rafmacalaba/datause-encoder-data huggingface dataset:
IHS2 used a two-stage stratified sample selection process. The primary sampling units (PSU) were the Enumeration areas (EAs), selected from each stratum on the basis of probability proportional to size (PPS). The household population figures used to select the EAs at the first stage originated from the 1998 Population and Housing Census. The 30 IHS2 strata are distributed over 3 regions of the country, namely North, Center, and South, and include all 4 urban centers, namely the cities of Mzuzu, Lilongwe, Blantyre, and Zomba, and 26 administrative districts, which excluded Likoma Island and were all considered rural. In total, 564 EAs were selected for the survey. At the second stage, each selected EA was relisted, and 20 households were randomly selected. The final IHS2 sample included 11,280 households.

The IHS2 featured a multi-topic household questionnaire that solicited (i) detailed information on household income, consumption, and agricultural production, and (ii) data on demographics, education, health and labor at the individual-level, among other topics. Weight and height information were recorded for children aged between 6-59 months in kilograms and centimeters respectively. In terms of agriculture, the household reported detailed information on cultivation and production for the reference rainy and dry agricultural seasons, and on tobacco production specifically, including a history of tobacco cultivation covering the period of 1999/2000-2003/2004 rainy agricultural seasons.<sup>10</sup> On the whole, we limit our analysis to smallholder households, defined to hold arable land of seven acres or less.<sup>11</sup> In exploring the effect of household tobacco adoption on child nutrition, we identify for each child whether his/her household was a tobacco grower in the year of or the year after his/her birth. This dummy variable is the main explanatory variable of interest.<sup>12</sup> We limit our cash crop production definition to tobacco producers, as Malawian production of other inedible cash crops like cotton or tea pale in comparison to tobacco (World Bank, 2007).

Few Malawian farmers monocrop tobacco. Due to food necessities and risk reduction through

> 10The retrospective questions on previous tobacco cultivation activities only captured incidence. Hence, with the exception of the reference rainy season, we do not have data on the extent of tobacco cultivation on the farm and the associated production outcomes for the past seasons.

> 11The seven acre cutoff covers 95 percent of the farming Malawian household population. Our results are robust to alternative cut-off points. Box-plots underlying our decision for the seven acre threshold and empirical results under different thresholds are available upon request.

> 12Our results robust to alternative definitions of household tobacco production, including tobacco production (i) in the year of and the year after child birth, (ii) only in the year of child birth, (iii) only in the year after child birth, or (iv) only during the staple food price shock of interest.

8
20

**Table 2.1.** Markets for new housing and secondary markets in Russia (price of one square meter of total floor space, fourth quarter, 1996.)

|Type of Housing|Price leve|l (thousand rubl|es)|
|---|---|---|---|
||Bottom|Top|Average|
|Low quality apartments<br>secondary market|734|4,314|1,969|
|Standard apartments||||
|newly constructed|1,220|4,493|2,794|
|secondary market|679|4,319|2,166|
|Ratio of primary/secondary market prices|1.8|1.0|1.3|
|Apartments with improved planning and design<br>newly constructed|882|6,046|2,934|
|secondary market|645|5,754|2,603|
|Ratio of primary/secondary market prices|1.4|1.0|1.1|
|Apartments in top-quality buildings||||
|newly constructed|1,613|11,987|4,644|
|secondary market|1,721|11,630|4,595|
|Ratio of primary/secondary market prices|0.9|1.0|1.0|

Source: Goskomstat _Socio-economic situation in Russia,_ No 12. Moscow, 1996.

These figures give an idea of the' environment in which the housing market is taking shape. Without question, the very high cost of housing in Russian is an important constraint, among others, affecting Russian households' behvaior as the attempt to deal with this issue.

