rafmacalaba/data-use-mentions-extended huggingface dataset:
- Burda, M., D. Hamermesh, P. Weil. (2007). “Total Work, Gender and Social Norms.” Working Paper Series 13000, National Bureau of Economic Research, Cambridge, MA.

- Campaña, J.C., J.I. Gimenez-Nadal, and J.A. Molina. (2015). “Gender Differences in the Distribution of Total Work-Time of Latin-American Families: The Importance of Social Norms.” IZA Discussion Paper 8933, Institute for the Study of Labor, Bonn

- Cascio, E. (2006). “Public Preschool and Maternal Labor Supply: Evidence from the Introduction of Kindergartens into American Public Schools.” Working Paper Series 12179, National Bureau of Economic Research, Cambridge, MA.

- Charmes, J. (2006). “A Review of Empirical Evidence on Time Use in South Africa from UNsponsored Surveys.” In Mark Blackden and Quentin Wodon (Eds.), _Gender, time use, and poverty in Sub-Saharan Africa_ (pp. 39–72). Washington, DC: The World Bank.

- Connelly, R., and J. Kimmel. (2010). _The Time Use of Mothers in the United States at the Beginning of the 21st Century_ . Kalamazoo, MI: W.E. Upjohn Institute.

- Craig, L. and K. Mullan (2010). “Parenthood, Gender and Work-Family Time in USA, Australia, Italy, France and Denmark.” _Journal of Marriage and Family_ 72(5), pp. 1344–1361.

- Craig, L., and K. Mullan (2009). “The Policeman and The Part-Time Sales Assistant: Household Labour Supply, Family Time and Subjective Time Pressure in Australia, 1997–2006.” _Journal of Comparative Family Studies_ , 40(4), pp. 545–560.

- Dayioglu, M.; K., M. Kirdar. (2010). “Determinants of and Trends in Labor Force Participation of
There is strong evidence that children of better educated parents in developing countries are healthier and our results are consistent with this evidence. Although a reduced-form framework does not permit us to infer the mechanisms through which parental education affects child anthropometry, our findings show that these effects are robust to the inclusion of potentially confounding factors at both household and community levels.<sup>22</sup> Model 1 shows the strong correlation between mother's education and child height observed in the EGSF sample. Roughly, one additional year of formal education is associated with a one-tenth standard deviation increase in anthropometric score. However, a non-linear pattern emerges. In the univariate regression, children of mothers with secondary or higher education are one standard deviation taller than children of mothers with no education (a huge difference). The differential remains substantial and significant when we control for all exogenous covariates. On the other hand, the effect of primary education is reduced by more than three-quarters and is no longer significant when all the variables are included as covariates (Models 6 and 7). The effect of father's education on children's height-for-age outcomes is similar to that of mother's education, although generally smaller in magnitude. One exception is the huge differential in height observed between children of fathers with no education and children of fathers with post-secondary education (1.7 and 0.8 standard deviations in the z-score respectively, before and after controlling for all other factors).

There is some uncertainty in the literature about the size and significance of effects of household resource availability on child anthropometry (Thomas and Strauss 1992; Lavy _et al._ 1996). Our findings indicate that, when measured in terms of expenditure per capita, household resource availability has a substantial and highly significant effect in Guatemala. Models 6 and 7 indicate that, other variables being equal, children living in families with a per capita monthly expenditure at the **90 th** percentile (52.5 quetzals) are 0.3 standard deviations
- 26 -

> For computational simplicity, _I_ is treated as a continuous variable, instead of binary. Note

> that _I_ is observable in our case. This can be estimated by the Nelson-Olsen (1978) two-stage

