rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 6226

# **Abstract**

The failure of trade economists to anticipate the extreme drop in trade post Lehman Brothers bankruptcy suggests that the behavior of trade in exceptional circumstances may still be poorly understood. This paper explores whether uncertainty shocks have explanatory power for movements in trade. VAR estimations on United States data suggest that domestic uncertainty is a strong predictor of movements in imports, but has little effect on exports. Guided by these results, the paper estimates a bilateral model with focus on the impact of importer uncertainty on foreign suppliers. It finds that there is a strong negative relationship between uncertainty and trade and that this relationship is non-linear. Uncertainty matters most when its levels are exceptionally high.

The paper does not find evidence of learning from past turmoils, suggesting that prior experience with major uncertainty shocks does not reduce the effect on trade. In line with the expectations, the negative effect of uncertainty shocks on trade is higher for trade relationships more intensive in durable goods. Surprisingly, however, the effect of durability is nonlinear. Supply chain considerations or the possibility that the relationships with the highest durability lead to important compositional effects may have a bearing on the results. The results are robust to excluding the post Lehman shock, suggesting that the trade response during the 2008–2009 crisis has been similar to past uncertainty events.

This paper is a product of the International Trade Department, Poverty Reduction and Economic Management Network. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The authors may be contacted at dtaglioni@worldbank.org or veronika.zavacka@graduateinstitute,ch.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
|Country|Covered<br>employees<br>(%)|Firms subject<br>to union<br>agreements<br>(anylevel,%)|Firms having<br>firm-level<br>agreements (%)|Firms subject to<br>higher level<br>agreements (%)|
|---|---|---|---|---|
|Austria|95|98|23|96|
|Belgium|89|99|35|98|
|Czech Republic|50|54|51|18|
|Estonia|9|12|10|3|
|Spain|97|100|17|83|
|France|67|100|59|99|
|Hungary|18|19|19|0|
|Ireland|42|72|31|68|
|Italy|97|100|43|100|
|Lithuania|16|24|24|1|
|Netherlands|68|76|30|45|
|Poland|19|23|21|5|
|Portugal|56|62|10|59|
|Slovenia|N/A|100|26|74|
|Total|67|76|33|65|
|EU-15|84|94|36|88|
|CEE|24|31|26|9|

Note: Responses are weighted, using employment in each cell as weights.

# **_3.3 Incidence of Wage Cuts_**

Before moving on to examine the reasons for avoiding wage cuts, we must establish that they are indeed rare amongst the firms being examined. The survey provides quantitative information on the proportion of firms that have cut wages and also on the proportions of workers affected by wage cuts in these firms. Specifically, firms were asked if they had ever cut base wages during the past five years.<sup>12</sup> If they responded “yes” to this question, they were further asked what percentage of their workforce this cut had applied to. Firms were instructed to answer the wage-setting questions with reference to their main occupational group, which was defined earlier in the survey.

> 12 In this question firms were asked to refer to a cut in base wages only. Other questions in the survey inquired about other margins of adjustment of their wage bill, (e.g., a reduction in bonuses, hours worked). See Babecký et. al (2012) for a discussion of these other adjustment margins.

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**<u>Table 6: Long-term Effects on Number of Parcels Registered Initially with One or More Female Co-owners</u>**

