rafmacalaba/datause-encoder-data huggingface dataset:
paper on the remaining other questions. The main reason is simply that household consumption data are often either unavailable, or infrequently collected, particularly for low-income countries.

Using the World Bank’s PovCalNet database, which covers the period 1981-2014, we plot in Figure 1 the number of data points of poverty estimates against a country’s income level (as measured by its consumption level in the household survey). For better presentation, we also graph the fitted line for the regression of the former outcome on the latter outcome. The estimated slope of this regression line is positive and strongly statistically significant, suggesting that countries with higher incomes more frequently implement household surveys. Indeed, a 10 percent increase in a country’s household consumption is associated with almost one-third (i.e., 0.3) more surveys. Figure 1 thus helps highlight the—perhaps paradoxical—fact that poorer countries with a stronger need for poverty reduction also face a more demanding challenge of poverty measurement given their smaller numbers of surveys. This is unsurprisingly consistent with a prevailing perception among some development practitioners that collecting survey data may not be the top priority for many developing countries (see, for example, Devarajan (2013)).

The observed pattern between missing data and national income is also particularly relevant for assessing methods to fill data gaps. As one example, the World Bank monitors the global headcount of people living in extreme poverty and these counts are used by many institutions, including the United Nations, previously for the purposes of monitoring the Millennium Development Goals, and now the Sustainable Development Goals.<sup>2</sup> Jolliffe _et al._ (2015) indicate that the approach used by the World Bank to imputing poverty rates for those countries with missing poverty data is simply to assume that the data are missing at random within each region. So, if poverty data are missing for the Democratic People’s Republic of Korea, the global poverty

> 2 For recent examples of the literature documenting the World Bank’s global poverty counts, see Ravallion, Chen and Sangraula (2009) and Ferreira _et al._ (2016).

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12 contraception. These equations are estimated by applying the GMM estimation strategy to the tobit method. The final dependent variable is births in the last five years and GMM is used along with the ordered probit model.

# IV. **The Data Set and Descriptive Statistics**

The data used in this analysis is the Demographic and Health Survey conducted in Tunisia in 1988. The sample is a nationally representative sample of ever married women between the ages of 15 and 49 in 156 sample segments. The total number of women in the sample is 4184. The sample used in the analysis dropped women who were not currently married (approximately 150 women), women who were over 45 years of age (approximately 300 women), women who had been sterilized more than five years (approximately 160 women), and women who were from two sample segments where there was no seivice availability data gathered (approximately 80 women). The end result was a sample size of 3482 women with 1366 women from rural communities and 2116 women from urban communities. The current use analysis dropped women who were pregnant (approximately 400 women) while the five year analysis dropped women who had not been married at least five years (approximately 850 women).

An issue that has important policy implications is the effect of infant mortality on fertility intentions. It has been a fundamental tenet of the theory of demographic tr.-.nsitions that mortality decline stimulates fertility decline through behavioral and biological channels. In particular, it is hypothesized that parents are more likely to use contraception to lower the risk of child death. (See Cochrane and Zachariah, 1983.) Since mortality figures based on our current sample of women would be unreliable because the sample size is too small and since mortality for the current respondents would have to be treated as an endogenous variable, we calculated community mortality measures from census data along the lines suggested by Montgomery (1985). The Tunisian census data from 1987 were used in the calculations of the community mortality measures using indirect estimates based on the number of births and surviving children by age of mother. The probability of dying between birth and age 5 was the measure of mortality selected for this analysis. These estimates were made for urban and rural areas in each governorate.

Appendix Tables 1 and 2 present descriptive statistics on all variables used in the analysis as well as their definitions. Since our preliminary model specifications indicated that there were major urban-rural differences, we initially stratified the entire analysis into separate urban and rural samples. Therefore, the descriptive statistics in the Appendix are also presented separately for the urban and rural samples. In the final analysis, however, given the relatively small size of the rural sample, the analysis was executed for the sample as a whole and interaction terms were used to determine the differential access to services in urban and rural areas.
- (i) <u>Familiarity with insurance provider: In an environment where a product is not well</u> understood, it seems plausible households will draw inferences based on their degree of experience and familiarity with the vendor, BASIX, and their trust in it.

