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38
"Dead Firms Walking" 2004. _The Economist._ September.

Feldman, Robert. 2004. "Japan Gets Ready to Surf A Wave of Mergers." _Financial Times_ , Nov. 3 Finn, Bridget. 2004. "Material World." _Business 2.0_ , Oct.

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39
Table 2: Regression on the gender disparity in mortaLity, enroLlment, and treatment

|VariabLe|<br>Chi|ld Mortali|ty (4q1)|Enr|oLLed in s|chool|No treatm|ent for fe|ver or ARI|
|---|---|---|---|---|---|---|---|---|---|
||All*|Non<br>South<br>Asia|South*<br>Asia|<br>ALL*|Non<br>South<br>Asia|South*<br>Asia|<br>AlL*|<br>Non<br>South<br>Asia|South*<br>Asia|
|Income<br>(ln)|-0.070<br>(-1.34)|-0.021<br>(-0.30)|-0.122<br>(-0.43)|0.117<br>( 2.83)|0.114<br>t 1.46)|0.203<br> t1.67)|0.006<br>( 0.06)|-0.025<br>(-0.12)|-0.128<br>(-0.36)|
|Dummy for<br>SthAsia|0.240<br> (3.31)|**.**<br>t<br>**.)**|**.**<br>**(**<br>**.)**|-0.156<br>(-3.22)|**.**<br> C<br>**.)**|**.**<br>**C**<br>**.)**|0.279<br>(2.17)|(<br>**.)**|(<br>**.)**|
|Rur. Pop.<br>Ag. land|-0.013<br>(-1.09)|0.010<br>( 0.57)|-0.039<br>(-1.38)|0.021<br>( 2.43)|0.026<br> (1.35)|0.007<br>C 0.58)|-0.012<br>(-0.57)|0.080<br>( 1.49)|-0.030<br>(-0.83)|
|Labor frce<br>in agric|-0.171<br>(-1.31)|-0.031<br>(-0.15)|-0.077<br>(-0.25)|0.177<br>(2.01)|0.316<br>( 1.55)|0.148<br>( 1.20)|-0.228<br>(-0.98)|-0.801<br>(-1.56)|-0.043<br>(-0.11)|
|Share<br>Muslim|0.021<br>C 0.34)|-0.004<br>(-0.06)|0.540<br>(1.18)|-0.289<br>(-5.72)|-0.434<br>(-5.47)|0.098<br>(0.49)|0.016<br>(0.12)|0.240<br>(1.12)|-0.002<br>( 0.00)|
|Share Ag<br>Wheat|1.280<br>( 5.84)|1.013<br>( 1.76)|1.409<br>(3.24)|-0.127<br>(-0.84)|0.561<br>(0.88)|-0.145<br>(-0.77)|0.197<br>( 0.53)|-2.410<br>(-1.30)|0.405<br>(0.74)|
|Share Ag|0.159|-0.036|0.053|0.077|1.016|0.048|-0.236|-1.614|-0.345|
|Rice|(1.11)|(-0.12)|(0.17)|( 0.80)|( 2.28)|(0.40)|(-0.97)|(-1.16)|(-0.88)|
|Share Ag<br>Maize|0.060<br>(0.19)|0.102<br>(0.29)|-1.323<br>(-1.01)|-0.028<br>(-0.12)|-0.667<br>(-1.97)|-0.104<br>(-0.20)|0.507<br>(0.86)|0.519<br>(0.50)|0.121<br>(0.06)|
|Dummyfor|**.**|.|-0.422|.|.|-0.262|.|.|-0.481|
|<br>Pakistan|(<br>**.)**|(<br>**.)**|(-0.81)|C<br>**.)**|(<br>**.)**|(-1.28)|(<br>**.)**|**(**<br>.)|(-0.74)|
|Dummy for<br>India|<br>**.**<br>C<br>**.)**|.<br>**C**<br>**.)**|0.118<br>(0.46)|**.**<br>**(**<br>**.)**|.<br>(<br>**.)**|0.141<br>(1.00)|**.**<br>(<br>**.)**|.<br>**(**<br>**.)**|-0.355<br>(-0.42)|
|Cons|1.581<br>(3.68)|1.131<br>( 1.85)|2.176<br>(1.02)|-0.027<br>(-0.08)|-0.058<br>(-0.09)|-0.879<br>(-0.97)|1.146<br>(1.35)|1.648<br>( 0.90)|2.750<br>( 0.98)|
|R-sq|.6227|.2591|.5099|.4639|.6105|.5074|.1982|.2461|.1762|
|A R-sq|.5796|.1487|.1949|.3859|.5095|.2741|.0490|.0063|-.4416|
|Nun. Obs.|<br>79|55|24|64|35|29|52|30|22|

t-statistics in parentheses

* IncLudes regional subgroupings within India and Pakistan but excludes observations at the national Level for those countries.

