rafmacalaba/datause-encoder-data huggingface dataset:
# **(7.2) Effects of Trade Cost Shock on the Employment Pattern**

We utilize two rounds of census data to explore the impacts of trade/transport shock on the employment structure along the Abidjan and non-Abidjan routes. In addition to employment in manufacturing and agriculture, we also consider employment in trade and other services. During the pre-crisis period, 91 percent of workers were engaged in agriculture in the study countries. Among those employed in non-agriculture, most were traders (5 percent), followed by manufacturing (2 percent) and rest in services (2 percent). The second round of census data corresponds to 2006 for Burkina Faso and to 2008 for Mali, both years are at around the time of the truce and conclusion of peace treaty in Cˆote d’Ivoire in 2007. The estimates for employment shares should thus be considered conservative as they relate to the immediate post-crisis years.

# **(7.2.1) Identifying the Comparison Autarkic Hinterland**

An important insight from proposition (2 _.a_ ) is that the areas with very poor market access are essentially isolated hinterland, and their employment structure is not likely to be significantly affected by the trade shock. The empirical analysis in section (7.1) above provides suggestive evidence that, during the pre-CIV crisis period, the isolated hinterland consists of the areas in the lower 60 percent of the market access distribution, i.e., _σ ≤_ 0 _._ 60. Thus the average effects over a route is a weighted average of the zero effect in the autarkic hinterland and the effect of the trade shock on the integrated areas. Since the theory suggest that the trade shock to the Abidjan route will shift the SMI outward in the alternate nonAbidjan routes, we expect the extent of the isolated region to shrink after the shock. If an area remains isolated after the shock, the employment structure will not be affected by the CIV trade shock, and any changes would reflect only the long-term factors in the economy. This provides us with an estimate of the changes in the employment shares due to the underlying trend in the economy and we need to account for this trend when considering the effects of the CIV trade shock on employment share in the areas with better market access.

To identify the autarkic areas unaffected by the CIV shock, we define a series of dummies 24
service providers, harmonization of Armenian standards with EU standards and reduction of trade and border costs. We have also assessed the impact of preferential liberalization of services with the CIS region and compared all results with wider non-discriminatory liberalization. We find that Armenia would lose from a shallow free trade agreement with the EU, that is, a free trade agreement that preferentially liberalizes good alone. This is due primarily to a loss of productivity from excluded varieties of manufactured products from the non-EU regions. But Armenia would gain substantially from a DCFTA with the EU, due to inclusion of services liberalization, reduction of trade and border costs and harmonization of standards.

Armenia would obtain only small gains from a preferential reduction of barriers in services with the CIS region in our central elasticity case. Gains from liberalization with the EU region are considerably larger because of the larger amount of technology diffusion associated with trade and FDI with large industrialized countries, captured in our model as a relatively large increase in the number of varieties. Non-discriminatory liberalization of goods and services, however, will produce gains about three times larger than preferential liberalization of goods and services with the EU alone.

# **References**

- Arnold, Jens, Aaditya Matoo and Beata Javorcik (2007), ―Does Services Liberalization Benefit Manufacturing Firms: Evidence from the Czech Republic,‖ World Bank Policy and Research Working Paper Number 4109.

- Baldwin, Richard E., Forslid, Rikard and Haaland, Jan (1999), "Investment Creation and Investment Diversion: Simulation Analysis of the Single Market Programme," in R. Baldwin and J. Francois (eds.), _Dynamic Issues in Applied Commercial Policy Analysis_ , Cambridge: Cambridge University Press.

- Balistreri, Edward J. and James Markusen (2009), ―Sub-national differentiation and the role of the firm in optimal international pricing,‖ _Economic Modeling_ , 26(1), 47 - 62.

- Balistreri, Edward J., Thomas F. Rutherford and David G. Tarr (2009) ―Modeling Services Liberalization: The Case of Kenya,‖ _Economic Modeling_ , Vol. 26 (3), May, 668-679.