- (ii) <u>Networks: Closely related, households are likely to rely on information gleaned from</u> social networks, such as whether other trusted farmers also purchase insurance.

- (iii) <u>Limited cognition: Households may vary in their cognitive ability to understand the</u> product, as well as their willingness to experiment with it. We study whether members of the Gram Panchayat (local council), and self-identified “progressive” households disproportionately purchase insurance. We also hypothesize that younger and more educated household heads will understand the product more easily, and be more likely to participate.

# **5. The Survey**

Our data comes from a household survey conducted after the 2004 Kharif, designed to study households’ experiences with ICICI Lombard rainfall insurance. The survey questions were developed by ourselves and implemented by ICRISAT (International Crops Research Institute for the Semi-Arid Tropics) in late 2004.

The survey sampling frame is a census of landowner households across 37 villages in Mahbubnagar and Ananthapur. We survey all villages where at least five households purchase insurance in 2004, accounting for the selection of 25 of the 37 villages. The other 12 villages are “controls,” villages identified by BASIX as being suitable for insurance marketing, but where no policies were sold in 2004 due to time constraints. Since there is no participation in the control villages, empirical analysis in this paper is based only on data from the 25 marketed villages.

Among marketed villages, we select a stratified random sample of households, so as to survey as many purchasers of insurance as possible. Details of the stratification are presented in Table 4. The three strata used are: (i) household purchased insurance (267 households), (ii) 14
groups, (iii) migration stocks by gender and reason for migrating, and (iv) migration stocks by gender and duration of stay at the destination.<sup>7</sup> Using the compiled data, we distinguish four subgroups of the population: (i) nonmigrants, (ii) intra-district migrants, i.e. those who moved from one enumeration area to another one within the _same_ district, (iii) inter-district migrants within the same state, i.e. people who moved _across_ districts within the _same_ state, and (iv) interstate migrants, i.e. those who moved _across_ states. Table 2 presents the sizes of these groups by gender. Migrants account for close to 30 percent of the population in 2001, albeit with considerable divergence in patterns across genders. The share of migrants among females (43.3 percent) is almost three times larger than among males (16.3 percent). This gap is due to the well-known migration of women within the same or neighboring districts for marriage. The share of intra-district migrants among women is 29.5 percent, over three times the level among men. Inter-district (but intra-state) migration among women is 9.8 percent, over twice the level among men. Finally, inter-state migration among women is 4 percent, slightly higher than among men.

The low level of internal migration in India, its spatial variation and gender gaps are further illustrated by district-level heat maps of Central India in Figure 1. In each map, state boundaries are outlined with thick lines, and districts are color-coded so that darker-shaded districts have relatively higher shares of the relevant migration measure.

Figures 1a and 1b plot the share of all inter-district migrants (the sum of intrastate-inter-district migrants and inter-state migrants) by gender among the existing population in each district. Figure 1b is much “darker” in color, indicating that interdistrict migration is higher among women. In 337 districts, over ten percent of the current female population are inter-district migrants, while only 101 districts have

> 7Each table has over 350,000 rows and between 10 and 16 columns. The 2001 administrative division of India has 593 districts, 9 of which are districts in Delhi. In our analysis, we combined the nine districts in Delhi, and treat Delhi as one single district. This leaves us with 585 districts in the empirical analysis.

