rafmacalaba/datause-encoder-data huggingface dataset:
14 over the course of the COVID-19 pandemic, and use this to articulate the range between the best case scenario (no pandemic) and the worst case scenario. Doing so assumes that COVID-19 is a more or less representative of a severe pandemic.<sup>28</sup> Thus, both the spread of the pandemic through the community and the required mitigation measures would likely be similar.<sup>29</sup> As far as the disruption to schooling is concerned, COVID19 was the first shock where schooling (particularly higher education) could switch to an online or hybrid system, particularly in wealthy countries, and as such it is likely to provide a relevant benchmark for how schools might respond to future pandemics.<sup>30</sup> We first analyze pandemic shocks as though they affect countries _symmetrically_ , to highlight the ways that a given shock might affect countries differently based on their level of development. Finally, note that we ignore the impact of mortality as it mostly affects the non-working population (see Web Appx. Section B for details).

**3.1.1. Schooling Shock**

**Incidence** For primary and secondary students, the shock lowers the amount of schooling by some share _ν ∈_ [0 _,_ 1] during the period of impact. Thus, we have that during the shock equation (1) becomes: _hit_ = _hi,t−_ 1 + (1 _− ν_ ) _rs_ ( _sit_ ). Factors leading to values of _ν >_ 0 include school closures, imperfectly effective hybrid or online education, and temporary voluntary withdrawal from school. This does not affect the probability of proceeding to the following year _πe_ ( _·_ ), but slows human capital accumulation.

On the other hand, we assume that the accumulation of human capital in college is not disrupted. This is because the evidence does not suggest significant disruption to college enrollment numbers nor to the effectiveness of college education during the COVID-19 pandemic. See Web Appx. Section C for further details.<sup>31</sup>

**Duration & Severity** We explore all possibilities between no disruption and total disruption, over 2 years. We study scenarios whereby primary and secondary schooling

> 28Since 1900, global pandemics have indeed almost all been respiratory diseases: COVID-19 (2019present), H1N1pdm09 (2009-2010, now endemic), H3N2 (1968-1969), H2N2 (1957-1958) and H1N1 (1918-1920). See U.S. Center for Disease Control and Prevention for details: https://www.cdc.gov/flu/ pandemic-resources/basics/past-pandemics.html, last checked 10/28/2021.

> 29An exception is the HIV/AIDS global epidemic (1981-present). However, the HIV virus requires different mitigation measures (in particular, neither workplace nor school shut-downs are required).

30There have also been severe pandemics in terms of mortality, such as the Black Death (14th century) and the American smallpox epidemic (16th century). These are cases where mortality itself is a key channel of resource destruction (Jedwab et al., 2021a), rather than the mitigation measures.

> 31Were college education disrupted, it would increase the relative magnitude of the shock in richer nations where college education is disproportionately important. Our results would thus be reinforced.
# **4. Mental Health Patterns in Tonga**

We begin by using the samples taken in Tonga to examine the correlates of mental health status in Tonga. These results will then be used later in the paper as an aid to help understand the possible channels through which migration may be affecting mental health. Table 1 presents the results of regressing the MHI-5 score on socioeconomic characteristics of the respondents. These regressions are used merely to describe the patterns present in the data, since variables such as education, labour force status, religion, and physical health, which are used as regressors, may themselves be affected by mental health status.

Column 1 presents a base regression, relating mental health status to some of the most common correlates studied in the literature. Mental health is seen to be significantly higher for females than males. While this is in contrast to the patterns found in many other countries, it is consistent with previous evidence from Tonga, at least amongst school-age youth (UNICEF, 2001). Mental health declines with age up until age 52, after which it starts increasing again. As all applicants to the migration lottery we study must be aged 45 or below, this means that mental health is declining with age among the eligible age range for migration. Mental health status is found to be lower for individuals born on the main island of Tongatapu compared to those born on the other islands in Tonga. Most importantly for assessing the impact of migration on mental health, we see a positive association between household income and mental health, and between employment and mental health. A one standard deviation increase in monthly household labour income is associated with a 0.34 increase (or 0.15 standard deviation) increase in mental health and being employed is associated with a 0.37 (or 0.16 standard deviation) increase in mental health.

