<br>-1........... -2-.............. 2~ ~~~~~.. .. .... . .................. . ... ........ ................ .....................<br>OECD S.S. AfrIca N. Africa S. Asia Indonesia China Indla PAk.aiJtan<br><!-- End of picture text --> Npop'i f emale-50) Source, World Development Report. 1989
stable. South Asia saw a huge jump in remittances of 35% in 2008 and the level was maintained in 2009.<sup>24</sup> The economic crisis had a magnified effect on world trade. Trade fell faster than income around the world- a result of the confluence of the decline in commodity prices, the decline in financing and the globalised production structure for many goods. In 2009 world GDP fell by - 0.8% but trade fell by 12.3%. Production for many manufactures is spread out among different countries and each time a product crosses borders its value is recorded in gross value terms while GDP only measures the value added of production. So when GDP declines, the measured decline in trade may be much larger.<sup>25</sup> East Asia was hit particularly hard by the decline in trade. The example of trade in semiconductors produced in the East Asian region illustrates how integrated production structures have affected trade volumes. Product semiconductors need to be customized for various end uses which today range from automobiles and heavy machinery to consumer electronics and personal computers. This allows firms/clusters located in different countries to specialize in particular varieties of semiconductor design and manufacture. Most newly industrialized East Asian economies such as Korea, Taiwan, and Singapore are simultaneously significant exporters and importers of semi-conductors. For example, Korea exports almost 90% of the chips it manufactures, but imports almost a comparable volume of chips<sup>26</sup> . Countries in Latin America by contrast, suffered less from this type of effect as production structures tend to be less integrated across countries.

> 24 Migration and Development Brief 11. Migration and Development Team, DECPG, WB. Nove 3, 2009. S. Ratha, Mohapatra, and Anil Silwal.

> 25 This latter effects means that a final good that is traded between countries A and B for example, may have been partly assembled in C and partly in D before final assembly in B for shipping to A. B’s GDP will record only the value added by B but the trade value to A will include the value added in C and D. so while B’s GDP falls by the smaller amount, the decline in trade reflects the whole value of the product . Note that when D stops manufacturing the intermediate input to ship to C, it will record a decline in value added in GDP and a commensurate decline in trade values. C will record a drop in the whole value of its contribution as well as the contribution of D.

> 26 Wendy Dobson (1997), Crossing Borders: Multinationals in East Asia in Dobson and Yue (ed) Multinationals and East Asian Integration, International Development Research Centre(IDRC), Ottawa and Institute of South-East Asian Studies (ISEAS), Singapore, pg 223-225

19
We end by investigating the prevalence of opportunistic behavior by inspectors during the RCT period focusing on declarations receiving detailed comments but no valuation advice. Analogous to the analysis presented in Section 4.3, we examine whether inspector actions and customs outcomes vary, depending on the amount of tax revenue at stake, controlling for initial undervaluation.<sup>35</sup> The initial undervaluation is measured relative to the internal reference price and a new high-stakes indicator is equal to one if the hypothetical taxes exceed 10,000 USD (with hypothetical taxes being defined as the product between the declaration’s tax rate and the hypothetical value of the declaration calculated using the internal reference unit price). We find evidence of differential treatment by inspectors of high-stakes highly undervalued declarations in that they detect less fraud and adjust significantly less their value and taxes, but the impact on fraud is not statistically significant. Not surprisingly, these results are generally weak due to the small sample size and the effects are not robust to adjustments for multiple hypothesis testing. But overall, this evidence is consistent with detection of fraud combined with a lower than theoretically optimal adjustment, especially for declarations for which opportunities for graft are largest.

In summary, we find a significant response by inspectors to the provision of detailed comments. The fact that inspectors increase substantially their recording of fraud but that the tax adjustments are small and that they are smaller for high-stakes declarations suggests that inspectors are engaging in box-ticking behavior.

# **6. Conclusion**

The performance of customs inspectors is an important determinant of tax collection in developing countries but may be constrained by asymmetric information and inadequate monitoring of their behavior in conjunction with limited performance incentives. This paper has used uniquely rich administrative data and a randomized control trial to examine the impact of the provision of information to inspectors and of monitoring them on fraud detection, tax revenue, and clearance times.

Using an instrumental variables strategy exploiting variation in the supply by an external third party of declaration-specific valuation advice, we have shown that such advice increases scrutiny.

> 35 This analysis is not conducted for the sub-sample of declarations with valuation advice during the RCT due to the very small number of observations (fewer than 100) with valuation advice and detailed comments.

33
presence in foreign-owned firms, as 70 percent of strikes occur in unionized enterprises (cf. ILO, 2011).

