rafmacalaba/datause-encoder-data huggingface dataset:
## **Savings methods among savers in economies with the highest use of “other” methods** Adults saving any money in the past year, by savings method used (%)

<!-- Start of picture text --> 100<br>80<br>NEITHER<br>60<br>40<br>FORMAL ONLY<br>COMMUNITY<br>20 BASED AND<br>FORMAL<br>COMMUNITY<br>0 BASED ONLY<br>WORLD TURKMENISTAN KYRGYZ SYRIAN ARMENIA UZBEKISTAN CAMBODIA TAJIKISTAN BURUNDI GEORGIA AZERBAIJAN SAVERS AS<br>36 45 REPUBLIC ARAB 11 31 31 14 25 7 11 % OF ALL<br>36 REPUBLIC 48 ADULTS<br>Note: “Other” savings methods exclude using a formal financial institution, informal savings club, or person outside the family.<br>Source: Demirguc-Kunt and Klapper 2012.<br><!-- End of picture text --> methods, they might include saving through asset accumulation (such as gold or livestock) and saving “under the mattress.” These adults account for 29 percent of savers worldwide and more than half of savers in 55 economies.

Among the 11 economies with the highest use of such alternative savings methods, 7 are in Europe and Central Asia (figure 2.5). These include the Kyrgyz Republic, Turkmenistan, and Uzbekistan, where more than 85 percent of adults who report having saved in the past year did so using neither a formal financial institution nor a community-based savings scheme. Interestingly, more than 85 percent of all savers in these three economies report saving for a wedding, an education, or another future expense, a larger share than report saving for a future emergency. This suggests a potential market for financial products that cater to specific savings goals, such as the education savings bonds that are common in many highincome economies.

# **How does the motivation for saving vary?**

Adults who save at a formal financial institution are more likely to report having specific savings goals than those who save using other methods. Worldwide, 67 percent of formal savers report having saved for future expenses such as an education, a wedding, or a big purchase. Concrete savings goals are reported by 63 percent of savers who use a community-based savings group and not a formal financial institution, and by 59 percent of savers who use neither. Though it is unclear from the data whether these differences arise because people with concrete savings goals are more likely to open a formal savings account, there is other evidence that simply having an account encourages people to save toward a specific purchase or investment.<sup>2</sup>

> 1. See, for example, Chawla, Betcherman, and Banerji (2007), who provide an overview of the challenges of aging populations in Eastern Europe and the former Soviet Union.

> 2. Dupas and Robinson 2009, 2011.

**35**

MEASURING FINANCIAL INCLUSION
to making detours over fields or unpaved roads.<sup>9</sup> These delays translate also into higher direct costs of travelling between areas of the West Bank.

These barriers have been varying across space and time. According to the data collected by ARIJ, their number increased along with the intensity of the conflict in the early 2000s and then peaked in 2007-08, when they started to decrease slowly. In 2009 our data record 89 checkpoints, 268 roadblocks and earth mounds and 122 barrier gates. The trend in the number of checkpoints between 2000 and 2009 is similar to that of the other barriers, showing a steady increase until 2008 when the number started to decline (Figure 1). The only exception is the drop in 2004, which may be partly due to incomplete reporting as that was the first year in which OCHA started recording the closure data. For that reason we also check the robustness of the results below to excluding the year 2004 from the analysis. All the results are virtually unchanged to this exclusion (results available from the authors upon request). In the analysis we exploit this variation in closures over time and across small spatial units in the West Bank to identify their impact on local labor markets.

