rafmacalaba/datause-encoder-data huggingface dataset:
# **_5.1 Export product diversification_**

Export diversification is at the top of the agenda of policy makers in the region. Designing policies to reduce the export product concentration of countries in Africa —especially those countries that are abundant in natural resources— is not a trivial issue. There are several drivers that policy makers may take into consideration such as trade, market access, trade costs, FDI, human capital, public investment, exchange rate volatility, terms of trade, financial development, infrastructure, quality of institutions, and resource wealth, among others.

Assessing the main drivers of export diversification is beyond the scope of this paper. However, empirical evidence suggests that export diversification can deliver successful outcomes for developing countries. For instance, firms in Malawi, Mali, Senegal and Tanzania that continue operating beyond the first year of business are those that diversify in terms of products and markets (Cadot et al. 2011). Tanzania stands out as an example of export diversification and greater output. Since the late 1990s, mining, manufacturing, construction and services experienced sustained growth while the production and exports of traditional cash crops (e.g. cotton, coffee, tea, sisal, cashew nuts, and tobacco) declined. The broader selection of higher-value added goods came along with changes in the export market shares. For instance, the share of exports to the euro zone diminished while intra-regional trade —particularly within the East African Community— increased (Papageorgiou and Spatafora 2012).

Rwanda is another example of trade diversification in Sub-Saharan Africa. The government introduced a series of changes to the trade framework from 2000 to 2016; for instance, policies to mitigate the effects of swings of international commodity prices —including quality upgrading of traditional exports (e.g. coffee) and measures to diversify trade destinations. As a result, the share of traditional exports (i.e. tea, coffee and minerals) in total exports decreased from 41 to 25 percent, and yet their overall export value more than tripled —it increased from US$ 415 million to US$ 4,125 million (World Bank 2019a).

# **_5.2 Emerging inter-regional partners and rising intra-regional trade_**

Policies to promote trade integration in Sub-Saharan Africa can help increase overall economic activity and total factor productivity. Figure 2 plots Sub-Saharan Africa’s average export and import shares over the period 1990-2018 within the region and with trading partners outside the region such as China, India, and the United States + euro area. Three facts emerge from this figure: first, China and India 31
Table 5

# Trust Fund Bureau and Postal Savings Balances (trillion yen)

||Trust Fund<br>Bureau|Share in Total|Postal Savings|
|---|---|---|---|
|1945|0.066|17.5%|0.047|
|1950|0.256|8.6%|0.155|
|1960|2.1|6.0%|1.1|
|1970|14.6|7.2%|7.7|
|1980|100.1|12.4%|62.0|
|1990|244.9|11.5%*|133.7|

- Notes: Total includes deposits, savings, certificates of deposits and bank debentures given in Table 1.

- Refers to share in 1988.

- Source: Bank of Japan, _Economic Statistics_ Annual, 1991 (pp. 101 and 104) and 1988 (pp. 99 and 102).

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Table S.13: Impacts on children’s time use: other activities <u>(OLS)</u> Dependent var. Per-day minutes (average from Sep 2013 to Apr 2014) spent on:

||Sports|Cooking<br>and bath|Household<br>chores|Other|
|---|---|---|---|---|
||(a)|(b)|(c)|(d)|
|**With no baseline co**|**ntrols**||||
|All products|-0.811|0.130|3.821|-7.672|
||(2.196)|(1.843)|(4.686)|(11.285)|
|One product|0.958|-2.127|7.523|-11.918|
||(2.636)|(2.005)|(5.449)|(12.838)|
|All = one (p-val.)|0.558|0.311|0.316|0.665|
|All + one = 0 (p-val.)|0.969|0.531|0.239|0.381|
|R-squared|0.213|0.228|0.159|0.391|
|No. of obs.|620|620|620|620|
||(e)|(f)|(g)|(h)|
|**With baseline contro**|**ls**||||
|All products|0.108|-0.945|3.589|-7.719|
||(2.297)|(1.899)|(4.670)|(10.812)|
|One product|1.474|-2.325|7.516|-14.044|
||(2.590)|(1.945)|(5.342)|(12.483)|
|All = one (p-val.)|0.655|0.544|0.315|0.574|
|All + one = 0 (p-val.)|0.682|0.299|0.237|0.295|
|R-squared|0.250|0.284|0.173|0.402|
|No. of obs.|620|620|620|620|
|School FE|Yes|Yes|Yes|Yes|
|Village FE|Yes|Yes|Yes|Yes|
|Mean _y_ (control)|67.28|71.10|123.75|585.46|

