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“Anonymity or Distance? Job Search and Labour Market Exclusion in a Growing African City,” _Review of Economic Studies_ , forthcoming.

- Abebe, G., S. Caria, and E. Ortiz-Ospina (2020b): “The Selection of Talent: Experimental and Structural Evidence from Ethiopia,” Manuscript, University of Bristol.

- Abel, M. (2019): “Unintended Labor Supply Effects of Cash Transfer Programs: New Evidence from South Africa’s Pension,” _Journal of African Economies_ , 28, 558–581.

- Abel, M., R. Burger, and P. Piraino (2020): “The Value of Reference Letters: Experimental Evidence from South Africa,” _American Economic Journal: Applied Economics_ , forthcoming.

- Ahn, S., R. Dizon-Ross, and B. Feigenberg (2019): “Improving Job Matching Among Youth,” Working Paper, Columbia University.

- Aigner, D. and G. Cain (1977): “Statistical Theories of Discrimination in Labor Markets,” _Industrial and Labor Relations Review_ , 30, 175–187.

- Alfonsi, L., O. Bandiera, V. Bassi, R. Burgess, I. Rasul, M. Sulaiman, and A. Vitali (2017): “Tackling Youth Unemployment: Evidence from a Labor Market Experiment in Uganda,” Manuscript, University College London.

- Almlund, M., A. Duckworth, J. Heckman, and T. Kautz (2011): “Personality Psychology and Economics,” in _Handbook of the Economics of Education_ , ed. by E. Hanushek, S. Machin, and L. Woessmann, Elsevier, 1–181.

- Alonso, R. (2018): “Recruiting and Selecting for Fit,” Manuscript, LSE.

- Altmann, S., A. Falk, S. Jäger, and F. Zimmermann (2018): “Learning about Job Search: A Field Experiment with Job Seekers in Germany,” _Journal of Public Economics_ , 164, 33–49.

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- Anderson, M. (2008): “Multiple Inference and Gender Differences in the Effects of Early Intervention: A Reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects,” _Journal of the American Statistical Association_ , 103, 1481–1495.

- Arcidiacono, P., P. Bayer, and A. Hizmo (2010): “Beyond Signaling and Human Capital: Education and the Revelation of Ability,” _American Economic Journal: Applied Economics_ , 2, 76–104.

- Attanasio, O., A. Kugler, and C. Meghir (2011): “Subsidizing Vocational Training for Disadvantaged Youth in Colombia: Evidence from a Randomized Trial,” _American Economic Journal: Applied Economics_ , 3, 188–220.

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33
changes following the onset of crisis actually work on average in the direction of lowering poverty (though this is not statistically significant), except in Latin America. Attempts to detect whether certain country institutional characteristics or crisis characteristics (using the Honohan, 2000, classification system) influence this effect were broadly unsuccessful. Interestingly, though, the KKZ indicator of corruption was associated with favorable distributional changes: possibly suggesting that in corrupt countries the crises were associated with a loss of previous privilege by beneficiaries of corruption. These regressions have large outliers, though (notably Kenya and the Kyrgyz Republic), and the size and significance of coefficients are sensitive to the inclusion or otherwise of these outliers (Regression Table 3).

This overview of the evidence from cross-country data confirms that the short-run pattern of inequality movements around banking crises differs from country to country. Outside of Latin America, these short-term changes are more likely than not to have been favorable to distribution.

The small number of specific crises for which appropriately-timed inequality data are available and the likely high noise-to-signal ratio in the inequality data militate against successful multivariate statistical analysis to control for independent causal factors influencing inequality movements around the time of the crisis.

# **5. Policy conclusions**

Available cross-country data on movements in inequality around the time of banking crises do not send very clear messages on the factors that influence whether crises will be associated with more or less inequality. This contrasts with recent evidence that financial deepening is a causal factor in reducing poverty (Honohan, 2004a and b, Beck DemirgűçKunt and Levine, 2004).

This is not the place to discuss general policy measures to reduce the frequency and scale of systemic banking crises and to improve containment and resolution of those that occur. Banking policies that improve the soundness of the banking system, thereby reducing the frequency of crises, are likely to be associated with less inequality and faster reduction in poverty in the long run.