While the system of internal closures was not targeted on the basis of local economic conditions, it did (and still does) however have large effects on the Palestinian economy and the West Bank in particular. Virtually all reports on the Palestinian economy in the last decade have argued that the movement and access restrictions are a key constraint to Palestinian economic development (e.g. World Bank (2004, 2007a, 2010 and 2011a), IMF (2010), UNCTAD (2011)). Internal closures stifle economic activity by raising the cost of doing business and increasing uncertainty (World Bank 2004, 2007a, 2007b). The closure system has fragmented the West Bank territory into small and disconnected “cantons” (World Bank, 2007b). A recent World Bank (2011b) study investigates the effects of the checkpoints on price differentials and finds that they have a significant and large positive effect on spatial price differences. The lower bound estimate of the effect of two or more checkpoints between locations is an added price difference of up to 10 percent, which is comparable to the transaction costs incurred when crossing the U.S.-Canada border. These types of effects are clearly larger during periods of curfew when “the wheels of the economy come to a grinding halt” (World Bank, 2004, p.1). Closer to the analysis in this paper, World Bank (2011a) also finds that an increase in the number

> 9 For example the travelling time between the city of Bethlehem (just south of Jerusalem) and Ramallah (just north of Jerusalem) has more than doubled since Palestinian vehicles were not allowed to take the most direct route via East Jerusalem (Palestinian Ministry of National Economy and ARIJ, 2011).

8
Table 2. Coefficients of variation (xIOO) of commodity prices from city to city in state stores (cont.)

|sugar|broad 1|brcad 2|millet<br>groats|vermicelli|potates|cabbage|onions|apples|cigaretUs|
|---|---|---|---|---|---|---|---|---|---|
|86|43|77|-|63|56|53|48|68|96|
|63|8|40|56|62|46|_55_|37|23|8|
|62|18|19|_55_|41|51|63|42|30|63|
|45|17|18|41|44|38|40|38|44|57.|
|73|28|21|58|41|32|36|37|37|S8|
|104|45|23|52|38|41|40|39|31|42|
|100|47|43|51|38|42|62|41|38|53|
|101|44|46|57|31|32|58|45|39|48|
|88|47|S1|49|27|39|62|39|41|35|
|87|S7|56|45|26|36|56|42|37|40|
|92|_55_|51|48|30|47|64|39|37|41|
|70|54|S0|45|34|43|49|29|33|41|
|68|57|54|46|33|43|41|26|40|26|
|69|62|S0|_4S_|26|44|48|27|42|35|
|64|S9|51|40|27|43|49|33|49|31|
|58|61|56|45|29|41|43|27|48|31|
|52|61|59|45|31|39|25|43|38|33|
|45|52|_55_|47|33|38|24|38|39|20|
|47|54|52|40|30|40|23|41|27|34|
|48|49|48|36|31|39|2S|34|_3S_|26|
|52|_55_|_53_|37|33|41|31|32|29|30|
|48|52|51|36|32|42|30|32|40|29|
|47|49|49|52|29|54|37|29|26|25|
|46|40|41|45|27|47|52|30|52|18|
|43|40|44|42|32|36|60|44|58|23|
|39|36|41|43|30|34|43|39|44|18|
|32|38|40|41|29|37|47|71|56|25|
|35|37|40|_51_|27|43|49|71|53|28|
|33|35|39|49|25|38|52|67|60|35|

13
26 Transfers, and Schooling in Nicaragua **.”** _World Bank Economic Review_ 22(2): 271-290.

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often have present value adjusted payback periods measured in decades. Foreign firms must be convinced that friendly policies are likely to continue, sometimes for very long periods, before FDI looks financially attractive.

To capture _regime durability_ , we take from the PolityIV database the number of years since the country’s most recent regime change. A “regime change” is not a simple change in leadership or parliamentary majority. Rather, a “regime change” is defined as a change of threepoints or more in the country’s _responsible government score (_ polity2 in the database) within a span of three years or less. Or, a regime change is defined as the end of unstable political institutions characterized by foreign invasions or anarchy.

Our ultimate objective is to see whether Chinese FDI inflow is “exceptional”, or follows patterns similar to FDI flowing into other countries at similar levels of development. This is important because a marginal institutional improvement in Denmark is not necessarily comparable to a similar improvement in Indonesia. Going from one to two phones per Dane is not equivalent to going from zero to one phone per Indonesian. The former is likely inconsequential, while the latter could be highly important.

