rafmacalaba/datause-encoder-data huggingface dataset:
using data from the 2000 census (Figure 2). This may be due to the preponderance of correspondence schools and part-time degree programs in the 1980s and 1990s, which enabled individuals to obtain higher education degrees later in life, and the relatively low overall college enrollment rates as a share of the total population. Also, entering classes contain students from multiple birth cohorts, so that cohort differences in educational attainment will be less obvious than time differences in enrollment. In contrast, the census data plotted in Figure 2 does show clearly the shock to high school completion for Cultural Revolution cohorts.

Reinforcing disruptions created by closure of high schools and universities early in the Cultural Revolution was the “sent-down youth” program (also known as the “rusticated youth” program), under which youth from urban areas were sent to the countryside to live in rural communities. The stated ideological motivation for the program was to help young people get in touch with the revolutionary origins of the Party and to contribute to the country’s rural development. However, the program also has been viewed as a pragmatic response to social unrest in urban areas created by a vast youth population that was neither employed nor in school and stirred to revolutionary furor at the start of the Cultural Revolution (Meisner, 1986). Starting in 1968 large numbers of urban youth of high school and early college ages were dispatched to rural areas, guided by education officials and local revolutionary committees established during the Cultural Revolution. Families were typically allowed to choose one child to keep in the city, but all other children of appropriate age were sent to the countryside. As the Cultural Revolution progressed, students were sent to the country at progressively later ages and some students with favorable class backgrounds were allowed to defer service in the countryside. While rusticated youth were initially expected to spend two years in the countryside, many spent much longer periods of time there because 9
completed. One impediment to early payouts is that the Indian Meteorological Department takes an average of two months to verify rainfall data. ICICI Lombard is in the process of setting up a private network of automated rain gauges, which in the future will facilitate faster payouts as well as minimize basis risk. A second potential innovation would be to combine insurance with a shortterm loan that helps credit-constrained households pay for the premium (stated differently, BASIX could offer state-contingent loans). We raised this possibility with BASIX; they are currently reluctant to mix products in this way, because they want to clearly establish to customers the conceptual difference between insurance and micro-credit products.

The overall conclusion of our empirical work is that, early in its introduction, the insurance product we study has not yet succeeded in proportionately reaching the most vulnerable households (e.g. poor, credit-constrained households, or households that are not members of social networks), that presumably would benefit most from protection against drought. These stylized facts likely in part reflect persistent real barriers to trade in insurance such as credit constraints, but also in part are due to a normal pattern of diffusion of a new product. Still in the early stages of introduction, the insurance product is not fully understood, and takeup rates are low. Early adopters are likely to be households where the cost of experimenting is low; participation will then filter through to other households over time.

A less sanguine perspective is provided by Morduch (2004), who highlights potentially adverse general-equilibrium implication of differential rates of insurance participation between rich and poor households. Morduch suggests that if rainfall insurance is only purchased by the wealthy, such households may have additional income to bid up the price of local non-traded goods during periods of low rainfall, making non-purchasers worse off. He also suggests that formal insurance may undermine existing risk-sharing mechanisms, by raising the threat point of households that seek to withdraw from implicit risk sharing arrangements.

31
Figure 3B. Lowess nonparametric fit of likelihood of school and log per capita expenditure, by years <!-- Start of picture text --> Prob. of work<br>rural boys rural girls<br>5 6 7 8 9 5 6 7 8 9<br>log per capita expenditure 98/99 value log per capita expenditure 98/99 value<br>bandwidth = .8 bandwidth = .8<br>urban boys urban girls<br>5 6 7 8 9 10 5 6 7 8 9 10<br>log per capita expenditure 98/99 value log per capita expenditure 98/99 value<br>bandwidth = .8 bandwidth = .8<br>...... 1998/99 ------- 2001/02 ____ 2005/06<br>1 1<br>.8 .8<br>.6 .6<br>prob .4 prob .4<br>.2 .2<br>0 0<br>1 1<br>.8 .8<br>prob .6.4 prob .6.4<br>.2 .2<br>0 0<br><!-- End of picture text --> Note: capita is adjusted with adult equivalence.

25
The benefits of market integration, however, can not be achieved without improving the infrastructure.

**Infrastructure:** Infrastructure is like second-nature geography, which can reduce the time and monetary costs to reach markets and thus overcome the limitations of physical geography.<sup>18</sup> Improved infrastructure that enhances connectivity and contributes to market integration is the best solution to promoting growth as well addressing rising inequality between regions. The Ganga Bridge in Bihar in India is a good example of second-nature geography. The bridge has reduced the time and monetary costs of farmers in the rural areas in north Bihar to reach markets in Patna, the largest city in Bihar. The Jamuna Bridge in Bangladesh is another good example of spatially connective infrastructure. The bridge has opened market access for producers in the lagging Northwest areas around the Rajshahi division. Better market access has helped farmers diversify into high value crops and reduced input prices.

