rafmacalaba/datause-encoder-data huggingface dataset:
year. Such a profile is obtained from the official mortality tables (RV85) used by the LICOs until 2003. Considering this payment profile and the annuity interest rates, we obtain estimated annuity costs.

# **3.3 Annuity Cost Behavior**

With the adjusted annuity rates (TVA) and the profile just described, we estimated an index representative of the annuity cost (CRV). For descriptive purposes, it can be related to the TVA. Considering that interest rates changed significantly in the sample period, we estimated the durations in a slightly unconventional way, as a function of interest-rate levels, and by doing so we take into account that durations change with interest-rate levels. Notice that we do not assume an adjustment lag or anything of that sort. It is just an ad hoc definition. <mark>Table 2</mark> shows empirical estimations of the annuities’ modified duration and (marginal) convexity.

Given the relationship that (by definition) exists between the annuity cost and the TVA, the regression adjustment is almost perfect. The (empirical) modified duration (which measures the percentage change in the annuity cost given a 1 percent change in its interest rate) turns out to be 10 − 44 _TVA_ t-1. For example, for an adjusted interest rate of 4 percent, the modified duration is 8.24. The (empirical marginal) convexity (which accompanies the quadratic term) is 22. For comparison, we perform the same exercise for a PRC20 total return wealth index, which assumes monthly investment in a new PRC20. We get a modified duration of 9.72 − 32 _PRC20_ t-1. With 4 percent interest rates, the modified duration is 8.44. The empirical or residual convexity of this instrument is marginally larger than that of the annuity.

We can thus conclude that the PRC20 may be a reasonable instrument for matching LICO assets and liabilities, at least in the case of 65-year-old male. However, it is important to keep in mind that many of the annuities paid by insurance companies actually correspond to early retirees, and for that purpose the duration of the PRC20 is too short.

# **3.4 Causality Relations**

As part of the descriptive information, it will prove useful to perform Granger causality tests. Considering the full sample and the interest-rate levels, several statistical criteria indicate an optimal lag of about 3 months. Considering changes in interest rates, the optimal lag is 2. <mark>Table 2.C</mark> shows the results. As expected, they indicate that PRC20 interest-rate levels and changes “cause” the levels and changes in annuity interest rates. The inverse relation does not exist, implying that annuity interest-rate levels and changes do not anticipate future interest rates. These results do not change much considering other sample periods.

# **4. METHODOLOGY AND RESULTS**

Here we estimate the long-term elasticity of annuity interest rates to changes in the risk free rates, look for structural changes and present robustness checks.

7
observations across countries and rounds and preserve representativeness. Table 2 presents data on unweighted sample sizes from the round 1 survey of each country.

Table 2. Respondent characteristics, round 1

||Indonesia|Lao PDR|Mongolia|Myanmar|Philippines|PNG|Vietnam|
|---|---|---|---|---|---|---|---|
|Relation (%)||||||||
|Head|93|55|52|52|36|72|63|
|Spouse|3|21|40|23|35|0|25|
|Other|4|24|8|24|30|28|12|
|Average age||||||||
|(years)|46|38|47|41|41|45|51|
|Male (%)|85|60|33|58|35|51|53|
|Sample size|4,216|2,500|1,026|1,500|2,924|3,368|6,213|

Note: Respondent refers to the subject of the employment module. In all countries, respondents were restricted to adults, defined as 15 years old or older. In Indonesia, the respondent was asked to answer questions in the employment module about the household head or breadwinner. In Mongolia Round 3-4, the respondent (if different from the previous respondent) was asked to report on the previous respondent. In PNG, respondents and household heads (if not the same) are pooled.

In the remainder of the paper, we focus on six key indicators to examine the distributional impacts of the pandemic in the context of the aforementioned three questions. Table 3 provides summary statistics by country and round for each of these six outcomes.

Table 3. Summary statistics of outcomes (percentage)