Notes: (1) Figures ( ) are standard errors. *** denotes significance at 1%, ** at 5%, and * at 10%. (2) Standard errors are robust to heteroskedasticity and clustered residuals within each school-grade. (3) The baseline controls include age (years); education (years); gender (dummy); household size; no. of males; head age (years); head education (years); head gender (dummy); and land size (natural number).

58
# **BACKGROUND**

Bangladesh provides an interesting context for this analysis. The growth of education, especially secondary education for girls, has been perhaps Bangladesh’s most dramatic achievement in the last two decades. Compared to other low income countries, Bangladesh stands out as a shining success story in female secondary education, along with countries such as Nicaragua, Vietnam, and some countries of the erstwhile Soviet Union. Bangladesh’s success is especially commendable because the growth in female education took place within a democratic regime and started from a really low base.

What is more startling in Bangladesh is that this spectacular increase in girls’ education has led to the reversal of a number of well-established patterns. First, girls gross enrollment at all levels except the highest has outstripped that of boys leading us to talk about a “boys left behind” phenomenon (World Bank, 2007; Figure 1 and Table 1). Second, there has been a dramatic increase in women who are marrying men less educated than them (Table 3, see the next section). This is a product of the marriage market where spousal age-gap has not changed much and younger cohorts of girls are more educated than the cohorts of men they marry in a strange “education squeeze.”

[Figure 1 about here]

[Table 1 about here] The growth in education and the attendant social change has probably been the most important but there are others as well. Starting from a very low base of 9 percent, female labor force participation picked up to over 22 percent during the years 1993-2003. Evocative images of hundreds of young girls walking every morning to the garment factories have been etched in the popular imagination as a metaphor for progress. Infant 5
From this experience, we are inclined to recommend that price collection best be conducted as an exercise separate from the household survey data collection. This could usefully occur in the context of an upgrading of existing price collection for the Consumer Price Index, which in most African countries currently covers only the capital city, or at most, selected urban areas. Our empirical results for CMte d'Ivoire indicate that regional price variation is significant and makes a major difference in the proper estimation of welfare and poverty levels, particularly in rural areas. It is thus essential that price coverage includes rural areas. Likewise, the regional price pattern for food items was found to be very different from that of non-food items, so that a good coverage of both types of expenditures is necessary.

# 3.4 **Basic Needs**

Basic needs information collected in the CILSS included health, education and housing. The general experience with the CILSS modules covering these topics is very positive. We did not discover any major measurement problems. The modules contain rnore information than what is needed to construct indicators which would capture the basic needs aspects of welfare (they were in fact designed to permit full analysis of the demand for these services). Nevertheless, the education section missed some useful information for assessing the impact of economic conditions on school attendance. In particular, it would have been useful to be able to calculate drop-out and repetition rates, especially in primary school, since those are warning signals of deteriorating household conditions which force parents to pull their children out of school.

The CILSS results have made it clear that it is essential to combine in poverty analysis information on household expenditure and on basic needs since the two do not necessarily display the same pattern. The incidence of poverty in CMte d'Ivoire, as measured by expenditure, increased by 50% between 1985 and 1988, while most basic needs indicators showed only relatively moderate changes. Combining the two items of information yields a poverty profile which is very useful for the design of poverty10
Policy Research Working Paper 7035

# **Abstract**

This paper analyzes the dynamic nature of rainfall insurance purchasing decisions, specifically looking at whether and why receiving an insurance payout induces a greater chance of purchasing insurance again the next year. This analysis uses customer data from the Indian micro-finance institution BASIX, and finds that receiving an insurance payout is associated with a 9 to 22 percentage points increased probability of purchasing insurance the following year. This affect appears to be driven by behavioral effects of receiving a payout, and cannot be explained by trust, learning, or direct effects of weather. Overall, low repurchasing rates even after payouts suggest that current rainfall index insurance products are likely to continue struggling to achieve significant sales at market prices.

