rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> AFR EAP ECA LCR MNA SAR APD EC NAM<br>400<br>Scenario<br>oEe im Delta Invest<br>| Delta Maxinvest<br>200<br>20302050 20302050 20302050 20302050 20302050 20302050 20302050 20302050 20302050<br><!-- End of picture text -->
31 Table **1:** Summary Data on EGS Before **and** After the Wage Increase.

||Sub-peri|od:||
|---|---|---|---|
|1<br>April 87<br>May 88|2<br>- June 88-<br>November 88|3<br>December 88-<br>February 90|Percentage<br>change<br>1 to 3|
|Total cost<br>132.6<br>(RpxlO <sup>6</sup>/month,<br>April 1987 prices)|<br>123.6|105.3|-21|
|Of which:<br>Wage cost<br>88.7|<br>91.0|76.0|-14|
|Non-wage cost<br>43.9|<br>32.6|29.2|-33|
|Employment<br>(Person-daysxlO<br>6/month) 9.9|6.5|6.1|-38|
|Average wage rate<br>9.0<br>(Rp/day, April 1987<br>prices).|15.1|12.4|37|
|Average non-wage cost<br>4.6<br>per worker (Rp/day,<br>April 1987 prices)|5.9|4.9|7|
an independent Markov Chain by using Tauchen & Hussey (1991). Furthermore, the differences between Core and Periphery are not only in their income process, but also the Core is richer than the Periphery by 20% on average according to Eurostat data. A period in the model refers to a quarter, and the risk free interest rate is set equal to 1.7% as in Arellano (2008). The utility function displays a constant coefficient of relative risk aversion form, The risk aversion coefficient _σ_ is set to 2, which is a common value used in real business cycle studies. All the Eurobonds proposal had some form of borrowing limit for the member states in order to mitigate moral hazard concerns. For this reason, I set an exogenous borrowing limit, of 66% debt-to-income ratio for the Core and of 83% for the Periphery.<sup>8</sup> As in Arellano (2008), I assume that default entails some direct output cost of the following form: where _γ_ is the exogenous output cost that I set equal to 0.96, as in Arellano (2008). Finally, I calibrate the discount factors of the benchmark model to match the sovereign spreads of Core and Periphery and I set them equal to 0.89 and 0.88, respectively.

Table 2 presents some results on the performance of the benchmark models in comparison with the data. To derive the business cycle statistics, I run many simulations of the model over time until a default occurs and I evaluate the mean statistics of these simulations.

The model matches relatively well the spread for both countries. It predicts that the mean interest rate spread for the Core is 0.5%, while in the data is 0.6%. The model is less successful for the Periphery, since it generates a mean interest rate spread of 1.9%, while in the data is 2.4%. Moreover, the model has an exogenous debt-to-output ratio to match the data.

The model predicts lower volatility than the data. The volatility of the interest rates for the Core is 0.9 % and the Periphery is 2.16% in the data; the model under-predicts the volatility for both countries, since for the Core is 0.019% and the Periphery is 0.042%.

> 8I am in the process of relaxing this assumption. I am solving for the economy that has no exogenous borrowing constraints.

11
demand them, for the scheme to function as intended in Bihar. In an attempt to minimize corruption, the scheme’s designers made its take-up procedures rather complex, and possibly too complex for many potential participants. Low schooling attainments may impede learning about one’s rights and self-efficacy; about half the adults in rural Bihar are illiterate, and it is an even higher proportion of those from poor families. Then intervention through a public information campaign makes sense. However, while public knowledge of program eligibility and take up procedures is a necessary condition for people to demand their rights, it is far from obvious that it is sufficient in this setting. Nor it is even obvious that the new information will be accepted.

Motivated by these observations, this paper aims to address the following questions about BREGS: How aware are people of their rights under the Act? Can their awareness be changed by a well-designed information intervention? If so, does that new knowledge generate better results—changes in BREGS’s ability to reach poor people? Or does it only create new social perceptions that are largely divorced from reality?