> estimation technique. First, the reduced form for _q_ is estimated by the tobit method over _X_   _X_ 1<sup>,</sup><sup>_X_</sup> 2  , and the reduced form for _I_ is estimated by OLS over _X_ . Then, the equation of _q_ is estimated by the tobit method replacing _I_ with _I_<sup>ˆ</sup> _._ Letting the covariance matrix of this estimator be _V_ 0, the asymptotic covariance for  ˆ1, <sup>ˆ</sup> 1  can be written by:

where _G_   2<sup>,</sup><sup>_J_</sup> 1  . Π2 is a vector of the coefficients from the first stage OLS regression for _I_ . _J_ 1 is the matrix consisting of zeros and ones so that _XJ_ 1  _X_ 1 . _d_ is calculated by _d_   12  22  2  1  12 . While  12 is a variance of the error term of the first stage tobit estimation,  12 is a covariance between the two reduced form equations. See Amemiya (1979) and Maddala (1983) for further detail.

# **V. DATA**

The paper uses household surveys in Albania for four consecutive years: 2002-2005. It pools the four surveys. Note that the number of observations for 2002 is relatively small, because of lack of information required for the analysis (see Table 3). The total number of observations is 4,762. The summary statistics is presented in Table 5. The sample average of monthly power consumption is about 350 kWh with a wide range from less than 1 kWh to over 4,000 kWh per month. The metering rate is
information handling and aggregate FDI inflows). Yet, it can be difficult and misleading to reduce the effectiveness of an IPA to a single outcome indicator.

This paper avoids some of these limitations by taking a data-driven approach to identifying ‘effective’ IPAs. Namely, it uses a model to identify those countries which see a statistically significant relationship between their IPA’s sectoral targeting and increased FDI stock in that sector. For this, the paper uses an empirical specification with country-pair, host-sector, source-, host-, and sector-year fixed effects. It then compares these to other IPAs across a highly detailed set of characteristics using the 2019 WB-WAIPA survey, to assess the potential dynamics that may shape the effectiveness of IPAs in attracting FDI.

While this paper provides a wide range of controls to address endogeneity concerns, the non-randomness of priority sectors may still happen for reasons not accounted for by the comprehensive set of fixed effects. An additional limitation comes from the analysis of characteristics of effective IPAs. Using t-tests to distinguish between high-performing and other IPAs does not allow us to make causal inference about IPA characteristics to drive FDI dynamics. As such, these results should be considered as indicative only.

# **2. Literature Review**

## **2.1 The effect of IPAs on attracting foreign investment**

The rationale for investment promotion agencies stems from the existence of information asymmetries and transaction costs in capital markets (Williamson, 1985; Wells and Wint, 2000, Loewendahl, 2001). International investors, who intend to invest in a foreign market, often lack specific information on the potential host country’s available business partners, government regulation, and wider investment climate (OECD, 2015). IPAs are expected to influence the investment decisions of multinational corporations (MNCs) by solving these information/perception gaps about the host economy (OECD, 2018).

3
WPS3432

# **Growth, Inequality and Simulated Poverty Paths for Tanzania, 1992-2002**

Gabriel Demombynes and Johannes G. Hoogeveen*

**Abstract** – Although Tanzania experienced relatively rapid growth in per capita GDP in the 19952001 period, household budget survey (HBS) data shows only a modest and statistically insignificant decline in poverty between 1992 and 2001. To assess the likely trajectory of poverty rates over the course of the period, changes in poverty are simulated using unit-record HBS data and national accounts growth rates under varying assumptions for growth rates and inequality changes. To this end the projection approach of Datt and Walker (2002) is used along with an extension that is better suited to taking into account distributional changes observed between the two household surveys. The simulations suggest that following increases in poverty during the economic slowdown of the early 1990s, recent growth in Tanzania has brought a decline in poverty, particularly in urban areas. Unless recent growth is sustained, the country will not meet its 2015 Millennium Development Goal (MDG). Poverty reduction is on track in urban areas, but reaching the MDG target for bringing down poverty in rural areas, where most Tanzanians live, requires sustaining high growth in rural output per capita.

* Demombynes is a consultant to the World Bank. Hoogeveen is with the World Bank. Please send correspondence to both gabriel@demog.berkeley.edu and jhoogeveen@worldbank.org.

