rafmacalaba/datause-encoder-data huggingface dataset:
but lower than what would be expected of a permanent increases in income as it is supposed to be.

I find positive, but modest impact of migration of household members on child education. While the impact on girls is strong and positive, the impact on boys is statistically indistinguishable from zero. It appears that, at least for boys, the increase in demand for education due to increases in income is at least partly offset by increases in returns to low-education through low-skilled migration abroad.

I also find largely positive impacts of migration on wages and labor force participation. The increase in the Gulf-Malaysia migration rate between 2001 and 2011 has increased overall wages by 8 percent (14 percent for females), and agricultural wages by 14 percent. The increase in migration during this period also increased labor force participation, both for individuals from households with and without a current migrant. This effect is remarkable, particularly in light of the _falling_ LFP during this period. The effect in participation is driven by increases in male participation in farming, and female participation in non-farm activities. Taken together, migration benefits both male and female workforce, especially those without a current migrant in their households, either through increases in wages and/or increases in participation. High migration rates in a village reduce participation in non-farm activities of men from migrant-households, plausibly because of their access to better job-search options abroad. This reduction is accompanied by increases in male non-farm wages, particularly in rural areas.

These findings broadly echo the findings in the literature on the impacts of migration and can be used to inform the policy discussion on the impact of migration, particularly, in the context of Nepal.

# **References**

- Adhikari, J., and M. Hobley (2013): _Everyone is leaving, who will sow our fields?: The effects of migration from the Khotang district to the Gulf and Malaysia_ . Nepal Institute of Development Studies.

- Akram, A. A., and A. M. Mobarak (2016): “Seasonal migration to increase incomes of poor households in Bangladesh,” _Working Paper_ .

- Antman, F. M. (2012): “Gender, educational attainment, and the impact of parental migration on children left behind,” _Journal of Population Economics_ , 25(4), 1187–1214.

- Bryan, G., S. Chowdhury, and A. M. Mobarak (2014): “Underinvestment in a profitable technology: The case of seasonal migration in Bangladesh,” _Econometrica_ , 82(5), 1671–1748.

- Clemens, M. A. (2011): “Economics and emigration: Trillion-dollar bills on the sidewalk?,” _The Journal of Economic Perspectives_ , pp. 83–106.

- Clemens, M. A., C. E. Montenegro, and L. Pritchett (2008): “The place premium: Wage differences for identical workers across the US border,” _Center for Global Development Working Paper 148_ .

16
- 14 -

# Direct and Indirect Taxes on Agriculture

The most comprehensive and consistent evidence on direct and Indirect taxation of agriculture comes from the World Bank comparative study of the political economy of agricultural pricing policies directed by Anne 0. Krueger, Maurice Schiff & Alberto Valdes (hereinafter KSV). This study provides estimates from country-level research studied for eighteen deveioping countries. The Initially published results focus on the Inpact of direct and Indirect policles on the prices of major export and Import-competing commoditles. For details of the KSV methodology, the reader Is referred to thelr published papers.

Table 1 (from KSV) presents their estknates of nominal direct, Indirect and total Intervention for representative export crops In sixteen countries over the perlod 197579 and 1980-84. The numbers on direct Intervention give an estknate of the percentage by which domestic producer prices devlated from the border price (adjusted for transport, storage, other costs and quallty differentials) measured at the official exchange rate. The estinates of Indirect effects make allowance for the effect of trade and macroeconomic policies on the real exchange rate and the extent of protection given to nonagricultural commoditles. The total effect Is simply the sum of the direct and Indirect Interventions. For the sixteen countries and representative export crops listed In Table 1 the total of direct and Indirect taxation of agricultural exports averaged about 40 percent over the 1975-84 period. For most countries, there was significant taxation of exports at both the direct and Indirect levels. All of these countries are Bank borrowers, and many have borrowed to finance Irrigation projects--e.g., 'gypt, Malaysia, Pakistan, Philippines, Portugal, Sri Lanka, Thailand, and Turkey. Clearly, the dominant pattern Is one of heavy taxatlon of agricultural exports, with Indirect taxatlon via trade and macroeconomic pollcy accounting for about two-thirds of the total on average.
_Table 6: Determinants of applicant making it to the longlist_