|Country|Survey<br>round|Survey<br>period|Stopped<br>working<br>(% of those<br>working<br>pre-<br>pandemic)|Reduction<br>in labor<br>income<br>(% of<br>households<br>with labor<br>income)|Sold assets<br>or increased<br>debt to<br>cope<br>(% of<br>households)|Used<br>savings to<br>cope<br>(% of<br>households)|Food<br>insecurity <br>(% of<br>households)|Interactive<br>distance<br>learning<br>(% of<br>households<br>with children<br>attending<br>school pre-<br>pandemic)|
|---|---|---|---|---|---|---|---|---|
||1|May-Jun20|24|76|||||
||2|May-Jun20|||59|32|27|43|
|Indonesia|3|Jul-Sep20|10|44|||25||
||4|Oct-Dec20|||54|25|25|70|
||5|Jan-Mar21|10|36|||24||
|Lao PDR|1|Jul-Sep20|13|56|4|19||7|
||2|Jan-Mar21|10|41|||||
||1|May-Jun20|19|59|28|5|10|33|
|Mongolia|2|Jul-Sep20||37|||9||
||3|Oct-Dec20|52|48|17|6|7|38|
||4|Apr-Jun21|32|51|27|7|4||
||1|May-Jun20|57|82|48|34|10||
|Myanmar|2|May-Jun20|33|62|59|39|13||
||3|Jul-Sep20|23|53|59|33|16||
||4|Oct-Dec20|34|75|56|40|24|5|

7
transfers.<sup>17</sup> Soares et al (2006) also highlight the importance of the pension system changes over the period 1995-2004 for poverty and inequality reduction in Brazil.

These various changes suggest a plausible division of the period under study into two sub-periods with distinct policy regimes. The first sub-period corresponds to the classical stereotype of Latin American macroeconomic populism, with persistent budget deficits, high inflation, ubiquitous trade distortions, extensive government ownership of productive enterprises in certain sectors and, ironically, an inefficient and poorly targeted social security system that did not reach the poor.<sup>18</sup> The second sub-period corresponds to what critics characterize as “neoliberal reform”: inflation was brought under control; fiscal balance restored; quotas were replaced by tariffs, which were then harmonized and lowered; some state-owned enterprises were privatized. In the Brazilian case, as indicated above, these policies were accompanied by a significant expansion of social security and assistance transfers, at least some of which also became better targeted over time. As we will see, this component of the policy reforms (which did not figure among the standard recommendations of the so-called “Washington consensus”) played a key role.

As with any binary division of a period into two, the choice of the precise cut-off point is inevitably somewhat arbitrary. Some of the policy reforms of the mid-1990s—notably trade liberalization, privatization and expansion of the social security system—were implemented over a number of years, beginning in the late 1980s. Nevertheless, if a single year has to be chosen, 1994 was in a number of ways the obvious point of the regime change. This was the year of the _Plano Real_ and also the year when effective rates of protection reached their lowest level. After describing our data sources in the next section, we turn to an econometric assessment of whether the growth-poverty relationship in Brazil changed between these two sub-periods.

# **4. Data sources and descriptive results**

We study the poverty impacts of economic growth in Brazil using state-level information on poverty, output by sector and a number of controls disaggregated at the state level, for a period spanning 20 years. Four main sources of data were used for this study. Firstly, the poverty statistics were computed from Brazil’s national household survey ( _Pesquisa Nacional por_

> 17 Similar results are obtained from a different methodological approach by Kakwani et al. (2006).

> 18 Dornbusch and Edwards (1991) described “economic populism” in Latin America as a combination of policies relying on “deficit financing, generalized control, and a disregard for basic economic equilibria” (p.1).

9
24

**Table 4.14: Incidence of Extreme Poverty in Nigeria by State, 1985-93 (Poverty Line=N197.71)**

|||1985/86|||1992/93||
|---|---|---|---|---|---|---|
|**STATE**|**P0**|**PI**|P2|Po|P,|P2|
|Lagos (Urban)|0.043|0.015|0.006|0.072|0.021|0.008|
|FCT|0.000|0.000|0.000|0.252|0.108|0.067|
|Anambra|0.055|0.012|0.005|0.061|0.014|0.006|
|Bauchi|0.221|0.066|0.025|0.208|0.075|0.040|
|Bendel|0.099|0.017|0.005|0.052|0.020|0.011|
|Benue|0.187|0.043|0.014|0.174|0.078|0.054|
|Bomo|0.160|0.042|0.017|0.163|0.067|0.036|
|Cross River|0.090|0.021|0.009|0.157|0.062|0.034|
|Gongola|0.144|0.043|0.021|0.117|0.033|0.017|
|Imo|0.062|0.015|0.005|0.080|0.025|0.012|
|Kaduna|0.156|0.036|0.013|0.079|0.031|0.016|
|Kano|0.128|0.027|0.009|0.167|0.053|0.025|
|Kwara|0.071|0.015|0.004|0.115|0.055|0.034|
|Lagos (Rural)|0.076|0.019|0.006|0.131|0.057|0.023|
|Niger|0.295|0.129|0.076|0.176|0.084|0.053|
|Ogun|0.069|0.019|0.006|0.076|0.020|0.008|
|Ondo|0.168|0.040|0.013|0.104|0.043|0.023|
|Oyo|0.011|0.001|0.000|0.076|0.026|0.013|
|Plateau|0.140|0.043|0.019|0.180|0.078|0.044|
|Rivers|0.028|0.006|0.002|0.154|0.052|0.024|
|Sokoto|0.182|0.043|0.014|0.280|0.107|0.055|
|**All Nigeria**|**0.120**|**0.042**|**0.016**|**0.136**|**0.085**|**0.034**|
|Source: NCS, 198|5/86 and 199|2/93.|||||

