rafmacalaba/datause-encoder-data huggingface dataset:
WPS3784 particularly strong impacts on small farms which support the poorest households. For example, facilitating the shift out of subsistence into coffee production for those farmers that have a propensity to produce coffee would reduce the national poverty headcount index by more than a quarter. However, substantial barriers to rural development will constrain movements out of subsistence into commercial production. The key barriers are the lack of availability of credit in rural areas to finance the transition and lack of knowledge of relevant techniques for cultivating and preparing new crops. While these constraints remain and transport costs are high the poverty reducing potential of trade in Rwanda cannot be realized. Rwanda is severely constrained in addressing these structural barriers, which should be a focus of assistance from the international community.

The rest of the paper is organized as follows. The next section analyzes the main determinants of poverty in Rwanda, emphasizing the role of trade costs. Sections 3 and 4 examine the impacts of lower transport costs and higher quality coffee on poverty, respectively. Section 5 looks at the structural implications of those two simulations on the rural economy. It highlights the poverty impact of switching from subsistence to more commercial agriculture and open trade. The main policy implications and conclusion are drawn in section 6.

# **2. Trade Costs as Key Determinants of Poverty in Rwanda**

The first step in putting in place policies and programs to reduce poverty is to gain an understanding of its key determinants. We investigate here the role of trade costs, among other key determinants, in allowing individuals to escape poverty in Rwanda. To do this, we estimate the determinants of the probability of not being poor in Rwanda through a probit regression, using the Rwanda Household Living Conditions Survey (EICV). The EICV was carried out between October 1999 and July 2001 and it contains community characteristics indicators together with information from 6,420 households distributed among Rwanda’s 12 provinces.<sup>1</sup> The overwhelming majority of the households interviewed live in rural areas, where the incidence of poverty is highest. Indeed, the

> 1 The sample design contains three main strata: (i) rural prefecture; (ii) urban Kigali and (iii) other prefecture urban centers. The rural prefecture stratum is further subdivided into 11 sub-strata corresponding to each rural prefecture.

3
This data were collected for all women who were the primary caregivers of children between 0 and 8 in treatment and control households. Mental health was measured using the Center for Epidemiological Studies Depression scale (CESD). The CESD is a widely-used measure of depression (Radloff, 1977), and consists of 20 questions on self-reported depression. Besides calculating an aggregate depression score, these questions also allow to look separately at positive and negative feelings, including expectations about the future. Finally, the questionnaire also included direct questions about aspirations and perceptions on upward mobility. This allows us to consider the relationship between attitudes towards the future and social interactions.

In addition to the quantitative data, qualitative work preceded each round of data collection. The qualitative work consisted of focus groups and semi-structured interviews with a wide set of beneficiaries and other local actors in treatment and control communities, and in municipal headquarters, and explored qualitative evidence of the program’s impacts as well as issues related to program implementation (Aguilera et al., 2006).

# _3.2 Identifying social interactions_

Our identification strategy relies on two key program design elements, namely the randomized allocation of beneficiaries to one of the three program packages, and the random allocation of these same packages among local female leaders. This allows us to explore whether program impacts depend on social interactions between beneficiaries and leaders.

Table 1 presents the randomization results for the full sample of eligible households. The baseline differences between treatment and control communities are generally small and not statistically significant. The last three columns in Table 1 show the P-values for differences between the three intervention packages (i.e. the result of the participatory lotteries in the communities). As expected, there are only a small number of statistically significant differences and the direction of the differences suggests there is no systematic bias.

The variables used to identify social interaction effects rely on the random allocation of female leaders to one of the three intervention groups. We consider both _promotoras_ and other women with leadership positions. Specifically, information was collected for each household member on 13
"/Cross-sectional regression of PE on per capita GNP (in $000) yields the following result: PE = 0.36 GNP + 0.59 R<sup>2</sup> = 0.32 (0.09) This estimate was used to normalize each country's PE to the sample's median per capita GNP level ($630); hence: PE = PE - 0.36 ' (GNP - 0.63) k/Excluding Botswana (extremely high PRM) and Nigeria (very low SRA, reflecting only federal budget).

Sourc: World Development Report 1989.

