rafmacalaba/data-use-mentions-extended huggingface dataset:
-- - - - 40,000<br>170,000 ------- 20,000<br>150,00 1991 1992 1993 1994 1995 1996<br>-a- Able-bodied people of working age _- Officially Employed<br>-Officially Unemployed (right axis)<br><!-- End of picture text --> 20. The changes over time are 0 dramatic.'<sup>5</sup> In only six years the number of able-bodied people of jJ3clearly working age in Ulaanbaatar has grown nv by 12%. During the same period of time, the percentage reporting that they *taidoff are employed has declined from 73% to 59%, and the percentage of the not- Figure 1 employed who do not provide a reason _Source: SSO_ has grown from 38% in 1991 to 76% in 1996. Furthermore, the percentage of the not-employed that are registered at 330,000 the labor exchange has fallen from 310,000 -------42% to 13%. The dramatic increase in 290,000 - the level of people "not-employed," 270,000 coinciding with relatively low official 250,000 unemployment rates, is suggestive of 230,000 marked changes in the labor market 210,000 ------that may be escaping the official 190,000 - statistics.

21. Figure 2 depicts the changes 150,00 over time in the number of able-bodied people of working age, the level of -aofficial employment, and the level of -Officially official unemployment for Ulaanbaatar I city.'<sup>6</sup> At the same time that the level **Figure 2** of employment has decreased, and the _Source: SSO_ number of people not-employed in the annual censuses has increased, the number of registrations, upon which the official unemployment rate is calculated, have actually fallen slightly since 1991. Unemployment as registered at Ulaanbaatar'
|-0.018|
||||(0.011)|
|Men in household|2113|1.763|-0.053<br>(0.047)|
|Women in household|2113|1.721|-0.011<br>(0.053)|
|Boys aged 6-16|2113|1.553|-0.171<sup>_∗∗∗_</sup><br>(0.063)|
|Girls aged 6-16|2113|1.209|-0.020<br>(0.058)|
|Number of elderly individuals over age 65|2113|0.095|0.000<br>(0.014)|
|Children under 5 years old|2113|1.829|-0.050<br>(0.066)|
|Number of shocks in last year|2113|1.856|-0.115<sup>_∗_</sup><br>(0.061)|
|Household assets (PCA)|2113|8.415|0.095<br>|
||||(0.132)|
|Pre-program cotton area (ha)|1804|3.648|0.027<br>(0.092)|
|Pre-program cotton production (kg)|1804|3769.341|10.267<br>(133.957)|

Note: Robust standard errors reported in parenthesis. *, **, and *** indicate significance at the 90, 95, and 99% confidence intervals. Pre-program cotton area and production come from cotton producer database.

18
declines and school participation increases with higher levels of household welfare. The high cost of schooling - both official and unofficial fees - pushes children into labor market to enable them to afford school or pulls them away from school as they can not afford it. The quality of education and its relevance to labor market needs to be given priority in future education reform to ensure that parents send their children to school. The current rates of child labor and non-school attendance are not conducive to meet Ghana's growth challenge for the twenty-first century.

# 6. **Rural-Urban Earnings Difference**

The subject of the last two sections was the nature and determination of labor earnings in the formal sector of Ghana's labor market. We now turn to the important topic of differences in the levels of earnings between the rural and urban labor markets. In the discussions of African labor markets this topic has been given the pride of places. The alleged bias towards the urban economy and the maintenance of labor earnings in the urban sector at a relatively high level have been singled out as one of the chief distortions in the functioning of labor markets in Africa-causing welfare loss both in terms of equity and efficiency (see, for example the long discussion of the literature in Jamal and Weeks).