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**Figure 3: Percentage of individuals older than 17 who used a condom during their last sexual intercourse, by education category at several rounds of the General Population Cohort (MRC), Rural Uganda.**

<!-- Start of picture text --> 25<br>20<br>15<br>10<br>5<br>0<br>Round 4 Round 7 Round 8 Round10 Round 11 Round 12<br><!-- End of picture text --> <!-- Start of picture text --> No education<br>Primary<br>Secondary<br><!-- End of picture text --> 47
<!-- Start of picture text --> 8000<br>7000<br>—— Dar es Salaam<br>= 6000<br>S —— Nairobi<br>2. 5000 .<br>=> ——— Addis Ababa<br>S 4000 ? .<br>=I 7 = — -Antananarivo<br>= 3000 7<br>a 2 ——— Lusaka<br>@ 2000 o> —— Mombasa<br>1000 -<br>—— Maputo<br>fe}<br>1995 2005 2015 2025<br><!-- End of picture text -->
Within the corporate sector, the highest share of total credit exposure is to industrial firms (Table 3). At the end of 2010, this credit exposure was 35.4 percent of the total credit exposure to the corporate sector and 15.8 percent of the total credit exposure to all sectors (financial and nonfinancial). Credit support to wholesale and retail firms, at 31.7 percent, holds the second largest share in the total credit exposure to the corporate sector. Most of the credit support to households is channeled through consumer loans, which alone constituted 34.2 percent of the overall credit exposure to this sector at the end of 2010. The other types of loans that are generally used for consumer purposes (overdrafts and credit cards) jointly account for 38.9 percent of the overall credit exposure to households. Housing loans still form a relatively small part of the credit portfolio of households: 20.5 percent as of December 31, 2010.

# <u>The role of the capital market</u>

The gradual increase of trading and price levels on the Macedonian Stock Exchange (MSE) began at the end of 2005 (Chart 7). The largest activity on the secondary capital market occurred in 2007, when the turnover on the stock exchange realized by classic trading was almost equal to the overall turnover realized in all other years in the period from 2005 to 2010 (except 2007). The growth of capital market turnover in the period 2005-2007 was mostly an outcome of the increased interest for trading of shares on the stock exchange, above all on the part of foreign institutional investors (mostly on the purchase side of the stock exchange turnover), but also on the part of the local public (particularly natural persons). This increase in trading was accompanied by a gradual growth of price levels on the MSE. One could even talk about a possible presence of a ―price bubble‖ during 2007, when the MSE Index (MBI-10) reached a level 10 times higher than its basic value.<sup>8</sup> The growth of prices resulted in an increase of total market capitalization of listed shares and bonds, which reached its maximum in 2007 and represented 84.7 percent of GDP. However, since the last quarter of 2007, with the first signs of the world economic crisis and the increased inflationary pressures worldwide, there was a trend of constant decrease of the stock exchange turnover, increased trading restraint of potential investors, and outflow of foreign investors’ funds. Price levels on the MSE registered a significant downward correction during 2008 and mild but rather variable and unpredictable growth in the course of 2009 and 2010. Market capitalization of shares and bonds showed a significant drop, and at the end of 2010 was reduced to a level of 31.9 percent of GDP (as opposed to the ratio of banking system assets to GDP of 71.2 percent). If bonds are excluded, market capitalization is just 28.9 percent of GDP.

> 8 The MBI-10 index was introduced as of January 4, 2005, when the basic value was set to 1.000 index points.

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# **5 Discussion and conclusion**