||<br>**Naïve Regression**|<br>**With Pol**|<br>**icy Reforms**|
|---|---|---|---|
|||**2006 only**|**2006 and 2010**|
|**Panel A: Total registered parcels**||||
|No. registered parcels (NARP)|0.692***|-0.238***|–0.284***|
||(0.011)|(0.057)|(0.055)|
|NARP post-2006||0.935***<br>(0.059)||
|NARP post-2006 & pre-2010|||–0.004<br>(0.071)|
|NARP post-2010|||0.986***<br>(0.058)|
|R-squared<br>|0.871|0.878|0.885|
|**Panel B: Systematic and sporadic separately**<br>||||
|Systematic parcels (NSyst.P),γ3<br>|0.733***|0.734***|0.734***|
||(0.009)|(0.009)|(0.009)|
|Sporadic parcels (NASpP),γ1<br>|0.260***<br>(0.070)|-0.314***<br>(0.052)|–0.338***<br>(0.051)|
|NASpP # post-2006,γ0<br>||0.610***<br>(0.079)||
|NASpP # post-2006 & pre-2010,γ4<br>|||–0.001<br>(0.069)|
|NASpP # post-2010,γ2<br>|||0.674***<br>(0.087)|
|γ3−γ2|||0.060<br>0.089|
|<br>R-squared|0.893|0.896|0.899|

_Note:_ All regressions are for 250m x 250m blocks for 2000–19. The dependent variable is the number of parcels registered for the first time in a given year at block level with a female co-owner. LCSystP denotes the lagged cumulative number of parcels systematically registered and LCSRP the number sporadically registered, with LCSpP instrumented by its lagged value as described in the text. Throughout, population density, share of built-up area of the block, block and year fixed effects, and a constant are included, with coefficients not reported. Standard errors are in parentheses. A two-period lag of the number of parcels registered is used as an instrument. The number of observations throughout is 30,280 (1,514 blocks over 20 years). Mean and standard deviation for the dependent variable are for the pre-program period. Standard errors are in parentheses. * p<0.10, ** p<0.05, *** p<0.010.

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**Table 9: Marginal determinants of non-agricultural employment including inheritance, schooling and parental characteristics**

||(1)<br>All|(2)<br>Rural|(3)<br>Urban|(4)<br>Men|(5)<br>Women|(6)<br>Rural men|(7)<br>Rural<br>women|(8)<br>Urban<br>men|(9)<br>Urban<br>women|
|---|---|---|---|---|---|---|---|---|---|
|Father in farming|0.0239|0.00973|0.0474|0.0496|0.00176|0.0369|-0.0150|0.0414|0.0710|
||(0.0274)|(0.0295)|(0.0389)|(0.0378)|(0.0347)|(0.0468)|(0.0334)|(0.0422)|(0.0625)|
|Mother in farm.|-0.0199|-0.0329|0.0443|-0.0801*|0.0272|-0.114**|0.0125|0.0300|0.0420|
||(0.0318)|(0.0326)|(0.0482)|(0.0445)|(0.0402)|(0.0504)|(0.0377)|(0.0523)|(0.0728)|
|Father in non-farm|0.0486*|0.128***|0.00437|0.102**|0.0237|0.202***|0.0994|0.0353|9.27e-06|
||(0.0294)|(0.0460)|(0.0343)|(0.0436)|(0.0388)|(0.0749)|(0.0616)|(0.0395)|(0.0522)|
|Mother in non-farm|<br>0.135***|<br>0.130**|<br>0.125***|<br>0.176***|<br>0.113***|<br>0.240***|<br>0.0623|<br>0.105***|<br>0.131***|
||(0.0313)|(0.0545)|(0.0302)|(0.0476)|(0.0394)|(0.0889)|(0.0623)|(0.0317)|(0.0470)|
|Father‘s schooling|-0.0479|-0.0960*|0.0114|-0.00532|-0.0591|0.0161|-0.0980*|0.0329|-0.00552|
||(0.0330)|(0.0540)|(0.0356)|(0.0612)|(0.0393)|(0.124)|(0.0555)|(0.0453)|(0.0510)|
|Mother‘s schooling|0.0115|0.173*|-0.0323|0.0843|-0.0237|0.268**|0.135|0.0321|-0.0619|
||(0.0513)|(0.0946)|(0.0559)|(0.0854)|(0.0582)|(0.128)|(0.121)|(0.0692)|(0.0769)|
|Own schooling|<br>0.0483*|<br>0.0814*|<br>0.0276|<br>0.161***|<br>-0.0130|<br>0.247***|<br>0.0266|<br>0.117***|<br>-0.0383|
||(0.0269)|(0.0452)|(0.0297)|(0.0380)|(0.0339)|(0.0747)|(0.0508)|(0.0351)|(0.0432)|
|Inherited land|-0.0121|0.00565|0.0185|0.0154|-0.00516|0.0735|-0.0372|-0.0216|0.0726|
||(0.0317)|(0.0364)|(0.0444)|(0.0440)|(0.0419)|(0.0582)|(0.0426)|(0.0487)|(0.0632)|
|Inherited house|0.0150|-0.0101|0.00849|-0.00725|0.0239|-0.0129|0.00961|-0.0205|0.0250|
||(0.0281)|(0.0352)|(0.0345)|(0.0396)|(0.0351)|(0.0551)|(0.0437)|(0.0383)|(0.0477)|
|Inherited other|0.0392|-0.00184|0.104***|0.0189|0.0783**|0.00184|0.0269|0.0631*|0.152***|
||(0.0279)|(0.0313)|(0.0350)|(0.0372)|(0.0367)|(0.0482)|(0.0390)|(0.0349)|(0.0536)|
|Observations<br>|3,169|1,624|1,538|1,409|1,747|705|893|682|849|
|Pseudo R<sup>2</sup>|0.208|0.142|0.154|0.259|0.179|0.217|0.145|0.149|0.148|

