rafmacalaba/datause-encoder-data huggingface dataset:
However, the addition of skills measures does not further explain male-female differences in wages.

**Table 10. Quantile Decompositions of Log Wage Gaps**

||Controls|+ Schooling|Addi<br>|ng Reading<br>Scores|Adding|Noncognitive<br>skills|
|---|---|---|---|---|---|---|
|||Proportion||Proportion||Proportion|
|Wage<br>percentile|Wage<br>Gap|<br>explained by<br>characteristi<br>|Wage<br>Gap|<br>explained by<br>characteristi<br>|Wage<br>Gap|<br>explained by<br>characteristi<br>|
|||cs||cs||cs|
|ARMENIA|||||||
|10<sup>th</sup>|**0.363**|0.30|**0.359**|0.27|**0.358**|0.31|
|50<sup>th</sup>|**0.523**|0.12|**0.526**|0.13|**0.535**|0.17|
|90<sup>th</sup>|**0.522**|-0.10|**0.520**|-0.11|**0.525**|-0.05|
|BOLIVIA|||||||
|10<sup>th</sup>|**0.680**|0.12|**0.646**|0.23|**0.659**|0.35|
|50<sup>th</sup>|**0.459**|0.18|**0.449**|0.23|**0.469**|0.33|
|90<sup>th</sup>|**0.328**|0.25|**0.329**|0.26|**0.352**|0.36|
|COLOMBIA|||||||
|10<sup>th</sup>|**0.349**|0.10|**0.348**|0.10|**0.329**|-0.05|
|50<sup>th</sup>|**0.291**|0.00|**0.290**|-0.01|**0.300**|0.00|
|90<sup>th</sup>|**0.296**|-0.10|**0.297**|-0.09|**0.332**|-0.02|
|GEORGIA|||||||
|10<sup>th</sup>|0.312|0.22|0.327|0.17|**0.372**|0.20|
|50<sup>th</sup>|**0.392**|0.04|**0.385**|0.01|**0.384**|-0.04|
|90<sup>th</sup>|**0.434**|-0.11|**0.426**|-0.15|**0.388**|-0.21|
|KENYA|||||||
|10<sup>th</sup>|**0.341**|0.33|**0.357**|0.31|**0.360**|0.32|
|50<sup>th</sup>|0.154|0.88|0.165|0.87|0.144|1.12|
|90<sup>th</sup>|-0.011|-10.40|0.000|302.48|0.064|3.06|
|UKRAINE|||||||
|10<sup>th</sup>|0.051|2.25|0.027|2.48|0.045|1.45|
|50<sup>th</sup>|**0.355**|-0.01|**0.348**|-0.02|**0.363**|-0.09|
|90<sup>th</sup>|**0.441**|-0.06|**0.440**|-0.07|**0.414**|-0.09|
|VIETNAM|||||||
|10<sup>th</sup>|**0.373**|0.22|**0.369**|0.22|**0.392**|0.25|
|50<sup>th</sup>|**0.288**|0.23|**0.287**|0.28|**0.282**|0.34|
|90<sup>th</sup>|**0.203**|0.19|**0.197**|0.18|**0.203**|0.29|

_Note_ . Coefficients in bold face are significant at the 5% level or lower. The dependent variable is the log hourly earnings. Controls include potential experience, squared term for experience, and occupation dummies. The sample includes 25- to 64-year-olds (both men and women) in self-employment or wage work.

_Source_ : STEP Surveys (2014).

29
**Table 2:  Household** **<u>characteristics of the poor, the food poor and the malnourished</u>**

|**Household characteristics**|**Overall**<br>**population **|**Poor**|**Non-poor**|**Chronically**<br>**malnourished**|
|---|---|---|---|---|
|Household size (median)|9|11|12|10|
|Age dependency ration|1.4|1.6|1.6|1.5|
|Female headed (proportion of column)|7%|6%|4%|7%|
|Head’s age (median)|48|51|52|50|
|Head: no education|77%|87%|90%|85%|
|Head: primary education only|11%|9%|9%|10%|
|Head: secondary education or more|12%|4%|2%|6%|
|**Total**|**100%**|**100%**|**100%**|**100%**|
|Total number of sample households|6449|2335|1159|2095|
|Population estimate- individuals|10.8|5.1|2.6||
|(millions)|||||
|Population estimate – households|0.9|0.35|0.16|0.31|
|(millions)|||||

