rafmacalaba/datause-encoder-data huggingface dataset:
not strictly comparable because they refer to different sets of price changes, they do suggest that the scope for substitution is lower at coarser levels of disaggregation.

# **4.  Conclusions**

This paper has empirically investigated the welfare effects of the large depreciation in Egypt between 2000 and 2005 operating through exchange-rate induced changes in consumer prices. I find a significant, and very heterogenous across products, degree of pass-through from the exchange rate to consumer prices. On average, the welfare cost of these price changes was 7.4 percent of households' initial expenditure. Since estimated pass-through for food items was significantly greater than for non-food items, the effects of the depreciation disproportionately affected poor households.

One should however keep in mind three major caveats about these results. The first is that I have looked only at the effects of the exchange rate working through consumer prices. The depreciation is also likely to have had heterogenous impacts on the earnings of households employed in different sectors, and these effects are not capture for lack of (a) detailed information in the household survey of the sector of employment of households, and (b) evidence on the effects of exchange rate changes on wages across sectors in Egypt.

A second limitation is that data limitations have also forced me to work at a fairly high level of aggregation. At this coarse level of aggregation, estimated substitution effects in response to price changes are small, and so I am likely to be overestimating the effects on household welfare. Consider for example the study of the Indonesian depreciation of 1997 by Friedman and Levinsohn (2002). They worked with a much more highly disaggregated set of expenditure items, and found that substitution effects were roughly half the size of the direct effects. If similar substitution behavior occurred for households in Egypt in response to the (much smaller) set of price changes, but was missed at the coarse level of aggregation at which I have worked, then the adverse welfare effects of the depreciation will be considerably overstated and could be much smaller.

20
**Table 3. Poverty rates by geographic area, headship, and displacement**

|||Poverty rate<br>Non-||||Observati<br>Non-|ons||
|---|---|---|---|---|---|---|---|---|
||Settleme<br>nt IDP|settlement<br>IDP|Non-<br>IDP|All|Settleme<br>nt IDP|settlement<br>IDP|Non-<br>IDP|All|
|All|77|77.3|66.5|70|2,220|2,427|21,610|26,257|
|_Geographic_<br>_area_|||||||||
|Urban||62.7|64.1|64||1,562|17,469|19,031|
|Rural||83.5|72.8|76.3||865|4,141|5,006|
|_Headship_|||||||||
|Female|70.8|70|64.8|66.7|1,254|1,009|10,969|13,232|
|_de jure_|68.8|64.4|67.7|67.5|383|292|3,124|3,799|
|_de facto_|70.1|72.2|63.2|65.7|856|690|7,627|9,173|
|Male|84.7|81.1|68|72.9|966|1,418|10,641|13,025|

_Source:_ Own elaboration based on Somali HFS 2017-18 _. Note_ : IDP stands for internally displaced persons. Observations correspond to the number of individuals in the dataset.

# **_6.1. Are female-headed IDP households poorer than male-headed IDP households and maleand female-headed non-IDP households?_**