Nevertheless, the discussion does suggest some general policy recommendations. For one thing, the way in which withdrawals of wealthy and well-informed depositors are often financed by central bank liquidity lending needs careful consideration at the containment stage of crises: all too often national foreign exchange reserves are deeply eroded in this process without any realistic hope of such lending preventing the need for bank intervention and indeed devaluation. These policies may occasionally be an appropriate tactic to head off an irrational speculative attack on a fundamentally sound banking system, but all too often are employed in circumstances where they are unlikely to succeed. Then, while they may buy a postponement of the crisis, this will be at the cost of an adverse distributional impact.

16
**Earrdxgs Rmttom ((IS): Prigy and Secxmdy Ii-or Maiets (0ependrit Variable: IDg Annal hale Farns)**

||I|tRegres|sion<br>Mbdel (1)|||Regre|ssion<br>Mode|l (2)|
|---|---|---|---|---|---|---|---|---|
|Variables|Prima|ry<br>Sector|Seconda|ry<br>Sector|<br>Prima|ry<br>Sector|SeconD|ary<br>Sector|
||b|t|b|.|b|t|b|t|
|(a) Ruman<br>|||||||||
|omii<br>variable|s||||||||
|SCH|0.0420|3.03|-0.0223|-0.90|0.0513|3.54|-0.0177|-0.69|
|EXP<br>|0.0183|1.78|-0.CCD13|<br>-0.08|0.0134|1.19|0.0018|0.11|
|EXP<sup>2</sup>|-0.0003|-2.66|-0.0002|-0.97|-0.0003|-2.53|-0.0002|-0.95|
|EXP.SCH|-0.0004|-1.08|0.0007|1.08|-0.0001|-0.16|0.0003|0.35|
|PRESWK|0.0119|5.03|0.0025|<br>0.71|<br>0.0123|5.20|0.0026|0.76|
|_Co)_Control<br>variables|||||||||
|RESID|0.0030|1.83|0.0080|<br>3.16|<br>0.0030|1.82|0.0081|3.17|
|WEEKS|0.0323|12.26|0.0361|<br>10.04|<br>0.0327|12.45|0.0363|10.02|
|HOLRS|0.0093|3.54|0.0031|<br>0.67|<br>0.0092|3.51|0.0028|0.60|
|EIHNIC|-0.0920|-2.41|-0.1178|-2.25|-0.0984|-2.58|-0.1168|-2.22|
|Economic|Sector:||||||||
|IND|0.1745|1.17|0.2865|3.07|0.2202|1.46|0.2809|3.00|
|ELEC|0.1773|1.00|*****|*****|0.2174|0.43|*****|*****|
|aCS1R|0.1339|0.86|0.2850|2.64|0.1884|1.20|0.2783|2.57|
|folM|0.0455|0.29|0.1796|<br>1.61|<br>0.0938|0.59|0.1836|1.63|
|IRANS|0.0902|0.58|0.2483|<br>2.02|<br>0.1439|0.93|0.2351|1.89|
|FINANC|E 0.1824|1.17|*****|*****|0.2364|1.50|*****|*****|
|HUVATE|0.0276|0.17|0.2524|<br>1.70|<br>0.0696|0.43|0.2426|1.62|
|(c) Ciaracte|ristics||||||||
|ofmariag<br>variable|e<br>s||||||||
|WSCH|0.0857|2.03|0.0267|<br>0.27|-0.052|8 -0.69|-0.0507|-0.30|
|MARR|0.0007|0.22|-0.0001|<br>-0.29|<br>0.0089|1.10|-0.0055|-0.79|
|WSCH.MAR|R-|-|-|-|0.0088|2.15|0.0044|0.59|
|SCH.MARR|<br>-|-|-|-|-0.0011|-1.38|0.0005|0.63|
|CCNS'ANr|6.8690|23.39|7.3888|<br>17.78|<br>6.7785|22.95|7.3659|17.54|
|R2|0.4568||0.4764||0.4629||0.4795||
|n|488||198||488||198||

Notes: b: Coefficient t: t-statistic

**The** following variables are inrxluded in the constant term: Ethnic origin: westem Economic sector: agriculture Wife's education: less than 12 years of **scdhoolirg**

