rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 6531

# **Abstract**

This paper analyzes and reconciles macro and micro evidence on savings and factors that affect savings, as well as possible policy implications. At the aggregate level, the main question is how savings are affected by growth and macroeconomic policies and variables (fiscal policy, exchange rate, for example) and the breadth of financial markets. Some of these macro determinants can be reconciled with microeconomic evidence of the savings behavior of households. Using macroeconomic quarterly data and household survey data, the analysis explores the determinants of the savings rate at the macroeconomic and microeconomic levels, using the typical econometric models used in the literature (long-term co-integration relation and short-term error correction model for the macro determinants; linear multivariate models for the micro determinants). The long-term relationship indicates that a 10-percent increase in gross domestic product per capita would add 3.7 percentage points to the savings rate in the long run. The short-term relationship depicts a strong catch-up process to the long-run equilibrium, with quarterly changes in gross domestic product per capita and openness strongly correlated with quarterly changes in the savings rate. The characteristics of households that represent the volatility of expected income, such as education and access to borrowing or remittances, significantly impact saving rates. The macroeconomic and microeconomic analyses of the determinants of saving rates in Armenia point to three policy areas: the macroeconomic environment, the financial sector, and the role of remittances.

This paper is a product of the Poverty Reduction and Economic Management Unit, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The main author may be contacted at scoulibaly2@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<!-- Start of picture text --> (a) Share currently working women - Morocco<br>0,45 |<br>0,350,4 ere =<br>0,3 a -<br>-— --" An-~=--.<br>0,25 — — —=~— - ' s @<br>= = ; 7 rd<br>0,2 -<br>~-.----4<br>0,15<br>0,1<br>0,05<br>0<br>1992 2003 2004 2007 2014<br><!-- End of picture text -->

<!-- Start of picture text --> (b) Share currently working women - Egypt<br>0,45 ;<br>0,350,4 . — - ae<br>m~<br>0,3 iN<br>be ' ~<br>0,25 i. i ' -~.<br>I io — al —_— —_<br>0,2 ~-<j{l7>><br>0,15 ~<br>0,1 |<br>0,05 !<br>0<br>1989 1993 2005 2008 2014<br><!-- End of picture text -->

<!-- Start of picture text --> (c) Share currently working women - Jordan<br>0,25<br>1<br>0,2<br>-27<br>ae lie” -<br>>< a rr<br>0,1 e -eE— — =I<br>!<br>0,05<br>1<br>fe)<br>1990 1997 2001 2007 2009 2012<br>—e—Control,mean —@®=—Treatment, mean<br><!-- End of picture text -->
SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 20. Moderate power tariffs in Sudan**

<!-- Start of picture text --> 50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>US cents per KWh<br>Liberia Chad Burkina Faso South Sudan Cape Verde Senegal Mali Uganda Kenya Congo, Rep. Rwanda Benin Niger Cote d'Ivoire Namibia Madagascar Cameroon North Sudan Ghana Botswana Mozambique Lesotho Tanzania South Africa Ethiopia Zimbabwe Nigeria Malawi DRC Zambia<br><!-- End of picture text --> _Source:_ Briceño-Garmendia and Shkaratan 2009; World Bank 2007; World Bank 2011 _. Note:_ South Sudan’s price is the median of its price range of 18–29 cents. DRC = Democratic Republic of Congo.

**Figure 21. Benchmarking historic power production costs in Sudan**

<!-- Start of picture text --> 45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Source: Briceño-Garmendia and Shkaratan 2009; World Bank 2007; World Bank 2011.<br>Note: DRC = Democratic Republic of Congo.<br>Mali Niger Congo Benin North Sudan Cape Verde Cameroon Rwanda Burkina Faso Madagascar Kenya Tanzania Chad Ghana Senegal Namibia Cote d'Ivoire Lesotho Mozambique Uganda Zimbabwe Nigeria Malawi Ethiopia DRC Zambia South Africa Botswana<br>Total costs of power production<br>Sierra Leone (2001-07) Sierra Leone 2009/10<br><!-- End of picture text --> In spite of the generous subsidies received from the government, the NEC reported large financial losses. The utility lost $12 million each year in 2003 and 2004, $0.02–$0.03 per unit of electricity sold. Even the 10 percent increase in power sales from the previous year did not stabilize the financial performance of the NEC.

