rafmacalaba/datause-encoder-data huggingface dataset:
countries, heterogeneity across households, heterogeneity across goods, and heterogeneity across sources of gains (namely, consumption and income effects). We illustrate these varying forms of heterogeneity by slicing the results from our simulations in different ways.

To begin, Table 2 shows that the aggregate gains are highly heterogeneous across countries. While the gains can exceed 5% of real household income, as in Zambia (6.93%), Bhutan (6.53%), Jordan (6.55%), Cameroon (6.25%), and Ecuador (5.05%), there are two countries with income losses, namely Burundi (-3.23%) and Ghana (-0.50%) and a few other countries with small gains (e.g., Cambodia, 0.17%). Figure 7 clearly illustrates the cross-country heterogeneity with a map of the relative magnitudes of the gains from trade. We can also use these data to show an interesting result: there is a positive correlation between the aggregate gains from trade and the log of per capita GDP (Figure 8). Roughly speaking, this suggests that richer countries stand to gain more from unilateral agricultural liberalization than the poorest ones.

Second, we examine household heterogeneity and explore distributional impacts, which is one of the main advantages of the Household Impact of Tariffs database. There is a burgeoning literature on this topic, including Porto (2006) and more recent contributions from Fajgelbaum and Khandelwal (2015), Atkin, Faber and Gonzalez-Navarro (2018), Faber (2014) and Atkin and Donaldson (2015), Antras, de Gortari and Itskhoki (2018), and Galle, Rodriguez-Clare, and Yi (2017), and Artuc, Porto and Rijkers (2019).

We start by plotting the developing world distribution of the gains from trade to show how HIT data can be used to analyze global implications of trade with a specific focus on farm households.<sup>8</sup> The variability in the gains from trade across countries is sizable; the average standard deviation of the income gains across the income distribution is 1.01% across countries. In Ukraine, the country with the lowest variance in the gains from trade, the standard deviation is 0.19 percentage points while in Burundi, the country with the highest variability, the standard deviation is 2.68 percentage points. It is interesting to note that the variability

> 8Artuc, Porto and Rijkers (2019) investigate the trade-off between the income gains and the inequality costs of liberalization for countries separately using raw data. Here, we show how related policy problems can be tackled at the global level using conveniently organized HIT data.

9
moving elderly into the households of migrant adult children. Reforming these obstacles to mobility would facilitate movement of the rural elderly and continued use of support through coresidence with adult children.

# **References**

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25
# 1. Introduction

Countries around the world have increased their participation in preferential trade agreements (PTAs) especially in the last two decades **.** From the 1950s onwards, the number of active PTAs increased steadily to almost 50 in 1990. Thereafter, PTA activity accelerated noticeably, with the number of PTAs more than doubling over the next five years and more than quadrupling by 2010, to reach close to 280 PTAs presently in force (see Figure 1).

Figure 1: Trade agreements have proliferated over time <!-- Start of picture text --> 300<br>250<br>200<br>150<br>100<br>50<br>0<br>Number of agreements in force<br><!-- End of picture text --> Source: Authors’ calculations using World Bank PTA data set (2016) The existing literature suggests at least two reasons for the significant increase in the number of PTAs. First, the lack of progress in trade negotiations at the multilateral level has improved countries’ incentives to engage in bilateral or regional preferential negotiations.<sup>1</sup> Second, the fear of market share loss by being excluded from existing PTAs has pushed more countries to sign PTAs ‐ a “domino effect” of PTAs.<sup>2</sup> The extent of preferential trade across countries has been previously estimated. However, the analysis is usually limited to a subsample of countries, due to the limited availability of data on preferential tariffs. Grether and Olarreaga (1998) estimated the share of preferential trade flows for a sample of 53 countries, representing around 85 percent of world trade for respectively 1990 and 1995. They estimated that the share of preferential trade was around 40 percent in 1995. Fugazza and Nicita (2010) calculated a bilateral index of preferential access, using a data set based on HS6‐digit tariff lines and trade data ranging from 2000 to 2007 and covering 85 countries; they also tested the impact of preferences on bilateral trade. Figures included in their paper suggest that 40 percent of world trade was at zero MFN rates and 30 percent duty‐free

> 1 Capling and Low (2010) and Bhagwati (2008).