- Blalock, Garrick and Paul Gertler ( 2008), ―Welfare Gains from Foreign Direct Investment through Technology Transfer to Local Suppliers,‖ _Journal of International Economics_ , 74:401-21

- Broda, Christian and David Weinstein (2004), ―Variety, Growth and World Welfare,‖ _American Economic Review_ , 94 (2), May, 139-144.

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evidence on this question by providing randomly allocated grants to a portion of microenterprises in a panel survey, as described in more detail below.

A simple framework results in two predictions, which we test with the data. Assume firms have Cobb-Douglas production functions and operate in perfectly  (1  ) competitive output markets. The output of firm _i_ is _Yi_  _AK i Li_ , with K, and L representing capital and labor, and Y representing the output of goods with unit price. Prior to the shock, We allow the opportunity cost of both capital and labor to be firm specific, reflecting less than perfect input markets. Since the enterprises in our sample employ few workers outside the family, _wi_ ( _w_ ) reflects the individual’s opportunity cost of time, which depends on the market wage rate, _w_ .

The tsunami appears to have destroyed a larger share of the capital stock than the labor force. The estimated value of the lost assets was US$1 billion, just over 1 percent of the country’s pre-tsunami capital stock. About 35,000 people were killed, less than 0.2 percent of the population.<sup>11</sup> A decrease in capital relative to labor leads to an increase in the returns to capital at the margin, and a decrease in the return to labor at the margin.

But at least three factors might offset this. First, the psychological trauma following the devastation may have increased at least temporarily the opportunity cost of the owner’s time. Owners reported (to us and to others) that they did not feel like working in the weeks and months following the event. Second, the tsunami caused shifts in the demand for output produced by the firms. Some evidence suggests that the

> 11 Because of the timing of the event and other factors, women were disproportionately likely to be killed. Since women have lower labor force participation rates, the effect on the labor force was likely less than 0.2 percent.

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# **How Does Bank Competition Affect Systemic Stability?**

**Deniz Anginer** , **Asli Demirguc-Kunt, Min Zhu**<sup>**1**</sup>

_JEL Classifications:_ G21, L11, L14 _Keywords_ : Systemic risk, bank competition, credit risk, merton model, distance to default, default risk, lerner index, bank concentration

> 1 Anginer, World Bank, danginer@worldbank.org; Demirguc-Kunt, World Bank,  ademirguckunt@worldbank.org; Zhu, World Bank, mzhu1@worldbank.org. This paper’s findings, interpretations and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank, their Executive Directors, or the countries they represent.
construction in district _k_ . We use a normalized measure so that _Inpk ∈_ [0 _,_ 1] _._<sup>30</sup> For details of the construction of the program intensity variable, please see section (3) above. For sons we use _c_ = _s_ , and for daughters _c_ = _d_ as the superscript. The intercept eect of the Inpres schools is captured by _β_ 4 and the slope eect by _β_ 7 .

As discussed above, our identication strategy closely follows that of Duo (2001, 2004). Following Duo (2001, 2004), we include birth district xed eects ( _αk_ ) and birth year xed eects ( _τt_ ) for a child, and the following interactions (denoted by _Wk × τt_ ): year of birth interacted with 1971 enrollment (before Inpres program), year of birth interacted with the number of school-age children in 1971, year of birth interacted with water sanitation program, with all the variables measured at the birth district level. Note that the district xed eects absorb the level eect of the Inpres school intensity variable _Inpk_ as it varies at the district level, and the birth year dummies absorb the level eect of the _Expt_ dummy. The estimating equation for the linear model becomes: A large literature on intergenerational educational mobility based on the linear CEF focuses on the parameter _β_ 1 , called the intergenerational regression coecient (IGRC, for short) which is a measure of relative mobility (see, for example, Azam and Bhatt (2015), Lou and Li (2022), Neidhofer et al. (2018)). Our focus is on the parameter _β_ 7 that captures the eects of Inpres schools on IGRC. Note that IGRC provides an estimate of intergenerational persistence in education, and, a higher persistence implies lower relative mobility. When _β_ 7 _<_ 0 , Inpres schools improve relative mobility, the IGRC estimate for the exposed cohorts is smaller in this case, i.e., ( _β_ 1 + _β_ 7 < _β_ 1) _._ A focus of our analysis is on the eects of Inpres schools on the intercept of the mobility equation as captured by _β_ 4 . As noted earlier a higher intercept ( _β_ 4 _>_ 0) implies a positive impact of the Inpres schools on the expected educational attainment level of children from the most disadvantaged households (fathers with no schooling). The intercept can be interpreted