# **6.1 The Pre-Crisis Economy**

Unlike East Africa, Ghana was not distinguished by a spectacular rural-urban difference in income or labor earnings before the economy began its long slide downwards. An early report comes from Knight (1972) who presented data from a national sample survey of household expenditure survey conducted in different regions of Ghana in 1961-2. Some selected data from this source are given in Table 4.1. It is seen that consumption per household was highest in Accra-about a third higher than the hinterland rural areas. The rural-urban difference was somewhat lower when we look at smaller towns in the individual administrative regions and compare them with their rural hinterlands, and non-existent in the Volta and Western regions. The average size of households was
_Social Capital, Household Welfare and Poverty in Indonesia_ 26 household's endowment of social capital. Human capital is measured conventionally by the years of education of the adult members of the household.'<sup>6</sup> The LLI study data set contains information on land, cattle and farm equipment owned by the household. Direct inclusion of these variables as regressors in equation (1) is problematic due to possible endogeneity. Indeed, as Table 3 indicated, 26% of households sold assets to pay for consumption expenditures. Unfortunately, the data do not contain the stock of assets at the beginning of the consumption reference period. For that reason, we chose not to include the asset variables as regressors. Instead, we created a dummy variable to indicate whether the head of households was a farner. This must be seen as an occupational variable as well as a proxy for ownership of agricultural assets.'<sup>7</sup> In addition, the regressions include demographic variables, such as household size and gender of the head of household. Age of the head of household and its squared term were included to capture the life cycle of household welfare. Lastly, two dummy variables were included to indicate province (Jambi was used as omitted category). These variables capture the general economic and social conditions of the provinces along dimensions other than those which we were able to include in the model.<sup>1 8</sup> The first column in Table 4 replicates, as far as the data permit, the model that was used by Narayan and Pritchett (1997) for Tanzania. It consists of one aggregate social capital index, which is a multiplicative index between the density of associations, their internal heterogeneity and the index of active participation in decision making. The

> **16** The LLI questionnaire recorded only the level of educational achievement of each adult in the household and the number of years of education was imputed from that information. 17 In order to assess
- 14 -

rental payments. This cash deposit can be increased by the landlord during tenancy and must be refunded in its entirety by the landlord when the tenant vacates the dwelling. The interest foregone by the renter is expected to cover the implicit rental value of the dwelling. Instead of raising rents when the rental market becomes tigp, or operating costs increase, landlord increase the amount of chonsei.29. This pervasive practice developed spontaneously in response to earlier economic and financial market conditions when inflation was high, regulated interest rates were negative. Urban housing was in extremely short supply due to the destruction of the housing stock during the war and to high rates of urbanization. Chonsei is the most conspicuous aspect of the financial repression imposed on the household sector in Korea. The financial magnitudes involved in this housingrelated financial claim are very large both for an individual tenant and in comparison with the institutional housing finance system.

30. Household surveys in the mid-1980s show that chonsei represents a very high share of the capital cost or market value of the rented space or unit. Typical chonsei ratios range between 35 percent and 40 percent of unit market value, these ratios are higher in large cities. For modern apartment units they can go as high as 70 percent of the unit's value. Thus the financial claim can be startlingly large. For instance, the Office of National Tax Administration (ONTA) reported in 1987 that a Hyundai apartment in Apkujong-dong a quality district of Seoul was posted at 243 million won for tax purposes, or 3 million per pyong (one pyong is 3.? m2). For a typical 55-pyong Hyundai apartment the Chonsei deposit can therefore vary between US$75,000 and US$133,000 when per capita income in Korea was $2,800. Because, it is not often possible to accumulate such contracts exist such as partial or declining chonsei, which large financial savings for housing, various forms of rental include blends of cash deposits and monthly payments. Such large financial claims also explain
giving the relationship in Figure 1, whereby poorer states have no higher participation in MGNREGS, despite the greater demand for work on the scheme.