Hidden costs diverted significant resources from the utility, causing economic losses of almost $500 million ($484 million) in 2008 Underpricing is one major problem—the $0.09 per kilowatt-hour tariff recovers only 50 percent of the $0.18 required to produce a kilowatt-hour. Despite subsidization, this underpricing added almost $380 million to the NEC’s 2008 losses.

Network losses were around 22 percent in 2008. Though generally consistent with what most African utilities encounter, losses of this magnitude are double international best-practice standards. Network losses contributed $106 million to the NEC’s 2008 financial losses (table 14).

> <sup>31</sup>
the statutory restrictions on hours, and de facto hours can therefore differ from the hours that it reports. According to Kullander (2012), the maximum statutory length of the workweek in Sweden has always been 40 hours. But the agreed length of the workweek in 2011 was 37.2 hours and the actual number of weekly hours worked on the main job by full-time employees averaged 35.8.<sup>10</sup> Until 2009 Sweden had a lower number of weekly hours worked on the main job than the averages for the OECD, G7 and EU15 and for other Nordic economies (Figure 7). But during the crisis the number of hours worked rose above the Nordic and EU15 averages, reflecting the agreements between unions and employers to allow longer hours to save jobs.

Figure 7: Full-time employees work fewer hours per week on their main job in Sweden than their counterparts in many comparator economies <!-- Start of picture text --> 38<br>37<br>36<br>35<br>2005 2006 2007 2008 2009 2010 2011<br>OECD average G7 average Finland<br>Sweden EU15 average Nordic average<br>Average weekly hours worked on the main job<br><!-- End of picture text --> Source: OECD (2014b). Note: The Nordic average excludes Sweden.

# **2.4. Regulations on redundancy**

The main objective of regulations on redundancy is to protect employees from unfair dismissals, though they can also have other benefits for both employees and employers. For example, they can lead to higher productivity by increasing the job security and satisfaction of employees, the cooperation between employee and employer and the training incentives of both parties because they

> 10 Doing Business records only the regulations stipulated in labor law. The only exception is when collective bargaining agreements cover more than 50 percent of the manufacturing sector and the agreed terms automatically extend to firms that are not party to the agreements, in which case Doing Business records the regulations set by the collective bargaining agreements. Since this is not the case in Sweden, the labor law is taken as the basis.

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# **Box 2. Penang Skills Development Centre**

First established by the Penang State Government and private industry as a joint training center, the Penang Skills Development Centre (PSDC) has evolved into a purely private education institution providing certificate and diploma level training. The Malaysian Government invests in the Center and uses it to carry out public training programs. PSDC, with more than 100 member companies, now runs both standardized and customized programs. It charges at cost and is basically self-financing. Companies recoup their training expenditure from the Human Resource Development Fund (HRDF), a training fund financed by a 1 percent levy on payrolls.

The initiative for PSDC came from the Penang State Government, which provided the land and buildings. The founder members included large multinational companies with training traditions of their own. Members donate equipment, laboratories, training modules and trainers. They have access to shared training facilities without having to duplicate with their own in-house capability. SMEs enjoy technology transfer from and can benchmark their standards against multinationals. Multinationals, in turn, receive better support services. Vendors donate equipment to familiarize the company workforce with their products and promote sales.

None of this is to say that the PSDC is easily replicated. Eleven other Malaysian States (out of 13) have launched similar centers, with varying degrees of success, usually a function of state government commitment, regional growth, and private sector ingenuity (for example, the State of Johor invited equipment suppliers to stock and update equipment used in the JDC so training equipment was always current and comparable to those used in companies). As usual, the success of this sort of enterprise depends on a number of circumstances, most importantly the people initiating and managing the process. Centers like PSDC cannot be imposed; they need to grow in already fertile ground.

|Source: Pillay (2005)|
|---|

The state can also play an expanded role in helping organize tailored training programs that target groups of SMEs operating in the same sector and geographic regions. This is already being done on a limited scale by SIDO, Tanzania’s Small Industries Development Organization.<sup>37</sup> It might enlist the help of existing industry associations, such as those in the textile and garments sector, or help establish new industry associations when none exist. It would work with industry associations and SMEs to identify their skill and other technical needs and match them with the most qualified training providers, either public or private. Training would be group-based to reduce unit costs

> 37 Five enterprises cited SIDO as a major source of TVET skills and a provider of external training, for hotels and construction in Zanzibar, transportation in Arusha, textiles in Mwanza and fabricated metals in Dar es Salaam.