***** No observations in these categories.
**Table 1 Data sources**

|Indonesia|The sample includes information from the Manufacturing Survey of Large<br>and Medium-Sized Firms (Statistik Industri). It provides plant-level data<br>from 2000 to 2015.<br>The sample focuses on a subset of Indonesian enterprises. The 2016<br>economic census indicates that 98.3% of enterprises are micro and small<br>business with less than 20 employees. However, the remaining 1.7% of<br>firms, the ones interviewed for the Statistik Industri, employ a non-negligible<br>amount (23.7%) of workers.|
|---|---|
|Malaysia|2000, 2005 and 2010 census of manufacturing firms with at least 10 workers.<br>There are 37,211 firms across the three waves of census.<br>In the 2010 manufacturing census, 36.7% of firms have at least 10 workers.<br>They account for 66.4% of employment in the manufacturing census.|
|Philippines|The sample includes data from 2001 to 2014 surveys on manufacturing firms<br>with at least 10 workers.<br>The economic census data is not disaggregated by firm size, so we refer to<br>the latest Enterprise Survey which indicates that firms with 5-19 workers<br>account for 52% of the sample. Firms with less than 20 workers employ<br>almost 12% of the labor force.|
|Vietnam|The sample includes data from 2009 to 2014 on manufacturing firms with at<br>least 1 worker. There are 259,721 manufacturing firms between 2009 and<br>2014.|

The potential gains from reallocation are large, even after accounting for measurement error (Table 2). We present three different measures of the potential productivity gains that can be achieved from reallocation. First, consistently with Hsieh and Klenow (2009), Panel A provides the estimates of potential gains if distortions (taxes) to revenue and labor are removed and factors of production (labor, capital and intermediate inputs) are reallocated towards their most productive uses. Panel B accounts for the presence of measurement error. Accounting for measurement error significantly reduces the magnitude of the potential gains from reallocation, these changes are as low as 6 percentage points in the Philippines and up to as high as 72 percentage points in Indonesia. Yet, even after accounting for measurement error, the estimates indicate that productivity can substantially increase where distortions are removed.

**Table 2 Potential gains from reallocation**

||**Malaysia**|**Indonesia**|**Philippines**|**Vietnam**|
|---|---|---|---|---|
|||_Panel A: w_|_ithout correction_||
|**2000-2007**|95.9|169.9|98.0|-|
|**2008-2015**|96.0|176.3|103.5|98.4|
|||_Panel B: with correc_|_tionfor measurement_|_error_|
|**2000-2007**|109.7|107.5|91.9|-|
|**2008-2015**|75.0|104.4|82.8|25.8|

18
This third variable is an indicator of exogenous security against the occurrence of terrorism incidents.

All three variables are based on past incidents computed from the ITERATE dataset from 1968 until 2002. Past incident-frequencies are defined over 5 years (i.e. the time horizon over which authorities formulate their beliefs is assumed to be 5 years ( _T_ = 5))<sup>10</sup> . We thus basically ask what is the effect of the past 5 years of incidents, on US imports.

Further, ITERATE delivers information on the country of location of each incident. This enables us to split terrorism incidents _njt_ between those perpetrated in the country of origin ( _Origjt_ hereafter), those located in the targeted country (i.e. in our case, the US) and those located in third countries ( _Thirdjt_ ). In particular, we expect observations on past incidents in third countries to be the most exogenous to US security at the borders. The reason is that terrorism in third countries should be in return much less affected by trade between the US and the origin country. In contrast, terrorism located in either the US or the origin country could be related directly or indirectly to trade between them. For instance, higher flows from an origin country to the US could reduce security measures at US borders for reasons discussed earlier, thus increasing the probability of incidents to take place inside the US. Besides, an escalation to war between a given state and the US can reduce bilateral trade but might also independently increase terrorism activities inside the former. In either of these cases, the parameter on frequency of past incidents would be biased.