This paper is a product of the Impact Evaluation Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dstein@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
of dirt, moisture and damaged seeds exceed that level.

Once the final price is set, the farmer’s produce is bagged and weighed on a manually operated balance scale. After weighing, the full value of the farmers produce is calculated and the farmer is paid in cash. Oilseed grading is undertaken in an unscientific manner in nearly all _mandis_ ; formal testing of the oil content to discern quality is not performed (World Bank 1997). It is important to clarify the various dimensions of soybean quality in this context. The transactions between farmers and buyers are based on observable features of quality such as the amount of moisture, dirt and damaged seeds in each lot of produce offered for sale. This is mostly dependent on the storage technology used by farmers, which is likely to be highly correlated with the farmers’ income. However, there is an unobservable dimension of quality that refers to the amount of oil and protein content of the seed. This aspect of quality is dependent on the variety of seeds planted by the farmers, the timely application of pesticides and fertilizers and use of farming techniques. Discussion of the impact of the intervention on overall improvement in both observable and unobservable aspects of quality is presented in a later section.

Soybean prices in India are determined to an extent by conditions prevalent in the international market. Soybean is processed to extract edible oil and a high protein residue called de-oiled cake (DOC) commonly used as animal feed.<sup>8</sup> The _mandi_ price of soybean in MP tracks the Chicago Board of Trade (CBOT) prices of soybean, DOC and oil reasonably closely. The correlation coefficients of the monthly _mandi_ price of soybean in MP with the CBOT price of soybean, soy-oil and soy-meal are 0.84 (0.06 s.e.), 0.42 (0.03 s.e.) and 0.87 (0.09 s.e.) respectively.<sup>9</sup>

# _B. Description of the e-Choupal Intervention_

ITC Limited, an Indian private sector company, implemented an _e-Choupal_ program that enabled it to buy soybean directly from the farmers starting in October 2000.<sup>10</sup>

> 8The processing of soybean is done by solvent-extraction process. In India, soybean is composed of 82 percent DOC and 18 percent oil (World Bank 1997).

> 9The CBOT price is listed in US Dollars/Ton. The monthly _mandi_ mode price listed in Rupees/Quintal was converted to US dollars/Ton using monthly exchange rates. CBOT prices of crops and currency exchange rates are available from the _International Financial Statistics_ of the International Monetary Fund.

> 10 The word _choupal_ stands for a village gathering place in Hindi.

8
Source: Own estimates based on Turkey 2016 HBS. Note: The figure shows the Gini coefficient for each income concept described in Figure 1. M.I. refers to before and after fiscal interventions, Disposable income refers to after direct taxes and transfers, Consumable Income refers to after indirect taxes and Final Income refers to after in-kind (education and health) transfers.

Figure 3 presents the overall impact of fiscal policy on inequality in peer countries to benchmark Turkey’s performance. For comparability, in all cases the baseline scenario considers market income plus pensions as starting point, treating old-age pensions as deferred income. Qualitatively speaking, income inequality falls in all countries after taxes and transfers, which implies that there is fiscal redistribution in all these economies. The differences are in the magnitudes. The highest absolute decrease in the Gini is observed in Argentina, at 0.18 Gini points, and the smallest drop is found in Russia, at 0.06. Among peer countries, Turkey shows a below median performance, close to Mexico and just ahead of Chile and Russia. The differential performance with comparator countries is driven by the inequality-increasing impacts of indirect taxes, first, and the relatively moderate inequality-decreasing impacts of direct transfers and taxes, second.

