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- Niimi, T. (2005). An analysis of household responses to price shocks in Vietnam: can unit values substitute for market prices? PRUS Working Paper no. 30, University of Sussex, UK.

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- Phu, L.V. (2020). Electricity price and residential electricity demand in Vietnam. _Environmental Economics and Policy Studies_ , 22:509–535.

Poi, B. P. (2012). Easy demand-system estimation with QUAIDS. _Stata Journal_ , 12: 433–446.

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- Stone, J. R. N. (1954). Linear Expenditure Systems and demand analysis: an application to the pattern of British demand. _Economic Journal_ , 64: 511- 527.

- Thai, D.M., Falch, M. (2018). Universal service in Vietnam: An institutional approach. _Telecommunications Policy_ , 42: 323–332.

- Trinh, A.N. (2018). Local Insights from the Vietnamese Education System: the impacts of imperialism, colonialism, and neo-liberalism of globalization. _The International Education Journal: Comparative Perspectives_ , 17(3): 67-79.

- Vu, L.H. (2020). Estimation and analysis of food demand patterns in Vietnam. _Economies_ , 8, 11.

- World Bank (2016). Vietnam-Household Registration Study 2015. http://micro data.world bank.org/index.php/catal og/2729

- World Bank (2020). Vibrant Vietnam: Forging the Foundation of a High-Income Economy. World Bank, Hanoi. https://openknowledge.worldbank.org/handle/10986/33831

- Yin H, Zhou H, Zhu K (2015). Long- and short-run elasticities of residential electricity consumption in China: a partial adjustment model with panel data. _Applied Econonomics_ , 48(28): 2587-2599.

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Javorcik, Beata S., and Gaia Narciso, 2008. “Differentiated Products and Evasion of Import Tariffs.” _Journal of International Economics_ 76(2): 208-22.

Khandelwal, Amit K., 2010. The long and short (of) quality ladders. _Review of Economic Studies_ 77: 1450-76.

Lall, Sanjaya, 2000. “The Technological Structure and Performance of Developing Country Manufactured Exports, 1985-1998”, QEH Working Paper Series – QEHWPS44.

Liu, Xuepeng, 2013. “Tax Avoidance through Re-imports: The Case of Redundant Trade.” _Journal of Development Economics_ 104: 152-64.

Mishra, Prachi, Arvind Subramanian, and Petia Topalova, 2008. “Policies, Enforcement, and Customs Evasion: Evidence from India.” _Journal of Public Economics_ 92(10-11): 1907-25.

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Rauch, James E., and Joel Watson, 2004. "Network Intermediaries in International Trade", _Journal of Economics & Management Strategy_ , 13: 69-93.

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32
The GTAP simulation of the Doha scenario predicts a relatively large increase in the world import price for foodstuffs (2.23 percent), which is partially offset by a reduction in the tariff rate from 12.2 to 11.9 percent. This leads to a significant drop in foodstuff imports (6.34 percent) in favor of locally-produced substitutes (increase of 0.09 percent).

On the whole, the Doha scenario involves a rise in world import prices (0.47 percent) and a resulting substitution of local demand toward domestically-produced substitutes: imports and exports decline (by 0.64 and 0.25 percent respectively) and locally-sold production expands (by 0.06 percent). The food-processing and foodstuff sectors are the most affected and strongly influence the overall impacts on the economy.

In terms of impacts on factor markets (Table 7), we first note that agricultural labor moves from cash crops to foodstuff, while food processing draws non-agricultural labor from most of the other non-agricultural sectors. All factor remuneration rates increase, although the changes are very small. Wage rates rise more for unskilled labor (0.33 percent) than for skilled labor (0.19 percent). Average returns to capital and land increase respectively by 0.21 and 0.40 percent. On average, changes in remuneration rates are more favorable to agricultural factors (+0.50 percent for agricultural labor; +0.44 percent for agricultural capital; and +0.40 percent for land) than to nonagricultural factors (+0.13 percent for nonagricultural labor; and +0.19 percent for nonagricultural capital).