One way to deal with the problem is to introduce non-linear terms of institutional measures. That, however, would induce collinearity. Also, interpreting higher order terms of categorical variables can be tricky. Moreover, FDI flowing into less developed regions appears to have motivations (e.g. resource extraction) different than that flowing into highly developed economies (local market access).

We therefore instead choose a sample of benchmark countries that, collectively, resemble China. That is, we consider countries in which the impact of a marginal improvement in institutions on FDI is likely to be in the same range as in China. We thus select all countries in the bottom 40 percent of our institutional measures (e.g., the bottom 40 percent of “executive constraints”). The 40% cutoff is not critical. Rerunning our analysis using, e.g., a 50% cutoff instead, generates similar results.

17
# **III. Medium run**

In this section, I examine the role monetary authorities should play beyond the business cycle horizon. I first explore the issue of complementarity between fiscal and monetary policy in fossil fuel exporters. Then, I discuss the need for macro-prudential policies. I then draw lessons from the recent collapse in oil prices and the associated policy responses.

## **Complementary between fiscal and monetary policies**

Commodity exporting countries in general—and fossil fuel exporters are no exception—tend to over-spend in good times leading to excessive indebtedness and crisis in bad times (Arezki and Brueckner, 2012a). The effectiveness of CBs’ contribution toward stabilization thus rests on the existence of a credible/sustainable fiscal anchor. The cost of borrowing rises with falling commodity export prices (Chart 3) (Arezki and Brueckner, 2012b). Weaker political institutions typically make things worse considering the risk premium associated with the latter. There is a need for fiscal (and credit rules) to limit the amplification of the effect of TOT shock. Interestingly, many countries such as Chile have graduated from pro-cyclicality by setting up fiscal rules (Frankel et al., 2013).<sup>2</sup> Chart 3. Rising sovereign bond spreads

### **Oil Exporters Global Bond Spreads**

<!-- Start of picture text --> (Average J.P. Morgan EMBIG Sovereign Spread Index)<br>1200<br>1000<br>800<br>600<br>400<br>200<br>0<br>Jan‐14 Apr‐14 Jul‐14 Oct‐14 Jan‐15 Apr‐15 Jul‐15 Oct‐15 Jan‐16 Apr‐16 Jul‐16<br>Source: Bloomberg, L.P.; and IMF staff calculations.<br>Note: Oil exporters are comprised of Angola, Bolivia, Colombia, Ecuador, Gabon, Iraq, Kazakhstan, Nigeria, Russia,<br>Trinidad and Tobago and Venezuela.<br><!-- End of picture text -->

> 2 Pieschacón (2012) compares the differentiated macroeconomic impact of oil shocks on Mexico and Norway. The latter country is at odds with the former considering the fiscal discipline to which it has subjected itself.

8
Table 2. Debt (Percent of GDP)

||2010|2019|2020|
|---|---|---|---|
|**Total debt**||||
|World|214|233|262|
|Advanced economies|264|269|300|
|EMDEs|118|180|206|
|Commodity-exporting EMDEs|78|109|125|
|Commodity-importing EMDEs|151|212|237|
|**Government debt**||||
|World|77|84|97|
|Advanced economies|98|104|120|
|EMDEs|37|54|63|
|Commodity-exporting EMDEs|30|47|55|
|Commodity-importing EMDEs|43|57|67|
|**Private debt**||||
|World|137|149|165|
|Advanced economies|167|165|179|
|EMDEs|81|126|142|
|Commodity-exporting EMDEs|48|62|70|
|Commodity-importing EMDEs|108|155|170|

Note: Aggregates are weighted averages using current GDP in U.S. dollars as weights. The classification of countries follows World Bank (2021).

Table 3. Primary balance (Percent of GDP)

||2010|2019|2020|
|---|---|---|---|
|World|-4.2|-2.1|-9.3|
|Advanced economies|-6.0|-1.5|-10.3|
|EMDEs|-0.7|-2.9|-7.7|
|Commodity-exporting EMDEs|-0.1|-1.0|-6.6|
|Commodity-importing EMDEs|-1.2|-3.8|-8.2|

Note: Aggregates are weighted averages using current GDP in U.S. dollars as weights. The classification of countries follows World Bank (2021).