Table 2: Educatiofl Sector Resourc Availabilit I-HGH SRA LOW SRA **HIjGHi** Botswana Cameroon MRM CAR Guinea C6te d'Ivoire Jordan Costa Rica Lesotho Honduras Malawi Kenya Moazambique Morocco Nigeria Senegal Tunisia Zimbabwe Turkey Zambia **_LIW_** Burkina **Faso Bangladesh** ERM Colombia Bolivia Ghana Haiti Guatemala Madar Mali Mexico Niger Palistan Peru Sierra **Ieone** Philippines Tanzania Rwanda

# Note: Low-income countries are in bold.

'/Federal spending on eiucation is only about one-third of total sector spending. Nigeria would be a high SRA country if state expenditures were taken into account.

72
obtained from data generated by different survey methods. At a practical level, they also underline the importance of consistency and best practice in survey design.

# **6. Conclusion**

This paper investigates whether survey design matters for estimating returns to education using a field experiment, and finds that it does. Using a randomized intervention that implemented two variations of survey design, namely use of a commonly used short versus a detailed labor questionnaire, and self‐response compared to response by proxy, we find that estimated returns to education differ dependent on the survey instrument, but not on the type of respondent, for both men and women. The short questionnaire leads to biased estimates of returns to education relative to the detailed questionnaire. The biases are substantial and significant, resulting in higher estimates in the short questionnaire, ranging from 6 and 8 percentage points higher for tertiary educated men and women respectively, to 14 percentage points higher for primary educated women. These results are robust when accounting for non‐linearity of education and taking both endogeneity of education and selection into wage work into account, making use of commonly applied estimation and identification methods. The results are consistent with suggestive evidence from qualitative research, including respondent debriefing studies in the US which show that screening questions can have important effects on the labor statistics they generate.

These observed differences are of a similar magnitude as the estimation bias related to endogeneity which is the subject of considerable attention in the literature; they are also of a similar magnitude as the differences in estimated returns between gender, levels of education, across sectors (public, formal private and informal private sectors) observed in the region, and therefore deserve attention.<sup>36</sup> While this paper does not aspire to obtaining more accurate estimates of returns to education for Tanzania ‐ the data was not collected with this aim in mind and using standard questionnaires, and the sample is also small and not nationally representative ‐ it is useful to consider the existing results for Tanzania, in particular because they rely on data from different surveys. Nerman and Owens (2010), using the 2001 and 2007 waves for the nationally representative

> 36 A review of key overview papers for the region indicates differences between 2 and 18 percentage points across these dimensions (see Teal and Baptist 2014; Schultz 2003; Kuepie et al 2009).

20
evidence of a slowdown in recent years. Variance has declined since the mid-1990s, and actual growth has tended to be above and closer to the trend (table 3).

<!-- Start of picture text --> Figure 4: Per capita income Figure 5: Growth rate of per capita income<br>2800 8<br>4<br>2400<br>0<br>100 8<br>2000 -4<br>4<br>0 0<br>-4<br>-100 -8<br>1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br>GDP per capita Trend Cycle Growth Trend Cycle<br>Source: Arbache and Page (2007a) Source: Arbache and Page (2007a)<br>Notes: Right axis = per capita income; left axis= cycle of p.c. income Notes: Right axis = growth of p.c. income; left axis= cycle of<br>growth rate<br><!-- End of picture text -->

**_A trend break in 1995._** At the regional level, growth in per capita income increased substantially during 1995–2005 and was accompanied by a sharp reduction in the coefficient of variation. This story is consistent with that about aggregate GDP growth discussed earlier. The per capita growth rate rose from −0.23 percent of the previous decade to 1.88 percent during 1995–2005, and the absolute value of the coefficient of variation fell  from (-25.5) percent to 3.2 percent (table 1.3). This shift implies an increase of 2 percent in growth, which is about three times the long-term growth rate of 0.7 percent. Income per capita went up to $2,486 in 1995–2005, which represents an increase of about $300, or 11 percent, compared with previous periods. Recursive residual estimations, Chow breakpoint tests, and Chow forecast tests do not reject the hypothesis that a structural break in the growth series occurred between 1995 and 1997 (Arbache and Page 2007b