Columns 2 through 4 of Table 1 then consider the relationship with additional factors. Column 2 shows that being Catholic is associated with higher mental health than other denominations. Column 3 adds several measures of physical health status and health behavior. Individuals who report their health status as very good (as compared to good, average, bad or very bad) have higher mental health, but not significantly so. Smokers have lower mental health than nonsmokers, while binge drinking has no significant or sizeable relationship with mental health. Column 4 shows that adding a quadratic term in household labour income does not significantly 10
vector of fixed effects purges systematic variation in the frequency of inspections across combinations of firms, products, countries of origin, and years.<sup>4</sup> The remaining variation acts as an instrumental variable for time spent under customs control, which affects growth in the value of imports in a second stage regression of our instrumental variables (IV) framework. This identification strategy follows that in Volpe Martincus _et al._ (2014), who apply it in a study of Uruguayan exports.

Our main findings are as follows. First, we show that conditional reductions in annual physical inspection rates (evaluated as a change from 50% or greater to less than 50% of shipments inspected) produce a 7% reduction in the median number of days spent in Albanian customs at the importing firm - HS6 product - origin country level. In turn, this reduction in median days in customs generates a 7% increase in the value of imports. Second, the effects of clearance time on imports had differentially larger impacts on imports from richer countries and from preferential trading partners. Most of this effect operates through increased imports from the EU, which appear to be more sensitive to time savings than imports from other high income countries. Further evidence on changes in the decomposition of import flows suggests reforminduced increases in unit prices and average quantities, as well as in the number of shipments. Third, there is reform-induced growth along extensive margins of trade, in particular the number of importing firms per product-country pair and the number of countries per firm-product pair. Fourth, we find little evidence to suggest that the reforms had differential impacts on imports of time-sensitive goods or on differently sized firms. Finally, we find evidence that reduced rates of inspection reduced the variability of time spent in customs, which in turn led to increased imports. The increase in imports due to reduced variability of clearance times survives even after we control for reductions in median clearance times.

A small number of recent studies examine the effect of customs procedures on trade flows, generally relying on cross-country gravity equations and aggregate measures of trade costs from the _Doing Business_ database. Djankov, Freund, and Pham (2010) link longer trading times to lower exports in the cross-section, and show that these effects are stronger for time-sensitive

> 4 Among other things, the fixed effects serve as controls for macroeconomic fluctuations and changes in trade policy that occurred during the period.  Fixed effects at the HS6 product - origin country - year level control for developments that are specific to country-product pairs, such as growth of imported fabrics used in processing trade with the Italian apparel sector.

3
trips. This presents both benefits and costs. The main advantage of our approach is that trips are exogenously chosen. Unlike Couture _et al._ (2018), we do not need to worry about the simultaneous determination of some variables such as trip length and speed, which could affect the estimates of city fixed effects in equations (2) and (3).<sup>20</sup> Conceptually, this approach is similar to measuring price indices from store price tags instead of from consumers’ transactions.

This is also a limitation of our method. The instances that we exogeneity potential trip query do not correspond to actual trips and may not be representative of the travel conditions faced by urban travelers when they demand to travel. If our trips are far enough from representative, and if the speed of various types of trips varies across cities, then our mobility indices will be mismeasured.

To this criticism, we have four answers. The first is that some of the trips we created were designed to resemble what we know about actual trips in other cities, with respect to either their direction, the type of destination (and their frequency), or their length. Second, our four trip types (radial, circumferential, gravity, amenity) are designed to reflect reality in distinct ways. We show below that when we introduce a comprehensive sets of controls for other trip characteristics, the economic significance of the trip type indicators in equation (3) is small. Third and most important, our large sample allows us to estimate mobility indices for each trip type, destination, time of day, distance to city center, and various other subsamples. These indices are all highly correlated with our baseline index. As argued earlier, this result implies that our indices do not depend in an important way on the particular utility weight that each counterfactual trip could receive. Finally, Akbar and Duranton (2018) use Google Maps in Bogotá to measure the speed of actual trips reported in a transportation survey and counterfactual trips designed using the same strategy as here. Within short time intervals within days, the speeds of the two types of trips are virtually indistinguishable from each other, and from measures of speed reported by Uber for comparable trips.