So far, South Asia has achieved impressive growth rates despite poor infrastructure. This may be difficult to sustain in the future. Poor infrastructure and restrictive labor laws (to be discussed later) are among the major factors that have restrained the growth of manufacturing sector and prevented firms from growing.<sup>19</sup> Table 2 shows that the manufacturing share of value added in India is smaller than that share in other large developing economies, though it is similar to that share in smaller countries with GDP per capita similar to that of India (such as Vietnam). However as table 3 shows, the growth of value added in manufacturing in India is noticeably lower than that in these smaller similar income countries. Indeed the sectoral growth comparisons in table 3 are rather striking. The growth of value added in _services_ in India is comparable to that in China, and about 10 percentage points higher than that in other countries. In rather stark contrast the growth of value added in manufacturing in India is only about _half_ that in China and Vietnam.

The service sector in India has done well because it relies less on transportation and is less energy intensive than manufacturing. South Asia has the highest share of services in its exports at 31 percent, which is higher than OECD high income countries. ICT exports and global outsourcing have benefited from the use of the internet which has reduced information transmission costs dramatically. While other countries can emulate India’s successful efforts to boost services export, sustained high growth will require a substantial effort to raise manufacturing growth in all South Asian countries. In general, poor infrastructure has constrained the growth of labor intensive manufacturing firms in South Asia and prevented the region from making use of its most important asset, its people.

> 18 See Kanbur and Venables, 2005a.

> 19 See Fernandez and Pakes, 2008.

23
In Figure 2, we summarize the evolution of Zambian export concentration in terms of both destination countries and products, using the Herfindahl index. This index ranges from 1/ _N_ , where _N_ is respectively the number of destinations or products, to 1. The lower the value of this index, the more diversified are export flows. The graph shows a trend towards export diversification in the early 2000s, followed by a steep increase in concentration after 2004.<sup>3</sup> This observed reversal in 2005 is without doubt due in large part to the increases in global commodity prices that started in that period and thus boosted the weight of traditional exports in the total. Another way of putting this, though, is to point out that any efforts at export diversification have failed to compensate for the reemerging dominance of mineral exports due to increased foreign demand for Zambian copper.

Figure 3 places Zambian export concentration in international context. Panel A shows Zambia to have the fourth-highest Herfindahl index, surpassed only by Botswana, Laos and Niger. Panel B reveals that in terms of its reliance on the biggest export product category, Zambia is second in the world, behind only Botswana.

In sum, Zambia remains exceptionally reliant on a single export sector, copper and related products, and this reliance has been increasing over the last decade – to a large extent due to rising global commodity prices.

# **2.2 Firm-level exports: Basic patterns**

We now turn to the analytical level at which the customs micro-data are uniquely informative: individual firms. In Table 9, we show that the number of formally exporting firms has more than tripled over our sample period, from 516 in 2000 to 1,754 in 2011. The number of exported products has more than doubled, from 813 to 1,764, and the number of destinations has risen by

> 3 The steep increase of the Herfindahl indices observed in 1999-2000 is most likely explained by incomplete data in our initial sample year.

6
projected before the disaster. The deviations in these variables were all much more pronounced than in the control group, with the exception of the changes in public debt, and both revenues and expenditures moved in the opposite direction to the control group.

**Table 3. A Hurricane Erika on Dominica (August 2015): Deviations of macro-fiscal outcomes from projections made immediately prior to the disaster (percent of GDP)**

||**Dominica**||**Other**<br> <br>**SIDS**|**Caribbean**|
|---|---|---|---|---|
||2015/16|2016/17|2015/16|2016/17|
|GDP growth|-1.4|-6.2|-0.8|-1.0|
|Govt Revenue|26.4|23.8|-2.1|-2.3|
|Govt Expenditure|11.1|19.3|-2.7|-2.2|
|Net Lending|15.3|4.4|0.7|-0.1|
|Gross Public Debt|-5.1|0.6|-5.6|-5.0|

_The figures in the table show the difference between the outcome for each variable and the projection made just before the disaster. Net lending is equivalent to the overall fiscal balance._

# <u>Source: WEO database</u>

**Table 3. B Hurricane Maria on Dominica (September 2017): Deviations of macrofiscal outcomes from projections made immediately prior to the disaster (percent of GDP)**

||**Dominica**||**Other**<br>**SIDS**|**Caribbean**|
|---|---|---|---|---|
||2017/18|2018/19|2017/18|2018/19|
|GDP growth|-1.3|-2.9|0.1|-0.3|
|Govt Revenue|13.3|9.2|-2.5|-1.7|
|Govt Expenditure|27.5|19.6|-3.6|-2.3|
|Net Lending|-14.1|-10.4|1.1|0.6|
|Gross Public Debt|3.9|13.3|-4.6|-4.8|