Notes: Robust standard errors in parentheses, clustered at the household level; *** p<0.01, ** p<0.05, * p<0.1. Marginal effects are reported. The regressions include controls listed in Table 5.

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# Bibliography

Fernando M. Aragon and Juan Pablo Rud (2013). ”Natural Resources and Local Communities: Evidence from a Peruvian Gold Mine,” _American Economic Journal: Economic Policy_ , 5(2): 1-25, May Fernando Aragon & Juan Pablo Rud (2012). ”Mining, Pollution and Agricultural Productivity: Evidence from Ghana,” Discussion Papers dp12-08, Department of Economics, Simon Fraser University Manuel Arellano and Stephen Bond (1991). ”Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations.” _Review of Economic Studies_ , 58(2): 277-297.

Abhijit Banerjee, Esther Duflo and Nancy Qian (2012). ”On the Road: Access to Transportation Infrastructure and Economic Growth in China”, MIT (mimeograph) Julia Bird and St ̈ı¿œphane Straub (2014). ”The Bras ̈ı¿œlia Experiment: Road Access and the Spatial Pattern of Long-term Local Development in Brazil,” TSE Working Papers 14-495, Toulouse School of Economics Nathaniel Baum-Snow, Loren Brandt, J. Vernon Henderson, Matthew A. Turner and Qinghua Zhang (2014), ”Roads, Railroads and Decentralization of Chinese Cities”, mimeo Hoyt Bleakley and Jeffrey Lin (2012). ”Portage and Path Dependence,” _The Quarterly Journal of Economics_ , 127(2):587-644 Carranza, E.J.M, Owusu, E.A., and Hale, M. (2009). “Mapping of prospectivity and estimation of number of undiscovered prospects for lode Gold, southwestern Ashanti Belt, Ghana”. _Miner Deposita_ , 44: 915-938 Amitabh Chandra and Eric Thompson (2000). “Does public infrastructure affect economic activity? Evidence from the rural interstate highway system. ”, _Regional Science and Urban Economics_ , 30(4): 457-490.

Walter Christaller (1933). _Die zentralen Orte in S ̈ı¿œddeutschland_ (The Central Places in Southern Germany), Gustav Fischer, Jena.