|**Table 3: Per capita in**|**come, mean gr**|**owth, and variation fo**<br>**Growth rate**<br>|**r different period**<br>|**s in Africa**|
|---|---|---|---|---|
|||**Standard**|**Coefficient**||
|**Period**|**Mean**|**deviation**|**of variation**|**GDPper capita**|
|1975-2005|0.70|6.27|8.96|2,299|
|1975-1984|0.13|6.92<br>|53.23|2,180|
|1985-1994<br>|-0.23|5.87<br>|-25.52|2,183|
|1995-2005|1.88|5.99<br>|3.19|2,486|
|1975-1994<br>|-0.07|6.33<br>|-90.43|2,182|
|(1995-2005) minus (1985-94)|2.11|0.12<br>|28.71|303|
|(1995-2005)minus (1975-94)|1.95|-0.34<br>|93.61|304|
|Source:Arbache andPage (2007a)|||||

10
Table 9: Product dropping

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Log Export Value|-.212***||-.192***||-.147***|
||(.005)||(.005)||(.006)|
|Share of Product||-.736***|-.478***||-.534***|
|in Total Exports||(.028)|(.028)||(.035)|
|Log Years of||||-.206***|-.146***|
|Export Experience||||(.010)|(.010)|
|Year FE|Yes|Yes|Yes|Yes|Yes|
|Clustered (frm-level) SE|Yes|Yes|Yes|Yes|Yes|
|No. Obs.|32,217|32,217|32,217|15,367|15,367|

Notes The dependent variable is equal to one if producer p discontinues exporting product i at time t, and zero otherwise. All explanatory variables are lagged one period.

# 5 Information asymmetries, trust and search costs

Recent theoretical (Rauch and Watson 2003) and empirical contributions (Besedes and Prusa 2006) points towards the importance of information asymmetries, trust and search costs as crucial determinants of trade patterns. In this section, we compare some of the implications of these models with descriptive statistics and stylised facts emerging from the analysis of Mexican firm-product level data.

## 5.1 New exporters start small . . .

Firms entering foreign markets for the first time start small in terms of the number of exported varieties and the export volume. This is illustrated in Figure 5 which relates the average number of export varieties to the firm’s experience in export markets.<sup>6</sup> The year when a firm begins to export is identified by zero on the X-axis. As illustrated with the dashed line, new exporters begin their sales in foreign markets with one or two export varieties (1.7 varieties on average). Two years later the average number of exported varieties increases to 2. The total number of varieties sold by the firm (continuous line) increases slightly in the year the firm enters foreign markets and remains stable afterwards. This suggests that most firms enter foreign markets by introducing varieties already sold

> 6New exporters are defined as firms which were not exporting in the previous year and are exporting in the current one

17
Policy Research Working Paper 10324

# **Abstract**

An increasing number of regional trade agreements contains provisions that ease access to visas among member countries, which reduces the administrative cost of crossing the border. Combining United Nations data on bilateral stocks of immigrants in the period 1990–2020 with World Bank data on the content of 279 regional trade agreements, this paper presents robust evidence of a positive effect of visa provisions in regional trade agreements on bilateral migration: the presence of visa provisions in regional trade agreements increases the bilateral stock of immigrants by 5.9 percent. This result is robust to an instrumental variable strategy addressing the endogeneity problem. The effect of the inclusion of visa provisions in regional trade agreements is particularly effective among country pairs with different income levels (such as North-South). For this type of country pairs, the presence of visa provisions in regional trade agreements increases the bilateral stock of immigrants by 13 percent. Finally, the paper shows that the effectiveness of visa provisions in regional trade agreements reduces with the anti-immigration sentiment of voters in the destination.

This paper is a product of the Development Research Group, Development Economics and the Macroeconomics, Trade and Investment Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at anthonin.levelu@dauphine.psl.eu, amm223@georgetown.ed, and gianluca.orefice@dauphine.psl.eu.