24
Table 8 Expected Loss for Banks and the Government

||(1)<br>Expected<br>Loss/GDP<br>(%)|(2)<br>Guarantee<br>(%)|(3)<br>Default<br>Probability<br>(%)|(4)<br>Deductible<br>(%)|(5)<br>Efective<br>Guarantee<br>(=((3)-(4))<br>x(2)/(3))<br>(%)|(6)<br>Government<br>Expected<br>Loss/GDP<br>(=(1)x(5))<br>(%)|(7)<br>Banks<br>Expected<br>Loss/GDP<br>(=(1)<br>x(100-(5)))<br>(%)|(8)<br>Banks<br>Expected<br>Loss/Bank<br>Capital<br>(%)|
|---|---|---|---|---|---|---|---|---|
|_Panel A: Risk Groups, Formal Firms_|||||||||
|High Risk|0.04|82.5|18.2|4.4|62.4|0.03|0.02|0.11|
|Medium Risk|0.04|79.9|9.8|3.9|48.0|0.02|0.02|0.15|
|Medium-Low Risk|0.04|77.0|5.7|3.5|29.6|0.01|0.03|0.20|
|Low Risk|0.03|72.1|2.1|3.0|0.0|0.00|0.03|0.22|
|No Risk Data|0.10|82.5|18.2|4.4|62.4|0.06|0.04|0.26|
|Total: Formal Firms|0.27|76.4|7.4|3.5|40.0|0.11|0.16|1.07|
|_Panel B: Formal Firms + Natural Persons_|||||||||
|Formal Firms|0.27|76.4|7.4|3.5|40.0|0.11|0.16|1.07|
|Natural Persons|0.19|82.5|18.2|4.4|62.4|0.12|0.07|0.47|
|Total: Formal Firms + Natural Persons|0.45|77.8|9.8|3.7|45.0|0.22|0.23|1.54|

This table shows the distribution of the aggregate expected loss borne by the government and the banking system as a result of the credit program, for natural persons and the formal firms sample. Panel A reports statistics across the firms’ risk distribution. Panel B reports statistics separately for formal firms and natural persons. Column 1 shows the total expected loss as a share of GDP. Columns 2–4 show the guarantee, the default probability of each category using the model in Table 1, and the first-loss deductible for each category, while Column 5 shows the effective guarantee, estimated as Guarantee _×_ (Default Probability _−_ Deductible)/Default Probability, directly by category. Columns 6 and 7 show, for each category, the fraction borne by the government estimated as Expected Loss/GDP _×_ (1 _−_ Effective Guarantee), and the fraction borne by the banking sector, estimated as (Default Probability _−_ Deductible) _×_ Guarantee)/Default Probability, respectively. Column 8 normalizes Column 7 by the effective capital of the banking system. Values in Columns 2–4 are weighted by the total amount of guaranteed credit granted to each firm as a share of the total guaranteed credit granted to all the firms within its category. Totals by panel reported in Columns 2–5 are calculated as the sum of the product of each category’s statistic by its relative weight (Column 3 of Table 7). Firms are classified across risk categories based on the distribution quartiles of the fitted values of the regression specifications reported in Table 1, Columns 4 and 8. Firms with a missing risk category are assigned the risk from the high-risk category.

50
Second, several other factors were contemporaneous in the Kenyan economy around the time of the caps, thus complicating the ability to directly measure and isolate the effects of the caps on the financial sector. There was a general decline in the Kenyan economy even prior to the introduction of the interest rate caps, and overall demand for credit was low across many economic sectors. According to data from the National Treasury, loan growth to businesses and homes in 2016 had decreased to 4.3% from the previous year’s growth of 20.6%.