45
The cost effectiveness of teacher salaries (normalized to 1) is by far the lowest. Material inputs which provide amenities to the school and teachers, such as teacher tables, toilets, bookcases, have a cost effectiveness on average 7.7 times larger than teacher salaries.

Instructional materials, which provide benefits to teachers only insofar as they increase scores, have cost effectiveness ratios between 17 and 34 times as large as the impact of additional spending on teacher salary increases. The prediction of the simple optimizing model, that cost effectiveness is equalized, is wrong by at least an order of magnitude.

Table **1: Ratio of** test **score gain per dollar in Portuguese and Mathematics for various inputs relative to teacher salary** (= 1), average estimates from Northeast Brazil

||Grand average<br>(ears sbjects|Second gra<br>1981<br>1981|de (Average<br>,83,85)<br>838519838<br>5i|Fourth grad<br>198|e (Average<br>3,85)|
|---|---|---|---|---|---|
||y, u,<br>grades)|<br>Portuguese|,__<br>,<br> I Mathematics|Portuguese|I Mathematics|
|Material inputs||||||
|______<br>Textbook usage|17.7|33.9|22.7|14.5|0.0|
|Writing materials|34.9|11.8|13.8|58.8|55.3|
|Software*|19.4|19.4|13.4|24.5|20.3|
|Infrastructure inputs|<br>|<br>|<br>|||
|lHardware*|[<br>7.7<br>|1<br>3.4|]<br>3.5<br>|<br>16.0||<br>8.1|
|Alternative teacher education strate|gies|||||
|Curso de Qualificacao|5.0|1.2|2.5|_5.0_|11.3|
|Logos|8.3|8.3|6.8|13.5|4.6|
|4 years primary school|6.7|8.5|13.2|0.0|5.3|
|<br>13 years secondary school|1.9|2.5|3.9|0.0|1.5|
|Teacher salary||||||
|Teacher salary|1.0|1.0|1.0|1.0|1.0|

* Hardware: water, bookcase, teacher table, pupil chair, pupil desk, two classrooms, large room, director's room, kitchen, toilet, store cupboard Software: writing material, chalk, notebook, pencil, eraser, crayons, textbook usage Source: Derived from Harbison and Hanushek (1992), Table 6-2

Table 2 provides similar estimates from a large scale study of student achievement in eight states in India (World Bank, 1996a). The cost effectiveness of increasing teacher salaries is again normalized to 1. The cost effectiveness of spending on improving physical facilities is 17
Second, we also had to choose how much to _coarsen_ each covariate (see appendix S2 for an explanation of _coarsening_ ). By way of example, we can choose to coarsen baseline math scores into quintiles, meaning that we can choose to create five equally sized bins of students based on the quintile of their baseline math score. It is by choosing how much to coarsen or bin each covariate (such as baseline math scores) that we can decide ex ante on the maximum amount of imbalance in covariates between the treatment and control groups. In our actual CEM analysis, we choose to coarsen the distributions of each of our baseline exam score variables (computing, math) into six equally spaced bins. We next coarsen age by year (where a year is defined by the calendar of a typical school year, e.g., from Sept. 1, 1985 to Aug. 31 1986). We also configure the CEM procedure to match students within (and not across) prefectures. All of the other covariates in Xij are dummy variables. As with exact matching, the CEM procedure uses the two values of each dummy variable to help create the bins on which we match treatment and control students.

The CEM procedure produces balance across the observable covariates. After applying the matching procedure, the vocational and academic high school students look similar on all of the baseline characteristics in equation 1 (appendix table S3 versus appendix table S4, and appendix table S3 and S4 in the supplemental appendix, available at http://wber.oxfordjournals.org/). As a robustness check, we also coarsen the baseline math and computer exam distributions into finer bins (from six up to fifteen bins each). Although the size of the matched sample decreases with the finer coarsening, we obtain similar results across the various matching specifications. The balance in baseline covariates is not just at the mean but
access only among the richest 60 and poorest 40 percent households; we do not have internet access information for every income level. As a result, our measure could be under-estimating connectivity for the relatively richer, and over-estimating connectivity for the relatively poorer households. To address this concern, we linearly interpolate internet access by income level based on those two estimates for each country to allocate internet. As seen in Figure 2c, this assumption does not change the results significantly either. The share of jobs that can be done in high income countries is identical due to widespread internet access. Similarly, the change in low-income countries is also minimal, but due to overall lack of internet access. The biggest change occurs in middle-income countries. In both upper- and lower-middle income countries, the share of jobs that require but lack internet access declines by around 0.5 percent, increasing the share of teleworkable jobs by the same amount.