# **2.2. Households' near-term plans and intentions to improve their housing conditions 2.2.1 General assessment of the housing situation**

Sociological surveys indicate that households' assessments of their housing conditions have recently changed for the better (Fig. 2.1). The number of households who said they were satisfied with their housing has risen by 1.6% in Moscow, 6.4% in Vladimir and 3.6% in (3orodets. Nevertheless, 36.8, 47.6 and 48.2% respectively still considered housing to be a pressing problem.

Fig. 2.1. Satisfaction With Housing Conditions (% of households polled) <!-- Start of picture text --> 70- 61.3 62.9<br>60- 54 52.4 51.8 51.8<br>50 - 46 48.2 48.2<br>40 -<br>30-<br>20-<br>10<br>0<br>1993 1996 1994 1996 1994 1996<br>Moscow Vladimir Gorodets<br>I Esatisfied U dissatisfied<br>]1<br><!-- End of picture text -->

**Sources: Household Survey in Seven** Cities **Moscow, Decemnber** 1993; T'he Moscow Longitudinal **Household Survey, January 1996; Vladliniir and Gorodets Longitudinal Household Survey,** Oct-1994 **and** Dec.1996.
Return Migration and Labor Market Outcomes: Evidence from South Asia<sup>_∗_</sup> Laurent Bossavie<sup>1</sup> and He Wang<sup>1</sup>

> 1World Bank

**JEL codes:** J21, J61, F22, O13

**Key words:** Return migration, labor supply, occupational choice, wage premium

> _∗_ This paper was prepared as a background paper for the World Bank policy report and book ”Towards Safer and More Productive Migration for South Asia”. We are very thankful to our colleagues and co-authors S. Amer Ahmed and Caglar Ozden for multiple discussions on the topic of temporary migration in South Asia which contributed to improving the paper. All remaining errors are ours. Funding for this research was provided by the Rapid Social Response Multi-Donor Trust Fund, the DEC Research Support Budget (RSB), Knowledge for Change Program (KCP) and the Multi-Donor Trust Fund for International Trade within the World Bank.
Mexico), the age profiles of excess deaths are similar to those of official COVID-19 deaths, and those under age 65 account for on average 37 percent of excess deaths.<sup>5</sup> Figure 3 shows the hypothetical age distribution of COVID-19 deaths according to official reports, as well as excess deaths, applying the United States standardized age distribution. This controls for the differing population age distributions across countries. Unsurprisingly, as compared to the actual, observed distributions, these figures show an older profile of deaths in low- and middle-income countries. However, the pattern of a younger profile of deaths in less wealthy countries remains, both with official data and with excess mortality data. The numbers shown in Figures 2 and 3 can also be found in Appendix Tables A1 and A2.

Notably the United States has a much younger profile of death—using both official COVID-19 death counts and excess mortality—than countries with similar income levels. This is the case even after controlling for differences in population age distribution. The age distribution of deaths in the United States is roughly similar to that of Chile. In both countries, 27 percent of excess deaths were among those under age 65.

Figure 4 presents a visual representation of all the mortality rates by age group across all countries and economies with available data. The vertical axis represents mortality rates per 100,000, and it is important to note that the scale varies widely across countries, reflecting the different levels of exposure to the pandemic and to its related mortality across the globe. Overall, mortality rates increase with age, albeit with different gradients. With the excess mortality data, a few deviations from this gradient are likely due to the relatively low mortality as well as the overall small number of deaths (e.g. Iceland). Australia, New Zealand and Taiwan, China have close to zero official COVID-deaths and show negative estimates of excess mortality rates, especially for the older age groups, likely reflecting averted deaths due to isolation policies.

> 5 Data is also available to produce excess death estimates by age for Brazil, but not with our preferred age group breakdown.

6
Sub-Saharan Africa: A case study of a metalworking cluster in Nairobi. _Small Business Economics_ , forthcoming.

Sonobe, T. & Otsuka, K. (2006). _Cluster-Based Industrial Development: An East Asian_ _Model._ Basingstoke: Palgrave Macmillan.