The incidence of poverty is higher for male-headed households than female-headed ones (73 versus 67 percent) (Table 3). This result is driven by the large gender gap in poverty (against maleheaded households) among IDPs (11-14 percentage points), as well as the smaller but significant difference in poverty between male- and female-headed non-IDP households (3 percentage points). Even when distinguishing between _de jure_ and _de facto_ female-headed IDP households poverty remains lower than male-headed households. For non-IDPs there is no significant 17
|**Table 6: Comparison of Characteristics of High an**|**d Low Profi**|**t Women**<br>Low|High||
|---|---|---|---|---|
||Men|Initial Profit<br>Women|Initial Profits<br>Women|Sri Lankan<br>Women|
|Monthly profits in January 2009<sup>a</sup>|||||
|Mean|130|38|173***|28|
|Median|91|37|137***|20|
|Monthly sales in January 2009|||||
|Mean|502|187|822***|87|
|Median|240|120|500***|50|
|Total Capital Stock in January 2009|||||
|Mean|611|251|456***|207|
|Median|255|102|162***|100|
|Age of Owner|35.4|35.9|37.0|41.1|
|Age of Firm|9.1|6.0|7.4**|9.5|
|Ever had a formal loan|0.07|0.08|0.15**|0.23|
|Keeps accounts|0.45|0.31|0.44**|0.29|
|Years of Education|10.04|7.80|8.63**|9.44|
|Digitspan Recall|5.70|4.59|4.80|5.68|
|Chose sector as it had low capital requirements|0.17|0.40|0.32*|n.a.|
|Chose sector for profit potential|0.18|0.11|0.18**|n.a.|
|Willingness to Take Risks|5.64|4.28|4.40|6.08|
|Save regularly|0.71|0.62|0.73**|0.67|
|Household Asset index|0.29|‐0.40|0.14***|n.a.|
|Household has a Cellphone|0.94|0.88|0.91|0.22|
|**Sample Size**<br>Notes:<br>Means shown unless indicated otherwise. Trimme<br>*, **, and *** indicate high profit women statistica<br>at the 10%, 5% and 1% levels respectively.<br>a. Figures for Sri Lanka are reported as of March 20<br>an approximate exchange rate of 100 Sri Lankan ru<br>n.a. indicates not available in Sri Lankan data.|**290**<br>d subsampl<br>lly differen<br>05 Sri Lank<br>pees to 1 c|**296**<br>e used.<br>t from the low p<br>an baseline, con<br>edi.|**179**<br>rofit women<br>verted at|**190**|
# **Trade Barriers or Catalysts? Non-Tariff Measures and Firm-Level Trade Margins**

Dela-Dem Doe Fiankor<sup>*</sup> Woubet Kassa<sup>†</sup> Abraham Lartey<sup>‡</sup> _Keywords:_ Non-tariff measures, Intensive margin, Extensive margin, Product quality _JEL Classification:_ D22, F14, L15, O13, 014, Q17, Q18

> *University of Goettingen, Germany †World Bank

> ‡SOAS, University of London, UK
<!-- Start of picture text --> rey<br>2<br>cx2<br>a<br>s<br>3° NN]<br>. | —_____—__—__ _~____-<br>o<br>Qe _ OS<br>2000 2005 2010 2015 2020<br>Year<br>— LAC — SAR<br><!-- End of picture text -->

<!-- Start of picture text --> iQ<br>2<br>sg ~<br>aw<br>§<br>20<br>—<br>__|&<br>We a _<br>‘<br>2000 2005 2010 2015 2020<br>Year<br>—— LAC ——— SAR<br><!-- End of picture text -->
7. For each Republic, proxy E (E*) could be estimated, based on data from the International Comparison Program (ICP) or similar studies. Proxy K (K*) could be found from fiscal data on indirect taxes and subsidies for traded and nontraded products or GDP estimates by sectors at factor cost. More specifically, E* can be computed as the ratio of the average domestic prices of major tradables to the average international prices: E* = SUM[wiPt(d)i]/SUM[wiPt(w)i] -Pt(d)/Pt(w)... (6) where wi is weights defined either as [Vi/Pt(d)i]/SUM[Vi/Pt(d)i] with Vi =ruble value of valueadded for, or expenditures on, tradable group i.

K* could be established as the ratio of average "resource costs' for non-traded to traded goods: Let Pn(d)([Vn - tnl/Vn} = (K*) Pt(d){[Vt - tt]/Vt} ... (7), where Vn=ruble value of non-tradable production, tn=net indirect tax on nontradables, Vt=ruble value of tradable production, tt=net indirect tax on tradables. The assumption here is that the resource costs are much less distorted than the "established" prices in FSU Republics.

# To estimate GNP(w) for a FSU Republic,

First, the value added or gross output of the nontradable sectors should be multiplied by [(Vn - tn)/Vn]/[(Vt - tt)/Vt]; i.e., QnPn(d){[(Vn - tn)/Vn]/{[Vt - tt]/Vt}. Let this be QnPn*(d)....(8), which is equal to Qn(K*)Pt(d), from (7) above; Second, (8) is added to the value-added or gross output of the tradable sectors: QnPn*(d) + QtPt(d) = Qn(K*)Pt(d) + QtPt(d) ..... (9).