We have examined the risk preferences of rural Ethiopian households using certainty equivalents. The results confirm recent findings according to which people in poor countries are on average more risk tolerant than people in rich, industrialized, countries. The positive correlation of income proxies with risk tolerance we find is in agreement with a large (if not always consistent) body of evidence from industrialized countries (Dohmen et al., 2011; Donkers et al., 2001; Hopland et al., 2013). Taken together with the high levels of risk aversion typically found in Western, industrialized countries, this gives rise to a risk-income paradox (Vieider et al., 2013).<sup>13</sup> Most evidence on risk preferences in developing countries stems from studies using a single choice list (Binswanger, 1980; Giné, Townsend and Vickery, 2008; Yesuf and Bluffstone, 2009). Responses to such a list may, however, be contaminated by noise. One of the presumed virtues of the Binswanger list is that it does not allow for _any_ noise to register in the response, given that subjects are asked to pick their favorite amongst a list of lotteries. This, however, makes it impossible to tease apart econometrically how much noise played into the response, and in general preference data and noise can thus not be separately identified. Andersson et al. (2013) showed how noise may systematically be counted towards risk aversion in some choice list designs, thus resulting in spurious correlations. This criticism particularly applies to the Binswanger design—given that the choice list is capped at risk neutrality, random choices will be systematically counted towards risk aversion. The advantage of our design is that it is easy to deploy and to explain, and that it can pick up a whole spectrum of risk preferences.

The findings of considerable risk tolerance by our subject raises the question

> 13Vieider et al. (2012) explain this paradox recurring to unified growth theory, and in particular the hypothesis developed by Galor and Michalopoulos (2012). In poor societies that find themselves in a Malthusian equilibrium, relatively affluent, risk tolerant people have the largest number of children. Since risk preferences are transmitted within the family, risk tolerance becomes prevalent. As societies grow richer, however, the affluent are the first to substitute quality for quantity of children (Becker, Murphy and Tamura, 1990). As more affluent families decrease the number of children, poorer families at first increase them, since the income constraint is no longer binding. This in turn leads to an inversion of the equilibrium and the spread of risk aversion.

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I1

# **4. DETERMINING ELIGIBILITY FOR THE SUBSIDY**

The intended beneficiaries of subsidy schemes are invariably those living under the poverty line, usually expressed as an annual threshold for per-capita income or expenditure. However, in practice, it is very difficult to measure income levels directly, and, therefore, to determine whether a particular household should benefit from the subsidy. To get around this problem, it is necessary to develop eligibility criteria that:

- show a high degree of correlation with the underlying poverty variable of interest;

- can be objectively measured and are easily observed;

- are difficult to falsify or misrepresent.

There are two basic approaches to determining eligibility for a subsidy programme, which were considered in the Panama case:

- zonal eligibility;

- individual eligibility.

# **Zonal** Eligibility

A popular eligibility criterion for subsidy schemes is zone of residence. Under this approach, subsidies are allocated to all households living in zones that are considered to contain a relatively high concentration of poverty.

As a criterion, zone of residence carries the advantage of being easy to observe and relatively hard to falsify. Whether it is highly correlated with the underlying poverty measure of interest depends on the extent to which poverty is geographically concentrated. This, in turn, often depends on the size of the 'zones' used for targeting because the larger the zone, the greater the likelihood that it will contain households with a diversity of socio-economic circumstances. Where the correlation between geographical location and poverty is imperfect, two types of failures tend to occur.

- On the one hand, subsidies will fail to reach poor households who happen to reside outside of the eligible zones.

- On the other hand, subsidies will be misallocated to households who do not meet the poverty criteria but happen to reside in the eligible zones.

In the case of Panama, there was already considerable experience with the use of zonal subsidies. As noted above, IDAAN had traditionally used zonal criteria in determining eligibility for existing water subsidies.

More generally, the Ministry of Planning has developed a 'poverty map' intended to guide the allocation of social investments. The map is based on a combination of socio-economic data collected as part of the 1990 census and more detailed data from the 1997 Living Standards Measurement Survey. The data were used to construct a poverty index that
TANZANIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Tanzania currently devotes $1.2 billion per year to meeting its infrastructure needs. The public sector is the largest source of finance for infrastructure in Tanzania and accounts for 56 percent of total expenditure (table 11). ODA and the private sector are also important sources of financing and account for 25 percent and 18 percent of total expenditure, respectively. Transport and power each capture onethird of total funding, and ICT and the water sector each capture 18 percent.