# **3.2   Household income effects**

These small increases in factor remunerations result in a slight rise (0.21 percent) in household gross income at the national level (Table 2). On the basis of the initial factor endowments of household groups and changes in the remuneration rates of various factors, total 11
**Table 7: Household‐level regression**

|**Independent variables**|**Category**|**Coeffi**|**cients**|
|---|---|---|---|
|||Without countryFE|With countryFE|
|Residence|Urban|||
||Rural|‐0.22|‐0.23|
|Demographic structure||||
|Two or less adults|0 kids|||
||1‐2 kids|‐0.49|‐0.42|
||More than two kids|‐1.00|‐0.80|
|Two or more adults|0 kids|‐0.29|‐0.27|
||1‐2 kids|‐0.57|‐0.50|
||More than two kids|‐1.04|‐0.82|
|Marital Status of head|Married|||
||Never married|‐0.09|‐0.02|
||Divorced|‐0.10|‐0.17|
||Living together|‐0.18|‐0.15|
||Widowed|‐0.09|‐0.01|
|Characteristics of household||‐008|000|
|head||.|.|
|Age of household head|0‐14||.|
||15‐24|<br>0.10|0.26|
||25‐34|<br>0.10|0.23|
||35‐44|<br>0.15|0.26|
||45‐54|<br>0.17|0.29|
||55‐64|<br>0.23|0.33|
||65 and up|0.24|0.33|
|Gender of household|M|||
|head|an|||
||Woman|0.19|0.08|
|Educational attainment of|No education|||
|head|Incomplete primary|0.20|0.11|
||Complete primary or<br>some secondary|0.33|0.26|
||Complete secondary|0.48|0.45|
||Tertiary|0.89|0.73|
|Type of work of head|Head not working|||
||Head working in<br>agriculture|‐0.15|‐0.04|
||Head working outside<br>of agriculture|0.19|0.19|
||Head 0‐14|||
|Constant||.|.|
|Adjusted r2||1.84|1.68|
|Number of countries||0.42|0.5|
|Number of observations<br>||76|76|
|Weighted number of||1718873|1718873|
|observations(‘000s)||,,|,,|

Note: All coefficients are statistically significant at 1 percent confidence level. Standard errors are not reported because they are not adjusted for sample survey design due to missing PSU identifiers for many counties. In a subsample of 38 countries, all coefficients are statistically significant. Grey shading indicates that the category is excluded from the regression specification or omitted due to multicollinearity.

25
# **1 Introduction**

Early childhood, when brain plasticity and neurogenesis are very high, is an important period for cognitive and psychosocial skill development.<sup>1</sup> Investments during this period create the foundations for the evolution of the cognitive and psychosocial skills that are key determinants of lifetime earnings.<sup>2</sup> Young children who experience negative shocks such as economic downturns, extreme weather, and infectious diseases suffer long lasting consequences in terms of their educational and labor market outcomes.<sup>3</sup> The seeds of inequality are planted in early life with remediation being less effective and more expensive later in life.<sup>4</sup> Today more than 200 million children under the age of 5 living in developing countries are at risk of not reaching their full developmental potential. The vast majority of these children live in extreme poverty.<sup>5</sup> These children start disadvantaged, receive lower levels of parental investments, and are likely to continue to fall further behind without help than are children from more affluent environments.<sup>6</sup> Based on a growing body of evidence demonstrating positive impacts, early childhood development (ECD) interventions aimed at skill development are being promoted as cost-effective remediation policies to help these children.<sup>7</sup> While these ECD interventions are estimated to have substantially higher rates of return than investments in the human capital of the disadvantaged later in life,<sup>8</sup> there is little rigorous evidence on the long-term effects of ECD on earnings and inequality for developing countries.

> 1See Huttenlocher (1979, 2002) and Thompson and Nelson (2001).

> 2See e.g. Knudsen et al. (2006), Borghans et al. (2008), and Almlund et al. (2011a).

> 3 See van den Berg et al. (2006), Almond et al. (2007), Bleakley (2007), Maccini and Yang (2009) and Almond and Currie (2011).

> 4See Carneiro and Heckman (2003), Cunha et al. (2006), Heckman (2008) and Cunha et al. (2010).

> 5See Grantham-McGregor et al. (2007) and Walker et al. (2007).

> 6See Paxson and Schady (2007), Fernald et al. (2011), Fernald et al. (2012) and Engle et al. (2011).

> 7See e.g. Engle et al. (2007), Heckman (2008) and Engle et al. (2011).