- Finally, (9) is converted at E* Pt(d)/Pt(w): [QnPn*(d) + QtPt(d)]/E* = Qn(K*)Pt(w) + QtPt(w) = GNP(w)*.

8. For practical reasons, it could be assumed that the tradables are products from agricultural, mining and manufacturing sectors and the non-tradables are those from construction and service sectors.

9. Given the general pattern that the prevailing exchange rate tends to understate significantly the relative purchasing power of the local currency for the economies with price controls, the method discussed in paras 6 and 7 above may be considered as a special case where an alternative GNP conversion rate is sought because of the "overly" appreciated or depreciated prevailing exchange rate.

35
# **Highway to Success in India**

The Impact of the Golden Quadrilateral Project for the Location and Performance of Manufacturing Ejaz Ghani, Arti Grover Goswami, and William R. Kerr January 2013

**Keywords:** Highways, roads, infrastructure, India, development, manufacturing, density, urbanization, spatial development, rent.

**JEL Classification:** L10, L25, L26, L60, L80, L90, L91, L92, M13, O10, R00, R10, R11, R14

_Author institutions and contact details:_ <u>Ghani: World Bank, Eghani@worldbank.org; Grover Goswami:</u> World Bank, <u>agrover1@worldbank.org; Kerr: Harvard University, Bank of Finland, and NBER, wkerr@hbs.edu.</u> _Acknowledgments_ : We are grateful to Montek Ahluwalia, Arunish Chawla, Peter Ellis, Nupur Gupta, Sudhir Krishna, Om Prakash Mathur, Barjor Mehta, Partha Mukhopadhyay, Stephen O’Connell, Onno Ruhl, Hyoung Gun Wang, Ming Zhang and seminar participants at The Planning Commission for helpful suggestions/comments. We are particularly indebted to Sarah Elizabeth Antos and Henry Jewell for excellent data work and maps. We thank the World Bank’s South Asia Labor Flagship team for providing the primary datasets used in this paper. Funding for this project was provided by a Private Enterprise Development in Low-Income Countries grant by the Centre or Economic Policy Research and by World Bank’s Multi-Donor Trade Trust Fund. . The views expressed here are those of the authors and not of any institution they may be associated with.
a worker training program that began in the 1990s, finding short-term effects of the training program of approximately one percentage point increase in employment and seven to eight percent increases in monthly wage rates. Related to the current study, Corseuil et al. (2012) evaluate an apprenticeship-based youth employment program in Brazil, finding that apprentices have a higher probability of getting a formal job in the years after the program.

Causal evaluations of vocational training are available for a newer generation of programs that focus on disadvantaged youth, offer training through private providers, and combine coursework with an internship or work experience at a private firm. Despite remarkably similar in program design, the rigorous experimental evaluations of such programs that exist for Colombia and the Dominican Republic find markedly different results. Attanasio et al. (2011) show that this training in Colombia improved women’s, but not men’s, employment and wages. While their original study was based on a surveyed sample, a more recent study that uses administrative records to track the full applicant pool finds positive effects for both men and women in the short and long run (Attanasio et al., 2017). Results for the Dominican Republic (DR) indicate no effect on employment and only very modest effects on earnings Card et al. (2011). A follow-up study indicates positive results in terms of tested skill acquisition and short term employment for women, a negative effect on employment and no skill acquisition among men, and no long term effects except on women’s expectations and self-esteem (Acevedo et al., 2017).

A strand of the recent literature has argued for the effectiveness of new approaches to reduce unemployment, including cash or capital injections to small businesses or providing business training to potential entrepreneurs (see Blattman and Ralston (2015) for an in-depth review of this literature). Another dimension of this debate focuses on whether existing programs can be reformulated to more closely involve the private sector in determining which skills to provide (World Bank, 2013). Although there are many means by which skills training can be aligned more closely with the needs of the private sector, few of the proposed 4
loans declined from Tk. 909 in 2000 to Tk. 721 in 2007. The decline in loan size is smaller only for dropouts who were members in 2000 but not in 2007 (9.9 percent of sample).