> 30We note that the main conclusions of the paper do not depend on this normalization.

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ITU collects data on connection fees, broadband wireless charges, broadband tax rates, business telephone installation fees, broadband caps, monthly subscription charges and broadband speeds. From these measures we compute a cost index using simple principal components. An increase in the index implies that costs are rising over time. Note that data are standardized across countries and hence an increase should reflect idiosyncratic increases in costs that imperfectly controls for inflation, quality changes or upgrades. To construct this index, all the input variables should have the same sign - so that the interpretation of an increase implies a higher cost relative to a global mean (see Appendix A, Table 41).

The estimated indices are summarized in Figure 6. Costs have been rising over time for all countries in the sample. These costs, associated with higher fees, and lower comparative speeds indicate entry barriers and hence market concentration. Although rising total costs could also indicate quality improvements, the measures here are mainly associated with regulatory costs.

A second measure to compute gaps in the input market uses the statistic from Bartelme & Gorodnichenko (2015) and computes the deviations from a frontier market using inputoutput tables. This measure tracks distortions in input markets by sector, on the assumption that an undistorted economy functions like a market leader. A further assumption is that all countries use the same intermediate goods technology. The distortion is then computed as a ratio of inputs in LAC countries relative to a frontier country in the provision of ICT goods (here the Republic of Korea). A higher ratio implies that the input distortion has risen relative to the frontier economy.

The undistorted share of intermediates of country _i_ in sector _j_ in period _t_ ( _bi,j,t_ ) is related to the distorted share ( _γi,j,t_ ) via a scalar that captures distortions ( _τi,j,t_ ):<sup>1</sup> Given that input-output tables are based on observed variables (i.e., they do not distinguish between distorted and undistorted states); the size of the distortion is then expressed relative to a frontier country (e.g., the United States or Korea). We extract the relative distortions using IO data:

> 1The firm profit maximizing problem reads as max���� _Pj,tYj,t−wNj,t−rKj,t−_<sup>�</sup><sup>_n_</sup> _i_ =1<sup>(1+</sup><sup>_τi,j,t_)</sup><sup>_PiMi,j,t_,</sup> _Kj,t,Nj,t,Mi,j,t_

where the Cobb-Douglas production function is _Yj,t_ = � _Kj,t_<sup>_α_((</sup><sup>_Aj,tNj,t_)</sup><sup>_α_)</sup> �1 _−γj_ � _ni_ =1<sup>_M γ_</sup> _i,j,t_<sup>_i,j_,where</sup><sup>_N_is labor,</sup> _K_ capital stock, _M_ intermediate inputs, _P, w, r_ the price of output _Y_ , labor and capital respectively. The first order condition for the intermediate input is _bi,j,t_ =<sup>_Pi_</sup> _P_<sup>_M_</sup> _j Y_<sup>_i,j,_</sup> _j_<sup>_t_</sup> =<sup>_<u>γ</u>_</sup> _τi,j_<sup>_i,j_.Theundistortedeconomy(i.e.,without</sup> the tax wedge) is equal to _γi,j,t_ . The wedge in this setup reduces the input share coefficient.