> 8See Heckman (2000, 2008), Cunha et al. (2006), Almond and Currie (2011).

1
the probability of finishing upper secondary on time.

In order to measure how expectations about the returns to education have changed in response to the intervention, we rely on the data from a nationwide survey administered to 20% of all 12th grade exam takers, the so called _ENLACE de contexto_ . In our sample, 730 students were administered the _ENLACE de contexto_ . The 2012 survey gathers, among others, information on expected monthly earnings at ages 30 to 40 on two hypothetical scenarios of educational attainments, high school completion and university degree. These questions read exactly the same as the ones asked in the baseline survey, but the answers are given using a pre-codified set of brackets.<sup>18</sup> The _ENLACE de contexto_ does not collect objective measures of student effort but it elicits self-reported assessment. The respondent is asked how the statement “I am a person who works hard in school” describes him or her in one of the following ways: 1) it does not describe me at all, 2) it describes me a little bit, 3) it describes me, 4) it describes me a lot, 5) it fully describes me. Students are also asked which subtrack they chose as part of the technological school curriculum.

# **3.4 Perceptions about Returns to Schooling**

As discussed in section 2.1, the baseline survey asked both about student’s own expected earnings and the student’s expected returns for the average person. While variation in the perceived returns for oneself reflects both possible misperceptions about the education returns and heterogeneity in subjective valuations of how well oneself can do in the labor market, dispersion in perceived average returns would just reflect misperception. Table 3 reports the gender specific mean and standard deviation of the expected monthly wages for themselves and for the average person, separately for the treatment and the control group. For none of the measures of perceived returns, the difference between the treatment and the control group is statistically significant.<sup>19</sup> At the baseline, the mean expected monthly wage for oneself, having completed high school as the highest degree, among boys in the control schools ($5,531 MX) is not statically different from the average wage for a man with high school degree between 30 and 40 using data from ENOE ($5,722 MX). When asked about the expected wage for an average man between 30 and 40, the average expected value is substantially lower ($4,282 MX). On

> 18The earnings brackets for both questions are: i) $4,000 MX or less; ii) $4,001 MX to $7,000 MX; iii) $7,001 MX to $10,000 MX; iv) $10,001 MX to $15,000 MX; v) $15,001 MX to $20,000 MX; and vi) more than $20,000 MX.

> 19The statistics displayed in Table 3 are generated trimming the top 1% and the bottom 1% of the earnings expectations, but the balancing properties still hold in the untrimmed sample.

11
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- Wu, Y., Tam, V. W. Y., Shuai, C., Shen, L., Zhang, Y., & Liao, S. (2019). Decoupling China’s economic growth from carbon emissions: Empirical studies from 30 Chinese provinces (2001–2015). _Science of The Total Environment_ , _656_ , 576–588. https://doi.org/10.1016/J.SCITOTENV.2018.11.384

- Xie, R., Fang, J., & Liu, C. (2017). The effects of transportation infrastructure on urban carbon emissions. _Applied Energy_ , _196_ , 199–207. https://doi.org/10.1016/j.apenergy.2017.01.020

- Xu, B., & Lin, B. (2015). Factors affecting carbon dioxide (CO2) emissions in China’s transport sector: a dynamic nonparametric additive regression model. _Journal of Cleaner Production_ , _101_ , 311–322. https://doi.org/10.1016/J.JCLEPRO.2015.03.088

- Yasmeen, H., Wang, Y., Zameer, H., & Solangi, Y. A. (2020). Decomposing factors affecting CO2 emissions in Pakistan: insights from LMDI decomposition approach. _Environmental Science and Pollution Research_ , _27_ (3), 3113–3123. https://doi.org/10.1007/s11356-019-07187-3

- Zhang, J., Fan, Z., Chen, Y., Gao, J., & Liu, W. (2020). Decomposition and decoupling analysis of carbon dioxide emissions from economic growth in the context of China and the ASEAN countries. _Science of The Total Environment_ , _714_ , 136649. https://doi.org/10.1016/J.SCITOTENV.2020.136649

20
# **Who Serves the Poor? Surveying Civil Servants in the Developing World**

Daniel Rogger<sup>_∗_</sup>

**Keywords: Structure, Scope, and Performance of Government; Bureaucracy, Administrative Processes in Public Organizations; Survey Methods**