To better understand this strong rationing effect of poverty, in Dutta et al. (2012) we study more closely the performance of the state of India with the highest poverty rate, Bihar. Drawing on various (qualitative and quantitative) data sources, including our own specialpurpose surveys, we argue that low administrative capacity for implementing the scheme and weak empowerment of poor people are at work in Bihar. Furthermore, we argue that changing one alone will not assure that MGNREGS will reach its potential in India‘s poorest areas. Effective action on both fronts will be necessary,

# **3. Is rationing undermining the self-targeting mechanism?**

By insisting that participants do physically demanding manual work at a low wage rate, workfare schemes such as MGNREGS aim to be self-targeted, in that non-poor people will not want to participate. The substantial rationing that we have demonstrated above raises the question of how well this self-targeting mechanism works in practice. The fact that there is rationing does not mean that targeting will not be pro-poor. For one thing, the manual work requirement at a low wage rate will still discourage non-poor people from wanting to participate. For another, the local authorities doing the rationing may well favor the poor. The local officials who are deciding who gets work could either enhance or diminish the scheme‘s targeting performance. There has been no comprehensive national assessment of targeting performance. The quantitative studies that have been done so far have been based on selected samples and the tests used have often been problematic.<sup>12</sup> What does the evidence from the NSS survey for 2009/10 suggest?

> 12 Jha et al. (2009) report evidence that households with larger landholdings were more likely to participate in the scheme in Andhra Pradesh (AP), though they find evidence of better targeting in Rajasthan. They conclude that the scheme
of the world.<sup>12</sup> Table 1 summarizes the sample statistics for 6 MENA countries and 26 non-MENA countries. The principal differences in export dynamics between these MENA and non-MENA countries lie in the number of exporters and the value of exports per exporter. The number of exporters and mean exporter size is much lower in MENA countries than in non-MENA countries. The average number of exporters in a MENA country is 5,552 and 13,258 in non-MENA countries.

A final point to illustrate using the EDD, is that MENA and non-MENA countries export different goods. In MENA, trade is concentrated in labor-intensive sectors; the goods sector with the largest number of exporters is food products (25 percent of all exporters are in this sector). However, less than 10 percent of exporters from non-MENA countries are in this sector. In the rest of the world, the sector with the most exporters is more industrialized (machinery and electrical goods). The predominance in MENA of exports in the non-technical industries is related to the low level of regional skills, human capital and technological sophistication.

> 11 Other countries exclude low-income countries.

> 12 The Exporter Dynamics Database is restricted to data from 2005 – 2010, we also exclude agriculture and extractive industries. Since no MENA country is low-income, we also exclude low-income countries from the rest of the world.

5
# **The Varying Income Effects of Weather Variation: Initial Insights from Rural Vietnam**<sup>*****</sup>

## Ulf Narloch

Sustainable Development Practice Group, World Bank, Washington, DC, USA

**Keywords:** Climate Change, Consumption, Households, Incomes, Livelihoods, Poverty, Shocks, Vulnerability

**JEL:** I30, I32, O10, Q10, Q54, R20

* **Acknowledgements:** This work is part of the programmatic AAA on Vietnam Climate Resilience and Green Growth (P148188) and was developed under the oversight of Christophe Crepin. It contributed to the global program on Climate Change and Poverty (P149919) under the oversight of Stephane Hallegatte. I am very thankful to the World Bank Vietnam team for providing the Vietnam Household Living Standards Survey (VHLSS) data and to Linh Hoang Vu and Ha Thi Ngoc Tran for helping with data questions. Mook Bangalore prepared the weather and geo‐spatial data. Maros Ivanic and Anne Zimmer contributed to earlier data preparations. Very helpful suggestions and comments on earlier versions of this work were received from Tuan Anh Le, Diji Chandrasekharan Behr, Gabriel Demombynes, Linh Hoang Vu, Christopher Jackson, Frederik Noack, Pamela McElwee, and Maurice Rawlins.
|Other income source|0.02|0.02|0.05|0.06|0.05|
|---|---|---|---|---|---|
|Aid/transfer sent to relatives/friends in Haiti|0.14|0.17|0.25|0.26|0.29|
|Aid/transfer received from relatives/friends in Haiti|0.11|0.11|0.20|0.22|0.23|
|Aid/transfer received from relatives/friends abroad|0.14|0.12|0.15|0.21|0.34|

Source: Own computations using June 2010 (EFSA II) surveys. Notes: The sample is restricted to the households residing in the six strata that cover areas directly affected by the earthquake.