Sonobe, T. & Otsuka, K. (2010). _Cluster-Based Industrial Development: A Comparative Study of Asia and Africa._ Basingstoke: Palgrave Macmillan.

- Syverson, C. (2010) What determines productivity? _Journal of Economic Literature_ , forthcoming.

Tewari, M. (1999). Successful adjustment in Indian industry: The case of Ludhiana’s woolen knitwear cluster. _World Development_ , 27 (9), 1651-71.

Tybout, J. R. (2000). Manufacturing firms in developing countries: How well do they do, and why? _Journal of Economic Literature,_ 38(1), 11-44.

28
**TableA7. Factors affecting the non-poor household's probability to live in the Lower Zambezi Park System GMA and, once in the GMA, the probability to** **<u>participate in the VAG/CRB</u>**

||Lower Zambezi|Probitmode|lsfortheh|ousehold's probability to<br>Participate in the|
|---|---|---|---|---|
|Variable|Variable description|Liveinthe|GMA|VAG/CRB<sup>_a_</sup>|
|||(1)||(2)|
|Hage|Age of the household head in years|-0.009||0.037<br>***|
|||(0.007)||(0.012)|
|Fhhh|Female-headed household dummy|-0.081||-0.104|
|||(0.113)||(0.792)|
|Maxedu|Education of the most educated household|-0.005||0.036|
||member in years|(0.014)||(0.042)|
|c0to14|Number of children below 15 years|-0.068|*|-0.193|
|||(0.039)||(0.165)|
|f15to60|Number of female members 15-60 years old|-0.084||-0.022|
|||(0.064)||(0.123)|
|m15to60|Number of male members 15-60 years old|0.032||-0.019|
|||(0.065)||(0.162)|
|m61plus|Number of members older than 60 years|-0.099||-0.397|
|||(0.161)||(0.457)|
|Kroad|Distance to the nearest main road in km|-0.054||-0.018|
|||(0.035)||(0.052)|
|Kbsch|Distance to the nearest basic school in km|0.012||-0.054|
|||(0.011)||(0.102)|
|Kheal|Distance to the nearets health centre in km|0.016|*|0.024<br>*|
|||(0.009)||(0.013)|
|Vacast|Value of consumer durable assets in ZMK|-0.092||0.097|
|||(0.112)||(0.081)|
|Dcoop|Participation in cooperatives dummy|0.049||0.437|
|||(0.226)||(0.362)|
|Nproj|Number of projects in the community|-0.112||0.139<br>***|
|||(0.078)||(0.051)|
|Dcfnd|CRB funded dummy variable|-0.343||1.891|
|||(0.398)||(1.534)|
|Npart|Number of participants in the CRB/VAG|1.103|*|0.274<br>***|
|||(0.599)||(0.058)|
|Cons|Intercept|0.075||-4.187<br>***|
|||(0.610)||(1.317)|

> _a_ Based only on the sub-sample of households that are located in the GMAs Level of significance: *=Significant at 10%; **=Significant at 5%; ***=Significant at 1% Values in parentheses are standard errors

22
# Appendix D. Robustness test for spatial dependence

Table A. Demand Side: Determinants of Income Per Worker<sup>a,b</sup> (standard errors corrected for spatial dependence in parentheses)

||(1)|(2)|(3)|
|---|---|---|---|
||Spatial GMM|Spatial OLS|Spatial GMM|
|Average Schooling|0.286***|0.280***|0.260***|
||(0.032)|(0.023)|(0.030)|
|Ln(marketpotential)|0.404***|0.048***|0.371***|
||(0.083)|(0.016)|(0.069)|
|Ln(no. workers)|-0.318***|0.005|-0.304***|
|[ln(population)for(3)]|(0.113)|(0.018)|(0.092)|
|Ln(inter-citytransport costs)|-0.246**|0.016|-0.218**|
||(0.122)|(0.024)|(0.102)|
|state capital dummy|-0.010|-0.090**|0.041|
||(0.157)|(0.039)|(0.143)|
|time dummies|Yes|Yes|Yes|
|Observations|369|369|369|
|Hansen J statistic<br>(overidentificationtest)|0.884||0.901|

*** significant at 1% level; ** significant at 5% level; * significant at 10% level.

- a. The instruments are semi-arid area dummy, ln(distance to state capital), ln(distance to São Paulo), manufacturing/service employment ratio (1970),  infant mortality (1970), ln(humidity), average years of schooling (1970), state capital and time dummies.