While the high and rising number of wildcat strikes are a symptom of the weaknesses of Vietnam’s industrial relations system, their consequences reach much farther. Strikes by workers (and lock-outs by employers) are typically taken as “last resort” measures, when no agreements can be reached on pay, working conditions or equally fundamental issues. Strikes are in most contexts an outcome of negotiations failing. Thus the pattern shown in Figure 7 raises the concern that the institutions for setting pay and other conditions of employment are not functioning in the manner expected, forcing workers to extreme measures. Whether wildcat or not, strikes and lock-outs have significant and harmful consequences. They lead to lost days of production, reduced profits and unpaid wages. That workers frequently resort to last resort measures and apparently have no recourse to less costly ways of voicing their concerns can in turn be seen as another symptom of the dysfunctional state of Vietnam’s industrial relations institutions.

**Figure 7 – Number of wildcat strikes in Vietnam by type of enterprise and annual CPI inflation**

<!-- Start of picture text --> 1000 25%<br>900<br>20%<br>800<br>700<br>15%<br>600<br>500 10%<br>400<br>5%<br>300<br>200<br>0%<br>100<br>0 -5%<br>State Enterprise FDI Private Enterprise Annual Inflation<br>Annual inflation<br>Number of wildcat strikes<br><!-- End of picture text -->

**Source: VGCL (2012) and World Development Indicators.**

**Notes: Annual inflation measured by changes in the consumer price index.**

Problems with this system have long been recognized. As a result, the 2012 revisions to the country’s Labor Code and Trade Union Law attempted to strengthen the system. The new Labor Code envisioned a more active role for the government in collective bargaining between employers and

- 18 -
cation across manufacturing industries. Thus, the measure of average size that our theory can speak to is one that abstracts from the international variation in production structures.

Our strategy to control for cross-country differences in production structures entails fixing the distribution of firms across two-digit manufacturing industries to be given by the distributionin the US.<sup>2425</sup> More specifically, letting _AvSize_<sup>_FD_</sup> _i_ stand for the average firm size in country _i_ under a fixed distribution of firms, and letting � _<u>MMs</u>_ � _US_ denote the share of firms in any given two-digit sector _s_ in the US, then Appendix C provides theoretical validation for our aggregation strategy in the context of a two-sector extension of the model, where comparative advantage is driven by exogenous sectoral differences in TFP.

Equipped with our preferred definition, we revisit the relationship between firm size and economic development. Figure 1 confirms the positive relationship between firm size and income per capita documented earlier in the literature,<sup>26</sup> showing a correlation between the log of size and the log of development that is equal to 0.28. Had we not controlled for the distribution of firm shares, the correlation would have been lower, equal to 0.15.

We turn now to estimating the elasticity between idiosyncratic distortions (TFPR) and idiosyncratic productivity (TFPQ). As in Hsieh and Klenow [2009], misallocation in our model manifests as a dispersion in the marginal technologies and common factor prices across firms is more likely to apply. Put differently, it is at the level of a narrow industry that it is more reasonable to expect that firms should behave identically across countries. At a more aggregate level, other factors (comparative advantage, geography) make it more plausible countries exhibit a different production structure and that such is an outcome.

> 24Appendix B.1 shows that controlling for industrial composition increases the average firm size for the majority of the countries relative to having adopted country-specific distributions of economic activity.

> 25We read the distribution of firm shares across two-digit industries in the US’ manufacturing sector from the County Business Patterns database for 2007.

26Salient examples of recent work documenting the relationship between average firm size and economic development in the data are Bento and Restuccia [2017] and Poschke [2014].

26
# **Table C.24: Robustness to different specifications and variable transformations**

## **Panel A: Women’s decision-making power (Index, Wave 1)**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|selected|0_._42*|0_._30|0_._40*|0_._25|0_._40*|0_._24|
||(0_._24)|(0_._25)|(0_._22)|(0_._25)|(0_._22)|(0_._25)|
|N|181|181|181|181|181|181|
|Nominated mean|_−_0_._48|_−_0_._48|_−_0_._48|_−_0_._48|_−_0_._48|_−_0_._48|
|Nominee SD|1_._38|1_._38|1_._38|1_._38|1_._38|1_._38|