**Figure 3. Gini coefficient before and after fiscal policy in Turkey and peer countries**

<!-- Start of picture text --> Market Income plus Pensions (M.I. P.) Disposable Income<br>Consumable Income Final Income<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>Argentina Brazil Chile (2013) Mexico Croatia Poland Russia USA Turkey<br>(2012) (2009) (2014) (2014) (2014) (2014) (2016) (2016)<br><!-- End of picture text --> Source: Argentina (Rossignolo 2017); Brazil (Higgins and Pereira 2017); Chile (Martinez-Aguilar et al. 2016); Croatia (Inchauste and Rubil 2015); Mexico (Scott et al. 2018); Poland (Goraus and Inchauste 2016); Russia (Popova et al. 2018); US (Higgins et al. 2016). Turkey: Own estimates based on Turkey 2016 HBS. Note: The figure shows the Gini coefficient for each income concept described in Figure 1.

# **_5.2 Impact on Poverty_**

19
The survey for the first wave of the dataset (Malawi’s Integrated Household Survey – IHS3) was conducted from March 2010 through March 2011, and covered 12,271 households in 768 enumeration areas (i.e. 16 households from each EA). A sub-sample of the households considered in IHS3 were re-surveyed in 2013 to create the second wave of the dataset (Integrated Household Panel Survey -IHPS). IHPS tracked and re-interviewed 4000 households (3,247 original households, and 753 split-off households) from 204 of the 768 enumeration areas. An overwhelming majority, 76.80%, of the 3104 baseline households did not split over time; 18.49% split into two households; and the remainder (4.70%) split into 3-6 households. Considering the 20 baseline household that died in their entirety between 2010 and 2013 and the fact that 4,000 households could be traced back to 3,104 baseline households, the dataset has an overall attrition rate of only 3.78% at the household level.

The households that were not resampled in IHPS were dropped from the dataset used in the analyses. Households from the urban enumeration areas were also dropped because farming in Malawi is predominantly rural. Among the rural households, the study focused on farm sub-plots on which maize is the main crop. In the end, the sample size for the analyses consisted of 4,688 households (2,175 from IHS3 and 2,513 from IHPS) and 6,619 maize plots (3,070 from IHS3 and 3,549 from IHPS). Attrition bias in the data could not be explicitly tested because there are no direct regression-based tests for attrition when FE estimators are used in a panel of only two waves (Wooldridge, 2010; Mason and Smale, 2013). That notwithstanding, we are confident that attrition bias is not likely to be a concern because, as indicated earlier, the attrition rate is only 3.78% at the household level.

# **Results**

_Descriptive Statistics_ 16
<!-- Start of picture text --> Urban/Rural Male-Headed/Female-Headed<br>Ethiopia Malawi Ethiopia Malawi<br>5 5 5 5<br>4 4 4 4<br>3 3 3 3<br>2 2 2 2<br>1 1 1 1<br>0 0 0 0<br>.2 .4 .6 .8 1 0 .2 .4 .6 .8 1 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Nigeria Uganda Nigeria Uganda<br>5 5 5 5<br>4 4 4 4<br>3 3 3 3<br>2 2 2 2<br>1 1 1 1<br>0 0 0 0<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Specialization Index Specialization Index Specialization Index Specialization Index<br>Rural Urban Male Female<br>Fractional Index 3<br>Ethiopia Malawi Ethiopia Malawi<br>5 5 5 5<br>4 4 4 4<br>3 3 3 3<br>2 2 2 2<br>1 1 1 1<br>0 0 0 0<br>0 .2 .4 .6 .8 1 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 .2 .4 .6 .8 1<br>Nigeria Uganda Nigeria Uganda<br>5 5 5 5<br>4 4 4 4<br>3 3 3 3<br>2 2 2 2<br>1 1 1 1<br>0 0 0 0<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Specialization Index Specialization Index Specialization Index Specialization Index<br>Rural Urban Male Female<br>Fractional Index 4<br>Ethiopia Malawi Ethiopia Malawi<br>5 5 5 5<br>4 4 4 4<br>3 3 3 3<br>2 2 2 2<br>1 1 1 1<br>0 0 0 0<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Nigeria Uganda Nigeria Uganda<br>5 5 5 5<br>4 4 4 4<br>3 3 3 3<br>2 2 2 2<br>1 1 1 1<br>0 0 0 0<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Specialization Index Specialization Index Specialization Index Specialization Index<br>Rural Urban Male Female<br>Density Density<br>Density Density<br>Density Density<br>Density Density<br>Density Density<br>Density Density<br><!-- End of picture text --> <!-- Start of picture text --> HHI 2<br><!-- End of picture text --> _Note:_ The table displays kernel density graphs for the pre-COVID-19 round for each of the six livelihood diversification indices. The first column of graphs shows densities for urban versus rural populations while the second column of graphs separates the data by male- versus female-headed households. Higher index values indicate more household specialization (less income diversification).