> 2 Baldwin and Jaimovich (2010).

2
- 38 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo, W. Velazquez / LCR Flagship Report on Cities and Productivity

method using Gaussian filters and frequencies of illumination. Remote Sensing of Environment. June 2018, 210, pp242-258.

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Angel, S; Parent, J; Civco, DL (2010b). Ten compactness properties of circles: Measuring shape in geography. Canadian Geographer, 54(4): 441–461. https://doi.org/10.1111/j.15410064.2009.00304.x Antrop, M., 2004. Landscape change and the urbanization process in Europe. Landscape Urban Planning, 37 (1–4), 9–26.

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Batty, M (2008). The Size, Scale, and Shape of Cities. Science 319, 769 Batty, M; Longley, P (1994). Fractal Cities: A Geometry of Form and Function Academic Press, London.

Boeing, G (2017). OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks. Available at SSRN: https://ssrn.com/abstract=2865501
- 26

(ILl) Production taxg T a t(F(L) y], when F(L) a y a 0 and where y is **the** norm or threshold level, (Lv) A lump sun tax, equal to a contantg T * t.

In Table 4.1 we derive the multlpliors from the VCF' maximization subject to the toro profit constraint and the particular tax rule. " Note that the tax parameter. are taken as **exogenous** by the WCP ln this arrangment. We distinguish between the tax rule itself and a prameter for the tax norm or threshold. The multLpliers in brackets are for specific functional form for the production function, namely a semi-Cobb-Douglas "I those outslid of brackets relate to a generic production function with decreasing returns to scale. 2 Several results stand out. A wage bill tax and a productlon tax have exactly similar effects and clearly result in lowr employment and a likely increase of wages. This equivalence is useful to illustrato the negative supply-sAe implications of the wage-blll tax. Thli tax rule effectively motivates firms to shed laborl not necessarily a desirable component for a market-base reform economy. Change ia the taxable norm (a and y) proves neutral vie-a-vim employment but wages unambiguously increase with an increase in 2 or y.

For the tax per worker, by contrast, an increase in the tax rate clearly lowers wages and raises employment. An increase in the norm (s) leads to higher employment but the impact on wages is ambiguous. This tax rule clearly has an emplayment bias in sharp contrast to the wage bill. It can be noted that so long as the wage coiling is above the resorvation wage or unemployment benefit, an increase ln the ceiling will stimulate employment and reduce wages.

" Detailed calculations available from the authors. 5 viz; F(L) - ALI, whore A > 0, and 0 < a < 1. F(0) a0, ' (L) D1 0, FR (L) < 0.
# **_VII. References_**

Acemoglu, Daron, Simon Johnson, James Robinson, and Yunyong Thaicharoen. 2003. “Institutional Causes and Macroeconomic Symptoms: Volatility, Crises and Growth. Journal of Monetary Economics. 50: 49-123.

Alfaro, Laura and Vladim Volosovych 2008. ”Why doesn’t Capital Flow from Rich to Poor Countries: An Empirical Investigation”. Review of Economics and Statistics, Vol. 90, p. 347-368 Buiter, Willem. 2010. “Public Debt Explosions in Developed Nations”. Citi Investment Research, March.

Caner, Mehmet, and Bruce Hansen. 2004. “Instrumental Variable Estimation of a Threshold Model”. Econometric Theory 20:813-843.

Catao, Luis A. V. and Marco Terrones. 2005. Fiscal Deficits and Inflation”. Journal of Monetary Economics 52: 529-554.

Cecchetti, Stephen, M.S. Moharty, and Fabrizio Zampolli. 2010. “The Future of Public Debt: Prospects and Implications”. Paper presented to Bank of India’s International Research Conference, February 12.

Cordella, Tito, Luca A. Ricci, and Marta Ruiz-Arranz. 2010 “Debt Overhang or Debt Irrelevance: Revisiting the Debt Growth Link.” IMF Staff Papers, forthcoming.