Household borrowing data are used to define the relative market shares of different sources of loans for both years. The market shares are plotted in Figure 2. In 2000, 47 percent of total credit to households came from MFIs, 27 percent from formal banks and 26 percent from informal sources. The market shares have changed dramatically by 2007, with MFIs accounting for 72 percent of total credit. Shares of informal sources halved to only 13 percent, and bank’s share fell to15 percent. In terms of absolute volume of loans, total volume of MFI loans nearly doubled between 2000 and 2007 while it declined for both bank loans and informal loans. In the case of informal loan, its level in 2007 was about 62 percent of its 2000 level. We find similar trends in market shares if we restrict our sample to all households with Tk. 100,000 or less loan outstanding. The evidence thus shows clearly that total loans from informal sources have declined in both absolute and relative terms between 2000 and 2007. The MFIs have driven not only informal lenders out of rural credit markets but also largely filled the gap left by withdrawal of public banks from rural areas.<sup>32</sup>

# **4. CONCLUSIONS**

Using two survey data sets from Bangladesh, we provide evidence on the effects of microfinance penetration into the village credit market, focusing on the effects on moneylender interest rates and household borrowing from informal sources. The implications of MFIs for the rural credit market have been a topic of intense debate among practitioners and policy makers, with sharply opposing views. However, a careful empirical analysis of the effects of the spread of MFIs on moneylender interest rates and household informal borrowing is lacking in the literature.

We consider the possible biases that can result from non-random program placement by MFIs and self-selection by households. It is extremely difficult, if not impossible, to find credible exclusion restrictions to solve identification challenges in the context of microfinance programs. It may also not be feasible to analyze the long-run general equilibrium effects of MFI penetration into rural credit markets by designing randomized interventions. To address selection biases, we develop an empirical approach that takes advantage of recent advances in econometrics that do not rely on exclusion restrictions required in the standard instrumental variables strategy. In particular, we implement the minimum biased normalized IPW estimator proposed by Millimet and Tschernis (forthcoming) and the heteroskedasticity

> 32 Direct evidence on the riskiness of MFI and informal borrowers is not available in any of the datasets on Bangladesh. Moreover, as noted before, sample size with positive informal borrowing is too small to conduct any meaningful empirical analysis.

26
Table 6: Regression coefficients in job switching equation (for fisherman in 07)

|VARIABLES|||Fish in 09|||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|IDIOSHOCK|-0.114**|-0.138**|-0.158**|-0.0881*|-0.145**|
||(0.0540)|(0.0679)|(0.0648)|(0.0531)|(0.0651)|
|AGGSHOCK|0.00203|-0.0151|-0.0663|-0.0643|-0.112|
||(0.0597)|(0.0673)|(0.0769)|(0.0661)|(0.0978)|
|AGGSHOCK * IDIOSHOCK||0.0639||||
|||(0.0884)||||
|Boat aid in 09|0.233**|0.237**||0.225**||
||(0.103)|(0.104)||(0.107)||
|D_bad house in 09|0.0382|0.0380|0.0963*|0.0431|0.0876*|
||(0.0455)|(0.0454)|(0.0536)|(0.0462)|(0.0497)|
|D_large boat pre-tsunami|0.0444|0.0433|-0.0326|0.0272|-0.00530|
||(0.0512)|(0.0512)|(0.0628)|(0.0458)|(0.0605)|
|Boat owner and captain pre-tsunami|0.0397|0.0382|0.142**|0.0610|0.127***|
||(0.0509)|(0.0514)|(0.0570)|(0.0451)|(0.0479)|
|Fraction of fish population in 07|0.121|0.119|0.213*|0.194*|0.130|
||(0.103)|(0.103)|(0.115)|(0.117)|(0.143)|
|Observations|357|357|242|355|241|
|Sample||Su|pplementary& Fish in|07=1||
||All|All|Boat aid in 07=1|All|Boat aid in 07=1|
|Mean of dependent variable|0.762|0.762|0.747|0.763|0.746|
|Seasonality is controlled|Y|Y|Y|Y|Y|
|Sampling heterogeneity is controlled 1/|Y|Y|Y|Y|Y|
|Regional fixed effects|Kab|Kab|Kab|Kec|Kec|