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The levels of Internet adoption in the exporter and importer have a different effect on the intensive and extensive margins. Internet adoption in the exporter has a larger impact on the extensive margin than the intensive margin, while it is the reverse for Internet adoption in the importer.<sup>6</sup> The result suggest that higher Internet adoption allows the exporter to find more overseas buyers for their products and increases the range of products exported. A 10 percent increase in Internet adoption in the exporter increases the extensive margin by 1.5 percent and the intensive margin by 0.4 percent. Riker (2015) also finds a similar result where higher Internet adoption in the exporter increases the probability of product entry into the U.S. market. Conversely, higher Internet adoption in the importer increases the intensive margin but decreases the extensive margin. A 10 percent increase in Internet adoption in the importer increases the intensive margin by 0.7 percent and decreases extensive margin by 0.15 percent. While the negative effect of the Internet on the extensive margin is puzzling, the aggregate trade data do not allow us to explore this further. Our intuition is that there is a substitution effect: higher Internet adoption in the importing country reduces the search and communication costs so buyers are able to purchase from a larger set of exporters, thus the number of products being exported between a given bilateral pair reduces.<sup>7</sup> This supports the intuition laid out by Freund and Weinhold (2004), where they infer that higher Internet adoption allows the importer to find more sellers and the equilibrium amount that each exporter will sell to the importer decreases.

**Table 3: Intensive and Extensive Margins and Internet Adoption**

|VARIABLES|(1)<br>ln (Total<br>exports)|(2)<br>ln (Average<br>value of<br>exports)|(3)<br>ln (Number of<br>product<br>exported)|
|---|---|---|---|
|Internet adoption exporter (log)|0.191***|0.0424***|0.149***|
||(0.0108)|(0.00883)|(0.00627)|
|Internet adoption importer (log)|0.0586***|0.0736***|-0.0150***|
||(0.00874)|(0.00736)|(0.00496)|
|GDP exporter (log)|0.359***|0.0604***|0.298***|
||(0.0156)|(0.0117)|(0.0112)|
|GDP importer (log)|0.0697***|0.0617***|0.00803|
||(0.0139)|(0.0107)|(0.0101)|
|Observations|215,796|215,796|215,796|
|R-squared|0.773|0.698|0.868|
|Year FE|Yes|Yes|Yes|
|Country-pair FE|Yes|Yes|Yes|

Standard errors are clustered at the country-pair. *** p<0.01, ** p<0.05, * p<0.1 The contributions of the intensive and extensive margins to the impact of Internet on bilateral export differ depending on whether Internet adoption changes in the exporter or importer. When Internet adoption increases in the exporter, a large proportion of the increase in bilateral exports (78 percent) can

> 6 The coefficients of the Internet in the exporting and importing countries are statistically different from each other for all margins of trade.

> 7 An alternative explanation is that the importer is buying fewer product but of higher unit value. Buyers use the Internet to access information about sellers and purchase the goods from the better sellers with higher quality.