## World Bank Policy Research Working Paper 3432, October 2004

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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working
noted above, in 2008, to circumvent the constraints of the legal framework in obtaining external funding, CFPA microfinance registered itself as a ‘company-like institution.’ After this transformation, CFPA was allowed to receive loans from commercial banks. To capture changes in portfolio quality and average loan size associated with this switch, we include the variable _Year 2008 and after_ as a control variable. It is a binary variable that equals one in years 2008, 2009, and 2010. Lastly, to control for social and economic differences across branches, we include _Annual income per capita of rural households_ measured at the county level.

It is possible that work experience prior to joining CFPA Microfinance influenced loan officers’ lending behavior. To explore this issue, we estimate the following alternative specification: _Yijt = a0 +a1*(EXPi × GOVi) + a2*(EXPi × FARMi) + a2*EXPit + a3*INCOME +eijt (2)_ In addition to the variables that are included in equation (1), in equation (2) we include two interaction terms ( _Months as a CFPA loan officer × GOV_ and _Months as a CFPA loan officer × FARM_ ) _._ The coefficients _a1_ and _a2_ measure the additional effect of CFPA experience for loan officers with government or farming backgrounds relative to all other CFPA loan officers.

A number of time-invariant unobservable factors could also be correlated with the independent variables of interest. To account for those factors, we exploit the longitudinal nature of our data and include a set of regressions that are estimated with loan officer fixed effects (columns 2 and 3 in Table 6; columns 2, 3, 5 and 6 in Table 7). Those fixed effects are intended, therefore, to summarize an officer’s suitability for the job based on characteristics that we do not necessarily observe.<sup>20</sup> As such,

> based on CFPA data. That said, our regressions do rely on explanatory variables that can be seen as proxies for those concepts
as in the CASE study, we would obtain more than one percent additional welfare gain as a percent of GDP.

# (iii) **Standards costs** .

Due to the lack of a survey for Armenia, both the CASE study and our study start with data for Ukraine (Jakubiak et al., 2006). The Ukrainian survey revealed that, on average for agriculture, mining and manufacturing, the cost of compliance with EU standards and regulations was 13.9 percent of production costs. Due to the extremely poor status of the National Quality Infrastructure in Armenia, both the CASE authors and our study assume that these costs are higher in Armenia than in Ukraine by 50 percent (or 20.9 percent of production costs on average). The CASE study assumes that Armenian production costs on exports to the EU will fall by 50 percent as a result of a DCFTA with the EU. Our estimates are smaller for two reasons. First, although the EU is likely to assist in the development of the National Quality Infrastructure, it is not likely to invest as heavily as it did in the countries involved in the Eastern Expansion of the EU. Consequently, we assume only a 25 percent decline in production costs. Second, it is necessary for Armenians to invest in the National Quality Infrastructure to be able achieve harmonization. We further limit the production costs cuts by two percentage points in agriculture and manufacturing to reflect the adjustment costs. In summary, we estimate that production costs of exports to the EU will fall, on average, from 20.9 percent of costs to 17.7 percent, whereas CASE assumes that they will fall from 20.9 percent of costs to 10.5 percent of the costs of exporting to the EU.

# **II. Comparative Steady State Estimates--Improvement in the investment climate**

The CASE study simulates improvement in the long run investment climate from the DCFTA in a scenario (called deep FTA+). This is the scenario that gives
level. We do the currency conversion at Purchasing Power Parity (PPP).<sup>3</sup> We also allow for extra costs of living in the US not fully reflected in the PPPs, such as higher housing costs.

Using these data, we can now explore the impact of creating a market for work permits by calibrating the model and going through a series of simulations.