*** p<0.01, ** p<0.05, * p<0.1; All estimates are marginal effects of probit model. Robust standard errors clustered at the village level are in parentheses. The inverse probability of remaining in the sample ("stayers") is used as a weight in Eq. (2) to account for attrition bias and Sampling heterogeneity is controlled by including 1[ _s_orig=1_ ].

AGGSHOCK=Fraction of bad aid boats IDIOSHOCK=D_have a bad aid boat in 09
-fi-

According to the 1990 IFPRI survey, credit accounts for a greater proportion of the expenditure of poor households. Interest rates on informal loans from sources other than friends or relatives are particularly high. Since informal loans are predominantly for consumption, it is not clear that these loans help recipients escape poverty.

The importance of credit availability can be seen by the fact that mean input expenditures per hectare are significantly higher for farmers with credit, regardless of their level of assets. Higher input expenditures are presumably associated with higher productivity growth.

The demand for credit in rural areas arises from agricultural investment, consumption smoothing by households, and nonfarm investment. These components are reflected in the demand for credit to the extent that these requirements cannot be financed from farm savings. Farm savings consists of selffinanced investments, bank deposits, and cash holdings. The savings rate in 1983 - 93 was almost 8 percent, which represents an increase over the savings rate in the 1970s. The increase can be traced to higher incomes, remittances from abroad, and rising land values.

Agricultural investment averages around 7 percent of agricultural valued added or gross domestic product (GDP) and it is estimated that an additional one percent agricultural growth is associated with investment of 2 percent of agricultural GDP. With these parameters, the annual institutional credit need for agriculture (production and investment credit) under the present growth scenario (4 percent annual growth rate) will be about Rs. 15.4 billion annually, which is about the same amount delivered by institutional sources in recent years. Thus, on aggregate, credit does not appear to be a constraint and the situation seems to have improved over earlier years (in the 1973 and 1985 rural credit surveys, the estimated gap between credit availability and need was estimated at 7 to 1 and 3 to 1 respectively).

But the credit situation faces several serious problems. First, as discussed next, the existing institutions delivering formal credit are facing a serious financial crisis and cannot be sustained. Second, as discussed earlier, the present rural finance system does not cover adequately the smallholders, who will be very important for future growth. Third, an increase in the growth rate of agriculture will increase the demand for credit. It is estimated that a 1 percent increase in agricultural output will create a demand for credit of 0.6 percent of agricultural GDP, assuming that farmers continue to self-finance and borrow from non-institutional sources at their previous rate (which would make the total investment up to 2 percent of agricultural GDP associated with a one percent additional growth of agricultral GDP). But while more rapid growth in agriculture raises the demand for credit, it will also raise incomes and thus the
<!-- Start of picture text --> DaR = x<br>100%<br>:<br>: : Case Line cv<br>No — 0<br>- vulnerability<br>Between w= = 0<CVI<1<br>Highest — 1<br>' vulnerability<br>=. x<br>7 6 5 4 3 2 1<br><!-- End of picture text --> DaRsx DaRso
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26
# **Who Are the Net Food Importing Countries?**

Francis Ng and M. Ataman Aksoy<sup>1</sup> JEL classification: F10, F13, Q17, Q18 Keywords: Agriculture, food policy, net food importing countries

> 1 Development Economics Research Group of the World Bank. Email correspondence to: <u>fng@worldbank.org</u> and Maksoy@worldbank.org. The authors would like to thank J. Baffes, D. Mitchell, M. Olarreaga for their helpful comments. The findings, interpretations, and conclusions expressed herein are those of the authors and do not necessarily reflect the views of the World Bank and its affiliated organizations.

1