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|**Table 18. Estimation Results of Model of the Det**|**erminants of**<br>Robust OLS|**Net Profit**<br>Endogen|ous SwitchingRe|gression|
|---|---|---|---|---|
||Full sample||No|Probit,|
||stepwise|Onlending|onlending|switch|
|Supply: Fund Capital|||||
|Initial VF capital|-0.056*|-0.004|-0.174*|0.284**|
||_(0.033)_|_(0.008)_|_(0.097)_|_(0.126)_|
|VF allows for saving|-0.052|-0.002|-0.087|0.072|
||_(0.037)_|_(0.006)_|_(0.060)_|_(0.086)_|
|VF requires members to save|-0.026|-0.005|-0.031|0.057|
||_(0.021)_|_(0.012)_|_(0.029)_|_(0.083)_|
|Supply: Characteristics of VF Committee|||||
|Proportion who have some higher education||0.003<br>|0.269<br>|0.090<br>|
|||_(0.015)_|_(0.265)_|_(0.166)_|
|Proportion who have accounting experience||<br>0.006|<br>-0.295|<br>0.011|
|||_(0.047)_|_(0.246)_|_(0.822)_|
|Need training in loan appraisal (yes=1)||<br>-0.009**|<br>0.050|<br>-0.028|
|||_(0.004)_|_(0.038)_|_(0.078)_|
|Accounts are computerized (yes=1)||0.005|0.020|0.045|
|||_(0.006)_|_(0.063)_|_(0.076)_|
|VF officers are chosen, not elected (yes=1)||-0.002|0.088|-0.013|
|||_(0.006)_|_(0.092)_|_(0.096)_|
|VF would cut loans equally if funds were short (yes=1)|0.075*|<br>0.004|<br>0.087*|<br>-0.085|
||_(0.039)_|_(0.039)_|_(0.047)_|_(0.078)_|
|Demand: Village Size and Organization|||||
|<br>Number of households in village|0.000|0.000|0.000|-0.000|
||_(0.000)_|_(0.000)_|_(0.000)_|_(0.000)_|
|Village has a market (yes=1)|<br>-0.033|<br>-0.001|<br>-0.048|<br>0.038|
||_(0.022)_|_(0.013)_|_(0.035)_|_(0.081)_|
|Rural (yes=1)|<br>0.083|<br>-0.001|<br>0.106|<br>-0.099|
||_(0.056)_|_(0.006)_|_(0.065)_|_(0.094)_|
|Demand: Household Characteristics|||||
|Proportion of households who are self-employed farmers|-0.219|-0.005|-0.460|0.215|
||_(0.149)_|_(0.020)_|_(0.297)_|_(0.224)_|
|Proportion of income generated by wages||0.003|0.136|-0.343|
|||_(0.014)_|_(0.193)_|_(0.253)_|
|Proportion of population with some secondary education||<br>0.001|<br>-0.214|<br>0.374|
|||_(0.017)_|_(0.233)_|_(0.279)_|
|Household assets (excluding land)|-0.022*|-0.010|-0.009|0.050|
||_(0.012)_|_(0.013)_|_(0.016)_|_(0.088)_|
|Proportion who have a loan from a formal bank||<br>0.011|<br>0.332|<br>0.388**|
|||_(0.039)_|_(0.243)_|_(0.188)_|
|Proportion of borrowers who are women||<br>0.008|<br>-0.242|<br>-0.282|
|||_(0.016)_|_(0.184)_|_(0.251)_|
|Expenditure per capita|-0.010*|<br>0.000|<br>-0.013|<br>-0.122***|
||_(0.005)_|_(0.008)_|_(0.010)_|_(0.028)_|
|Loan terms and conditions|||||
|<br>VF would borrow more if funds were short|-|||1.217***<br>_(0104)_|
|VF could lend out another 1m baht safely|-|||_._<br>0.572***<br>_(0.193)_|
|VF wants to borrow more from BAAC, GSB, etc.|-|||<br>0.464***|
|||||_(0154)_|
|Memo items:||||_._|
|<br>Number of observations|2,143|||1,963|
|R2|0.014||||
|pfor endogeneity||||0.055|
|<br>Source: Village Fund Survey 2010 and SES 2009. Robust stan|dard errors in pa|rentheses. * p<|0.05, ** p<0.01,|<br>*** p<0.001.|

29
across sectors.<sup>8</sup> These stocks are reported on a historical cost basis in millions of U.S. dollars for ten sectors.<sup>9</sup> GDP in current U.S. dollars was taken from the _World Development Indicators_ (WDI) and deflated by the U.S. producer price index from IFS to get the real GDP. Our measure of skill-labor abundance is the ratio between skilled and unskilled workers which were taken from the often-used education data provided by Barro and Lee (2000).