_Source: authors’ calculations using ELIM –MICS 2010_

# **B. Location characteristics**

While 77 percent of the overall Malian population lives in rural areas, nearly all the poor (90%) and the food poor (93%) are concentrated in rural areas and (table 3). Most (84%) of the malnourished live in rural areas. Households with malnourished children are spread across the country in proportion to the population. The only exception perhaps is Sikasso where the chronically malnourished are more overrepresented (19% of the chronically malnourished live there while only 16% of the population does) (table 3). While 11 percent of our population resides in the northern regions of Mali (Timbuktu, Gao, and Kidal) the share of poor and food-poor households living in the North is actually smaller than the national average. This is because the northern regions are the least populated in Mali. Poverty rates in the Sikasso region are fairly high despite despite good agricultural conditions and cotton production. This so-called ‘Sikasso Paradox’ is partly caused by the relatively high poverty line that is set for this region (Delarue etal. 2009, Dury and Bocoum 2012, World Bank, 2012).

6
utilization of informal credit sources show a negative economic gradient with lower overall utilization in 2004 than 1993.<sup>44</sup> The main change was a displacement of informal credit by formal credit (for both landed and landless) rather than an overall increase in credit utilization.

It is not surprising that the landless are less likely to get formal credit at given consumption, since they lack the collateral provided by land. But it is worth noting that the landless poor are also less likely to receive credit from the national anti-poverty program.<sup>45</sup> The 2004 survey asked whether respondents had participated in the anti-poverty programs (since 1999). Figure 15(a) gives the conditional probabilities of receiving subsidized credit through these programs for both farmers and the landless. We see that among the poor, the landless are appreciably less likely to receive credit from this source, which appears to be targeted instead to poor farmers. Possibly the lack of land is seen to make the landless a credit risk. However, the bias appears to go deeper than this, since we find a similar pattern in participation in the noncredit components of the anti-poverty programs, as can be seen in Figure 15(b). The sources of this evident bias against the landless poor merit attention in future research.

# **7. Poverty-increasing landlessness?**

There are a number of further observations that lead us to question the idea that rising landlessness has increased poverty, as a generalization of Vietnam’s experience over this period. We focus on the relationship between rates of poverty reduction across regions and the increases in landlessness. Clearly this relationship does not solely reflect the reforms; for example, the

> 44 Details are available from the authors. The mean proportion using informal credit in 1993 was 39.21% for farmers and 40.87% for the landless, while in 2004 the proportions were 20.36% and 21.13% (n=904).

> 45 Credit subsidies are targeted to Vietnam’s poor through the Hunger Eradication and Poverty Reduction Program and Program 135.  These programs also provide help with health care costs and local infrastructure, though micro credit is the main instrument.

29
GDP per capita. For the _Banking Sector_ , _Financial Markets and NBFIs_ , and _Liquidity and Funding_ categories, the curves appear to show that financial sector policy makers in more developed countries responded faster compared to less developed economies. For example, in the _Banking Sector_ category, almost all higher-income countries had taken at least one measure after 60 days since the WHO declaration - for lower-income countries, it took 120 days. In contrast, in the _Payments Systems_ category, less developed countries were faster in implementing payment systems measures. The log-rank test of the equality of the survival curves shows there is a significant difference (p-value < 0.01) in the policy reaction time between higher and lower-income countries, except for the _Payments Systems_ category (p-value = 0.25).<sup>16</sup>

# **Figure 5. Kaplan-Meier (KM) Survival Curve Estimates by Category: Countries Grouped by GDP per Capita**

**Panel A. Banking Sector**

<!-- Start of picture text --> 0 20 40 60 80 100 120 140 160 180 200 220<br>Days since declaration of Public Health Emergency of International Concern<br>Lower income Higher income<br>P-value: 0.000.<br>1.00<br>0.75<br>0.50<br>0.25<br>Countries implementing a measure (%)<br>0.00<br><!-- End of picture text -->

**Panel B. Financial Markets and NBFIs**

<!-- Start of picture text --> 0 20 40 60 80 100 120 140 160 180 200 220<br>Days since declaration of Public Health Emergency of International Concern<br>Lower income Higher income<br>P-value: 0.000.<br>1.00<br>0.75<br>0.50<br>0.25<br>Countries implementing a measure (%)<br>0.00<br><!-- End of picture text --> 16 For brevity, we just report results of the log-rank test of equality. As robustness, we also performed the Wilcoxon-BreslowGehan test (Breslow, 1970; Gehan, 1965), which gives more weight to events occurred at early time points; and the Peto–Peto– Prentice test (Peto and Peto, 1972; Prentice, 1978) to account for higher ratio of hazards earlier in the time period. These produce qualitatively similar results.