# **_3.3 Disentangling two sources of mobility: Uncongested mobility and congestion._**

Mobility can naturally be decomposed into two components: an uncongested or “free flow” speed, and a congestion factor. To separate the “intrinsic” slowness of a city from its congestion, we can adapt the approach proposed above. To measure mobility, we use as dependent

> 20For instance, as mobility gets better travelers may choose to travel to further destinations. In addition, the (counterfactual) trip instances that we query do not affect real traffic conditions.

11
- Haavelmo, T. (1944). The Probability Approach in Econometrics. _Econometrica_ , 12((Suppl.)):1–115.

- Hanna, R. and Oliva, P. (2015). The effect of pollution on labor supply: Evidence from a natural experiment in Mexico City. _Journal of Public Economics_ , 122:68–79.

- Hendry, D. F. (2017). _European journal of pure and applied mathematics._ , volume 10.

- Kalman, R. (1983). Identifiability and Modeling in Econometrics. _Developments in Statistics_ , 4:97–136.

- Lee, L. (2012). Environmental poverty, a decomposed environmental Kuznets curve, and alternatives: Sustainability lessons from China. _Ecological Economics_ , 73(1):86–92.

- Levinson, A. (2012). Valuing public goods using happiness data: The case of air quality. _Journal of Public Economics_ , 96(9-10):869–880.

- Lu, X., Su, L., and White, H. (2017). Granger Causality and Structural Causality in Crosssection and Panel Data. _Econometric Theory_ , 33(02):263–291.

- L ̈utkepohl, H. (2005). _New Introduction to Multiple Time Series Analysis_ . Springer Berlin Heidelberg, Berlin, Heidelberg.

- Neuberg, L. G., Neuberg, and Gerson, L. (2003). Causality: Models, Reasoning, and Inference, by Judea Pearl, Cambridge University Press, 2000. _Econometric Theory_ , 19(04):675–685.

- Nsiri, S. and Roy, R. (1993). On the Invirtibility on Multivariate Linear Processes. _Journal of Time Series Analysis_ , 14(3):305–316.

- Panayatou, T. (1997). Demystifying the environmental Kuznets curve: turning a black box into a policy tool. _Environment and Development Economics_ , 2(4):S1355770X97000259.

- Pearl, J. (2000). _Causality : models, reasoning, and inference_ . Cambridge University Press.

- P ̈otscher, B. M. and Prucha, I. R. (1997). _Dynamic Nonlinear Econometric Models_ . Springer Berlin Heidelberg, Berlin, Heidelberg.

- Preker, A. S., Adeyi, O. O., Lapetra, M. G., Simon, D. C., and Keuffel, E. (2016). Health Care Expenditures Associated With Pollution: Exploratory Methods and Findings. _Annals of Global Health_ , 82(5):711–721.

- Quiggin, J. (2009). Six Refuted Doctrines. _Economic Papers_ , 28(3):239–248.

- Ranieri, R. and Almeida Ramos, R. (2013). Inclusive growth: Building up a concept.

- Reinsel, G. C. (2003). _Elements of multivariate time series analysis_ . Springer.

34
# **Reference**

Banse, M., van Meijl, H., Tabeau, A., Woltjer, G., 2008. Will EU biofuel policies affect global agricultural markets? European Review of Agricultural Economics , 35 (2), 117-141.

Barker, T., Scrieciu, S.S., Foxon, T., 2008. Achieving the G8 50% target: modelling induced and accelerated technological change using the macroeconometric model E3MG. Climate Policy, 8, 30-45.

Birur, D. K., Hertel, T. W., Tyner, W. E., 2008. Impact of biofuel production on world agricultural markets: a computable general equilibrium analysis. GTAP Working Paper No. 53.

Birur, D., Hertel T., Tyner W., 2007. Impact of Biofuel Production on World Agricultural Markets: A Computable General Equilibrium Analysis. GTAP Working Paper No. 53, presented at the 10th Annual Conference on Global Economic Analysis, Purdue University, USA, 2007 Edenhofer O., Carraro C., Kohler J., Grubb, M., 2006. Ednogenous Technological Change and the Economics of Atmospheric Stabilization. The Energy Journal. Special Issue.