To address these questions, we began with an extensive, state-wide, survey of rural households’ knowledge about BREGS. On the basis of the results and complementary field work, we determined that public awareness of rights under the Act is low. To assess whether poor awareness is a causative factor in determining the program’s low participation rate, we designed and implemented a randomized control trial (RCT) for an information intervention in the form of a high-quality and entertaining fictional movie, which aims to inform people of their rights under the Act. We were drawn to this intervention by our baseline survey results and qualitative field work, which suggested that for the information campaign to be successful, it had to meet several criteria: (i) it would need to engage viewers emotionally if it was to be relevant to them; (ii) it should influence public knowledge and not just that of participants; and (iii) it should be relatively easy to scale up if it proved effective in the trial. After showing the movie, we did a second round of surveys, returning to the same villages and households. The paper’s key result is that, while the information intervention was successful in enhancing knowledge of entitlements under BREGS, it did not result in better program performance on average, although we do find gains for illiterate individuals. We infer that complementary actions are needed on the supply side to assure that the scheme’s potential is realized.

5
# **3. Summary of Key Findings**

This section highlights some of the key characteristics of informal entrepreneurs and their businesses based on the data covering seven countries and twenty-four cities. Informal businesses differ from formal firms along several key dimensions, which we also explore for countries where the survey data is available covering formal firms of comparable size. In five of the countries included in the analysis (India, Mozambique, Somalia, Zambia, and Zimbabwe), surveys of microenterprises, (i.e., formally registered business with fewer than five employees) were also conducted roughly at the same time as the Informal Sector Enterprise Surveys.<sup>7</sup> We use this data, where possible, to provide an overall comparison of informal businesses with micro-enterprises. Table 2 provides details and results of these simple comparisons across multiple dimensions. The analysis in this section is purely exploratory without aiming to uncover causal relations underlying the patterns that we highlight.

## **3.1 Characteristics of Informal Business Owners**

We find that owners of informal businesses across the seven countries are on average in their late 30s and early 40s, with an overall median age of about 36 years.<sup>8</sup> Only 3% of owners are over 60 years old. About half of the business owners are 35 years old or younger, ranging from 61% in Somalia to about 35% in India. There is no difference in age between female and male owners, with each averaging around 38 years of age, respectively. We find little age variation across the three groups of sectors covered by Informal Sector Enterprise Surveys; manufacturing businesses have the oldest owners (39.3 years on average), followed by retail (37.4) and other services (36.7).<sup>9</sup> Interestingly, the owners of informal businesses are similar in age to their counterparts in the micro-enterprises, averaging 37.8 years compared to 35.4 years.

The skills of the owner play a critical role in the growth and survival of small businesses (Fairlie and Robb, 2007; Akoten et al., 2009; Nechter and Goldmark 2009). It is widely documented that informal business owners have lower level of skills than owners of formal firms (La Porta and Shleifer, 2016). The Informal Sector Enterprise Surveys collect information on the

> 7 While the survey for Iraq also covers micro-enterprises, data collection for the micro-enterprises survey has not been finalized at the time of this writing, and thus not used in the computation of subsequent formal sector comparisons.

> 8 In what follows, estimates at the city level are calculated using sampling weights. When aggregating across cities and countries, each country is weighted as one.

> 9 These differences are not statistically significant at conventional levels.

11
# **IIntroduction**

Between 1999 and 2009, the average share of bank assets held by foreign banks in developing countries rose from 26 percent to 46 percent.<sup>1</sup> This significant transformation in bank ownership spurred a large literature looking at the consequences of foreign bank entry. For the most part, studies have found that foreign bank participation brought many benefits to developing countries, especially in terms of competition and banking sector efficiency.<sup>2</sup> Furthermore, research on the behavior of foreign banks during host country-grown crisis episodes, such as the Tequila 1994 crisis and the 1997 Asian crisis, indicate that foreign banks can have a stabilizing impact on the supply of credit in developing countries (Peek et al., 2000; Crystal et al., 2001, 2002; De Haas and van Lelyveld, 2006; Detragiache and Gupta, 2006). In particular, because foreign banks typically operate in many countries they can allocate liquidity and capital from their headquarters or from affiliates outside the afflicted host country to help stabilize local credit during host-grown crises.