David Donaldson (2014). “Railroads of the Raj: Estimating the Impact of Transportation Infrastructure”, _American Economic Review_ , (forthcoming) 27
and over) and for the group of all adults. In general the labor force participation of women of all ages is quite low (for no age group do overall labor force participation rates exceed 18%). In addition, the majority of women who do work tend to participate in unsalaried activities: this is particularly true of women over the age of 35. It is interesting to note the decreasing relative participation in salaried work versus unsalaried activities with age of women. Men, on the other hand, show a very high labor force participation rate, over 90% for men between the ages of 24 and 55. The majority of men are salaried workers, although the percentage in salaried work tends to decrease with age. For male workers over the age of 55, almost half participate in nonsalaried activities.

In particular, our sample includes 183,646 individual observations (89,207 observations on adult males and 94,439 observations on adult females) for 4 rounds of the PROGRESA evaluation sample (Nov 97, Oct 98, Jun 99 and Nov 99) that are divided in treatment and control localities. The analysis is first carried on the sample of those who are classified as “poor”<sup>11</sup> , and thus eligible to receive the program if they live in treatment localities (E=1). We then re-do the same estimation on the sample of those who are classified as non-eligible (E=0).

The vector **_X_** in equation (1) consists of individual and demographic composition variables. In particular, we include as individual characteristics his/her age, age squared, marital status, whether he/she is head of the household, speaks an indigenous language, and his/her level of education. The demographic composition variables include the number of children aged 0 to 2 and aged 3 to 5, boys and girls between 6 and 7 years old , 8 to 12, and 13 to 18, men and women aged 19 to 54 and men and women over the age of 55. At the community

> 11 This is the poor status after the _densifcacion_ , the revision of the eligibility that raised the number of households eligible for the program from 52% to 78%.  It has to be noticed that the fraction of households that actually ended up receiving the PROGRESA cash transfers during the two-year interval covered by the evaluation sample is just under 65%, due to administrative errors and delays in the final registration of beneficiary households.

15
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 9. Evolution of seats and city pairs in Niger**

<!-- Start of picture text --> a. Seats b. City pairs<br>450,000 20<br>400,000 18<br>350,000 16<br>300,000 14<br>12<br>250,000<br>10<br>200,000<br>8<br>150,000<br>6<br>100,000 4<br>50,000 2<br>- 0<br>2001 2004 2007 2009 (Est) 2001 2004 2007 2009 (Est)<br>Total International Intercontinental Total International Intercontinental<br>City Pairs<br>Number of seats<br><!-- End of picture text --> _Source_ : Bofinger, 2009.Derived from AICD national database (www.infrastructureafrica.org/aicd/tools/data). _Note_ : As reported to international reservation systems.

The fleet servicing Niger has been renovated. Around 95 percent of the seat capacity is new, largely due to the international nature of the services. Four airlines—Air France, Royal Air Maroc, Afriqiyah Airways, and Air Senegal International—comprised over 80 percent of the regularly scheduled traffic in 2007. In 2009 Air Senegal International, with a market share of 14 percent, ceased operations.

**Table 8. Benchmarking air transport indicators for Niger and select other countries**

|Country|Niger|Mali|Chad|Nigeria|Burkina<br>Faso|Central<br>African<br>Republic|
|---|---|---|---|---|---|---|
|_Traffic (2007)_|||||||
|Domestic seats (seats per year)|n.a.|n.a.|n.a.|9,304,568|20,245|n.a.|
|Seats for international travel within Africa (seats per year)|128,414|564,455|109,074|1,373,745|244,721|20,661|
|Seats for intercontinental travel (seats per year)|41,717|165,776|88,608|2,437,702|147,095|23,842|
|Seats available per capita|0.012|0.061|0.018|0.089|0.027|0.010|
|Herfindahl-Hirschmann Index—air transport market (%)|18.97|11.75|36.35|71.4|22.89|50.26|
|_Quality_|||||||
|Percent of seat-km in newer aircraft|94.3|95.6|99.5|71.4|93.4|100|
|Percent of seat-km in medium or smaller aircraft|44.9|51.8|93.6|27.6|46.7|23.5|
|Percent of carriers passing IATA/IOSA Audit|0|0|0|28.6|0|0|
|FAA/IASA Audit Status|No audit|No audit|No audit|No audit|No audit|No audit|

_Source:_ Bofinger, 2009. Derived from AICD national database (www.infrastructureafrica.org/aicd/tools/data).

n.a. = Not applicable.