_The figures in the table show the difference between the outcome for each variable and the projection made just before the disaster. Net lending is equivalent to the overall fiscal balance._ <u>Source: WEO database</u> 10
<!-- Start of picture text --> Density of PMT Score for Women<br>a Manipulation test with 90% Cl<br>8<br>S<br>©<br>2°<br>Fa<br>oO<br>g -<br>oS<br>5e<br>c<br>8<br>Biocme} Pill ea<br>“Ss<br>S<br>lo}<br>-5000 0 5000<br>PMT Score<br><!-- End of picture text -->
not to deny that, for given bulkiness of imports, countries with longer average import routes will tend to have greater fuel costs per value unit associated with imports; although these cost differences may be much less than in proportion to such average distances.<sup>47</sup> 66. **Impacts on landed import prices vary, but are in most cases small** . The determinants of freight costs are complex, varying with a range of factors such as distance and vessel type. While fuel costs are commonly a large part of those costs, however, freight costs themselves are generally a small part of the total cost of imports, around 5-10 percent, though higher, at perhaps 25 percent, for commodities. Estimates reported in AGF (2010b; p.38) suggest an impact from a charge of $25 per tonne of CO2 of less than 1 percent for most items, though up to 2-3 percent for some commodities.

67. **The impact on food prices is a particular concern, but seems likely to be fairly small** . According to calculations in Table 4 (from Stochniol 2011a) impacts range from about 0.2 to 1.0 percent for a $25 per tonne CO2 price. Developments in fuel and food prices over the next decade or two could well dwarf the impact on import and export prices of a modest carbon price.

**Table 4. Maritime Transport Costs by Product Category and Ship Segment**

|**Food category**|**AV**<br>**(percent)**|**Unit cost**<br>**$/Tonne**|**Price**<br>**increase**<br>**(percent)**|**Shipping**<br>**mode**|
|---|---|---|---|---|
|Live animals|19|821|0.79|Container|
|Meat|5|168|0.21|Container|
|Fish|4|172|0.17|Container|
|Dairy products, birds eggs, honey|3|110|0.13|Container|
|Live trees, plants, bulbs, cut|8|250|0.34|Container|
|flowers|||||
|Vegetables|22|154|0.92|Container|
|Fruit & nuts|13|123|0.55|Container|
|Coffee, tea, mate & spices|4|103|0.17|Container|
|Cereals|21|58|0.88|Clean Bulk|
|Milling products, malt, starch|11|70|0.44|Container|
|Oil seeds and oleaginous fruits|16|68|0.67|Clean Bulk|
|Vegetable plaiting materials|10|65|0.42|Container|
|Animal or vegetable fats, oils|5|62|0.21|Tanker|
|Sugars and sugar confectionary|9|52|0.38|Container|
|Beverages, spirits and vinegar|5|95|0.21|Container|
|Food industry residues & waste|25|124|1.05|Container|
|Tobacco|3|193|0.13|Container|

Source: Stochniol (2011a), which builds on UNCTAD (2010) and Vivid Economics (2010). Notes: AV = average transport cost share of total import value; Unit cost = Average absolute transport cost per tonne of transported goods.

68. **Average shipping costs for oil, as fraction of shipped value, are of similar order as for food products** . According to the IMO (2009), the fuel consumption of tankers in 47 Stochniol (2011b) finds much less difference in average fuel consumption associated with imports, than in average trade distances. This again rests on an argument that goods shipped longer distances are carried predominantly in larger and more fuel efficient vessels.

37
# **Box 1. Improving Remittance Data in Mexico**

Mexico’s official data indicates that remittance inflows increased from US $8.8 billion in 2000 to US $17.2 billion in 2004, an increase of 95%. According to Mexico’s central bank, this increase in remittance inflows reflects major improvements in the way remittances are reported by financial institutions to Mexico’s central bank, rather than a major increase in the number of Mexican migrants working in the USA, which increased during the same period by 22%.

**<u>Chart 4. Remittance Inflows to Mexico from the USA</u>**

<!-- Start of picture text --> 20 $20,000<br>New regulation to<br>improve remittance<br> statistics<br>10 $10,000<br>0 $0<br>2000 2001 2002 2003 2004<br>Migrant Population Remittances<br>Remittances in US Million Dollars<br>Mexican Migrants in USA in Millions*<br><!-- End of picture text -->

- *It refers to migrants born in Mexico and currently living in the USA.

Sources: CONAPO and Central Bank of Mexico.