Edenhofer O., Knopf B., Leimbach M. and Bauer, N., 2010. The Economics of Low Stabilization. The Energy Journal. Special Issue.

Edenhofer O., Bauer N. and Kriegler E., 2005. The impact of technological change on climate protection and welfare: Insights from the model MIND, Ecological Economics 54: 277-292.

Fargione, J., Hill, J., Tilman, D., Polasky, S., Hawthorne, P., 2008. Land Clearing and the Biofuel Carbon Debt. Science , 319, 1235 - 1238.

Goulder, L.H., 1995. Effects of carbon taxes in an economy with prior tax distortions: an intertemporal general equilibrium analysis. Journal of Environmental Economics and Management, 29: 271-297.

Goulder L.H., Parry, I.W.H., Williams III, R.C. Burtraw, D., 1999. The cost-effectiveness of alternative instruments for environmental protection in a second-best setting. Journal of Public Economics, 72: 329360.

Hertel, T.W., Tyner, W.E. & Birur, D.K., 2010. Global Impacts of Biofuels, The Energy Journal, 31(1):75-100.

Huang, J., can Tongeren, F., Dewbre, J., van Meijl, H., 2004. A new representation of agricultural production technology in GTAP. Presented at the 7th Annual Conference on Global Economic Analysis, Washington DC, USA .

37
block excluding the United States (Canada, Mexico, Chile, Peru) increasing their export volume in 2030 by $10 billion or increasing from 1.4 to 3.3 percent of total exports.

# **<mark>Figure 9. Destination of Indonesian exports in selected scenarios, 2030</mark>**

<!-- Start of picture text --> Baseline ($492.9b) EU-IDN ($528.4b) FTAAP ($518.1b) TPP-15 ($507.9b)<br>43 45 62 42<br>70 7 69 7 69 69 17<br>14<br>48 99 94 46 47<br>98 109<br>102<br>149 78 73 144 148<br>142 82 75<br>USA Other TPP TPP/RCEP joint members China Other RCEP members EU Rest of World<br><!-- End of picture text --> Figure 10 expands on trade diversion under these PTAs by showcasing the sectoral decomposition of exports with respect to baseline in 2030. Percentage point changes of exports by sectors are shown in horizontal bars, the sum of which is equal to zero. It can be seen that most of the sectoral reallocation would shift exports towards wearing apparel, textiles, and to a lesser extent into food, beverages, and tobacco under the EU-IDN CEPA and FTAAP. The former scenario would lead to redirection of exports to the European Union while the latter to the United States. By contrast, TPP-15 offers a more balanced distribution favoring sectoral growth in sectors typically associated with larger value added, such as transport and machinery equipment. Export destinations for these 20
**Table 12: Sectoral decomposition of growth in output per capita, 1987-2018**

|**Sector**|**Growth in output per**<br>**capita**<br>|**Share of contrib**<br>**(**<br>|**ution in growth of o**<br>**1987-2018) (%) from**|**utput per capita**<br>**:**<br>**Chanes in**|**Total sector**<br>**contribution to**|
|---|---|---|---|---|---|
||**(1987-2018)**<br>**(2015 RM)**|**Within-sector**<br>**productivity**|**Between-sector**<br>**shifts**|**g**<br>**employment**<br>**share**|**output per**<br>**capita (%)**|
|Agriculture|-757.17|10.2|3.3|-16.4|-2.8|
|Manufacturing|6,288.49|20.4|0.5|2.2|23.0|
|Construction|1,287.19|2.5|-0.7|2.9|4.7|
|Trade|6,287.73|15.6|-1.8|9.2|23.0|
|Transport|2,777.60|7.0|1.1|2.0|10.2|
|Business|3,475.03|9.9|1.3|1.5|12.7|
|Other|3,819.22|10.2|-0.7|4.4|14.0|
|_Total_|_23,178.11_|_75.9_|_3.0_|_5.9_|_84.9_|

Source: Authors’ calculations based on data from DOSM Note: Total contribution does not add up to 100 percent due to the omission of the contribution of changes in the demographic structure to the growth in output per capita.