- b. Coordinate variables are latitude and longitude. Cutoffs are 1.5 standard deviations of latitude and longitude (10.23, and 8.20), which correspond to about 900 miles.

44
percent of clients have fewer than 5 employees, 33 percent between 5 and 20 employees, 29 percent between 21 and 99 employees, and 22 percent of clients have 100 or more employees.

The second block of rows in Table 1 looks at what external signals of quality these providers use. It is very rare to use any form of money-back guarantee, with only 2 percent of providers offering a financial guarantee if customers are not satisfied – it is only a few marketing firms that offer this. Just over half say they will redo work if the customer is not satisfied, and 28 percent of the accounting firms say they will help clients if they fail an audit or have tax problems. Three-quarters of staff have tertiary qualifications, and 84 percent belong to some form of professional association, but only 39 percent of their staff on average have a professional certification of some form. Note that our sampling process captures the more formal and experienced providers who serve more customers – there are also likely to be some single-person firms providing accounting or marketing to a much more limited client base, and where information and quality concerns may be even more of an issue.

We then asked the providers how they find the majority of clients. The most common method is through word-of-mouth, which is the main method for 66 percent of firms. Only 16 percent of firms get most customers through advertising, and only 8 percent are walk-in customers. This reliance on word of mouth and lack of advertising is consistent with the lack of brand recognition seen in our survey of GEM firms, and shows firms using informal reputation as the main way of trying to overcome information and quality concerns.

Finally, we asked the providers why they thought more small firms do not use the types of services that their firm provides. The most common reason given was that firms lack knowledge of the value of these services, which 75 percent of providers gave as one of the three most important factors. Only 25 percent thought the services do not have a high enough return to warrant use by small firms, while 66 percent of providers thought that firms do not have enough money to pay the upfront costs. Not knowing what providers exist in the market was seen as a key factor by 33 percent of providers, and firms not being able to tell the quality of the providers that are on the market was seen as a key factor by 40 percent of firms. These suggest that information frictions 7
growth in Mongolia. Less than 1 percent of firms considered it to be one of the three biggest obstacles to growth. Estimates suggest that on average, in 2004 firms’ losses due to crime added up to 1.6 percent of sales, higher than the average for East Asia (1 percent).

# **3.4 Not all constraints a country faces are “binding”**

As shown in section 3.3.1, low level of capacity utilization in Mongolian firms implies that infrastructure services other than transport can be improved but at present are not binding constraints to private sector growth in Mongolia. Other constraints to private sector growth that are not binding but need to be addressed in the future in order to sustain growth over the medium term are discussed next.

# **_3.4.1 Mismatch of skills_**

While a high percentage of high skilled workers are employed in jobs well below their qualifications, over half of the large firms cite a shortage of skilled workers as a reason for not operating at full capacity. A third of the firms surveyed by the World Bank reported vacancies for professionals, and nearly two-fifths report vacancies for educated workers (World Bank 2007). Employers—especially large firms—complain about shortage of certain types of skills such as practical knowledge (e.g., English), technical knowledge (e.g., computers), and managerial, marketing and behavioral skills (work ethics and ability to work in teams).<sup>62</sup> The data on wages of different types of labor support these findings (Table 19).