20
# **1. Introduction**

Malawi has experienced several catastrophic droughts over the past few decades. The recurrent weather shocks have resulted in low economic growth and significant volatility from year to year. This is because Malawi’s agriculture constitutes about 40 percent of the economy, largely smallholder farming and mostly rain-fed maize production. As a result, weather patterns deeply affect agricultural production and Malawi’s GDP. Further since the Malawian diet is overwhelmingly dominated by maize consumption, which comprises over 50 percent of total calories, changes in maize production also have a direct impact on food security. Most recently, the adverse weather in 2001, 2002 and 2005 brought dramatic swings in agricultural output in the past decade.

In recent years the Government of Malawi has been pursuing innovative approaches towards developing a comprehensive national food security strategy. In this context, the World Bank has been exploring the use of _ex-ante_ market-based instruments to assist the government to manage the financial risks associated with volatility in maize production. This paper discusses an innovative instrument that has been designed to assist the Government of Malawi in managing the risks associated with the impact of weather shocks. The next section highlights some of the indirect costs faced by Malawians as a result of these shocks. Section 3 provides background information on the design and limitations of index-based drought risk management instruments. Section 4 describes the development of such an instrument for Malawi. Section 5 provides some steps ahead and conclusions.

# **2. The impact of weather shocks on the economy in Malawi**

Economic growth in Malawi has been elusive, reaching no more than 2 percent average during the period between 1999 and 2005, with significant volatility from year to year (Table 1 and Figure 1). Much of the poor performance has been the result of the recurrent weather shocks on <!-- Start of picture text --> Figure 1: Maize production and GDP growth in Malawi, 1985-2005<br>20 200<br>15 150<br>10 100<br>5 50<br>0 0<br>c<br>-5 -50<br>-10 -100<br>-15 -150<br>GDP Grow th Rate Maize Production Grow th Rate<br>Source: Malawi National Statistical Office<br>198519861987198819891990199119921993199419951996199719981999200020012002200320042005<br>(percent)<br>Rate (percent)<br>GDP growth rate<br>Maize Production Growth<br><!-- End of picture text --> 2
advantage of this measure is that it does not impose a limit for the share of top performing firms, which can vary with business cycles. A disadvantage is that the growth rate per se does not take into consideration the differences in initial size, nor relative average differences across countries.

An alternative to the OECD approach is a definition based exclusively on a fixed share of top performing firms, such as the top 1%, 5%, or 10% of highest growing firms in the period. Several studies adopted this measure (Goedhuys and Sleuwaegen 2010, Holzl and Friesenbichler 2010, Lopez-Garcia and Putente 2012, Bjuggren et al. 2010, Capasso et al. 2013, Coad et al. 2014, Bianchini and Secchi 2016, and Daunfeldt and Halvarssonin 2016). This definition is also based exclusively on the growth rate of the firms but keeps the share of HGFs constant. A drawback of these definitions is that, in case there is an unexpected event where all firms have low growth, it still identifies the top percentile of firms as HGF.

The Birch index (Birch, 1979) is likely the most well-known HGF definition in the second group, which also considers the initial size of the firm. The index combines both the absolute and relative growth rates. It is defined as �X� �X���� �� ~~,~~ where Xt is the number of employees at the period ���� t. The HGF definition following this method selects a given share of firms in a top percentile of the Birch index distribution.

Another index, following the aim of controlling for differences in the initial size of firms is �� � ���� provided by Davis, Haltiwanger, and Schuh (1996). The DHS index, defined as ~~� ]~~ , �.� ��������� where, X� is the number of employees at the period t. This index measures the growth rate of the firm relative to the average size. HGF definitions based on this method also select a given share of firms in the top percentile of the index distribution.<sup>9</sup> In this paper we focus on 3 definitions of HGF including: i) a variation of the OECD definition; ii) the Birch definition; and iii) the DHS definition, as described in table 1. However, due to the large number of small firms in developing countries, we do not impose a threshold on a minimal number of employees. In Tunisia, the median firm has 2 employees. For the sake of space and being concise with some of the descriptive statistics, whenever we present a table only with 1 definition, we use HGF1.

<u>Table 1 – Definition of HGF based on a three-year period of cumulative growth</u>

|**Definitions**|<br>**Description**|**Based on**|
|---|---|---|
|HGF1|Firms with average annualized growth greater than 20% per annum.|OECD|
|HGF2|Firms in the top 10 percent of the Birch index, over a three-year<br>period.|Birch|
|HGF3|Firms in the top10percent of DHS Index.|DHS|

Source: Authors elaboration.