5
firms in the GQ corridor relative to the hinterland, which continues to rise through 2013. Panel B shows similar results for employment in logging firms; we omit 2005 because logging firms were not distinguished from firms engaged in afforestation in the 2005 Economic Census. These two graphs suggest that demand for wood from downstream firms is a plausible explanation for local deforestation after construction of the GQ.<sup>24</sup> Note that logging firms were more common along the transport corridor even before the GQ was built, but there is no suggestion of a pretrend that could explain what we see after highway construction. Note that employment in other sectors of the economy, which also consume wood, exhibit similar treatment effects close to the new highways Ghani, Goswami and Kerr (2016).

Panels C through E of Figure 5 show the effects of the GQ upgrades on household fuel consumption, for which data are available in 2001 and 2011. We observe marginal increases in firewood and imported wood use, and comparable reductions in use of local non-wood fuels. These effects are not statistically significantly different from zero, nor are they large enough to explain a 20% reduction in forest cover in the neighborhood of the GQ corridor. Panel F shows that land use shifts slightly _away_ from agricultural uses following the construction of the GQ, breaking a previous upward trend, making agricultural extensification an unlikely explanation for the treatment effects.

It is difficult to directly test the last hypothesis that net forest loss has come from the expansion of land dedicated to settlement and industry, because data on land dedicated to settlement and industry only becomes available in the 2011 Population Census. However, it is implausible that settlement and industrial expansion could explain a 20% reduction in forest cover in a distance band as wide as 100km around a 6000km long highway corridor. In 2011, only 6.7% of of rural land was used for settlement and industry.

Note that the large relative forest losses along the GQ corridor are unlikely to be direct effects of the road construction process. Although we found evidence of these effects in the

> 24Appendix Tables A8 and A9 show complete regression results for all mechanism tests. As would be expected, employment in logging firms is more geographically diffuse, as those firms reach further into the GQ’s hinterland.

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on technology upgrading and innovation. Regarding exports, two important channels are at play. First, a scale effect increases the incentives to adopt new technologies. Bustos (2011) shows how tariff reductions in Argentina in the context of MERCOSUR incentivized firms to adopt new technologies given the larger scale and profits. This positive effect, however, concentrated on firms at the top of the productivity distribution. Lileeva and Trefler (2010) analyze the impact of tariff reductions in the U.S. on Canadian plants and show that this had a positive impact on exporters, especially on lower productivity plants that are export entrants. Thus, the positive scale effect can also benefit lower productivity plants, but only if they enter export markets. A second channel is the “learning” channel. Atkin et al. (2017) conduct an experimental design with Egyptian rug producers by randomly assigning export contracts and find an increase in quality and learning for those producers that get the export contract.

In sum, this literature finds that there is a positive “learning” effect, which also applies to imports of intermediates, and a “scale” effect for exporters that increases their incentives to upgrade their technologies. The evidence summarized in Shu and Steinwender (2019) finds some evidence for all these channels, with some of the positive effects concentrated among more productive firms.

Identifying the sign and magnitude of the effects of exporting on technology adoption is challenging for three reasons. First, disentangling the causal direction of these effects is difficult, given that more productive firms tend to export and participate in international markets and, accordingly, are more likely to be technologically sophisticated. In addition, in preparation for exporting, firms may upgrade their technologies to generate competitiveness gains and quality upgrades, allowing them to export. A second challenge is the lack of data on technology use. Most of the evidence focuses on indirect technology measures such as patents or R&D; only Bustos (2011) and Lileeva and Trefler (2010) use direct technology measures. Third, the use of technology is multidimensional in its application to different business functions. Establishments use different technologies for different tasks, and even within the same business function. Thus, the export effects on technology adoption may differ for different tasks and technologies.