**JEL Classification: H11, D73, C83**

> _∗_ Development Economics Research Group, World Bank. E-mail: drogger@worldbank.org. Web: www.danrogger.com. I gratefully acknowledge financial support from the Strengthening Research on the Civil Service initiative and Bureaucracy Lab of the Governance Global Practice of the World Bank. I would like to express my sincere gratitude to Elsa Araya, Sheheryar Banuri, Zahid Hasnain, Berhanu Legesse and Phil Keefer for providing me with their survey data for use in this paper. I am grateful to Chiara Bronchi, Zahid Hasnain, Kerenssa Kay, Phil Keefer, Asmeen Khan, Steve Knack, Arianna Legovini, Eric Mvukiyehe, Christian Schuster, Ravi Somani and Tony Verheijen for very useful comments. All errors remain my own.
|Haiti Business Development and Investment Project|Emiliano Duch Navarro|
|---|---|
|Haiti Business Development and Investment Project|Emiliano Duch Navarro|
|Haiti Business Development and Investment Project|Emiliano Duch Navarro|
|Haiti Business Development and Investment Project|Emiliano Duch Navarro|
|Haiti Business Development and Investment Project|Emiliano Duch Navarro|
|Haiti Business Development and Investment Project|Emiliano Duch Navarro|
|Haiti Business Development and Investment Project|Emiliano Duch Navarro|
|PEACH Grant to UGM on CapacityBuildingPrograms Related to|SubBastian Zaini|
|PEACH Grant to UGM on CapacityBuildingPrograms Related to|SubBastian Zaini|
|PEACH Grant to UGM on CapacityBuildingPrograms Related to|SubBastian Zaini|
|PEACH Grant to UGM on CapacityBuildingPrograms Related to|SubBastian Zaini|
|RY - ImprovingCorporate Financial Reporting|Moad M. Alrubaidi|
|RY - ImprovingCorporate Financial Reporting|Moad M. Alrubaidi|
|RY - ImprovingCorporate Financial Reporting|Moad M. Alrubaidi|
|Indonesia Second Power Transmission Development Project|Puguh Imanto|
|Indonesia Second Power Transmission Development Project|Puguh Imanto|
|Indonesia Second Power Transmission Development Project|Puguh Imanto|
|Indonesia Second Power Transmission Development Project|Puguh Imanto|
|Indonesia Second Power Transmission Development Project|Puguh Imanto|
|Indonesia Second Power Transmission Development Project|Puguh Imanto|
|Indonesia Second Power Transmission Development Project|Puguh Imanto|
|Indonesia Second Power Transmission Development Project<br>|Puguh Imanto<br>|
|Indonesia Second Power Transmission Development Project|Puguh Imanto|
|Indonesia Second Power Transmission Development Project|Puguh Imanto|
|Indonesia Second Power Transmission Development Project|Puguh Imanto|
|Indonesia Second Power Transmission Development Project|Puguh Imanto|
|First ConnectivityDevelopment PolicyLoan|Sjamsu Rahardja|
|LightingLives in Liberia|David Vilar Ferrenbach|
|LightingLives in Liberia|David Vilar Ferrenbach|
|LightingLives in Liberia|David Vilar Ferrenbach|
|LightingLives in Liberia|David Vilar Ferrenbach|
|LightingLives in Liberia|David Vilar Ferrenbach|
|LightingLives in Liberia|David Vilar Ferrenbach|
|LightingLives in Liberia|David Vilar Ferrenbach|
|LightingLives in Liberia|David Vilar Ferrenbach|
|LightingLives in Liberia|David Vilar Ferrenbach|
|Social SafetyNet Project|Rebekka E. Grun|
|Social SafetyNet Project|Rebekka E. Grun|
|Social SafetyNet Project|Rebekka E. Grun|
|Social SafetyNet Project|Rebekka E. Grun|
|Social SafetyNet Project|Rebekka E. Grun|
|Social SafetyNet Project|Rebekka E. Grun|
|Social SafetyNet Project|Rebekka E. Grun|
|Social SafetyNet Project|Rebekka E. Grun|
|Social SafetyNet Project|Rebekka E. Grun|
|Social SafetyNet Project|Rebekka E. Grun|
|Social SafetyNet Project|Rebekka E. Grun|
WPS4141