Frankel, Jeffrey, and David Romer, 1999.“Does Trade Cause Growth?” American Economic Review , 89(3): 379-399.

Hansen, B.E. 2000. "Sample splitting and threshold estimation," _Econometrica_ , 68, 575-603.

Hansen, B.E. 1996."Inference when a nuisance parameter is not identified under the null hypothesis." _Econometrica_ 64, 413-430 Leeper, Eric and Huitxin Bi 2010.“Sovereign Debt Risk and Fiscal Policy in Sweden.” NBER, Working Paper 15810, March.

Levine, Ross, and David Renelt. 1992, “A Sensitivity Analysis of Cross-Country Growth Regressions”. American Economic Review82(4): 942-963.

Pattillo, Catherine, Helene Poirson, and Luca Ricci.2002 “External Debt and Growth”.IMF Working Paper April.

Pattillo, Catherine, Helene Poirson, and Luca Ricci.2004. “What Are the Channels Through Which External Debt Affects Growth?”. IMF Working Paper January.

12
- 25 -

32. Schematically, AVI consists of three functional elements: a transponder (known as a 'tag') which stores a unique identification code; an interrogator which 'reads' a transponder and decodes its identfication; and a computer system which transmits, analyzes and stores data (Davies, 1989; Sommerville, 1991). There are four broad types of AVI technology:

- Optical and infra-red systems

- hIductive loop systems

- Radio frequency and microwave systems (including surface acoustic wave technology)

- Smart card systems

33. One of the earliest generations of AVI technology involves optical means (Davies, 1989; Sommerville, 1991).'3 It is not unlike bar code scanning available in grocery store checlstands (which are not close to being considered fully reliable). With optical systems, the coded labels are placed outside the vehicle. A laser beam from an optical scanner tracks the bar code sticker placed on side windows and analyzes the reflected light. However, this technology becomes less reliable under conditions of poor visibility (e.g., when there is dirt, snow, ice, rain or fog). Further, counterfeiting of bar codes is easily caried out and a vehicle's speed is lowered when attempting to meet the exacting requirements imposed by optical systems. Computer scanning of vehicle number plates have been tried on some French toll roads but still has not reached a tolerably low error rate in readability. In the 1970s, infra-red technology based on similar pnnciples of operation as the optical-based ones also floundered when subjected to poor environmental conditions."

- 13 Optica-basd aumtic car idficaton (ACI) is so usd i om, noaroa-bd tra ai envhionm_t, u exemplified by tho Washugton Meoto's uso of ACa since 1976 (Arms, 1984).

- 1 Automac or electsc rout guidao, which does not require very stinget requimen of reliability, curntly **uses** a **_mre ancod_ info*-rd** tchnology **bsod** an a dynamic two-way communicatos between an in-vehicle uit ad a roadside bem Static rout guidanc ue self-contained, mapping informaton stored in a veicle only, whereas dynmc rute guidance obtins dital map inmaton and routi eommedatons both **auraly** and visuly. **i loer** maoges **tafflc** flow by usn road-side inf-red

- beos to **raumit** and receive dynamc d to al moving vewle in _(ahost)_ ra tm (Cading, 1990). Siemen' Buo-eo sytm - thea genertion of the ali-out stem - has been in full scale field tral
Large water CGE models have been developed to address regional and international water trades for example Peterson _et. al._ . (2004)’s model analyzes the effect of water trade on the effects of water scarcity. They conclude that that allowing both intra- and interregional water trade among irrigators substantially lessens the impact of reducing water availability on gross regional product. Dywer _et. al.,_ (2005) extends the analysis of Peterson et al. (2004) to investigate the effects of expanding trade to include both irrigators and urban water users. They found that water trade compensates the losses from water reductions. Beritella _et. al._ . (2005a) develop GTAP-W, a large scale CGE model that extends GTAP Model of Hertel _et. al._ . (1987). They conclude that welfare losses are substantially larger in the non-market situation. Beritella _et. al._ . (2005b) introduce the tax policies to the model of Beritella _et. al._ . (2005a). They conclude that water taxes reduce water use and lead to shifts in production, consumption, and international trade patterns. Countries that do not levy water taxes are also affected by the taxes levied by other countries. Beritella _et. al._ . (2006) uses and estimates the impacts of the Chinese North-South water transfer project on the economy of China and the rest of the world. They conclude that China will benefit from the project at the expense of the International trade and global GDP. Kohn (2003) uses a Heckscher-Ohlin framework to examine the economic efficiency of international trade in water as a means to mitigate water scarcity when water is both an input and output among the trading countries. Both countries are better off with international trade.