Our final exercise uses data from the World Development Indicators (WDI) to allocate internet access. Since the data are not disaggregated by income level, we match the share of internet users by country by allocating connectivity to the highest earners. As an example, if 37% of the population has internet access, we assume that everyone in the top 37% of the income distribution has access and nobody in the bottom 63% has access. This procedure tends to increase the share of occupations amenable to teleworking across all country groups (Figure 2d). Higher wage occupations are more likely to be amenable to teleworking, and this allocation rule gives them preferential access to internet. The increase is about 2.3 percentage points in low-income countries, 5 percentage points in middle-income ones, and 3.1 percentage points in high-income countries. We also recalculated figure 1a under each scenario to see how the results change for each country and the results are available upon request. We see that the different assumptions explored in each of the scenarios (as presented in Figures 2a-d) do not lead to dramatic changes in our estimates, increasing our confidence in our results and further analysis.

The appendix presents additional robustness tests in which we explore how our results change with alternative assumptions about the ability of the self-employed, especially agricultural workers, to work from home. If anything, data from PIAAC surveys suggest agricultural employment in lower income countries is less amenable to working from home than in high-income countries. Based on these patterns, the DN2020 index (and our adaptation of it) does not appear to dramatically underestimate the ability of agricultural workers to work from home in developing countries.

12
tested territories and positive effects of refugee exposure to U.S. allies on mental health. However, it is worth noting that these effects are not statistically significant.

_Robustness and heterogeneous effects._ Table D.3 investigates potential heterogeneous effects of Taliban and indicates that there are no mental health differential exposure by gender effects.

# **V.D Income, consumption, and labor market access**

we examine the effects of Taliban U.S allies and contested Finally, exposure, exposure territories in labor market-related and income-related outcomes in Tables 6 and 7, rethe results effects of Taliban on these spectively. Although suggest negative exposure variables, the coefficients are not statistically significant potentially suggesting precision issues rather than null effects.

# **VI DISCUSSION**

This paper examines the effects of refugees’ exposure to religious extremism on refugees’ integration, educational attainment, mental health, income, and labor markets. For this we data from a census collected in which interviews all the purpose, employ Tajikistan Afghan refugees that fled their country after the Taliban took control of Afghanistan in 2021. Refugees’ exposure to Taliban presence is constructed as an average measure of the share of the territory controlled by the Taliban in the province of origin of each refugee between 2017 and 2021. We also contrast the effects of Taliban with refugees’ exposure that of U.S allies and contested territories exposure.

Our findings suggest that refugee exposure to extremism has significant and adverse implications for their integration, educational attainment, and mental health. Conversely, exposure to U.S. allies tends to have positive effects on these outcomes, although the effects are generally less precise. With regard to the detrimental effects of Taliban exposure on educational attainment, our expectations are confirmed, as these effects are primarily driven by a reduction in female education attainment. Furthermore, the negative impact 24
||**Oil**|**Gas**|
|---|---|---|
|**.**<br>Inflation Rate|3 percent|3 percent|
|Time period|25 yrs|22 yrs|
|Unit Price ($/baril or $/mmBTU)|~100 (2013)|8.5 (2016)|
|Fixed Cost of production (total, $ million)|n.a.|15|
|Upfront Capital Costs ($ million)/Capex|n.a.|600|
|Production (annual, barils or mmBTU)|2.5 mln|15.3 mln|
|Revenue (M$)|~250 mln|~130mln|
|Unit Cost of Production ($/baril or $/mmBTU)|45|2.95|
|**Natural Wealth Per Capital Estimate ($)**|**930**|**550**|

# **_Methodology and context for pasture land wealth estimates_**

For pastureland this paper decided to opt for the calculation of the production of some key pasture products, such as milk (of various animal species), meat, grease, leather, and so on. On this basis, wealth in 2013 was calculated with the following equation: Where W is the wealth of the pasture land sector, P is its unit price, and RR is its total rent.<sup>72</sup> Interviews were conducted in Nouakchott with experts and representatives from the Ministry of Rural Development, which also provided detailed data on meat, milk, and eggs annual production (tons). Lagged, sixyear-average (2007-2012) values of these productions were used to estimate production numbers to project from 2013 over the 25 year generation span. The pasture goods that this paper utilized as a proxy for the whole production are: camel milk, cow milk, goat milk, ovine milk, eggs (number of eggs produced), chicken meat, goat meat, sheep meat, beef meat, and camel meat. Price was computed as the average price of the period 20002012<sup>73</sup> .