More than half (11 states) of the states indicate an increase in the severity of poverty, as follows: Anambra (0.1 percentage points), Bauchi (1.5 percentage points), Benue (1.1 percentage points), Cross River (2.5 percentage points), Kano (1.6 percentage points), Kwara (3.0 percentage points), Ondo (1.0 percentage points), Oyo (1.3 percentage points), Plateau (2.5 percentage points), and Rivers (2.2 percentage points). Seven of the states that show increases in headcount also show increases in all three poverty measures. On aggregate there is a doubling of 1.8 points in the severity of core poverty in Nigeria. From Table 4.15, it can be seen that in 1985, three Northern states (Kaduna, Kano and Sokoto) accounted for 38 percent of all the core poor, contributed 34.0 percent to the depth of poverty and 28.7 percent to the severity of poverty. In 1992 the numbers had reduced by 4 percentage points for headcount, 8 and 11 percentage points for the depth and severity respectively. On the other hand, five states have shown dramatic increases in the headcount, depth, and severity of core poverty. They are Kwara, Oyo, Rivers, Plateau and Sokoto. Lagos urban areas made almost no contribution to extreme poverty in 1992/93.
Program (RWSSP) there is still an urgent need to reconfigure the institutional framework and retool the sustainability approach to achieve a lasting and high-quality delivery of water services in Tanzania.

# **3. Literature Review**

In recent years, statistics on water points functionality have been collected in a number of countries. One of the more comprehensive database is Akvo (2015) developed by the the Akvo Foundation. It is currently monitoring data for over 120,000 water points across 37 countries. Globally, 20 percent of the water points are not functional while an additional 10 percent are functional but with problems<sup>9</sup> . Other studies indicate that in Sub-Saharan Africa, it is estimated that about 30 percent to 36 percent of water points are not functional (Baumann, 2009; RWSN, 2009) with countries such as Cote d’Ivoire (65 percent) and Sierra Leone (65 percent) experiencing high rates of water points failures and countries such as Madagascar (10 percent) and Benin (22 percent) experiencing lower levels (Table 1, Annex)<sup>10</sup> .

Another problem is that there is no widely agreed upon definition of functionality. This is an important aspect of data accuracy, because the inaccuracy of the data collected – especially when the definition of functionality is not clearly defined – could adversely affect programs that are designed based on flawed data (Jimenez & Perez-Foguet, 2010). While most surveys use the binary distinction (Cairncross et al., 1980) of working or not working, which at times is considered insufficient (Carter & Ross, 2016), other surveys included the concept of partial functionality (Wilson et al., 2016). Initiatives such as the Akvo Flow and the Water Point Data Exchange (WPDx)<sup>11</sup> were created to not only collect information and indicators on water points across different countries but also harmonize the data definitions which are often collected and measured differently. A recent World Bank study which analyzes a range of indicators from countries and development partners, including 20 national monitoring systems and 20 monitoring frameworks from donors proposed a shortlist of indicators and associated metrics as a global framework: (1) service levels (the characteristics of water that users receive); (2) functionality (the physical condition and functioning of a supply system); and (3) upkeep (those factors, including external backup support, that affect the performance of the service provider in its roles of operation, maintenance, and administration).

> 9 Additionally, the Water Point Data Exchange (WPDx) was also launched in May 2015 with the aim of compiling and harmonizing statistics of water points globally which are often collected and shared using unique approaches. Data from Akvo Flow for example will also be shared on WPDx. 10

> 11 WPDx’s website: <u>https://www.waterpointdata.org/</u>

6
countries are more likely to build dams, especially those with downstream costs, when downstream countries bear some of the costs. We also investigate the role of international watershed management institutions in mitigating this effect.

We find evidence that dams are more prevalent in areas of river basins upstream of foreign countries, supporting the view that countries put lower weight on downstream countries’ costs than their own costs in deciding whether to build dams. We find suggestive, but very weak, evidence that international water management institutions reduce the extent of such free-riding.