The recent global crisis has reignited interest in studying the behavior of foreign banks in developing countries during periods of financial turmoil. In particular, the fact that the 20082009 crisis was a home-grown as opposed to a host-grown episode makes it an interesting case to analyze, since it creates the potential for foreign banks to transmit the shocks they suffer in their home countries to their affiliates overseas.<sup>3</sup>

> 1 These data come from the World Bank Regulation and Supervision Surveys. See http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,contentMDK:20345037~pagePK: 64214825~piPK:64214943~theSitePK:469382,00.html. 2 See Cull and Martinez Peria (2011) for a review of the literature on the drivers and the impact of foreign bank participation.

> 3 Previous studies of earlier crises such as the Japanese crisis (Peek and Rosengren, 2000) and the Russian crisis (Schnabl, 2012) in the 1990s offer evidence of how shocks to parent banks can be transmitted to their foreign affiliates, negatively impacting their lending.
12 Figure 2: Comparison of Africa in 2012 with other regions in 1990 <!-- Start of picture text --> Latin America & Caribbean 1990<br><!-- End of picture text --> _Source_ : World Bank staff analysis using data from World Bank 2016 and World Bank and IEA 2015. _Note_ : Bubbles are in proportion to population.

# Descriptive Statistics

Household surveys in the 22 countries show that, on average, two‐thirds of all people lived in rural areas (see Table A.2 in the annex). In terms of numbers of households, one‐third of households lived in rural areas in South Africa, but in all other countries the percentage was 42 or higher. In 16 countries, the average per capita expenditure in every rural quintile (calculated using population weights) was lower than in the corresponding urban quintile, and in seven countries household expenditure in every rural quintile (calculated using household weights) was similarly lower. The median for the percentage of people who were officially classified poor across the 22 countries was 46 percent. In rural areas, the poor constituted a majority, with a median of 56 percent of all people and a weighted average of 50 percent. About one‐fifth of all people lived in households headed by a woman. The median of the percentage of people living in female‐ headed households was 23 in urban areas, 20 in rural areas, and 21 percent of the total population. At the highest end of the spectrum, Botswana stands out with half of all people living in female‐headed households; at the opposite end is Mali with only 4 percent reporting living in female‐headed households. Across the 22 countries studied, the median monthly per capita expenditure in 2014 U.S. dollars<sup>8</sup> was $73 in urban areas and $37 in rural areas, giving a national median of $49 per capita per month.

# Measuring Access in Household Surveys

What information about access do household surveys provide? How many surveys provide enough information about kWh of electricity consumed by each household—essential information for designing targeted subsidies? How do access rates differ by income, between urban and rural, between the poor and

> 8 Expenditures were adjusted for inflation to 2014 and converted to U.S. dollars using the exchange rate in 2014.
the likelihood of death for newborns, excess rainfall can also be protective in certain circumstances as a result of positive income shocks. Finally, Kudamatsu et al. (2011), using data from 28 African countries, show that infants who in utero experience worse malaria conditions than the site-specific seasonal means (based on a weather-prone-to-malaria index) face a higher risk of death. In addition, infants born in arid-climate regions of Africa who in utero experience droughts face a much higher risk of death than other babies. The reported results are quantitatively important. For example, a six-month malaria epidemic in a place with little average exposure to malaria can raise infant mortality by more than 3.5 points. Interestingly, they report that rainfall during the growing season before birth has a protective effect on child health. They interpret this finding as the result of a positive income shock due to higher agricultural production.

Rainfall and temperature also affect child morbidity. Hoddinott and Kinsey (2001) use panel data for rural Zimbabwe to examine growth rates of young children after a major drought. They find that children aged 12-24 months lose 1.5 to 2 centimeters of growth in the aftermath of a drought. Wang et al. (2009) find that extreme precipitation and temperatures have significant impacts on the incidence of diarrhea and weight-for-height malnutrition among children under the age of three. The reported impacts are of considerable magnitude. They estimate the health costs of more frequent events by 2020 to be in the order of 0.2 to 0.5 percent of Sub-Saharan GDP.