_Note:_ The Herfindhal-Hirschmann Index (HHI) is a commonly accepted measure of market concentration. It is calculated by squaring the market share of each firm competing in the market and then summing the resulting numbers. A HHI of 100 indicates the market is a monopoly; the lower the HHI, the more diluted the market power exerted by one company/agent.

All data as of 2007 are based on estimations and computations of scheduled advertised seats, as published by the Dusiio SRS Analyzer. This captures 98 percent of worldwide traffic, but a percentage of African traffic is not captured by these data.

FAA = U.S. Federal Aviation Administration; IASA = International Aviation Safety Assessment; IATA = International Air Transport Association; IOSA = IATA International Safety Audit.

18
constant. We also assume that demand remains constant throughout the year and therefore divide annual demand into twelve roughly equal monthly demands.<sup>13</sup>

**Table A-1:Ranges for plausible demand futures (m3/year), Total Lima, 2040**

|||||**Popul**<br>|**ation**||
|---|---|---|---|---|---|---|
||||**SEDAPAL**|**5-Year**<br>**Plan**|**INEI**|**GIZ**|
|||**x**|**_12.5M_**|**_13.3M_**|**_11.7M_**|**_17.3M_**|
|**Water**|**SEDAPAL**|**_83_**|1,031M|1,101M|966M|1,425M|
|**Consumption**<br>**(m3/p/year)**|**5-Year Plan**|**_84_**|1,049M|1,120M|982M|1,450M|
||**INEI**|**_63_**|787M|840M|**737M**|1,088M|
||**GIZ - high **|**_102_**|1,282M|1,370M|1,201M|**1,773M**|

# Generating Streamflow Futures

In contrast to demand, there is significant variation in intra-annual streamflow due to seasonal variation in precipitation at the headwaters of each of these basins in the Andes (Vera _et al._ , 2006). Because of its extensive reservoir storage capacity, however, the system is largely insensitive to month-to-month changes in streamflow and much more sensitive to changes in seasonal in streamflow. Therefore, we represent each streamflow future with two values, one for the dry season (March-November) and one for the wet season (December-February).

Our method of estimating future streamflow is to use a delta change factor to adjust historical monthly flows, consistent with the principles of Decision Scaling (Brown, 2011). Under this method, we apply a range of percent changes to historical precipitation data to obtain projections of future precipitation. The precipitation data is then used in a rainfall-runoff model to estimate future streamflow.

We specify the range of delta factors to exceed that from downscaled climate projections. We use estimates of downscaled precipitation changes from global climate models and historical streamflow data from SEDAPAL. Figure 4-1 summarizes the changes in annual and seasonal precipitation projected by 15 climate models in 3 emissions scenarios. We use the minimum and maximum changes as the ranges for an LHS-generated set of monthly precipitation deltas. That is, each of the 300 futures contains 12 unique values of monthly change factors. Without a rainfall-runoff model, we

> 13 In many parts of the world, variations in temperature and precipitation within a year drive intraannual variations in demand. Demand increases in hotter or drier periods and decreases in cooler or wetter periods. Lima has little variation in intra-annual demand because temperature is roughly constant throughout the year and precipitation is negligible. This is consistent with SEDAPAL’s observations of historical demand.