**Supply of work permits: Calibrating the disutility of work** To calibrate supply – curve (2), we need some estimate of the disutility of work the parameter _γ_ . To that end, we draw on the surveys that have been done to determine what share of pre-retirement income will make a retiree no worse off on a pension. Binswanger and Schunk (2012) report their estimates from such surveys for the US. The mean replacement rate is 80%, but they find that it is higher for lower-income workers, as one would expect. Based on their results, we set that rate to 0.9. Namely, we henceforth assume that a US worker _i_ will sell her work permit at any price that exceeds 90% of her income.

**Demand for work permits** To calibrate the demand for work permits, we proceed in two steps. First, we need to estimate the wages that Mexican migrants would earn if they were to migrate and work in the US. Second, we calibrate the other parameters in equation (3) such as migration costs or the cost of living in the US.

**Mincer regression** To predict the expected wages of Mexican migrants in the US, we first estimate the coefficients ( _β_<sup>_US_</sup> ) of a Mincer earning regression for the log yearly earnings of US worker _i_ on a set of her productive characteristics using the CPS data:

where _εi_ is a standard error term with mean zero and variance _σ_<sup>2</sup> . We predict the expected ˆ earnings of a
# Factor Market Failures and the Adoption of Irrigation in Rwanda<sup>∗</sup>

Maria Jones<sup>†</sup> Florence Kondylis<sup>†</sup> John Loeser<sup>†</sup> Jeremy Magruder<sup>‡</sup> _Updated_ January 2021 JEL Classification Codes: O1, O12, O13, Q12, Q15 Keywords: Technology adoption, Irrigation, Factor markets ∗ This draft benefited from comments from Abhijit Banerjee, Chris Barrett, Paul Christian, Alain de Janvry, Simeon Djankov, Esther Duflo, Andrew Foster, Doug Gollin, Saahil Karpe, Elisabeth Sadoulet, John Strauss, Duncan Thomas, Chris Udry, and seminar audiences at Cornell University, Georgetown University, Georgia State University, Harvard University/MIT, Michigan State University, North Carolina State University, Northwestern University, University of California, Berkeley, University of Pittsburgh, University of Southern California, and the World Bank. We thank the European Union, the Global Agriculture and Food Security Program (GAFSP), the World Bank Rwanda Country Management Unit, the World Bank i2i fund, 3ie, and IGC for generous research funding. Emanuele Brancati, Anna Kasimatis, Roshni Khincha, Christophe Ndahimana, and Shardul Oza provided excellent research assistance. Finally, we thank the technical staff at MINAGRI, the staff of the LWH project implementation unit, and the World Bank management and operational teams in Rwanda for being outstanding research partners. We are particularly indebted to Esdras Byiringiro, Jolly Dusabe, Hon. Dr. Gerardine Mukeshimana, and Innocent Musabyimana for sharing their deep knowledge of Rwandan agriculture with us. Magruder acknowledges support from NIFA. This study was approved by UC Berkeley IRB (2016-06-8861), the Rwanda National Ethics Committee, and IPA IRB (10951), and is registered in the AEA RCT Registry (AEARCTR0001323). The views expressed in this manuscript do not reflect the views of the World Bank. All errors are our own.

> †Development Impact Evaluation, World Bank

> ‡
NEET compared to more than 70 percent of their female counterparts—a very large difference that points to severe retrenchment on the women’s side. It can be seen that, as age increases, the malefemale gap widens markedly. Additionally, although this widening of the male-female gap holds both in 2007 and in 2018, in relative terms it is far worse in 2018 than in 2007, because of the great gains the girls made in early education but then lost as they entered their 20s. In 2018, about 10 percent of women aged 24 were in school, and fewer than 20 percent were working, compared to about 20 percent of their male counterparts in school and about 60 percent working.

Both Figures 3 and 4 portray a situation for Morocco not dissimilar from other MENA region countries, where the share of employed youth is higher among young men than young women, and the share of NEET is higher for young women. Interestingly, both in Morocco and in the rest of the region (Doss et al., 2018), when the NEET information is crossed with marital status, married women—either with or without children—represented a significant percentage of female NEETs. When this married group is removed from the NEET calculation, however, the share of female NEETs (for example, those who are neither married nor have children) becomes comparable to the share of NEET men.