This leaves third country incidents much better candidates of exogenous security than all other incidents. Thus we define an alternative indicator of exogenous security based solely on third incidents. Let _Fjt_ ( _Third_ ) = _t_<sup>_′_</sup> _∈_ <u>[</u> _t...t−nTT_ <u>]</u><sup>_Thirdj.,t′_</sup> , be the frequency of past incidents perpetrated in third �<sup><u>�</u></sup> � countries, we thus define to be an alternative proxy of exogenous security at the US borders. Because they are the most closely linked to our theory, this variable, together with _πjt_<sup>_US_</sup> will be our main two variables of interest in the next sections.

The dependent variable we study is bilateral US imports. We have chosen to work with data at the product level in order to control for the relative specialization of countries which we already suspect (see section 2) to be correlated with both measures, bilateral trade and terrorism activities. As well, a product level analysis allows us to investigate the differential effects of transnational terrorism and

> 10We have also considered time horizons of 3, 7 and 10 years. The results qualitatively very similar to a 5 years horizon. They are available upon request

15
Table 11: RD Estimates for Schooling and Labor by Gender of Child

|||Boys|||Girls||
|---|---|---|---|---|---|---|
||Full<br>Sample|Less<br>Sickness|More<br>Sickness|Full<br>Sample|Less<br>Sickness|More<br>Sickness|
||(1)|(2)|(3)|(4)|(5)|(6)|
|**A.** Schooling outcomes|||||||
|School enrollment (0/1)|0_._015|0_._004|0_._004|0_._036**|0_._040|_−_0_._021|
||(0_._026)|(0_._056)|(0_._036)|(0_._016)|(0_._034)|(0_._023)|
|Class attendance (Log)|0_._009**|0_._005|0_._011***|0_._007**|0_._007**|0_._003|
||(0_._004)|(0_._006)|(0_._006)|(0_._003)|(0_._003)|(0_._007)|
|Hours homework|0_._098|_−_0_._212|0_._370|0_._228|0_._019|0_._755|
||(0_._187)|(0_._179)|(0_._314)|(0_._163)|(0_._149)|(0_._614)|
|School Fees (Log)|_−_0_._036|0_._014|0_._017|_−_0_._006|0_._016|0_._042|
||(0_._036)|(0_._116)|(0_._062)|(0_._036)|(0_._110)|(0_._040)|
|**B.** Labor|||||||
|Child work (0/1)|_−_0_._015|_−_0_._002|_−_0_._032|_−_0_._031|_−_0_._008|_−_0_._108***|
||(0_._036)|(0_._069)|(0_._036)|(0_._077)|(0_._093)|(0_._021)|
|Labor earnings (Log)|_−_0_._011|_−_0_._032|_−_0_._032|_−_0_._008|0_._001|_−_0_._024|
||(0_._021)|(0_._035)|(0_._046)|(0_._009)|(0_._013)|(0_._022)|
|Household Chores (Log)|_−_0_._003|_−_0_._001|_−_0_._008**|_−_0_._015***|_−_0_._017***|_−_0_._025***|
||(0_._002)|(0_._003)|(0_._003)|(0_._005)|(0_._005)|(0_._010)|
|_Number of households_|||||||
|_Before Cutof_|1,072|302|153|1,056|290|152|
|_After Cutof_|536|151|282|553|146|284|
|_Total_|1,609|453|435|1,584|436|436|

_Notes:_ See notes in Table 6.

to a reduction in reporting error that arise if households better remember the enrollment status of children in case they regularly attend class. We further estimate that the NHIS reduced the incidence of child labor of girls living in households with more sickness by 10.8 percentage points, and their hours spent on household chores by 2.5% (Column 6).

None of the effect estimates statistically differ between boys and girls for the full sample even though, in absolute terms, the impacts seem to be larger for girls (compare Columns 1 and 4). On the contrary, we find that the effect of NHIS on class attendance is significantly greater for boys relative to girls among households experiencing more sickness. Girls, however, benefit more than boys from NHIS introduction in terms of the reduction in child labor and household chores in the same subsample. In addition, we observe a remarkable gender-related asymmetry in the relationship between child work and class attendance. Our results suggest that reduced household chores did translate into increased class attendance only in the case of boys, even though the NHIS freed up less time for boys relative to girls (Columns 3 and 6). This is possibly due to larger baseline school enrollment rates of boys (see Table 10), which allow them to more flexibly attend classes.