We include two variables to account for investment costs. As a measure of the hostcountry political instability we follow Eichengreen and Tong year and use the Political Risk Rating from ICRG. This rating goes from 0 to 100, where higher scores indicate more political stability. To clarify the interpretation of this variable’s coefficient we redefine this measure as 100 minus the index. We thus expect a negative coefficient on this variable. As a measure of economic volatility we include the volatility of the real exchange rate. We calculate this as the standard deviation of the monthly growth rate of the real exchange rate.<sup>10</sup> For trade costs, we follow the KCM literature and use 100 minus the degree of openness, where openness is the ratio of merchandise imports over GDP. Merchandise imports were taken from the WTO database. Finally, the indexes on natural resources are the ratio of a country’s sector net exports divided by the labor force. These data were also taken from the WDI. Tables A1 and A2 in the appendix contain the summary statistics for the resulting dataset and the list of countries in our sample.

> 8 www.bea.org

> 9 BEA sector classification is SIC until 1998 and NAICS since 1999. However, total stocks in the manufacturing sector are comparable across classifications.

> 10 We calculate the RER as the product of the U.S producer price index and the market exchange rate, divided by the consumer price index. These data were taken from the International Monetary Fund’s International Financial Statistics database.

10
secondary cities and rural areas. In capital cities, the figure is also surprisingly low, with fewer than 65 percent of households having access 24 hours a day.

**Figure 4.9.  Reliability of infrastructure and energy services in ECA in early 2000s**

(of households reporting access, % receiving water or electricity 24 hours per day) <!-- Start of picture text --> Capital Other urban Rural<br>80<br>60<br>40<br>20<br>0<br>Potable water 24 hours per day Electricity 24 hours per day<br>%<br><!-- End of picture text --> Note: Average among 8 ECA countries for potable water; 8 for electricity.

Source: see Table 2.1 Available evidence also points to large disparities across countries in terms of availability and reliability of basic services, and within countries, between the capital city and other cities. Taking the example of water, one can see that the connection gap between the capital city and other cities is particularly pronounced in Moldova and Kyrgyz Republic, and to a lesser extent in Azerbaijan, Georgia, Kazakhstan, Turkmenistan and Uzbekistan (Figure 4.10).

**Figure 4.10. Water connection comparison in capital and other urban areas in ECA countries**

<!-- Start of picture text --> Capital Other urban<br>100<br>80<br>60<br>40<br>20<br>0<br>EU Accession Balkans/EE Caucasus Central Asia Slavic<br>%<br><!-- End of picture text -->

* Bosnia- urban & mixed settlements

Source: see Table2.1 30
# **Table A4:** **<u>Calculation of Paternal Orphans (Before Adjusting for Child EVD Deaths)</u>**

||Age 15-19|20-24|25-29|30-34|35-39|40-44|45-49|50-54|55-59|Total|
|---|---|---|---|---|---|---|---|---|---|---|
|_Guinea_|||||||||||
|Male deaths|129.2|109.4|90.7|74.1|60.2|50.0|79.6|67.7|55.9||
|Mean number of living children|0.0|0.2|0.8|2.0|3.4|4.6|6.0|7.0|7.6||
|Total paternal orphans|1.3|17.5|75.3|144.5|204.0|230.2|473.5|476.6|425.3||
|Paternal orphans <15 years old|1.3|17.5|75.3|143.8|194.4|188.6|318.3|247.1|168.2|1,354|
|_Liberia_|||||||||||
|Male deaths|250.0|220.9|193.5|161.3|131.9|106.1|137.1|109.5|84.6||
|Mean number of living children|0.0|0.4|1.4|2.6|3.9|4.3|5.6|6.6|7.2||
|Total paternal orphans|7.5|88.4|272.8|420.9|510.6|458.2|767.8|725.6|606.2||
|Paternal orphans <15 years old|7.5|88.4|272.8|416.1|457.9|308.6|409.9|301.8|240.6|2,504|
|_Sierra Leone_|||||||||||
|Male deaths|195.3|175.3|156.5|131.8|105.7|82.3|114.0|89.3|69.6||
|Mean number of living children|0.0|0.3|1.1|2.3|3.5|4.4|5.3|5.7|6.2||
|Total paternal orphans|3.9|52.6|167.4|301.8|368.0|365.6|605.2|508.9|427.9||
|Paternal orphans <15years old|3.9|52.6|167.4|299.2|336.2|277.5|344.2|198.2|119.0|1,798|
|Totalpaternal orphans < 15(gross)||||||||||5,656|

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