In this paper, we aim to narrow the existing gap in the literature in understanding the relationship between exporting and the technology gap. We use a unique and novel database, the Firm-level Adoption of Technology (FAT) survey and explore the impact of exporting on technology sophistication and the adoption of selected individual technologies. The survey includes more than 1,500 firms in Brazil and provides granular information on the adoption of more than 300 technologies for different business functions as well as participation in international trading activities.

2
information beyond their textbooks, is a key factor in increased learning achievement. How to develop good learning habits and motivation among students should be a challenge not only to teachers but also to parents.

- CM's assessment should not be made only on the basis of whether it helps to provide better pay for good teachers and retain them, but also on whether it pushes bad teachers to improve.  Testing this assessment will require a panel data of teachers.

- Linking teachers' pay to the rate of growth (not the level) in their students' grades in standardized tests would require a panel of students. Lopez-Acevedo (1997) shows that teachers’ salary is weakly correlated to changes in learning achievement.

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# **Figure A1. Investment growth slowdown**

_Investment growth in EMDEs has slowed sharply since the global financial crisis. In 2015, the share of EMDEs with investment growth below its long‐term average reached its highest level except during global downturns. Long‐term forecasts suggest continued weakness in investment growth. Weak investment growth has been accompanied by anemic productivity growth, compounding risks that the income catch‐up of EMDEs to advanced economy (AE) levels may slow._

## **B. Quarterly investment growth: EMDEs**

## **A. Investment growth**

<!-- Start of picture text --> Percent<br>1990-2008 average<br>12<br>2003-08 average<br>8<br>4<br>0<br>EMDEs AEs World<br>2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> <!-- Start of picture text --> Year-on-year, percent EMDEs<br>15 1990-2008 average<br>2003-08 average<br>10<br>5<br>0<br>2010Q1 2011Q1 2012Q1 2013Q1 2014Q1 2015Q1 2016Q1<br><!-- End of picture text -->

## **C. Share of EMDEs with investment growth below its long‐term average**

## **D. Five‐year‐ahead forecasts of investment growth**

<!-- Start of picture text --> Percent<br>EMDEs AEs World<br>10<br>8<br>6<br>4<br>2<br>0<br>2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> <!-- Start of picture text --> EMDEs AEs World<br>Percent of EMDEs 10<br>100<br>8<br>80<br>6<br>60<br>40 4<br>20 2<br>0 0<br>E. Share of world investment F. Change in TFP and investment growth, 2010‐15<br>Percent EMDE excluding BRICS Percentage points<br>Other BRICS 30<br>50 China 20<br>10<br>40<br>0<br>30<br>-10<br>20 -20<br>-30<br>10<br>-40<br>-15 -10 -5 0 5 10<br>0<br>1993-1999 2000-08 2010-15 Change in TFP growth<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2010 2011 2012 2013 2014 2015 2016<br>growth<br>Change in investment<br><!-- End of picture text -->

## **F. Change in TFP and investment growth, 2010‐15**

Sources: Consensus Economics; Haver Analytics; International Monetary Fund; Oxford Economics; Penn World Tables; World Development Indicators, World Bank.

A. Weighted averages. Long‐term average starts in 1991 for EMDEs due to lack of earlier data.

B. Weighted averages. Includes 28 EMDEs with available quarterly data. Long‐term averages start in 1991 for EMDEs and are based on annual data. Last observation is for Q2 2016.

- C. Long‐term averages are country‐specific and refer to 1990‐2008. Latest year is 2015.

- D. Each line shows five‐year‐ahead Consensus Forecasts as of the latest available month in the year denoted. Unweighted averages of 21 EMDEs and 25 advanced economies. World sample includes 46 countries. Latest data is as of October 2016.

E. Each column shows the period average of the share of global investment contributed by each respective group denoted. The world sample includes 100 EMDEs and 34 AEs.

F. Correlation of change in investment growth from 2010‐15 with change in TFP growth over the same period. Red dotted line denotes the linear regression line. Includes 40 EMDEs.

3