<!-- Start of picture text --> Figure 4: School-to-Work Transition for Population Aged 15 to 24 years old, 2018<br>Male Female<br>100% 100%<br>NEET<br>80% 80%<br>School only<br>60% 60%<br>40% 40% NEET<br>20% 20% School only<br>Work only<br>Work only<br>0% 0%<br>15 16 17 18 19 20 21 22 23 24 15 16 17 18 19 20 21 22 23 24<
to calibrate the ̃ elasticity of substitution between tasks, _σ_ . Since this parameter is related to the love for variety of tasks, we calibrate it to match the elasticity of the number of job options to exports in Table 5.<sup>47</sup> First, we simulate the model with a 20% decline in trade costs from Brazil to each partner country uniformly for all sectors. We then run the same regression described in Section 4.3 with the simulated data for changes in exports and the number of job options following the shock. The calibrated value of _σ_ ̃ is 2.4429 with which the elasticity of job options in Table 5, 0.652, is matched exactly. The calibrated value of _σ_ ̃ is larger than one and thus confirms the love for variety of tasks channel.

# **5.4 Counterfactual Exercises**

The benchmark counterfactual shock we explore is a 20% permanent decline in iceberg trade costs _d_ in the manufacturing sector from Brazil to each of its trading partners. This shock can be interpreted as a positive export shock to all manufacturing labor markets in Brazil. For the baseline counterfactuals, all the other model parameters are fixed. We then shut down our job options channel by setting the cost of task operation to be infinite and compare this alternative model to the baseline. Lastly, we simulate the benchmark shock with different levels of between-market moving costs to understand the role of mobility frictions in the transmission of trade shocks on workers’ welfare. We focus our analysis on Brazil for the of the counterfactual shock on outcomes of interest.

> 45As in Section 2, we use the residual wage after controlling for observable worker characteristics.

> 46These cost shares are assumed to be the same across Brazilian regions within the same sector, because the inputoutput data are not available at the detailed region level in Brazil.

> 47In addition to the reason related to the interpretation of this parameter, we do not match the other three elasticities in the table because they also depend
method can be leveraged in the context of a small, open economy. There are a set of possible extensions that have been implemented in other contexts, particularly promising is incorporating a higher level of granularity in the “down” part of the framework relying on firm-level, census, or new databases that use high-frequency data.

**Policy implications should aim to distribute the gains with more equity** . By understanding the varied impacts across regions and sectors, policy makers can design policies to mitigate negative impacts on selected groups of workers and facilitate the transition of workers across regions and sectors. In doing so, the gains from trade could be distributed more equally. Possible specific policies might include:

• **Skills for migrant workers** : Increasing economic activity in urban areas is expected with a major reallocation of labor from districts with large agriculture and vegetables/fruit sectors into urban areas that host emerging sectors (chemicals, textile and apparel, and other manufacturing). Policies that promote the acquisition of job-related skills will benefit both workers and firms. While workers’ technical knowledge and expertise are critically important, the emphasis on managerial and administrative capacities should be underscored, particularly for firms engaging in exporting activities.

• **Better mobility and communication** . Some non-tradable sectors, such as social services, construction, trade and transport sectors which are essentially in highly urbanized districts experience positive effects on the extensive and intensive margins of labor demand. Still, geographic labor mobility in Sri Lanka is very limited. Infrastructure and communication investment policies can help to reduce the gap between the less connected, isolated and job loss prone locations with the expanding urbanized districts. Domestic labor mobility could also be promoted with security and socio-political integration, which is crucial in the aftermath of a prolonged civil war.

**• Enforce progressive labor standards and policies that protect vulnerable workers.** Textiles and wearing apparel experience the highest rate of labor demand expansion with large increases in employment of women. Policies that prevent the exploitation of vulnerable workers