> 72 Subscripts _t_ and _i_ represent time and pasture product type, respectively. Methodology and data according to The Changing Wealth of Nations, The World Bank, 2010.

> 73 Source: Ministry of Rural Development of Mauritania.

35
these are important and there seems to be some substitutability between these and the “synergy effect”, nevertheless the inclusion of these variables does not alter our previous results.

# **4.3.2 Information and access to**

We now turn to an exploration of the mechanisms that could explain our results, primarily focusing on the hypothesis that the “synergy effect” is driven by the existence of some “information spillovers”.

First, this synnergy effect could indicate the presence of information externalities. For instance, when technical regulations or buyer policies change in the destination market, exporters may share information about upcoming changes, improving their ability to anticipate and adapt to these changes. Alternatively, buyers may take suppliers from a given country more seriously (and therefore share more information with them or show more flexibility in the face of glitches) when there is a critical mass of them and improve their reliability. If this conjecture is correct, we should expect a stronger synergy effect for products characterized by higher quality heterogeneity for which information asymmetries between buyers and producers are potentially more important. We proxy product _p_ ’s quality heterogeneity by _ρp_ , the coefficient of variation of its FOB unit value across exporters in 2000 (the initial value in our sample) using COMTRADE data, with a higher _ρp_ meaning more heterogeneous quality.<sup>14</sup> The results are presented in column (1) of Table 5. The coefficient on the interaction term _ρp ×_ ln _npdt_ is positive, although significant only at the 10% level, suggesting that the synnergy effect is stronger for products with a high unit-value dispersion, where information is more important.

Given the importance of finance, as shown by the survey discussed in Section 3, an alternative hypothesis could be that while information is still a key determinant of the synergy effect the mechanism behind it could be instead different. Consider the following scenario. A Senegalese firm is approached by a US buyer to provide a small trial order of t-shirts. Upon successful delivery and sale, the buyer is happy and contacts again the Senegalese firm for a larger order. Now the Senegalese firm has to ramp up capacity and,

> 14We explored results on sub-samples split by Rauch’s categories in a table that is available upon request.

25
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an improvement in its coverage of seasonal/circular migrants. At the same time, there are a number of shortcomings. First, while it covers the same households between 2004-05 and 2011-12, it does not specify the year in which household members migrated. It also does not specify the destination of migrants beyond whether they move within or across state boundaries. Secondly, the questionnaire design disregards the fact that quite often entire households and not individuals participate in both seasonal and longer-term migration. Third, the sample size of the survey is much smaller relative to the NSS (and obviously to the census) and is only representative at the state level.

In estimating international migration flows, a new field of research that uses ‘big data’ to proxy for migration flows has emerged. However, most big data sources are limited to online data users, generally biased towards younger, wealthier, and more urban citizens. Therefore, their potential to estimate internal migration flows in a developing country, such as India, may be considered limited. The Government of India’s recent Economic Survey presents an innovative railway passenger data–based migration metric. The key idea is to use monthly data on unreserved passenger traffic between every pair of stations as a proxy for work-related migrant flow. This class of travel serves less affluent people, who are more likely to travel for work-related reasons. It is also relatively unconstrained by capacity, hence reflecting the demand for travel, whereas reserved passenger traffic is more likely to be constrained by the supply of seats. Net annual passenger flows for the last five financial years starting 2011-12 averaged close to 9 million, considerably above levels suggested by the Census.

# **References**

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Bhattacharyya, B. (1985). The role of family decision in internal migration: The case of India. _Journal of Development Economics_ 18 (1), 51–66.

Bryan, G., S. Chowdhury, and A.M. Mobarak (2010). “The Effect of Seasonal Migration on Households during Food Shortages in Bangladesh,” ongoing study, Yale University.

Chandrasekhar, S. and A. Sharma (2015). Urbanization and spatial patterns of internal migration in India. _Spatial Demography_ 3 (2), 63–89.

Connell, J., B. Dasgupta, R. Laishley, and M. Lipton (1976). Migration from Rural Areas: The Evidence from Village Studies, Delhi: Oxford University Press.

23