_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
9 Sidorenko, A.A., and McKibbin, W.J. 2006. Global macroeconomic consequences of pandemic influenza. Washington, DC: The Brookings Institution: <u>https://www.brookings.edu/research/global-macroeconomic-consequences-of-pandemicinfluenza/; Fan, V.Y., Jamison, D.T., and Summers, L.H. 2018. Pandemic risk: how large are the</u> expected losses _?_ Bulletin of the World Health Organization 96(2): 129-134: <u>https://www.who.int/bulletin/volumes/96/2/17-199588/en/</u> 10 Jordà, O., Singh, S.R., and Taylor, A.M. 2020. Longer-Run Economic Consequences of Pandemics. Federal Reserve Bank of San Francisco Working Paper 2020-09: - - <u>https://www.frbsf.org/economic research/files/wp2020 09.pdf</u> 11 Hamermesh, D.S. 2020. Coronavirus and the labor market. IZA World of Labor Opinion: <u>https://wol.iza.org/opinions/coronavirus-and-the-labor-market</u> 12 Jordà, O., Singh, S.R., and Taylor, A.M. 2020. Longer-Run Economic Consequences of Pandemics. Federal Reserve Bank of San Francisco Working Paper 2020-09: - - <u>https://www.frbsf.org/economic research/files/wp2020 09.pdf</u> 13 Almond, D. 2006. Is the 1918 influenza pandemic over? long-term effects of in utero influenza exposure in the post-1940 U.S. population. Journal of Political Economy 114(4): 672-712: <u>https://www.jstor.org/stable/10.1086/507154?seq=1#metadata_info_tab_contents; Correia, S.,</u> Luck, S., and Verner, E. 2020. Pandemics Depress the Economy, Public Health Interventions Do Not: Evidence from the 1918 Flu. Available at SSRN: <u>https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3561560</u> 14 <mark>Keogh-Brown, M.R., Smith, R.D., Edmunds, J.W., and Beutels, P</mark> _<mark>.</mark>_ <mark>2010. The macroeconomic impact of pandemic influenza: estimates from models of the United Kingdom, France, Belgium and The Netherlands. Eur J Health Econ 11: 543–554: https://doi.org/10.1007/s10198-009-0210-</mark> <u>1.</u> 15 Oreopoulos, P., von Wachter, T., and Heisz, A. 2012. The Short- and Long-Term Career Effects of Graduating in a Recession. American Economic Journal: Applied Economics 4(1): 1-29: = <u>https://www.aeaweb.org/articles?id 10.1257/app.4.1.1</u>

> 16 <mark>Zhang, S. 2018. Effects of High School Closure on Education and Labor Market Outcomes in Rural China. Economic Development and Cultural Change 67(1): 171-191:</mark> <u>https://www.journals.uchicago.edu/doi/abs/10.1086/697564</u>

17 Rosenzweig, M.R. 1995. Why are there returns to schooling? American Economic Review 85:153-158: <u>https://www.jstor.org/stable/2117910?seq=1</u> 18 Card, D. 2001. Estimating the return to schooling: Progress on some persistent econometric problems. _Econometrica_ 69(5): 1127-1160: <u>https://onlinelibrary.wiley.com/doi/abs/10.1111/1468-0262.00237</u> 12
# **_Welfare effects_**

So far we have seen that flood prone Upazilas, and local rainfall variability pushes households to diversify their occupational choices. We have also seen that policy action variables like access to credit, safety net, and market may reduce or eliminate the need to diversify that stems from either living in flood prone Upazilas, or living in Upazilas with high rainfall variability.

To test the hypotheses that household members accept less productive occupations to cope with local rainfall variability, or flood, we focus on per capita consumption. We expect households living in high rainfall variability Upazilas to have lower per capita consumption welfare.

A similar argument can be made about flood risks and low productivity leading to low consumption. However, others have shown that “normal” flood may increase productivity and wage rates (Islam et al 2004, Quasem 1992, Banerjee 2007). Thus, productivity loss from diversification may be partly or fully compensated by the productivity gains from “normal” flood.

The key results of equations (3) are presented in Table 4. Columns 1, 2, and 3 of Table 4 show the estimation coefficients for the household consumption equation (3) for the pooled sample and for historically non-flood prone, and flood prone Upazilas respectively. In the pooled sample (column 1) the coefficient of the flood prone indicator variable has the right sign but is not statistically significant. That is, households living in historically flood prone Upazilas do not appear to have lower (or higher) consumption as compared with those in non-flood prone Upazilas after controlling for other factors.

The insignificant coefficient of the flood prone indicator may be explained by two reasons. First, the beneficial effects of low level floods may compensate the risks of high flood risks to some extent. Second, the ability to diversify occupational choices within household in the historically flood prone Upazilas as evidenced in Table 2 may allow households to better protect their consumption welfare.