|Model 1<br>Model 2|Model 3|Model 4|Model 5|Model 6|Model 7|
|---|---|---|---|---|---|
|b/se<br>b/se|b/se|b/se|b/se|b/se|b/se|
|Female<br>0.0188**<br>0.0193*|0.0193*|0.0222**|0.0179*|0.0208**|0.0278**|
|(0.0082)<br>(0.0106)|(0.0106)|(0.0105)|(0.0105)|(0.0105)|(0.0115)|
|Accounting||-0.1028***<br>(0.0223)||-0.0979***<br>(0.0221)|-0.0974***<br>(0.0222)|
|Marketing||<br>0.0083||<br>0.0046|<br>0.0051|
|||(0.0215)||(0.0214)|(0.0214)|
|IT||<br>0.0450*||<br>0.0137|<br>0.0127|
|||(0.0266)||(0.0312)|(0.0314)|
|Istanbul Asia|||||<br>-0.0015<br>(0.0193)|
|Ankara|||||<br>0.0147<br>(0.0231)|
|Total application size (100)|||-0.0026**|-0.0021**|<br>-0.0020**|
||||(0.0011)|(0.0009)|(0.0009)|
|Female * Istanbul Asia|||||<br>-0.0101<br>(0.0070)|
|Female * Ankara|||||<br>-0.0156*<br>(0.0091)|
|Female * Acct||0.0049<br>(0.0057)||||
|Female * Mrkt||<br>-0.0059<br>(0.0052)||||
|Female * IT||<br>-0.0015<br>(0.0039)||||
|Female * Total application size|||0.0002<br>(0.0003)|0.0002<br>(0.0003)||
|Vacancy and CV sectors match|0.0647***||<br>0.0606***|<br>0.0324|0.0327|
||(0.0192)||(0.0192)|(0.0212)|(0.0212)|
|Experience (in months)<br>-0.0001|<br>-0.0001|-0.0005|<br>-0.0001|<br>-0.0005|<br>-0.0005|
|<br>(0.0010)|(0.0010)|(0.0010)|(0.0010)|(0.0010)|(0.0010)|
|<br>Age<br>0.0351***<br>0.0359***|<br>0.0357***|<br>0.0396***|<br>0.0350***|<br>0.0389***|<br>0.0390***|
|<br>(0.0106)<br>(0.0135)|(0.0135)|(0.0134)|(0.0135)|(0.0134)|(0.0135)|
|<br> <br>Constant<br>-0.2250<br>-0.2395|<br>-0.2454|<br>-0.2979|<br>-0.2106|<br>-0.2666|<br>-0.2717|
|(0.2837)<br>(0.3321)|(0.3317)|(0.3307)|(0.3318)|(0.3307)|(0.3323)|
|N.obs.<br>10748<br>10748|10748|10748|10748|10748|10748|
|R-squared<br>0.002<br>0.002|0.004|0.015|0.007|0.017|0.017|

**Notes:** Models 1 to 7 report the results from OLS regressions. Cluster-robust standard errors at the vacancy level are shown in parentheses.  Dependent variable in all regressions is a dummy that takes on the value 1 if the applicant makes it through the first screening and into the longlist.  Variable _Female_ takes on the value 1 if applicant is female, 0 otherwise.  Variable _Vacancy and CV sectors match_ takes on the value 1 if the vacancy is in the same sector with applicant’s current or previous sectors. Variables _Accounting_ , _Marketing_ and _IT_ denote the occupational clusters of vacancies (and so of applicants), and the baseline category is sales occupations. Variable _Total application size_ denotes the total number of applications for the vacancy. Symbols ∗ ∗ ∗, ∗∗, and ∗ indicate significance at the 1%, 5% and 10% level, respectively.

17
Policy Research Working Paper 8123

# **Abstract**

This paper proposes a methodology to approximate individual income distribution dynamics using only time series data on aggregate moments of the income distribution. Under the assumption that individual incomes follow a lognormal autoregressive process, this paper shows that the evolution over time of the mean and standard deviation of log income across individuals provides sufficient information to place upper and lower bounds on the degree of mobility in the income distribution. The paper demonstrates that these bounds are reasonably informative, using the U.S. Panel Study of Income Dynamics where the panel structure of the data allows us to compare measures of mobility directly estimated from the micro data with approximations based only on aggregate data. Bounds on mobility are estimated for a large cross-section of countries, using data on aggregate moments of the income distribution available in the World Wealth and Income Database and the World Bank’s PovcalNet database. The estimated bounds on mobility imply that conventional anonymous growth rates of the bottom 40 percent (top 10 percent) that do not account for mobility substantially understate (overstate) the expected growth performance of those initially in the bottom 40 percent (top 10 percent).

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. 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 akraay@worldbank.org and rvanderweide@worldbank.org.

_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
## **Figure 12 - GDP growth rate in different scenarios**

Note: **HM2** : 2° increase in temperatures, with carbon revenues rebated in green incentives; **HM1.5** : 1.5° increase in temperatures, with carbon revenues rebated in green incentives and all countries increasing green public spending; **HM1.7** : 1.7° increase in temperatures, with carbon revenues rebated in green incentives and only AE and EME increasing green public spending.