**Table 19. Monthly average wages and salaries in 2006 (thousand tug)**

|Legislators, senior officials and managers|195.8|
|---|---|
|Professionals|152.2|
|Technicians and associate professionals|132.2|
|Clerks|93.4|
|Service workers and shop and market sales workers|103.2|
|Skilled agricultural and fishery workers|74.7|
|Craft and related trades workers|130.8|
|Plant and machine operators and assemblers|147.7|
|Elementary occupations|91.3|
|Average|128.5|

Source: Mongolia National Statistical Office.

Poor quality jobs for skilled workers and underutilization of the skilled labor force are also problems in Mongolia. Although the labor market is relatively flexible,<sup>63</sup> high social security taxes and the fact that these are applicable only to permanent workers, tends to result in poor-quality job opportunities by increasing the share of contract and informal

> 62 According to representatives from the Foreign Investment and Foreign Trade Agency marketing knowledge and skills are critical to export growth.

> 63 According to the Doing Business Database, and the Growth Competitiveness Index for 2005 minimum wages and firing costs are low, and temporary contracts are permitted in Mongolia.

34
Politically defined ‘districts’ are the primary unit for collection of socio-economic, environmental and geographical data for the Ricardian approach (i.e. the independent variables in the regression equations) and they are the basis of all analyses. In the current project, the approach is being applied to selected districts in 11 countries, representing a broad spectrum of climatic conditions (Figure 1; Table 1). Multi-disciplinary teams of researchers in each country have collected the data required for the analyses.

This report describes the methods and results of hydrological analyses to provide inputs for use in the Ricardian assessment of climate change impacts. The reported analysis is conducted in two phases. The principal objectives of the first phase of the study were to

- i. develop an approach that enables flow and runoff to be derived for the ‘districts’ of interest;

- ii. develop time series (1961–1990) of runoff and flow for the ‘districts’ which will provide a baseline for climate change scenarios; and

- iii. provide hydrologically relevant parameters that can be used as independent variables in the Ricardian analyses.

The objectives of the second phase were to

- i. extend the time frame for the hydroclimatic series from 1990 to the year 2000, using the same modeling methodology used in Phase I and updated input data from the Climate Research Center at the University of East Anglia to calibrate and simulate the hydrological model up to the year 2000;

- ii. provide results for the new district boundaries for Cameroon and Egypt (which have changed since the Phase 1 results were compiled) for the 1961–1990 period as well as for the proposed future analyses; and

- iii. provide climate change scenario analyses for the same hydroclimatic variables at district level.

6
# **Referentes**

- Aparicio, S. and R. Benencia (Coordinadores), 1999. “Empleo Rural en Argentina: viejos y nuevos actores en el Mercado de trabajo”. In Empleo Rural en Tiempos de Flexibilidad, La Colmena, Buenos Aires.

- Aparicio, S. and E. Tapella, 2002. “Algunos resultados preliminares del monitoreo de subproyectos”. Nacional Coordination Unit, PROINDER, Buenos Aires.

- Araujo, C., A. de Janvry and E. Sadoulet, 2003. "Measuring the role of social networks on behavior with an application to rural off-farm employment". Department of Agricultural and Resource Economics University of California Berkeley. Mimeo.

- Arias, Omar, K.F. Hollack, and W. Sosa, 1999. “Individual Heterogeneity in the Returns to Schooling:  Instrumental Variables Quantile Regression Using Twins Data,” Mimeo, University of Illinois.

- Brondo, A. and C. Luparia, 2001. “La libreta del trabajador rural”. In Neimanl G. (Compilador) Trabajo de campo: producción, tecnología y empleo en el medio rural. Ediciones Ciccus, Buenos Aires.

- Buchinsky, M., 1998. “Recent Advances in Quantile Regression Models – A Practical Guideline for Empirical Research". In The Journal of Human Resources, Vol. XXXIII, No. 1, pp. 88-126.

- Card, D., 1998. “The Causal Effect of Education on Earnings”. Forthcoming in Handbook of Labor Economics, Vol. 3, Ashenfelter, O. and Card, D. (eds.).