**Table 4: Effects of flood and local rainfall variability on consumption welfare**

||Sample:|(1)<br>Overall|(2)<br>Non-flood|(3)<br>Flood|
|---|---|---|---|---|
|Flooded Upazilas 1998||0.0141|||
|||(0.734)|||
|CV Rain CRU (β4)||**-1.109*****|**-1.642*****|-0.432|
|||(-2.891)|(-4.020)|(-0.625)|
|Observations||7,697|3,886|3,811|

Robust t-statistics in parentheses based on PSU level clusters. *** p<0.01, ** p<0.05, * p<0.1. Full estimation results are in annex Table A9.

The effects of local rainfall variability on consumption welfare is negative and significant in the pooled sample (Column 1) as well as in non-flood prone Upazilas (Column 2) and in flood prone Upazilas (Column 3). Thus households in Upazilas with higher rainfall variability have lower consumption after controlling for other factors. Beneficial effects of low level floods may compensate for some of the local rainfall variability as the size of the coefficient of local rainfall variability for flood prone Upazilas (Column 3) is 22 percent smaller than that for the non-flood prone Upazilas (column 2).

17
period between 1991 and 1998. The only exception among the large states was West Bengal. Second, in 10 of those 14 cases the proportion of that change which occurred within industries was strongly positive (over 60 percent), providing a preliminary indication of SBTC. Third, despite the similar sign of these changes, the size varied considerably across regions. This last finding—of diversity in skill upgrading—mimics the popular perception of widely different growth in manufacturing across regions.

**Table 4: Changes in Wage-bill Shares by State and Period**

|**State**<sup>*****</sup>|∆ Sn|**84-89**<br>% within|% ∆<br>wn/wp|∆Sn|**90-98**<br>% within|% ∆<br>wn/wp|**1998**<br>**Employment**<br>(1000s)|
|---|---|---|---|---|---|---|---|
|Andhra Pradesh|-0.91|125.62|-8.49|0.43|45.34|0.90|985|
|Assam|0.05|55.81|0.70|-0.33|111.58|-1.43|132|
|Bihar|-0.80|116.90|-2.33|0.26|143.20|-0.49|262|
|Chandigarh|-0.37|-25.32|-2.96|0.82|124.42|-0.20|11|
|Delhi|0.33|-51.82|-2.14|0.64|116.21|1.80|128|
|Goa, Daman and Diu|-0.94|69.80|-2.66|--|--|--|45|
|Gujarat|0.39|-60.98|-1.72|0.89|51.34|1.80|825|
|Haryana|-0.61|109.84|-5.22|0.82|67.60|1.91|277|
|Himachal Paradesh|-0.90|80.16|-0.77|0.85|55.57|2.54|38|
|Jammu and Kashmir|1.18|56.87|0.22|-0.02|-672.83|0.76|24|
|Karnataka|0.16|-120.96|-1.60|0.52|120.53|3.61|582|
|Kerala|0.18|18.22|-2.44|0.03|91.35|-3.67|360|
|Madhya Paradse|0.20|-8.72|0.82|-0.11|182.23|-1.02|380|
|Maharashtra|-0.43|144.10|-1.49|0.53|94.38|2.57|1,423|
|Orissa|0.06|-230.79|3.76|-1.44|99.53|-4.26|142|
|Punjab|-0.33|139.48|5.25|0.88|96.58|2.42|332|
|Pandicherry|0.89|-33.03|3.84|1.22|27.13|7.22|35|
|Rajasthan|0.84|72.33|0.91|0.13|-67.10|-0.31|242|
|Tamil Nadu|-0.44|78.40|-1.56|0.14|170.83|0.53|1,203|
|Uttar Pradesh|0.37|-0.78|-0.59|0.30|87.35|0.25|674|
|West Bengal|-0.55|83.01|15.68|-0.07|-109.40|0.86|760|
|**Total**|-0.23|175.31|0.26|0.37|89.45|1.07|8,900|

**Notes:   1. ∆** Sn is the change in non-manual wage-bill share × 100. The percentage “within” is the percentage of the change due to within industry increases, according to the decomposition discussed in the text. The percentage change in the relative wage of non-manual workers is also an annual average.

**2.** The table omits six state groupings with less than 10,000 employed persons in manufacturing: Dadar, Nagar and Haveli, Tripura, A&N Islands, Meghalaya, Nagaland and Manipur.

19