28
to creditors. The estimates of Lindert and Morton (1987), Jorgensen and Sachs (1988) and Eichengreen and Portes (1986, 1988b) agree on this point. Our owr. calculations, based on two large samples of foreign bonds issued in the 1920s, reveal in addition considerable variation in debtor and creditor experience.3AI ro summarize the performance of foreign loans issued between 1920 and 1929, we calculated the nomninal own-currency internal rate of return on more than 300 issues. These are summarized in Table 8. For dollar bonds, at one extreme the mealized return marginally exceeded the contractual rate, as in the cases of Norway and Canada, due to early debt retirement. At the other extreme, the realized internal rate of ret'rn might reach substantial negative levels, such as -7.4 per cent for Brazil, -9.8 per cent for Bolivia and -14.76 per cent for Hungary, indicating that not just interest but a substantial fraction of principal was written off by the creditors.

On average, however, both British and American creditors recovered their principal. Our calculations suggest that, for creditors with diversified foreign bond portfolios who were willing to hold out for final settlement, the defaults of the 1930s were not as disastrous as typically portrayed. The average nominal intemal rate of return (weighted by issue value) was roughly 4 per cent on dollar bonds and in the range of 5 per cent for sterling issues. While dollar bondholders setded for approximately half of contractual interest and sterling bondholders settled for only slightly more, on balance the former did only slightly worse than if they had held domesdc Treasury bonds, and the latter did slightly better. Ex ante risk premia were nearly sufficient to compensate American bondholders for the risks of foreign lending, while for British bondholders they more than sufficed. Although some debtor countries received what can be interpreted as substantial relief, overall the defaults of the 1930s did not inflict unsupportable losses on the creditors.

36
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**different trends when unraveling markups across sectors.** Because of their intrinsic characteristics, firms are different from each other and contrast in terms of performance, even within very narrowly defined sectors (Syverson 2004). These differences could persist either because of supply-side factors, such as management skills, R&D, or investment patterns (Bartelsman and Doms 2000), or because of demand-side factors related to product differentiation, customer-producer relationships, geographical segmentation, among others. That said, firms across high-level sectors—manufacturing, services, and mining—have shown varied performance. While the manufacturing (sales-weighted) average markup experienced a decline of −5.7 percent over the 2008–17 period (figure 1, panel b), there was an opposite, and much more rapid, trend in services, with an aggregate change of 42.2 percent (see figure 1, panel c). Weighted average markup in mining experienced an uneven trend throughout the same period. Nuances are unveiled even within sectors, as aggregate markup performance shows different patterns when breaking manufacturing down into groups of sectors according to their technology intensity.<sup>14</sup> This is also observed when services are broken down into groups of sectors according to their knowledge intensity<sup>15</sup> (see figure A1.1 and figure A1.2 in annex 1).

**Further, there is also evidence of changes in the markup firm distribution from 2008 to 2017, revealing an increasing proportion of firms with relatively high markups.** Figure 2 displays the firm-level distribution of markups in 2008 and 2017. Some changes are noteworthy. First, both the mean and variance increased over that period. But more importantly, the upper tail of the distribution became fatter over time suggesting that in 2017, more firms have higher markups than in 2008. This suggests that the rise in average markups is associated with large increases among the firms with the highest markups.

> 14 As per Eurostat classification, <mark>manufacturing sectors are classified as high-technology, medium-hightechnology, medium-low-technology, and low-technology, according to technological intensity (R&D expenditure or value added), and based on the statistical classification of economic activities in the European Community (NACE) at the 2-digit level. See t</mark> able A1. <mark>1 in annex 1, with sector aggregation.</mark>

> 15 As per Eurostat classification, service <mark>sectors are classified as knowledge-intensive services (KIS) and lessknowledge-intensive services (LKIS). KISs comprise knowledge-intensive market services, high-tech knowledgeintensive services, knowledge-intensive financial services, and other knowledge-intensive services; LKIS comprise less-knowledge-intensive market services and other less-knowledge-intensive services. Both KIS and LKIS are based on the share of tertiary educated persons at the NACE 2-digit level. See table A1.2 in annex 1, with sector aggregation.</mark>

12