The structure of the paper is as follows. The paper begins with a brief review of previous literature that considers dam placement and transboundary spillovers in rivers, as well as the potential mitigating impacts of international agreements. Section 2 describes the sources of our data and the GIS analysis conducted to generate our variables of interest. Section 3 presents the results of our main equations. Section 4 considers several extensions, including analyses that break down results by type of dam and consider the role of international water management institutions. Section 5 briefly concludes.

# **1. Previous Literature**

Substantial anecdotal evidence suggests that political jurisdictions free-ride in the allocation of shared water resources (Gleick 1993). Much of the economic literature on this topic develops the theory of common pool resources, using game theory and drawing upon specific case studies of shared rivers (Rogers 1969, Frisvold and Caswell 2000). Prior studies suggest that the incentive to free-ride in international surface water allocation can sometimes be overcome. Becker and Easter (1999) consider the U.S. states and Canadian provinces sharing the Great Lakes and show that a relatively small coalition 3
As a rule of thumb, Cohen (1977) suggests that effect sizes smaller than 0.20 should be considered as a “small effect”; effect sizes around 0.50 indicate a “medium effect”; while effect sizes greater than 0.80 constitute “large effects.” Where pure mean comparisons, standard deviations, and sample sizes for each experimental outcome are not reported directly we exhaust all possibilities to calculate or estimate effect sizes (� _)_ and its corresponding standard error from the range of available statistical data (cf. Lipsey and Wilson 2001).

In the estimation of summary effects of the literature, our main approach follows a full pooling least squares meta-regression framework (e.g., Card et al. 2015). Accordingly, the financial education treatment effect (�) can be explained by exogenous, observable characteristics, the impact � on an outcome _i,_ reported in study _j_ is expressed as a linear function where ���� is a vector of observable (exogenous) study-level covariates, such as intensity of intervention, α is an intercept, and ��� denotes an error-term independent from ����. We estimate our models using multiple effect sizes per study and account for heteroscedasticity by clustering standard errors at the study-level. Reassuringly, results are not sensitive to a set of changes in estimation strategy and accounting for publication selection bias (see section 6 and supplemental appendix S3).

# III.  SAMPLE DESCRIPTION

This section describes the selection of studies, the extraction of effect sizes and study-level covariates, and types of financial education programs.

# _Selection of Studies_

We follow the established meta-analytical protocol (cf. Lipsey and Wilson 2001: 23, Stanley 2001: 143). This starts with systematically searching the relevant databases, including working papers, for the following keywords: (i) financial literacy; (ii) financial knowledge; (iii) financial education; (iv) financial capability; and (v) combinations of these keywords with “intervention.” Moreover, we consider all records from meta-analyses (Fernandes et al. <u>2014; Miller et al. 2015) and narrative</u> literature reviews (Fox et al. <u>2005; Collins and O’Rourke 2010; Willis 2011; Xu and Zia 2012;</u> Hastings et al. 2013; Blue et al. 2014; Lusardi and Mitchell 2014). This search resulted in over 500 potentially relevant published journal articles and over 600 results from working paper databases with some apparent overlap. We stopped collecting studies in October 2016 (see appendix S1).

From this collection, we drop studies that do not meet our three criteria for inclusion: (i) reporting on impacts of an exogenous educational intervention on financial literacy and/or financial behavior; 6
# **Table 3: OLS and Levinsohn-Petrin Estiamtes of Cobb-Douglas Specification of Production Function**

**by sector**

Dependent variables= Log (annual value added)

||OL<br>estimates|S|Levinsohn-<br>estimates|Petrin|||
|---|---|---|---|---|---|---|
||Log<br>(fixed|Log<br>(wage|Log (fixed|Log (wage|Observations|Number<br>of|
||assets)|bill)|assets)|bill)||Firms|
|All firms|0.266<br>(57.88)**|0.832<br>(175.91)**|0.138<br>(5.20)**|0.551<br>(43.36)**|18842|7668|
|All nine sectors|0.280<br>(56.18)**|0.818<br>(158.98)**|0.133<br>(5.36)**|0.546<br>(42.93)**|16516|6662|
|Food /beverages|0.270<br>(17.01)**|0.836<br>(49.96)**|0.002<br>(0.02)|0.570<br>(18.62)**|2009|839|
|Textiles|0.302<br>(24.80)**|0.788<br>(60.02)**|0.172<br>(3.34)**|0.487<br>(24.04)**|3084|1216|
|Garments|0.305<br>(31.55)**|0.793<br>(83.45)**|0.114<br>(3.05)**|0.543<br>(26.35)**|4190|1686|
|Leather goods|0.276|0.840|0.203|0.514|1095|439|
||(16.09)**|(50.11)**|(3.16)**|(10.43)**|||
|Chemicals, Drugs and pharmaceuticals|0.297<br>(20.91)**|0.801<br>(52.74)**|0.251<br>(3.17)**|0.502<br>(15.37)**|2666|1126|
|Electrical goods/equipment|0.177|0.920|0.085|0.664|1322|537|
||(10.60)**|(52.95)**|(0.86)|(12.00)**|||
|Auto parts|0.269|0.820|0.074|0.558|1057|391|
||(12.93)**|(37.54)**|(0.96)|(6.75)**|||