Caballero J. M. (2005). “Rural poverty in Mexico” .World Bank, mimeo.

- CEDLAS, 2004. “Monitoring the Socio-Economic Conditions in Argentina, Chile, Paraguay and Uruguay: Argentina chapter”. Available online at http://www.depeco.econo.unlp.edu.ar/cedlas/monitoreo/pdfs/argentina1.pdf

- Craviotti Clara and S. Soverna, 1999. “Sistematización de estudios de casos de pobreza rural”. PROINDER, SAGPyA, Buenos Aires.

- Currie, Janet, 2001.  “Early Childhood Development Programs.” In Journal of Economic Perspectives, 15 (2), pp. 213–38.

- Elbers, C., J. Olson Lanjouw, P. Lanjouw and P. G. Leite, 2001. “Poverty and Inequality in Brazil: Estimates from Combined PPV-PNAD Data.”  World Bank, Mimeo.

- Heckman, James, 1999. “Policies to Foster Human Capital.” Working Paper No. 7288. MA: National Bureau of Economic Research, Cambridge.

50
**Table 6.  The Investment Equation by Ownership**

||**Sta**<br>|**te**<br>|**Non-st**<br>|**ate**<br>|**Fore**<br>|**ign**<br>|
|---|---|---|---|---|---|---|
||**OLS,**|**FE**|**OLS,**|**FE**|**OLS**|**FE**|
|ln(GDP per capita)|0.034||-0.034||0.047||
||(1.390)||(-1.416)||(1.161)||
|ln(city pop)|0.018||0.012||-0.012||
||(0.823)||(0.626)||(-0.394)||
|ln(firm age)|0.016||-0.053***||-0.121***||
||(0.754)||(-4.376)||(-2.747)||
|Loan|0.091***||0.168***||0.217***||
||(3.925)||(7.954)||(4.753)||
|Trade credit|0.014||0.090||0.070||
||(0.173)||(1.522)||(1.084)||
|UCA|-0.000||0.039***||0.010||
||(-0.025)||(5.800)||(0.219)||
|sales/lagged K|0.030**|0.080***|0.010***|0.042***|0.009**|0.059***|
||(2.177)|(15.933)|(5.882)|(26.696)|(2.119)|(15.620)|
|CF/lagged K|-0.023|-0.290***|0.121***|0.139***|0.072***|-0.035|
||(-0.621)|(-7.077)|(5.010)|(11.286)|(2.619)|(-1.617)|
|Lagged sale growth|0.028||0.016**||0.023||
||(1.283)||(2.370)||(1.222)||
|Tobin’s Q|-0.056|0.111|0.265***|0.151**|0.340**|0.184|
||(-0.391)|(1.362)|(3.102)|(2.435)|(2.311)|(1.413)|
|Number of observations|1,007|2,098|7,765|16,258|1,210|2,504|
|Adjusted R2|0.144|0.198|0.082|0.153|0.101|0.178|
|p-value for the test of equality of<br>CF/lagged K for OLS|State = No|n-State: 0.001;|State=Foreig|n: 0.063; N|on-State=Fore|ign: 0.174|
|p-value for the test of equality of<br>CF/lagged K for FE|State = No|n-State: 0.000;|State=Foreig|n: 0.000; N|on-State=Fore|ign: 0.000|
|p-value for the test of equality of<br>Loan for OLS|State = No|n-State: 0.013;|State=Foreig|n: 0.014; N|on-State=Fore|ign: 0.318|
|p-value for the test of equality of<br>trade credit for OLS|State = No|n-State: 0.457;|State=Foreig|n: 0.593; N|on-State=Fore|ign: 0.814|

*** p<0.01, ** p<0.05, * p<0.10.    Not reported are coefficients of area dummies.  Q for OLS is industry-level average of Q, and for FE is industry-year level of Q.

47