# **VI. Robustness and Caveats**

The results of long-term scenario analysis should be interpreted with caution. The long-term horizon per se is a source of uncertainty, as model simplifications and approximation that might not lead to large errors within a short horizon could compound over time and lead to large deviations in the long-run. One such element is related to interest rates. The OLG model used in this paper is an equilibrium model, which under baseline UN population projections and technology assumptions, leads to low equilibrium risk-free interest rates for a prolonged period of time. As more countries in the world significantly increase their borrowing levels for purposes largely unrelated to green spending needs, borrowing costs can rise. Borrowing costs can rise also if risk-premiums, which are not modeled in this paper, increase. A different dynamic of borrowing costs would require to reconsider the debt sustainability results. A similar argument works for the intensity of the climate damages. Assuming more intense effects of rising temperatures on the economy would weaken the growth prospects going forward, although it would make an even more urgent case for a rapid increase in green public investment.

More generally, while the results relative to the levels of the variables, especially GDP growth and public debt/GDP, should be taken with caution, more trust should be given to the 20
37

# CONCLUSIONS AND POLICY ISSUES

This paper has presented a picture of the recent situation in the emerging housing market in Russia. The process of mass privatization of housing, which began in earnest in Russia in 1992, has served as the catalyst to the development of this market. At the same time, as has been seen through the analysis of survey data, the peak of privatization appears to have passed or to be stalled due to some of the conditions that now prevail in the housing situation in Russia. The introduction of policies directed at resolving these problems could further the development of the housing market and the lead to the provision of a wider range of housing market services for which there is at present an unmet demand.

An important shift in household attitudes to the housing market is apparent in the survey data. Recent data indicate a greater willingness to turn to market methods to deal with the housing problem as opposed to the non-market methods that prevailed in Soviet times. This is especially true of provincial towns and to a lesser degree of Moscow. Market methods are clearly the preferred means in households' plans to improve their living conditions. Many households would like to take out long-term loans for the purchase of housing and it is increasingly clear to people that the practice of using waiting lists to obtain free housing, a vestige from the Soviet system of allocation of housing, has not proved effective.

Below follow some broad policy proposals that address the distortions in the development of the housing market that have been examined in this paper.

- Support the transition to predominantly market methods of dealing with the housing problem;

- Abandon the allocation of free newly constructed housing;

- Introduce a category of "social" housing (low-quality dwellings, which in Moscow could include Khrushchev-era buildings, and in small towns, the old low-rise stock without modern conveniences) to address the problem of housing allocation to the poor (possibly free of charge);

- Establish a low rent for the households living in the "social" housing;

- Formulate a housing policy to assist the households prepared to pay (in full or in part) to obtain better housing;

- Coordinate this policy with the concept of housing and utilities reform (targeted at the households ready to join the housing market);

- Facilitate the development of the appropriate financial instruments (e.g., home loans) to increase the availability of funds.

The Russian housing sector is important to the life of the country not onlv because it represents an important area of future growth for the recovering economy, but because it directly touches upon the lives of all the residents of Russia. Good practices and policies are needed to ensure that the development of the housing market and related areas of the economy takes place sooner rather than later.
# **IV Quantitative model**

In this section, we calibrate the model using the United States economy and then simulate it on a sample of 30 African and Latin American countries.

## **IV.1 Calibration**

The model is calibrated on the United States, which is considered as an economy with no distortions as is standard in the literature. Several of the parameter values are assigned following the literature. The period in the model corresponds to one year in the data. **Preferences.** The yearly interest rate is targeted to 4% as in Restuccia and Rogerson (2008) <u>1</u> and Bah and Fang (2015). This implies a value of _β_ = (1+0 _._ 04)<sup>= 0</sup><sup>_._96.</sup>

**Technology.** We follow the literature by using 0.85 as the returns to scale of the production function.<sup>8</sup> Parameter values _η_ and _γ_ are attributed to match capital and labor shares of income, respectively,<sup><u>1</u></sup> 3<sup>and</sup><sup><u>2</u></sup> 3<sup>ofthereturnstoscaleoftheproductionfunction.Wechoose</sup> _δ_ so that the investment to output ratio is equal to 20%, implying _δ_ = 0 _._ 08 as in Restuccia and Rogerson (2008). The range of employment across establishments in the data determines the range of establishment-level productivity. According to the United States data from the Census Bureau,<sup>9</sup> the number of employees at the establishment level ranges from 1 to 10,000. Hence, the minimum and maximum levels of productivity are chosen to obtain the range of employment, as in the data. Normalizing the lowest firm-level productivity to 1, the highest level of productivity is chosen to obtain the maximum number of employees of 10,000, as in the data. We approximate the distribution of establishment-level productivity with 100 grid points. We choose a log-spaced grid so that the invariant distribution of establishment size across employment level matches the data. Descriptive statistics about firms highlight one important stylized fact. Establishments with fewer than 20 employees represent 86.1 percent of all the establishments. However, these establishments account only for 24.9 percent of the

> 8See for instance Restuccia and Rogerson (2008), Atkeson and Kehoe (2005), and Pavcnik (2002).