## **Panel B: Women’s assignable goods expenditures (% in household income)**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|selected|2_._71**|3_._01***|1_._96|2_._67*|1_._96|2_._04**|0_._26|
||(1_._07)|(1_._10)|(1_._53)|(1_._40)|(1_._53)|(1_._03)|(0_._22)|
|N|363|363|363|363|363|363|363|
|Nominated mean|5_._32|5_._32|5_._32|5_._32|5_._32|5_._32|7_._28|
|Nominee SD|4_._48|4_._48|4_._48|4_._48|4_._48|4_._48|1_._53|
||**Panel C: G**|**irls’ assignable**|**goods expen**|**ditures (% i**|**n household**|**income)**||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|selected|1_._22**|1_._08*|1_._12|1_._38|1_._12|0_._73|0_._84|
||(0_._62)|(0_._55)|(1_._19)|(0_._87)|(1_._19)|(0_._67)|(0_._52)|
|N|146|146|146|146|146|146|146|
|Nominated mean|1_._65|1_._65|1_._65|1_._65|1_._65|1_._65|5_._17|
|Nominee SD|2_._41|2_._41|2_._41|2_._41|2_._41|2_._41|2_._74|
|||**Panel D: Rem**|**ittances (% i**|**n household**|**income)**|||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|selected|3_._48**|3_._47**|3_._04**|3_._35**|3_._04**|3_._48**|0_._66|
||(1_._40)|(1_._37)|(1_._50)|(1_._41)|(1_._50)|(1_._42)|(0_._64)|
|N|362|362|362|362|362|362|362|
|Nominated mean|6_._00|6_._00|6_._00|6_._00|6_._00|6_._00|3_._90|
|Nominee SD|8_._63|8_._63|8_._63|8_._63|8_._63|8_._63|4_._26|
|Method|PDS|OLS|OLS|OLS|OLS|PDS|PDS|
|Controls|PDS|No|No|Linear|Theory|PDS with interactions|PDS|
|Factory F.E.|PDS|No|Yes|No|Yes|PDS|PDS|
|Enumerator F.E.|PDS|No|Yes|No|Yes|PDS|PDS|
|Month F.E.|PDS|No|Yes|No|Yes|PDS|PDS|
|Imbalanced controls|PDS|No|Yes|No|Yes|PDS|PDS|
|Transformation|||||||IHS|

Notes: Selected is a dummy variable indicating whether the female sewing-line operator is quasi-randomly selected for the promotion program. Nominated is a dummy variable indicating whether the female sewing-line operator is an eligible, shortlisted runner-up that was quasi-randomly not selected for the promotion program, and represents the comparison group for the selected group. Standard errors clustered at the individual level in parentheses. IHS = Inverse hyperbolic sine transformation of the Taka amount.

88
# **Figure 8 Real Wages of Employed Migrants according to Three Data Sources, 2001–09**

<!-- Start of picture text --> 1,600<br>NBS RCRE PBC<br>1,400<br>1,221<br>1,200<br>1,140<br>1,000<br>953<br>889<br>800 821<br>666<br>644 756<br>703<br>600<br>2001 2002 2003 2004 2005 2006 2007 2008 2009<br><!-- End of picture text --> _Sources_ : NBS **(2010b)** RCRE; People’s Bank of China (2010).

Sources: National Bureau of Statistics (NBS, 2010); RCRE (various years); People’s Bank of China (2010).

26
describes that the island of Domica has nine potentially active volcanoes and concludes that it is “extremely vulnerable to volcanic hazard”.

Respondents in Haiti are most likely to indicate that they are exposed to climatological events, with 60 percent of households exposed to heat waves and 56 percent to droughts. Floods are the most common hydrological risk mentioned by 39 percent of households in Haiti, 44 percent of households in Suriname, and 33 percent of households in Belize.<sup>13</sup> It is common for households to indicate that they are exposed to multiple (at least 2) risks: Haiti (81 percent), Suriname (55 percent), Belize (63 percent), Dominica (79 percent), Saint Lucia (72 percent) (results not displayed).

# **3.3 Disaster risk management in the Caribbean**

The Caribbean Disaster Emergency Management Agency (CDEMA) leads the CARICOM’s regional disaster management efforts. Founded in 2009 as the successor to the Caribbean Disaster Emergency Response Agency (CDERA), it coordinates emergency response based on the principles of “Comprehensive Disaster Management”. In other terms, CDEMA’s function relates both to the preparation for disasters and the response to disasters. The former consists of promotion of the adoption of appropriate policies, response mechanisms and capabilities. The latter consists among others of “mobilizing and coordinating” the immediate response to disasters.<sup>14</sup> The countries in the region also have their own national disaster management agencies. These agencies too are involved both in a combination of preparation for disasters and postdisaster relief efforts. Their activities have a wide range, including prevention and mitigation assessments and strategies, preparedness for disasters at the household, community and national level, disaster warning systems, the coordination of actual emergency response, and post-disaster recovery support.<sup>15</sup> These agencies provide direct advice to citizens on how to prepare for

> 13 The importance of droughts and heat waves in Haiti, and those of floods in Haiti, Suriname, and Belize, are also reflected in other monitoring efforts. See the World Bank’s Climate Change Knowledge Portal. E.g.: <u>https://climateknowledgeportal.worldbank.org/country/haiti/vulnerability#pill-B</u> (last accessed on May 8, 2024). The importance of so-called ‘flash droughts’ in the Caribbean is described in Ramseyer and Miller (2023).