# **Do Weather Shocks Have Long-run Impacts on Child Health?**

Weather shocks in early life may also have long-term implications. For example, a shock during infancy may affect a child’s health so severely as to have an impact on their long-run health status. Alternatively, a weather shock which occurs in early childhood may also affect well-being during adulthood, not directly through health, but indirectly through other outcomes that are related to health status at any particular point in time; such as educational attainment.

In a recent paper, Maccini and Yang (2009) find that higher than normal precipitation during the first year of life leads to improved health, schooling, and socio-economic status of Indonesian women. Specifically, the authors claim that women that experienced above normal rains in their year and location of birth are less likely to self-report a poor health status; they also attain greater height, have more completed grades of schooling, and accumulate more wealth. The results reported by Maccini and Yang suggest that neither the environmental conditions while in utero nor those after the first year of age significantly affect long-term well-being; only those in the early stages of life seem to be critical in the long-run.

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- 57 -

### F&Sure 5

## POST-SECONDARY VrE

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5 more reliable one.<sup>3</sup> In addition, there are the usual arguments that are made in favor of total expenditures providing a better reflection of current living stand'.rds than current incomes.' This paper aluo makes use of the 1986/87 Potensi Desa, a survey which collects information on village and community level facilities. It is useful for linking up the unit record data on utilization with information on the availability of facilities in the household's district of residence.

Throughout the paper, utilization incidence is measured as the proportion of an eligible subgroup who makes usage of a social service. This is, in some ways, a crude indicator. For one, aggregation is often necessary in surveying and this may disguise underlying quality differentials which may be of considerable importance. In addition, utilization necd not fully reflect the actual benefits derived from a social service. But, despite its shortcomings, utilization incidence is clearly an important indicator of access and benefit from a social service.

So, the paper begins by characterizing the utilization of education and health facilities in Indonesia. Following this step, estimated government unit subsidies for the various facilities are attributed across households according to the utilization incidence, as a measure of the benefit from social sector spending. This is the methodology followed in most public expenditure incidence work of which perhaps the best known examples are the late 1970s World Bank studies by Meerman (1979) and Selowsky (1979) on Malaysia and Colombia respectively. In the literature, this type of exercise is usually referred to as benefit incidence which seeks to measure the distribution of consumption benefits.<sup>3</sup> Some of the "benefit" incidence 3 For a description of total household expenditures in the SUSENAS surveys and of the data generally, see van de Walle, 1988.

4 Though, this may not be the case for all purposes. See the paper by Chaudhuri and Ravallion (1991) which examines the performance of a series of welfare indicators in assessing chronic poverty using panel data for rural India.

5 In contrast, "expenditure" incidence studies examine the question of who receives government expenditures through, for example, being employed by the public sector (e.g. doctors, nurses, teachers).
# 1. **Introduction**

In developed countries, a vast range of forecasting tools have been used to predict growth and other economic variables of interest. In contrast, growth projections for many developing countries are typically based on much more informal techniques. For example, both the World Bank and the International Monetary Fund rely largely on the informed judgement of their country economists to produce forecasts for internal and external use.<sup>1</sup> In this paper, we consider two simple formal models for forecasting growth in a large sample of developed and developing countries: univariate time series models estimated country-by-country, and cross-country growth regressions. The time series models constitute a useful benchmark which illustrates how well forecasts based on extremely limited information (only the history of per capita GDP itself) can perform. The growth regressions are of interest given the vast empirical literature which argues that a significant fraction of the cross-country and time series variation in longer-term growth rates can be explained by a fairly parsimonious set of explanatory variables. A natural question to ask is whether this popular empirical framework has any value for predicting future growth.