# _(f) Damages and losses due to the earthquake_

Table 6 presents households damages and losses by pre-earthquake wealth quintile. Many households in the directly affected areas appear to have had their house damaged or destroyed (82.6% of all households). Concerning income sources, the richest households appear to have more experienced the death of one or more income earners (11.5%) compared to other groups (8.7% on average for all households). They have also experienced loss of savings more often. Compared to other households, the richest ones were more to lose: in February 2010, 86.5% experienced assets losses, compared to only 17.6% among the poorest. The main assets lost were a television, radio or fan among the richest; they consisted of a radio, cell phone and iron among the poorest. In June 2010, many of the richest households had recovered back to their pre-earthquake situation (16.0%), whereas the poorest households were more to lose. This feature of the
socioemotional skills (OECD 2019). Furthermore, workers in more digitally intensive sectors in those same countries earn higher wages when they have higher numeracy and self-organization skills (both cognitive skills) (Grundke et al. 2018). A similar result is found in the United States: using online job postings for professional occupations, Deming and Kahn (2018) find positive pairwise correlations of 0.5 between posted requirements for basic digital skills (computer literacy and office programs) and skills categories related to cognitive and socioemotional skills, as well as positive correlations of around 0.2 between advanced digital skills (programming and specialized software) and cognitive and socioemotional skills categories.<sup>1</sup> In this paper, we investigate the extent to which digital and non-digital (cognitive and socioemotional) skills are complements in digital and non-digital occupations and how these skills are distributed across the labor market in four Southeast Asian countries. Since detailed data on the skills requirements of occupations is not available for the study countries, we build a data set of skills requirements by occupation using data from more than half a million online job advertisements posted in Malaysia between 2016 and 2018. We classify the skills as digital, cognitive, socioemotional, and others. We use the resulting data set to create skills profiles at the occupation level, which we then match to employment data from nationally representative surveys in Cambodia, Malaysia, Thailand, and Vietnam. We define four levels of the digitalization of an occupation (very-low, low, medium, and high) based on the share of basic, intermediate, and advanced digital skills in each occupational profile. This allows us to answer two questions. First, we explore how digital, cognitive, and socioemotional skills correlate with each other within occupations, focusing on Malaysia where the primary source of data was collected. Second, we estimate the extent to which digital and nondigital skills are used in each country, given the occupational structure of the individual labor markets.

In addition to providing the answers to
confirms that the decline in actual fertility in firstborn-girl families that we document is not driven by a decline in desired fertility (i.e., preferences).

The coefficients of the stated preference terms reveal that actual fertility is, as we may expect, increasing in desired fertility and in the desired ratio of sons to daughters. However, excess fertility is decreasing in the ideal ratio of sons to daughters, suggesting that desired fertility rises more steeply with son preference than actual fertility.<sup>45</sup> Again, this makes sense, given that the desire to have sons leads to the widely documented phenomenon of son-biased fertility stopping (which is evident from the baseline statistics in the first row). Since actual fertility is not fully in the control of parents, it will tend to rise less than proportionately with desired fertility.

# **6.4 Birth Spacing**

We also study birth spacing between the first two births as an outcome. We focus on the sample of women who have two children to avoid selection in terms of fertility preference as most couples in India have at least two children. We expect two mechanisms to be at play here: a behavioral mechanism that distinguishes firstborn-girl from firstborn-boy families, and a mechanical effect which is strictly active only in the post-ultrasound period. The first is that firstborn-girl mothers choose shorter birth spacing than firstborn-boy mothers, driven by their desire to conceive a son— one may think of this as an intensive margin behavior corresponding to their fertility-continuation behavior (Bhalotra and van Soest (2008), Jayachandran and Kuziemko (2011), Milazzo (2018)). The second is the mechanical effect associated with abortions being unobserved in the fertility history data. The observed interval between births will tend to be longer when an abortion takes place since there is a conception but then no birth. This implies that firstborn-girl households will tend to have longer birth spacing post- relative to pre-ultrasound since (sex-selective) abortions are predominantly in these households. In sum, other things
occupation, and a formal job as the main occupation during the past 12 months. These women’s average age hovers around 32 and ranges from 17 to 58.