> 9The data can be downloaded from the website of the US Census Bureau: `http://www.census.gov/ econ/susb/data/susb2007.html` in the table "U.S. & states, totals".

20
of crisis despite a rapid recovery in consumption and income to pre-crisis levels.<sup>11</sup> In contrast, Stillman et al. (2006) examine the effect of a positive shock, winning an emigration lottery allowing migration from Tonga to New Zealand. They find migration to result in large increases in income, and to improvements in the mental health of the migrating households.

Although the Indonesian and Tongan case-studies show that large shocks can have significant effects on mental health, the effect they find may only be in part due to changes in income. It is likely that the financial crisis led to many other covariate changes in household circumstances, including, for example, changing availability of community level public services or household dislocation as a result of migration. Similarly, the reduced form impact of migration on mental health potentially encompasses a host of other changes other than income to the individual, which the authors explore in their paper.

Using the well-documented difference in mental health scores between men and women as a motivating question, the Indian study is an attempt to isolate exactly how a specific shock affects mental health, although the authors make less progress in identifying a causal impact. Specifically, Das and Das (2007) show that the male-female differential in mental health scores is directly related to the number of pregnancies a women has lost, either due to abortions and miscarriages or due to child-death. In households where there are no such losses, there is no gender difference in reported mental health scores. This finding is puzzling and can be interpreted in a number of different ways. By combining the quantitative analysis with anthropological narratives, the authors present qualitative evidence on the pathways through which these data may be realized.

Taken together, the three case studies provide strong evidence that while income and poverty are not strong predictors of mental health status, shocks that affect the economic or demographic nature of the household may have significant _influences_ on mental health. In the next section we turn to explore the possible _consequences_ of poor mental health for economic behavior.

# **4. Possible Consequences of Poor Mental Health**

The multi-purpose surveys enable us to examine associations between mental health and two development-related outcome measures--labor force participation and health care utilization. While we can not ascribe a causal effect to mental health (except under the strong condition of selection on observables), we view this as an exploratory step intended to show the extent to which poor mental health

> 11 Related evidence is found in de Mel, McKenzie and Woodruff (2007), who show no relationship amongst mental health recovery from the tsunami in Sri Lanka and income recovery of microenterprise owners.

- 10 -
activity. We also asked farmers to assess their spouse’s agricultural knowledge, after the knowledge portion of the intervention had been completed.

The baseline and endline surveys both contain detailed plot and crop rosters, allowing us to compute number of plots, surface cultivated, production and yields separately by crop. For each plot, we also collect detailed information about the use of labor inputs (number of workers from inside and outside the household by gender, which tasks they did, as well as the intensity of this work – allowing us to observe effort) and nonlabor inputs (phytosanitary products, fertilizer and hybrid seeds). We also collected information on the farmers’ agricultural knowledge, their perception of their spouse’s knowledge, and several variables capturing women’s decision making in agricultural production and control over crop revenue and household savings.

We also collected information on other income sources, household composition and other aspects related to the couple, such as relationship quality, and whether they still have the action plan two years later. The baseline survey was administered in June-July 2016, while the endline was administered in June-July 2018.

# **3 Empirical Strategy and Sample**

## **3.1 Empirical Strategy**

Our primary results specification in this paper is ANCOVA. Our results using survey data are estimated as follows: Yijs = α + βSeedlingSubsidyijs+ δ CouplesTrainingijs + μYijs Where

- Yijs is the outcome of interest for farmer i in village j in strata s, at follow-up.

- 0

- • Yijs is the outcome of interest for farmer i in village j in strata s, at baseline.

- SeedlingSubsidyijs equals one if farmer i in village j in strata s is assigned to receive a seedling subsidy and zero otherwise.

- CouplesTrainingijs equals one if farmer i in village j in strata s is assigned to couple’s training and zero otherwise.

- Xijs is a vector of covariates, containing all socio-demographic variables, such as age, education, marital status, household size, and farm characteristics (discussed further in Section 3.2). For agricultural outcomes (harvest and yield) we additionally control for whether rubber and cocoa were intercropped on that field.

- φs is a full set of strata dummies, indicating whether farmer _i_ is located in a particular APROMAC

- extension zone, whether village j had an above-median number of female applicants, which quartile of the number of beneficiaries that had never received rubber seedlings from APROMAC before are present in village j, and which age tercile corresponds to farmer i.

Standard errors are clustered at the village level to account for any geographic correlation and heteroscedasticity. The coefficient on SeedlingSubsidy shows the effect of being assigned to receive the 7