42
where _xg_ is the consumption of good _g_ and _θg_ is its share of total expenditure. To be consistent across the paper, we work with nine goods, eight traded goods and the non-traded residual sector. We represent those preferences with data on budget shares compiled by the International Comparison Program.<sup>13</sup> Production functions are also assumed to be Cobb-Douglas where _Qg_ is physical output of good _g_ , _Ag_ is a technology parameter, and _Lg_ and _Kg_ are labor and capital respectively. Labor is imperfectly mobile because, as before, workers can move across sectors after paying the moving costs _C_ . Instead, capital is assumed to be fixed as in Artuç, Chaudhuri, and McLaren (2010).<sup>14</sup> The parameters _αg_ are approximated with the share of wage bill in value added at a sectoral level. Assuming each sector pays a wage equal to the marginal product of labor, we then solve for the technology parameters (including differences in capital) where _A_<sup>�</sup> _g_ = _AgKg_<sup>1</sup><sup>_−αg_</sup> . Note that there is an important difference in the treatment of the traded and residual sectors. For the traded sectors, the UNIDO data include wages and employment and thus we can easily recover _A_<sup>�</sup> _g_ . For the residual sector, we only observe _Lg_ . For the purpose of the initial calibration, we thus set the wage to the average wage of the economy.

We focus first on shocks to Food and Beverages. We report the responses of employment allocations and wages for each of the 25 developing countries in our sample in Figures 8 to 15. Each graph shows six responses using solid lines for wages and dashed lines for employment. The responses of the affected sector (Food and Beverages) are plotted with a thick line and the responses of the residual sector, with a medium-thick line. To simplify the presentation of the results, we aggregate all the remaining traded sectors into one. These responses are plotted with a thin line. To streamline the exposition, all our results are presented as proportional changes relative to the initial steady

> 13Details can be found at `http://siteresources.worldbank.org/ICPEXT/Resources/ICP_2011.html`

> 14See Artuç, Bet, Brambilla, and Porto (2012) for simulations with imperfect capital mobility.

13
has achieved superior returns over the period. Furthermore, the Quebec scheme seems to have produced returns which are similar, if slightly lower, than those experienced in the private sector. The 1991 Report of the Committee on the Investment of the Canada Pension Plan Fund concludes that the performance of the Quebec scheme "is competitive with those of the other large funds considered." The table also suggests that the main pension scheme in the Philippines, while volatile, has managed to achieve positive rates of return on average over a long period. While data are not available, the fund of the special scheme for public employees is known to have performed much worse. There have been many reports of wasteful and fraudulent use of those funds. Jordan also maintained a modest positive average rate of return of about 1.6% between 1984 and 1988. On the other hand, recent reports on the Chinese municipal pension schemes cite high, negative real rates of return on pension reserves invested in treasury bills and bank deposits.

Pension Fund Investment Returns: Sources. Data on the investment performance of pension funds around the world are difficult to find for both public and privately-managed pension schemes. Most of the data presented in the table above are based on ILO/ISSA sources. Data for years prior to 1982 for Mauritius, Senegal, Pakistan, Sri Lanka, Fiji, Morocco and Tunisia were taken from the ILO's Report from the Meeting of Experts on the Investment of Social Security Funds, Geneva, 1983. Tunisian returns for 1985 and 1990 were taken from Vittas (1993). Data for the Philippines, Turkey and Jordan were taken from the "Report of the ISSA Regional Meeting for Asia and the Pacific on the Methods of Financing Social Security with Special Reference to Long Term Benefits" published in New Delhi in 1990. Returns for Singapore were taken from Asher (1992). Malaysian EPF dividends were taken from Fry (1992). An internal, unpublished World Bank report on the Egyptian financial sector reported nominal rates of return credited to the Egyptian scheme during the 1980s.

Zambian rates of return were taken from the ILO (1983) for early years and the Zambian Provident Fund Annual Report for years after 1982. The rates of return credited to members were taken from several reports of the National Provident Fund in Kenya. Data for Madagascar's partially-funded scheme came from the ILO report entitled, "Madagascar: Rapport au gouvemement sur la situation actuelle et les perspectives d'evolution de la protection sociale", Geneva, 1991.

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