We consider the relative forecast performance of two straighfforward models. Our time series model is very simple, and models (the logarithm of) real per capita GDP as following a first-order autoregressive process around a broken trend. We estimate this model country-by-country for 112 countries, for two time periods: 1960-1980, and 1960-1990. We then generate out-of-sample forecasts for the remaining years through 1997 based on these two information sets, and compare these forecasts with actual outcomes. Our growth model follows the vast empirical literature spawned by the neoclassical growth model. We estimate a dynamic panel regression of (the logarithm of) real per capita GDP on itself lagged five years, and a number of lagged explanatory variables which proxy for the steady-state of the neoclassical growth model and capture the effects of various policies on long-run growth: investment, population growth, trade openness, inflation, and the black market premium. We estimate this model using non-

> 1 The World Bank's Unified Survey projections, and the IMF's World Economic Outlook projections are produced in this way. Both organizations also use large macroeconometric models: the World Bank's Global Economic Model (GEM) is used to produce forecasts appearing in the Bank's annual Global Economic Prospects publication, and the IMF maintains MULTIMOD for research and simulation purposes.

I
|**State**|**Summary**|**Political support / interference**|**Status of interventions and fund**<br>**utilization**|**Range of solutions**|**Examples of problems fixed**|**Performance on the six**<br>**key indicators (2016)**|
|---|---|---|---|---|---|---|
|Abia|Implementation severely<br>hampered<br>by<br>malign<br>political<br>interference.<br>Very few interventions<br>undertaken so far and<br>little evidence of impact<br>despite<br>significant<br>expenditure.<br>Slowly<br>getting back on track,<br>with<br>some<br>innovative<br>plans in the pipeline but<br>impact of interference<br>likely to be felt for some<br>time.|**Highly**<br>**unsupportive**<br>**political**<br>**environment.**Direct interference<br>by a politically powerful civil<br>servant<br>has<br>led<br>to<br>financial<br>irregularities (including accusations<br>of<br>fraud),<br>suspension<br>of<br>procurement, staff transfers/ side‐<br>lining and a prolonged delay in<br>implementation<br>of<br>SOML<br>interventions.<br>The<br>political<br>currency of the interfering person<br>in question was such that efforts by<br>ADG to address the interference<br>were unsuccessful. Matters have<br>begun to be resolved in early 2018,<br>but<br>transferred<br>staff<br>appear<br>unlikely to be reinstated.|**Significantly**<br>**delayed**<br>**implementation and minimal**<br>**activities**<br>**despite**<br>**significant**<br>**expenditure.**The initial $1.5<br>million<br>for<br>SOML<br>implementation<br>was<br>only<br>accessible to the PMU from June<br>2017. The PMU team and<br>counterparts<br>undertook<br>implementation between June<br>and August 2017, but this<br>implementation<br>was<br>then<br>suspended<br>after<br>political<br>interference from the PS at the<br>time. It has only been reinitiated<br>since February 2018 after a<br>lengthy,<br>politically<br>charged<br>process to remove the PS. Very<br>few interventions are actually<br>underway despite the fact that a<br>large percentage of SOML funds<br>(reportedly 80%) have actually<br>been spent (indications that<br>expenditure has been poorly<br>linked<br>to<br>outcomes<br>and<br>emphasising<br>equipment<br>purchases that benefit specific<br>individuals).|**Very few solutions have been**<br>**implemented.**Even standard<br>interventions like the QISS have<br>only just begun implementation.<br>Proposed interventions, such as<br>the<br>installment<br>of<br>a<br>data<br>monitoring chart in all the health<br>facilities, show some promise<br>and an appetite for innovation<br>though whether such solutions<br>will<br>achieve<br>implementation<br>remains to be seen.|In<br>the<br>absence<br>of<br>implementation, no evidence<br>of specific problems being<br>fixed.|**Regressed.**<br>Results<br>according<br>to<br>the<br>5<br>indicators<br>that<br>were<br>captured during the 2016<br>MICS survey show Abia as<br>having regressed in all<br>except<br>one<br>indicator<br>(SBA). Their sum total<br>across<br>the<br>indicators<br>showed a regression of ‐<br>47.6%.|

30