Tirado, _et al._ . (2004) illustrate the gains from voluntary water rights exchange between the agriculture and urban sectors. Agricultural production is lower when there is a market for water rights than when there is not such a market. Tirado _et. al._ . (2005) introduce Water Framework Directive to the same model, while Tirado _et. al._ . (2006) analyze the effect of efficiency of irrigation on tourism sector and they conclude that water efficiency measures do not reduce economic pressures on water ecosystems.

CGE models have also been used to analyze the impact of water policies on equity and poverty alleviation. Letsoalo _et. al._ . (2005) test the triple dividend hypothesis discussed in section 3.2. They concluded that there could be a triple dividend of water policy that would simultaneously reduce water scarcity, improve economic growth and reduce poverty. Velazquez _et. al._ . (2007) develop the models of Cardenete and Sancho (2003) and André _et. al._ . (2005) to assess the impact of various of water tax policies. They concluded that although the tax policy does not yield significant water saving in the agricultural sector, a more efficient and more rational reallocation of water is achieved.

52
**Table 1: Cotton production**

||**_1990-94_**|**_1995-99_**|**_2000-04_**|**_2005-09_**|**_1990-94_**|**_1995-99_**|**_2000-04_**|**_2005-09_**|
|---|---|---|---|---|---|---|---|---|
|**_WORLD_**|||||||||
|||**_Thousa_**|**_nd tons_**|||**_Share of _**|**_World(%)_**||
|China|4,483|4,311|5,314|7,269|24.4|22.3|24.9|29.9|
|India|2,149|2,767|2,864|4,742|11.7|14.3|13.4|19.5|
|US|3,649|3,708|4,123|3,844|19.9|19.2|19.4|15.8|
|Pakistan|1,591|1,646|1,869|1,986|8.7|8.5|8.8|8.2|
|Brazil|556|462|1,013|1,285|3.0|2.4|4.8|5.3|
|Africa|904|1,316|1,422|1,142|4.9|6.8|6.7|4.7|
|Uzbekistan|1,356|1,095|994|1,061|7.4|5.7|4.7|4.4|
|Others|3,692|3,993|3,703|2,952|20.1|20.7|17.4|12.2|
|**WORLD**|**18,380**|**19,300**|**21,303**|**24,282**|**100**|**100**|**100**|**100**|
|**_AFRICA_**|||||||||

|||**_Thousa_**<br>|**_nd tons_**<br>|||**_Share of _**<br>|**_Africa(%)_**<br>||
|---|---|---|---|---|---|---|---|---|
|Burkina Faso|65|102|178|210|7.2|7.8|12.5|18.4|
|Mali|116|194|198|130|12.9|14.8|14.0|11.4|
|Zimbabwe|55|109|93|105|6.0|8.3|6.5|9.2|
|Nigeria|54|75|89|91|6.0|5.7|6.3|8.0|
|Benin|82|144|148|90|9.1|11.0|10.4|7.9|
|Tanzania|62|59|62|89|6.8|4.5|4.4|7.7|
|Côte d'Ivoire|101|133|129|68|11.2|10.1|9.1|6.0|
|Cameroon|51|78|99|63|5.7|6.0|6.9|5.5|
|Others|317|421|426|298|35.1|31.9|29.9|26.1|
|**AFRICA**|**904**|**1,316**|**1,422**|**1,142**|**100**|**100**|**100**|**100**|

**Source** : United States Department of Agriculture (http://www.fas.usda.gov/psdonline) **Note** : All figures in this table (and the paper) refer to cotton lint.