Figure 17 repeats the sectoral decomposition of growth in output per capita of Table 11 for the three subperiods from 1987 to 1997, 1997 to 2007, and 2007 to 2018. It shows that the manufacturing sector was the largest contributor to the growth of output per capita between 1987 and 1997 and between 1997 and 2007 while the trade services sector had the largest contribution between 2007 and 2017. In fact, growth in output per worker in the manufacturing sector was highest in the period from 1987 to 1997 and fell in the subsequent periods. With regard to the components of sectoral contributions to output growth, in the period from 1987 to 1997, the contributions of employment in the manufacturing sector as a share of the working age population and between-sector shifts were both positive. However, after 1997 the contribution of the manufacturing sector to growth in output per capita could be fully be attributed to improvements in within-sector productivity, with between-sector shifts and changes in employment contributing negatively to output per capita growth.

**Figure 17: Decomposition of growth in output per capita by sector (2015 RM), 1987-2018**

<!-- Start of picture text --> 14,000<br>12,000<br>10,000<br>8,000<br>6,000<br>4,000<br>2,000<br>0<br>-2,000<br>-4,000<br>1987-1997 1997-2007 2007-2018<br>Within-sector changes Between-sector changes Changes in employment<br>Contribution to growth in output per capita (2015 RM) Agriculture Manufacturing Construction Trade Transport Business Other Total Agriculture Manufacturing Construction Trade Transport Business Other Total Agriculture Manufacturing Construction Trade Transport Business Other Total<br><!-- End of picture text --> Source: Authors’ calculations based on data from DOSM 21
# Investigating the Gender Gap in Agricultural Productivity: Evidence from Uganda<sup>†</sup>

Daniel Ali, Derick Bowen, Klaus Deininger, Marguerite Duponchel<sup>‡</sup> _JEL Classification:_ J16, O13, Q12, Q18 _Keywords:_ Gender, Agriculture, Productivity, Decomposition, sub-Saharan Africa, Uganda

> † The authors would like to thank Talip Kilic, Marcus Goldstein, Will Martin, Siobhan Murray and Amparo Palacios-Lopez, as well as seminar participants at the 2013 Land and Poverty Conference, the 2014 Growth Week event, and the Kampala IFPRI seminar for their comments on earlier versions of the paper. They are also grateful to the Ugandan Bureau of Statistics for data access and constant support, and to the International Growth Centre for funding.

> ‡ Development Research Group, The World Bank
# **1 Introduction**

There is widespread evidence from developed countries that migration has relatively benign effects on the employment outcomes of non-migrants in the destination regions. Card (1990) was the first to show, based on the study of the Mariel Boatlift, that even a sudden large inflow of migrants virtually has no effect on native wages and employment probability. This research was followed by a plethora of studies which all drew similar conclusions.<sup>1</sup> The adjustment process that is typically advocated to explain why researchers find no labor market effects of immigration is capital mobility: capital inflows are expected to mitigate the negative impact of immigration on native employment outcomes. For instance, Angrist and Kugler (2003) show that the negative effect of immigration is much stronger in countries with high business entry costs than in countries with more flexible markets. In the same vein, Ruist and Bigsten (2013) demonstrate that international capital adjustment substantially diminishes the negative impact of immigration from developing countries on native wages in developed countries.

Surprisingly, little attention has been paid to the impact of migration on labor market outcomes in developing countries.<sup>2</sup> Yet, it is particularly developing countries that are subjected to large migration flows, although these concern mostly internal rather than international migration. For instance, Barrios, Bertinelli and Strobl (2006) note that rural-urban migration has accounted for roughly half of Africa’s spectacular urban growth between the 1960s and 1990s.<sup>3</sup> Moreover, one expects the negative effect of migration on labor market outcomes to

> 1See Hunt (1992), Friedberg and Hunt (1995), Carrington and de Lima (1996), Pischke and Velling (1997), Friedberg (2001), Suen (2000), Card (2005), Longhi, Nijkamp and Poot (2005), McIntosh (2008), Hanson (2009), Boustan, Fishback and Kantor (2010).

> 2Berker (2011) is an exception.

> 3The authors report that Africa’s growth rate of urbanization, defined as the share of urban to total

2