**Table 11.  Existing financing flows to Tanzania, average annual 2001-05**

<u>US$ million per year</u>

|Sector|O&M<br>Public sector|Public sector|Ca<br>ODA|pital expenditure<br>Non-OECD<br>financiers|PPI|Capital<br>expenditure<br>total|TOTAL|
|---|---|---|---|---|---|---|---|
|ICT|75|3|2|0|135|140|215|
|Power|260|16|40|0|44|99|358|
|Transport|194|82|117|7|9|214|408|
|WSS|15|33|143|4|29|209|224|
|Total|544|134|302|11|217|662|1,205|

_Source:_ Derived from Foster and Briceño-Garmendia 2009. _Note:_ PPI = private participation in infrastructure; O&M = operations and maintenance; CAPEX = capital expenditure.

Tanzania’s existing infrastructure spending is quite substantial relative to GDP. Tanzania currently devotes 8.6 percent of GDP to infrastructure spending, which is typical of nonfragile low-income countries and similar to its East African neighbors (figure 13). This translates to $30 per person per year in infrastructure spending, which is on par with Uganda and Ethiopia but only a fifth of what is spent by Kenya and a twelfth of what is spent by South Africa.

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# 2. Revenue Performance in Eastern Europe

# 2.1. Public Revenue

<!-- Start of picture text --> 120..<br>10 I I<br>- -o - Bulgaria , -- - ----<br>70 ao -- FonnerCzechoslovakia _<br>> Hungary e<br>So - Poland l --------<br>At,Romania<br>1986 1987 1988 1989 1990 1991<br>Year<br><!-- End of picture text -->

**Figure 1: Revenue in Eastern Europe (in constant 1986 prices)**

Current revenues of governments in Eastern Europe, after adjusting for inflation, are falling rapidly (see Figure 1)- In some cases, revenues during this transition can only buy half of what they used to buy before the collapse of the centrally planned economiesw As percent of the peak revenue in 1988189, the revenue figures reported in recent years (expressed in constant 1986 prices) are only: 36.9 percent in Bulgaria (1992); 64.2 percent in the former Czechoslovakia (1992); 76.4 percent in Hungary (1991); _50.4_ percent in Poland (1991); and 63.2 percent in Romania (1991).

An irnportant reason for the decline in revenue is the collapse of output. The dismantling of the Council for Mutual Economic Assistance (CMEA) in Eastem Europe has led to the disappearance of traditional export markets, while die prices of inputs such as fuel and raw material imports have soared. The resulting extemal shocks has caused widespread output declines (see Rodrik [1993])_ _Go: Reene Uncernainry_ _4_
Figure 2: Migrants in Gulf-Malaysia, and net remittance receipt <!-- Start of picture text --> 2008m1 2010m1 2012m1 2014m1 2016m1<br>Month<br>Nepali migrant workers in Gulf-Malaysia<br>Net remittances received (right)<br>2<br>6<br>1.8<br>1.6 5<br>1.4<br>1.2 4<br>1 Remittances (billions, in 2010 US$)<br>Estimated number of migrants (in millions) 3<br><!-- End of picture text --> Source: Census 2011, DoFE database on migrant permit, and The World Development Indicators (The World Bank, 2017) Note: Stock of migrants estimated from the stock in June 2011 from the census and migrant permit database at the Department of Foreign Employment. Each migrant is estimated to remain in the destination for 2 years in case of the Gulf countries, and 2.5 years in case of Malaysia.

Figure 3: Distribution of migration rates by villages <!-- Start of picture text --> 0 .02 .04 .06 .08 .1 .12 0 .05 .1 .15 .2<br>non-India migration rate in village India migration rate in village<br>2001 2011 2001 2011<br>30<br>50<br>40<br>20<br>Percent 30 Percent<br>20<br>10<br>10<br>0 0<br><!-- End of picture text --> Source: Author’s calculations from the 2001 and 2011 Census.

Note: This figure shows the distribution of migration rates for each of the villages in 2001 and 2011, for India and non-India migrants.

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