In addition, the collapse of two Kenyan banks in early 2016, Imperial and Chase, precipitated a crisis of confidence in the banking system, decreasing the magnitude of inter-bank borrowing and influencing a flight of depositors from smaller banks to larger counterparts. Many smaller banks were already facing liquidity shortfalls as depositors fled to perceived safer institutions and inter-bank funds became inaccessible.

The election cycle also potentially affects our ability to draw more definitive conclusions about the effect of interest rate caps. Presidential, parliamentary, and local elections were held in August and October 2017. Uncertainty surrounding the elections and future policy initiatives likely affects the behavior of both consumers and financial institutions. The private sector typically adopts a conservative approach to borrowing during an election cycle, preferring to defer major strategic decisions. These factors, in addition to the corresponding economic downturn, affect our ability to distinguish between the impact of the policy change and other contemporaneous effects on the financial sector.

Most importantly, since the interest rate caps affected the entire banking sector for which we have obtained data, it is difficult to conduct counterfactual analysis since all banks in our dataset were exposed to the caps. This lack of a counterfactual is further confounded by the factors mentioned above and undermines our ability to make causal inferences.

Given these identification challenges, we do not make causal claims in our analysis, and instead focus our attention on documenting key variations in loan and deposit composition and maturity across time, and on studying variations in responses across different formal financial actors (i.e. tier 1, tier 2, and tier 3 banks) and borrower types (e.g. corporate, consumer, SME, unsecured).

# **IV. Qualitative Evidence**

The qualitative interviews conducted as part of this study form an integral part of understanding the quantitative impacts of interest rate caps and how commercial banks responded to the policy.

At the financial institution level, several negative trends were identified by banking sector actors that characterize the microeconomic impacts of the interest rate caps across all lenders. The proportion of new borrowers fell by more than half, from a peak of 13% in March of 2016, to roughly 6% after the caps. Across the banking sector, returns on equity slumped, as did 8
# 1. Introduction

Inadequate infrastructure and low productivity of manufacturing firms tend to be closely related, especially in developing economies (Tybout 2000; World Bank, 1994; Bloom et al., 2010). A substantial literature has linked infrastructure quality to economic performance (Calderón et al., 2003; Canning, 1998; Jiemenex, 1995). However, fewer studies, typically region-specific, have established a link between infrastructure and the performance of the private sector. This is in sharp contrast to the large literature focusing on access to finance and firm performance (Davis et al. 2001). Furthermore, infrastructure encompasses roads, ports, power, communication facilities, and water, with the latter receiving less attention than the others (for example, Arnold et al., 2008 consider how communication and electricity infrastructure, and financial services affect firm performance in Sub-Saharan Africa, but do not consider the importance of water). Evidence of the lower prominence given to water infrastructure can also be seen in the World Bank’s Enterprise Surveys, which have the broadest and most consistent coverage of firm-level data globally. While the surveys do contain information on water outages, the questionnaire does not consider access to or reliability of water as a potential obstacle for operations. Specifically, when respondents are asked to rank the top obstacles they face – a legacy question that has existed since the inception of the surveys – the choices encompass power, communication, and transportation, while water is not presented as an option. Similarly access to water and water infrastructure does not feature in the World Bank’s _Ease of Doing Business Index_ while electricity is included. However, one would expect this to change in the near future, given the increased focus on climate change that has brought the issue of looming water challenges to the frontlines of policy.

The sidelining of water as an area of focus is surprising given its importance in driving the economy. Consider, for example, the prominent role water plays in the Sustainable Development Goals (SDGs). The SDGs were conceived to reflect the holistic nature of development and the integrated approach needed to end poverty, protect the planet, and ensure prosperity for all. The end result is 169 targets revolving around 17 goals. Water is unique in that it links nearly every SDG (World Bank, 2016). On top of SDG 6, which directly concerns clean water and sanitation, water is also crucial for food security (SDG 2), health (SDG 3), clean energy (SDG 7), new jobs (SDG 8), and the planet’s ecosystem (SDGs 13,14, and 15). Therefore, it is implicitly 2