Before 2002, only commercial banks reported their remittance transactions to Mexico’s central bank. On October 28, 2002, Mexico’s central bank issued a new regulation requiring all money transfer companies to register at the central bank and report the value of their remittance transactions on a monthly basis. As shown in Chart 4, after the new reporting system was introduced, remittance statistics started to see a rapid growth. While other factors may also have influenced the increase in remittance inflows, the improvement in data collection practices has largely contributed to it.

Moreover, at the international level it is also important to address some misclassification problems that may cause either sub- or over-reporting of remittance inflows. For example, are funds borrowed by a migrant in his host country and invested in his home country (e.g. purchase real estate) a remittance? Are guidelines followed by banks sufficiently clear to prevent certain trade-related transactions from being reported as remittances? Clearly, these are issues that need to be addressed at an international level so that remittance data is consistent across countries.

13
# I. Introduction

The macroeconomics of structural adjustment confers a central role to 18bor market response, since the achievement of a real devaluation demands both real wage flexibility and intersectoral labor mobility. However, it is usually argued that the effectiveness of expenditure switching and expenditure reduction policies is mitigated by obstacles in the labor market. These obstacles result from segmentation, job security regulations, wage indexation, aggressive minimum wage practices and other institutional sources of rigidities. Hence, a central issue in applied research is to determine how influential are economic forces as opposed to institutional forces in affecting labor market adjustment.

Although several analytical models single out the likely important effect of distortions and institutions in terms of a costly adjustment process, little recent empirical evidence is available for LDCs. In fact, previous research work has dealt with wage indexation, minimum wages policies, distortive non-wage costs and institutionally segmented labor markets in a theoretical perspective [see, for instance, Fischer (1984), Edwards (1988), Lal (1985), Lopez and Riveros (1989)]. However, and in spite of their potential importance in assessing the stylized facts of labor market perforwAnce in the adjustment, comparative studies of recent LDCs' experience in the framework of imperfect labor markets are all but abundant.

This paper reviews the recent experience of 23 LDCs' labor markets on the basis of ad hoc country studies prepared for the World Bank.l. The aim is to provide a synthesis of the main conclusions in light of the major implications of standard macroeconomic models. Given the diversity both in labor market structure and in recent economic experience among LDCs, this review is necessarily selective in its coverage and concentrates upon two main ques'ions as its main focus:
<!-- Start of picture text --> AFR EAP ECA LCR MNA SAR APD EC NAM<br>20 fF<br>45 Base year<br>—_ . i -- 2010<br>x A<br>8F<br>oO | Scenario<br>a 10 Oo Zerolnvest<br>—i 1) invest<br>| Maxinvest<br>0<br>2050 2050 2050 2050 2050 2050 2050 2050 2050<br><!-- End of picture text -->
work burdens and time constraints, help improve women's labor productivity, and assess the potential trade-offs that women may face among their multiple activities. Women's attempts to reallocate their labor in the face of competing demands on their time can have serious nutritional implications and can hamper efforts to tackle problems related to their roles as farmers and caretakers of their children.

- Available evidence from rural areas it 'Cenya points to women's significant

- time commitments. A ten community study undertaken in 1979 found that women spent 13 to 14 hours per day working. Resp'idents reported that, on average, tasks related to food preparation and nutri.Lon took up one-third of the day, while water and fuelwood collection, farming. ca.ring for animals and marketing took up the remainder. Water collection alone was estimated to take 2 to 4 hours per day (GOK and UNICEF, 1984). Demands on women's time have increased as more attention is paid to health and sanitation issues: 'adequate sanitation, for example, requires water for washing children, cleaning dishes and laundering clothes, over and above the water required for animals" (ILO, 1986). Women's labor input also increases when drinking water requiring boiling.

Table 1 Women's Weekly Time Allocations for Productive Activities

||High potent|ial<br>areas|Low poten|tial areas|
|---|---|---|---|---|
|.Activity|is Women|irs/wk.|3 Women|Hrs/wk.|
|Collecting firewood|90|5.25|81|6|
|<br>Farming|90|12.25|62|13.5|
|Caring for animals|66|19.5|53|12|
|Marketing|44|6|53|10.75|
|<br>Milking|35|5.75|50|8|

Source: Krystall and Gommes, 1979. Cited in World Bank, 1989.

A recent study in Siaya District shows that women spend most of their<sup>time</sup> on food crop production rather than on childcare or home maintenance. According to this survey, women have primary responsibility for land clearing and preparation, planting, weeding and harvesting. Women also are the ones to fetch water and firewood with some limited help from their daughters. When asked to indicate the most important task to carry out first if there were time constraints, about 85 percent of the women said that they would carry out childcare and other household activities as a priority (Menya, 1988).

3