> 9 Another option suggested by Clayton et al. (2013) classifies a firm with fewer than 10 employees as an HGF if it made a growth of 8 or more employees over a 3‐year period.

4
more evident for lower-caste households. Behrman and Deolalikar (1990) find that females eat less when food is scarce and the marginal value of food is high, and vice versa.

Considerable evidence against the unitary framework (Strauss and Thomas, 1995; Haddad et al., 1997) has made collective framework (Chiappori, 1988, 1992; Bourguignon et al., 1993, 1994) attractive. Using the latter, Thomas (1990) finds a mother’s unearned income has greater impact on daughters’ anthropometric outcomes than that of sons, while a father’s unearned income has the opposite effect. Using household food expenditure data, he also finds that the estimated impact of women’s unearned income is about seven times that of men’s unearned income for (per capita) calorie and protein consumptions. Schultz (1990) finds that women’s unearned income has significantly different effect (i.e., reduces more) than men’s unearned income on women’s labor supply, and women’s but not men’s unearned income has a significant positive effect on fertility. Hallman (2003) uses maternal shares of current assets, premarital assets, and marriage payments as proxy measures for resource controls, and finds that a mother’s assets are generally more beneficial for girls and a father’s for boys as far as child morbidity is concerned, which is consistent with Thomas (1994). She further finds that a greater share of marriage payments to husbands reduce child morbidity, regardless of a child’s sex. This is consistent with Rao (1997), who shows that lower dowries increase wife-beating and reduce child calorie intake during marriage. Targeting mothers for cash-transfers seems to increase secondary school enrollment, particularly for girls, and has positive effects on a child’s health, nutrition, and food consumption (Skoufias and McClafferty, 2003; Adato et al., 2003). Pitt and Khandker (1998) find that household consumption and child nutrition and education are significantly better when the micro-credit borrowers are women. None of these studies using collective framework, however, focuses on individual nutrient intakes - the topic of this paper.

While collective models provide useful insights, often it is difficult to distinguish (empirically) their predictions from those of unitary model (Behrman, 1990, 1997). For instance, a unitary model will predict that better schooled women are more efficient in household production and knowledgable about health and child bearing technology. A collective model will argue that the better schooled women bargain more effectively over household resources, and that women are more interested in nutrition than their husbands. Moreover, spouses’ education could be correlated with other unobserved factors, such as marriage market selection (Foster, 1998), and may pick up unobserved wealth or income effects. Using a unitary framework, Rosenzweig and Schultz (1982) demonstrate that the sex differences in infant mortality rates is influenced by the sex differences in labor-market participation rates, while Folbre (1984) argues that this relationship is supportive of a non-unitary framework in which women who have greater incomes have greater 5
- 12 -

Figure 1 - Matrix of Trading Characteristics

||~~~~~~~~~|O<br>r<br>d<br>**8**<br>r Odr<br>~~~~ Qut Drv<br>r<br>**V**<br>**C**<br>**fi**|QC<br>u<br>o<br>t<br> <br>e<br>D D<br>r<br>**V**<br>e<br>nn|e<br>**fis**<br>t<br>X<br>d|D<br>i<br>t<br>r<br>X<br>~~~b<br>t<br>d|C<br>o*A<br>nfun<br>t<br>**C**<br>it<br>i<br>uo0<br>0<br>ac<br>**u**|1<br>A<br>t<br>**0n**|_N_<br>c|
|---|---|---|---|---|---|---|---|
|Order Driven|X||X|X|X|X||
|QuotenDriven||X|X|X|X|X|X|
|Centralized'|X|X|X|XI'|X|X|X|
|Distributed <sup>2</sup>|X|X|X'|X|X|X|X|
|Continuous Auction|X|X<sup>3</sup>|X|X|X|X7||
|Call Auction|X|X|X|X|XE|X||
|Non-Auction<br>__|.n1.|X|X|X|||X|

- 1 Often called "Floor based' in reference to the traditional stock exchange trading floor but may also be a trading room such as in Warsaw or Tokyo.

- 2 Commonly a telecommunications and computer network linking broker/dealer offices.

- 3 Requires competing market makers who continuously respond to the quotes and sales occurring across the market.

- 4 After close of centralized trading session.

- S As above.

- 6 At opening of continuous auction session to clear pre-session quotes.