Absolute value of z-statistics in parentheses

* significant at 5% level; ** significant at 1% level

42
have bank accounts in the BRC and information session treatment arm (85 percent)<sup>27</sup> , while in the control group that proportion is 70 and 59 percent for women and men respectively. Therefore, the impact is larger for men as they close the gender gap.

The impact of the BRC and bank information session treatment on having a business bank account is also smaller for women. About half of the gender gap on having a business bank account is actually associated with lower take-up during the information sessions (as per table 4), despite a difference of only one percentage points in participation in the information sessions. The other half is equally explained by business closure among women-owned businesses and a higher proportion of women than men who report no business bank account at follow-up surveys, differing from the information collected through the administrative records.

The bank information sessions emphasized the importance of separating household and business expenses, while having a separate business bank account may facilitate this process. Table 8 examines the treatment impacts on measures of the separation of household and business money. We see a significant 6.6 percentage point reduction in the mixing of household and business expenses for the BRC plus business information session treatment group in the first follow-up round, but this effect is smaller and no longer significant by the second follow-up round. This short-run effect appears only to be present for male owners. We do see this treatment group being more likely to have an account that they use just for business purposes. At the same time, this is well below the penetration of accounts in the name of the business for this group. Indeed, 47 percent<sup>28</sup> of the firms (46 percent for men and 49 percent for women) in this third group with bank accounts in the name of business used the funds saved there for other purposes, namely personal expenses. We also see a 7 to 11 percentage point increase in the likelihood of keeping financial records for the group offered bank information sessions. There are few impacts of the other two treatments, although in the second follow-up round the BRC+TPIN group appears less likely to keep follow-up records than the control group.

Finally in table 9 we examine the impacts of the interventions after the first two follow-ups on the usage of credit and insurance. On average, there is no effect of the interventions on the

> 27 At the second follow-up, 88 and 86 percent of women and men respectively had a bank account in the group offering information sessions from the bank.

> 28 These are the results after the first follow-up, but are about the same (48%) after the second follow-up.

23
KENYA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 10. Indicative infrastructure spending needs in Kenya for 2006 to 2015**

|US$ million per year||Operations and||
|---|---|---|---|
|Sector|Capital expenditure|<br>maintenance|Total needs|
|ICT|485|44|529|
|Irrigation|13|2|16|
|Power (trade)|745|274|1,019|
|Transport (basic)|232|242|474|
|Water supply and sanitation|1,375|555|1,930|
|Total|2,850|1,118|3,968|

Sources: Mayer and other 2009; Rosnes and Vennemo 2009; Carruthers and others 2009; You and others 2009. Derived from models that are available on-line at http://www.infrastructureafrica.org/aicd/tools/models.

**Figure 12. Kenya’s infrastructure spending needs are greater than those of comparable countries**

Estimated infrastructure spending needed to meet targets, as percentage of GDP <!-- Start of picture text --> Sub-Saharan Africa<br>fragile low-income countries<br>COMESA<br>nonfragile low-income countries<br>resourch-rich countries<br>middle-income countries<br>Congo, Dem. Rep. of<br>Ethiopia<br>Kenya<br>Tanzania<br>Sudan<br>Uganda<br>South Africa<br>Cape Verde<br>0 10 20 30 40 50 60 70 80<br>% of GDP<br>capital expenditure<br><!-- End of picture text --> Legend: LIC – Low Income Country, MI C – Middle Income Country, COMESA – Common Market for Eastern and Southern Africa Source: Foster and Briceño-Garmendia 2009.

Kenya already spends a sizable amount ($1.6 billion per year) to meet its infrastructure needs (table 11). About 65 percent of the total is allocated to capital expenditure and 35 percent to operating expenditures. Operating expenditure is entirely covered from budgetary resources and payments by infrastructure users. The largest source of funding for infrastructure investment is the private sector, which accounts for half of the total amount. The public sector and overseas development aid to Kenya 23