_— 13 —_
Figure 3: Correlation between likelihood of laying off workers and change in sales revenue.

<!-- Start of picture text --> Likelihood of laying off workers Fraction of workers laid off<br>.25<br>10<br>.2<br>.15<br>5<br>.1<br>.05<br>0<br>0<br>-100 -50 0 50 -100 -50 0 50<br>Change in sales Change in sales<br>Men-led firms Women-led firms Men-led firms Women-led firms<br>Average fraction of workers laid off<br>Average fraction of firms thay lay off workers<br><!-- End of picture text --> Note: Binned scatterplots. Computation use weights equal to the inverse of the number of observations in each country. Variables in both axes are residuals from linear projections on fixed effects for size, sector, income group, geographical region, timing of the survey, and severity of the shock.

# **6.2 Digital technology adoption**

In this section we examine gender gaps in technology adoption during the COVID-19 crisis. Studies prior to the crisis have tested whether women are more or less likely than men to use digital solutions. Fatehkia et al. (2018) estimate gender gaps in both internet penetration and mobile phone usage across countries using digital trace data from Facebook, and find that gaps in internet usage and mobile phone usage between men and women decrease with the stage of development of the country.<sup>21</sup> EIGE (2016) documents that women’s usage of the internet to sell goods is 5 pp lower than men’s usage, while women’s usage of the internet to buy goods is 7 pp less than men’s usage. Finally, (Ono and Zavodny, 2003) examine differences in men’s and women’s usage of the internet using data from several surveys during the period 1997 to 2001. They find that the frequency and intensity of internet usage by women is less than that of men, however, women’s likelihood of having access to the internet is not significantly different from that of men since the year 2000. Other, more recent, studies document that women have lower digital literacy (Rowntree, 2018), and are on average, 14% less likely to own a mobile phone than men (De Paz Nieves et al., 2021). The literature, however, has remained relatively silent on whether such potential gender gaps exist during periods of large unanticipated economic shocks, which is the focus of this section.

> 21Women’s internet usage is 24% lower than that of men in low-income countries, 14% lower in lower-middle-income countries, 8% lower in upper-middle-income countries and 5% lower in high-income countries. On mobile phone gender gaps, women’s mobile phone usage is 21% lower than that of men in low-income countries, 10% lower in lower-middle-income countries, 4% lower in upper-middle-income countries and 2% lower in high-income countries.

24
Table 8b: Industry-level competition analysis in manufacturing sector - input trade tariffs

||Baseline estimation|Excluding ind<br>weights|ustry<br>Excluding initial level of<br>dependent variable<br>Includ<br>empl.|ing initial female<br>share for industry|Including initial female<br>empl. share for industry<br>and its change|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
||||A. Change in log total employment|||
|Decline in industry tariff|0.086|0.083|0.002|0.107|0.060|
||(0.095)|(0.248)|(0.081)|(0.080)|(0.053)|
||||B. Change in log female employment|||
|Decline in industry tariff|0.400|0.196|-0.077|0.441|0.371***|
||(0.246)|(0.272)|(0.211)|(0.264)|(0.093)|
||||C. Change in female employment share|||
|Decline in industry tariff|0.568|0.114|0.320|n.a.|n.a.|
||(0.354)|(0.169)|(0.431)|||
||||D. Change in male segmentation|||
|Decline in industry tariff|-0.131|0.003|-0.344***|-0.149*|-0.064|
||(0.091)|(0.136)|(0.059)|(0.087)|(0.088)|
||||E. Change in female segmentation|||
|Decline in industry tariff|0.200**|-0.006|0.164|0.238*|0.131**|
||(0.099)|(0.109)|(0.151)|(0.122)|(0.051)|
||||F. Change in combined segmentation|||
|Decline in industry tariff|-0.170|0.036|-0.250*|-0.105|-0.162|
||(0.183)|(0.141)|(0.148)|(0.125)|(0.099)|

Notes: See Table 8a.