# **A land of milk and honey with streets paved with gold: Do emigrants have over-optimistic expectations about incomes abroad?***

David McKenzie, _Development Research Group, World Bank#_ John Gibson, _University of Waikato_ Steven Stillman, _Motu Economic and Public Policy Research_

# **Abstract**

Millions of people emigrate every year in search of better economic and social opportunities. Anecdotal evidence suggests that emigrants may have over-optimistic expectations about the incomes they can earn abroad, resulting in excessive migration pressure, and in disappointment amongst those who do migrate. Yet there is almost no statistical evidence on how accurately these emigrants predict the incomes that they will earn working abroad. In this paper we combine a natural emigration experiment with unique survey data on would-be emigrants’ probabilistic expectations about employment and incomes in the migration destination. Our procedure enables us to obtain moments and quantiles of the subjective distribution of expected earnings in the destination country. We find a significant under-estimation of both unconditional and conditional labor earnings at all points in the distribution. This under-estimation appears driven in part by potential migrants placing too much weight on the negative employment experiences of some migrants, and by inaccurate information flows from extended family, who may be trying to moderate remittance demands by understating incomes.

**Keywords:** Expectations; Migration; Natural Experiment **JEL codes:** D84, F22, J61

World Bank Policy Research Working Paper 4141, March 2007 _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 Papers are available online at http://econ.worldbank.org._

* Financial support from the World Bank and Marsden Fund grant UOW0503 is gratefully acknowledged. We thank the Government of the Kingdom of Tonga for survey permission, the New Zealand Department of Labour Immigration Service for providing the sampling frame, Halahingano Rohorua and her assistants for excellent survey work and helpful comments on this paper, and the survey respondents. Helpful comments were received from Mary Adams, Jishnu Das, Betty-Ann Kelly, Helen Lee, John Roseveare and seminar participants at ANU, Canterbury, NEUDC, Stanford, and UCSD. The views expressed here are those of the authors alone and do not necessarily reflect the opinions of the World Bank, the New Zealand Department of Labour, or the Government of Tonga.

# Corresponding author: MSN MC3-300, World Bank, 1818 H Street NW, Washington DC 20433, USA; email: dmckenzie@worldbank.org.
|_harvest_end_month_|Harvest end<br>month|If different crops harvested at<br>various times, latest date is<br>chosen.<br>ETH: Dates had to be<br>converted, so there is a one-<br>week discrepancy between the<br>months in the data and the<br>months in our Gregorian<br>calendar.|UGA: absent wave 1|
|---|---|---|---|
|_planting_month_|Month of<br>planting|If different crops planted at<br>various times, earliest date is<br>chosen.<br>ETH: Dates had to be<br>converted, so there is a one-<br>week discrepancy between the<br>months in the data and the<br>months in our Gregorian<br>calendar.|TZA: absent<br>UGA: absent wave 1, wave 2|
|_harvest_kg_|Total harvest<br>in kg|ETH: using conversion factor<br>database, or enumerator<br>conversion if not available.<br>NGA: In w4 and w3, full<br>harvest quantity was imputed<br>based on realized harvest at<br>the time of the interview +<br>expected harvest after the<br>interview.<br>MLI: full harvest quantity<br>imputed based on a “% area<br>left to harvest” variable.<br>MWI: Post planting harvest<br>may have been reported in<br>post-harvest period.|Summed up to the plot level in the<br>plot dataset|
|_harvest_value_LCU_|Value of plot<br>harvest, in<br>LCU|<br>Harvest valued using median<br>prices per in a defined<br>geographical area. The area in<br>question is the EA (or admin<br>1 in some surveys) under the<br>condition that there are more<br>than 10 observed sales in the<br>area. If there are less than 10<br>sales, prices are calculated at<br>a higher geographical level.<br>Prices are calculated<br>independently for each crop<br>type.<br>UGA: sale prices multiplied<br>by 100 in wave 5 and divided<br>by 100 in wave 1 season1.|Summed up to the plot level in the<br>plot dataset|
|_harvest_value_USD_|Value of plot<br>harvest, in<br>USD|,<br>Harvest values<br>(_harvest_value_LCU)_are<br>converted to current USD and<br>deflated to 2020 dollar values,<br>using exchange rates and a<br>deflator extracted from World|Summed up to the plot level in the<br>plot dataset|

26