> 14 <u>https://www.cdema.org/index.php/about (last accessed on April 29, 2024).</u>

> 15 See, e.g., the website of Dominica’s Office of Disaster Management: https://odm.gov.dm/who-we-are-office-of- <u>disaster-management/</u> (last accessed on April 30, 2024).

10
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Table A2: Pre-Liberalization Industry Trends - 1980-1991

||unweighted,<br>bootstrapped|unweighted,<br>bootstrapped, omitting<br>agriculture|weighted|weighted, omitting<br>agriculture|
|---|---|---|---|---|
|1980-1991 change in log:|(1)|(2)|(3)|(4)|
|Panel A: average earnings|||||
|Regional tariff change|-0.345|-0.111|-1.029***|-0.510|
||(0.322)|(0.354)|(0.139)|(0.582)|
|Panel B: earnings premia (with ind|ividual controls)||||
|Regional tariff change|-0.203|0.017|-0.610***|0.235|
||(0.273)|(0.311)|(0.157)|(0.350)|
|Panel C: earnings premia (with ind|ividual and region controls)||||
|Regional tariff change|-0.135|-0.044|-0.184|-0.018|
||(0.177)|(0.209)|(0.158)|(0.222)|
|Panel D: employment|||||
|Regional tariff change|1.564|2.669**|-0.799*|1.638|
||(1.268)|(1.331)|(0.408)|(1.868)|
|Observations|20|19|20|19|

Decennial Census data. 20 industry observations (19 omitting agriculture). See text for details of dependent and independent variable construction. Column (1) weights industries equally, and presents standard errors based on pairwise bootstrap of the t-statistic. Column (2) uses the same estimator as Column (1), but drops agriculture. Column (3) uses heteroskedasticity weights and presents heteroskedasticity-robust standard errors. Column (4) uses the same estimator as Column (3), but drops agriculture. *** Significant at the 1 percent, ** 5 percent, * 10 percent level.

85
# **Coverage of Informal Channels**

Despite the increasing number of financial products available in the marketplace to transfer money between countries, some migrants may choose for several reasons to send money to their home country through informal channels, such as:

- Sending cash with people traveling to their home country.

- Putting the money in an envelope and mailing it.

- Using the services of Hawala-type of money transfers.<sup>6</sup>

<!-- Start of picture text --> Chart 3. Informal Channels for Transferring<br>Remittances<br>Percent of Central Banks<br>that Collect Data from Informal Channels<br>Costa Rica<br>yes<br>Ecuador<br>25%<br>El Salvador<br>Honduras<br>Indonesia<br>Mexico<br>Moldova<br>no<br>75% Nicaragua<br>Peru<br>Russia<br>Data is collected through:<br>� surveys to migrants visiting their home country<br>� household surveys<br><!-- End of picture text --> As shown in chart 3, of the 40 countries covered in the survey 10 central banks have developed tools to measure the volume of remittances inflows that occur through informal channels. In these countries, information is normally collected through surveys of migrants visiting their home country and/or household surveys. Countries like Ecuador and Honduras conduct surveys to migrants at the country’s main points of entry (usually airports) every year, especially during holiday seasons when the inflow of migrant visitors reaches its peak (e.g. Christmas and New Year, Easter, Chinese New Year, etc.)<sup>7</sup> Other countries such as El Salvador conduct household surveys every year to estimate the total value that they receive in remittances either through formal or informal channels.

6 Empirical studies indicate that the choice of mechanism used is not just related to transaction fees, but also to the speed of service and the availability of financial services to both the sender and the beneficiary. See Manuel Orozco (2004b).

7 The survey did not assess the scope, methodology and adequacy of the surveys conducted by this group of countries. This is an issue that deserves much more attention in the future.

11
expenditures in the regression. We examine these two hypotheses in turn.

Although a number of different variables may be used to measure country size, we followed Kuznets, Chenery and<sup>Syrauin</sup> and chose country population as our measure. (The analys.s was also conducted using the level of GDP as a measure of size, which did not significantly affect the results). The results are shown in Table 11. The size variable was included by itself and interacted with trade policy. If it is true that large countries benefit less from more open policies than small countries, then the coefficient on the interaction of trade policy and size should be opposite in sign to the cocificient on openness, mitigating its effect. The results in Table 11 do show a mitigating effect for 4 of the 7 variables, but none of these are statistically significant. The only trade policy variable<sup>for</sup> which the inclusion of size affects openness is the Choksi measure, yet it has the opposite of its expected effect: the impact of liberalization is positive and significant for larger countries.

Although the impact of size by itself varies with the number of countries included in the sample, country size is statistically significant (and always positive) in four specifications--those with the largest number of observations. This suggests that if the sample of countries is sufficiently large, one generally finds that larger countries have experienced higher productivity growth. This confirms results from earlier 19