- 7 As above.
We report the short-term correlations between quarterly GDP growth and employment growth in South Asia in Table 3. The results suggest that seasonal variation in economic growth affects the composition of employment. With higher seasonal growth, self-employment decreases while employment in other types of jobs increases. But these changes net out and we do not find evidence of an overall increase in employment with economic growth if the latter is not seasonally adjusted. Conversely, seasonally-adjusted economic growth has no impact on the composition of employment, but it has an impact on overall job creation. With each additional annualized percentage point of seasonally adjusted quarterly growth, employment increases by 0.16 percent.

More distant points are needed to assess how much employment is created per percentage point of economic growth in the longer term. We follow a four-step procedure to this effect. First, we compute comparable employment rates for all observations in our dataset. Second, we apply these employment rates to consistent estimates of the working-age population, obtained from readily-available demographic series, to obtain employment levels. The advantage of using demographic series is that they are comparable over time. Third, we compute percentage changes in the resulting employment for all the pairs of observations available for each country. And fourth, all the percentage changes in employment levels are divided by the percentage change in GDP over the corresponding period.

**Table 3.** Correlations of quarterly changes in employment rates and GDP growth.

|**South Asia**|**Correlation coefficient**|
|---|---|
|total employment|-0.03<br>(0.7206)|
|Between non-seasonally adjusted GDP and<br>self-employment|-0.30***<br>(0.0006)|
|rest|0.24***<br>(0.0078)|
|total employment|0.16*<br>(0.0794)|
|Between seasonally adjusted GDP and<br>self-employment|0.14<br>(0.1302)|
|rest|0.04<br>(0.6353)|

**Note:** *** p<0.01, ** p<0.05, * p<0.10. Number of observations (n=123). **Source:** South Asia Employment Dataset.

Each of the resulting figures is an estimate of the elasticity of employment growth to economic growth, or the percentage change in employment for every percentage point of GDP growth. We summarize the distribution of these elasticities for South Asian countries in Table 4. On average, one percentage point of economic growth has led to an increase of employment by 0.34 percent. When only considering the data points furthest away from each other, the elasticity decreases to 0.19 as in the long-run rising GDP per capita negatively affects the employment rate (World Bank 2018a). Using these long-run estimates, Bangladesh has generated 110,000 jobs per percentage point of growth, India 750,000, Pakistan 200,000 and Sri Lanka 9,000.

16
results presented in Box 3. It is then compared to the operating profit margin for different levels of profit margin generated by different farms. The crop business activity is thus viable if the operating profit margin is higher than the hedging costs.

## **Crop business viability**

<!-- Start of picture text --> Cocoa Bananas<br>25% 25%<br>20% 20%<br>15% 15%<br>10% 10%<br>5% 5%<br>0% 0%<br>1 2 3 4 5 1 2 3 4 5<br>farms farms<br>operating profit margin hedging costs operating profit margin hedging costs<br>average crop yield average crop yield<br><!-- End of picture text --> As shown in the figures above, Farm 1 producing cocoa is not viable (its profit margin is lower than its hedging costs), while the other four farms with a higher operating profit margin are viable. All farms producing bananas are viable, even Farm 1, which has the lowest operating profit margin. Source: Authors.

# **4. Insurance and Rural Finance**

# **_4.1. Combining Risk Financing Instruments_**

As we have shown in the previous section, the yield risk characteristics of a specified crop and the farm’s production costs allow to characterize the variability of operating losses and thus to estimate the minimum amount of capital required to finance the unexpected exogenous production shocks. In this section, we will demonstrate that this financial capacity does not need to come from one single source such as insurance or savings. In fact, a costefficient risk financing strategy will rely on multiple sources of risk financing ranging from the farmer’s internal stock of wealth (savings) to external capital provided by insurers or lenders. We demonstrate that a cost-efficient risk financing strategy should be custom-tailored to the specific needs of farmers and should take into account the characteristics of the underlying risk and the costs of available risk management instruments, with the likely outcome being a combination of insurance, savings, and contingent debt. Figure 5 provides an illustration of how the risk frequency, severity and co-variability characteristics determine the crop’s insurability and the choice of risk financing instruments to finance the minimum business capital requirements.

As can be seen from Figure 5, market-based solutions, including insurance, are inefficient to deal with high frequency / high severity risks. For instance, droughts occurring every three years, as it can be observed in some States of India or no less frequent floods in Bangladesh, cannot be financed using market-based insurance solutions. Crops, and consequently the farms involved in their cultivations, which are exposed to risks with such frequency/severity characteristics are unlikely to be viable. When the frequency of the 14