The working rate of women in 2016 was 93%. For comparison, Table A.1 in the Appendix also reports men’s employment rate, which is 6 percentage points higher with almost all men (99%) working. This male-female gap in the employment rate, however, was stable during the period. The 2014 and 2016 VHLSS include questions concerning the reasons for not working. These are very different for men and women. Figure A.1 in the Appendix presents the distribution of women and men by reasons for not working. In 2016, 90% of women did not work outside the home because they were occupied with housework. These activities include caring for one’s own home, caring for grandchildren so the daughter can work, caring for elderly women, or any additional range of tasks. Compared with women, the proportion of men not working outside the home for the same reason was much lower at 23% in 2016. For men, the main reasons for not working were retirement, sickness and disability. The unemployment rate (the unemployed comprise those who were unable to find a job during the past year) was very low, less than 1% in 2016.

Among working people, men work more hours (209 hours) in a month than women (190) in 2016. The gender gap in the number of people in a wage-earning job is larger, though this gap decreased over time. In 2016, around half of all men (51%) had a wage-earning job, while the corresponding figure was less than half for women (38%). This means that more than half of all women (55%) were self-employed. We classify self-employed work into farm and non-farm employment. In 2016, 18% of women had non-farm employment and 37% of women worked on farms.

We
products to GDP. This measure of vulnerability does not show the distributional or fiscal effects of a change in oil prices.

Vulnerability, so defined, can be related to a number of factors through an accounting identity. Such a decomposition analysis allows a study of how much changes in individual factors contribute to the overall changes in vulnerability. Following Bacon and Kojima (2008), this paper uses a refined Laspeyres index—rather than a log mean Divisia index—to enable the incorporation of both production and consumption effects, thereby allowing the inclusion of countries that are net oil exporters. The change in vulnerability (V) is decomposed into several factors between two dates. One advantage of this approach is that the decomposed factors are additive and their relative contributions to the overall change in vulnerability can be easily ascertained.

The identity is expressed as the sum of two terms that separate the production of oil and consumption of oil products, each of which is further decomposed into several individual factors:
that are fully owned by men. We know from ILO data that women are more likely than men to be engaged in the informal economy in the region; the fact that we see fewer of them formalizing (and, thus, represented in the Enterprise Survey data) suggests that there are characteristics associated with these firms that make them both less likely to formalize and less productive if they manage to do so. Obtaining a deeper understanding of the gender and informal entrepreneurship in the region would be an interesting avenue for further research but is beyond the scope of this study.

# **5. Conclusion**

In this paper we show that a legal environment that discriminates against women as employees and entrepreneurs is positively associated with the probability that a firm with female participation in ownership began operating as an informal enterprise. The association is strongest for firms in South Asia, but also holds for firms in Sub-Saharan Africa and in the Middle East and North Africa. We also document a similar, although less strong, relationship between legal equality and the duration for which firms 31
04|
|||(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|
|**_Poverty_**|Market income|0.06|0.06|0.05|0.06|0.02|
|**_severity_**||(0.001)|(0.002)|(0.001)|(0.002)|(0.001)|
||Disposable income|0.05|0.06|0.05|0.06|0.02|
|||(0.001)|(0.002)|(0.001)|(0.001)|(0.001)|
||Consumable income|0.06|0.06|0.06|0.07|0.03|
|||(0.001)|(0.002)|(0.002)|(0.002)|(0.001)|
||Final income|0.04|0.04|0.04|0.04|0.02|
|||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|

**_Source:_** _Authors’ calculations; Data from ESS 2018/19._ **_Note:_** _Standard errors in parenthesis._

In other words, the combined effect of fiscal policies in Ethiopia (taxes and direct transfers) increases poverty among children when comparing market and consumable income. While this finding is similar to the observation made for the Kenya (Save the Children 2021), it is relatively uncommon when compared to most other countries in which similar studies have been carried out.<sup>9</sup>