52
# **Figure 7. Bond yields, policy rate expectations and inflation**

# **A.  Yields on 2-year government bonds**

<!-- Start of picture text --> Percent Euro Area Sweden Percent<br>4 Japan Switzerland 12<br>Denmark Hungary (RHS)<br>3 10<br>2 8<br>1 6<br>0 4<br>-1 2<br>-2 0<br>2010 2012 2014 2016<br><!-- End of picture text -->

# **C. Policy interest rate expectations**

<!-- Start of picture text --> Percent<br>1.0 Euro Area - July 16<br>0.8 Euro Area - May 14<br>Japan - July 16<br>0.6<br>Japan - Dec 15<br>0.4<br>0.2<br>0.0<br>-0.2<br>-0.4<br>-0.6<br>2016 2017<br><!-- End of picture text -->

# **E. 2016 inflation forecasts**

<!-- Start of picture text --> Percent<br>4 Euro Area Sweden Japan<br>Switzerland Denmark Hungary<br>3<br>2<br>1<br>0<br>-1<br>Jan-15 Apr-15 Jul-15 Oct-15 Jan-16 Apr-16 Jul-16<br><!-- End of picture text -->

# **B. Yields on 10-year government bonds**

<!-- Start of picture text --> Percent Euro Area Japan Percent<br>Sweden Denmark<br>4 12<br>Switzerland Hungary (RHS)<br>10<br>3<br>8<br>2<br>6<br>1<br>4<br>0<br>2<br>-1 0<br>2010 2012 2014 2016<br>D. Term spreads<br>Percent Range from 2005-present<br>4<br>Pre-NIRP<br>3 Current<br>2<br>1<br>0<br>-1<br>F. Long term market-based inflation<br>expectations<br>Percent<br>3 Euro Area Sweden Japan United States<br>2<br>1<br>0<br>-1<br>2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> Sources: European Central Bank, Bloomberg, J.P. Morgan, Haver Analytics, World Bank. A.B. “Euro Area” yield is from the ECB’s fitted AAA-rated euro area government bond yield curve which includes all AAA rated euro area sovereigns. Last observation is July 2016.

C. Policy rate expectations derived from interest swap contracts. Last observation is July 2016.

D. Term spreads are the percentage point differences between 10- and 2-year government bond yields. “Pre-NIRP” is the level on from the day before each country entered into its negative policy rate regime. Last observation is May 2016. E. Consensus Economics inflation forecasts for 2016 over time. Last observation is June 2016.

F. Inflation expectations derived from 10-year zero coupon swap rates. Last observation is July 2016.

20
<!-- Start of picture text --> PA AverageNouts (afl dary) peat10 dayne<br>bi<br>i ”<br>a<br>oe :<br>~ - ar] a es Li] Pd “ ra<br>hours<br><!-- End of picture text --> <!-- Start of picture text --> “ outagedays, past 10 days<br>2<br>Is<br>°<br>1 4 4 4 2 ° 2 ‘ 4 8 0<br>Gays<br><!-- End of picture text -->