rafmacalaba/datause-encoder-data huggingface dataset:
registries, property ownership registration requirements, the bank’s reputation, news media, and so on. When you buy an airplane ticket, you do not know how safe the airplane is to fly or what rights you have if the airline cancels your ticket. The government has regulations requiring airlines to provide this information. When one purchases a toy, clothing or food, the seller knows what are the constituent elements and the quality of the good or service. Today, government regulations and consumer preferences expressed in buying trends, govern the degree of information provided. News producing and distributing institutions are very important in this regard (WB 2002, WB 2008). Freedom of information laws govern the information asymmetry existing between people and the public sector. By influencing the stock and flow of information available to people, institutions influence interactions. Clearly, economic outcomes are affected as institutions are used to restrict _<u>or</u>_ to enhance information flows.

# <u>Property Rights</u>

Laws, regulations and customs of all kinds determine who has what “ownership” rights in markets. Some common examples might be the right or title to a piece of land, or a car, the right to pollute a river, the rights of workers versus equity holders in a company,<sup>10</sup> the right to build commercial property on a piece of land, or the government’s right to your property through taxation or direct appropriation. Without formal and informal rules that determine the limits of individual ownership/rights to gain from a particular economic transaction, there could be no community as each would aim to maximize his/her own self‐interest.<sup>11</sup> Government’s main role is to capture resources

> 10 For example, from each market transaction, shareholders earn profit, but workers have a right to the firm’s income stream, an amount that is determined partly by government legislation and partly by the firm’s internal rules and regulations and contracts it makes with employees (minimum wages/ overtime pay, severance pay, pensions, insurance, expenses to ensure safety equipment). For the worker, these rules determined their rights to the income earned using their labor.

> 11 Note that even when self‐interest dictates cooperation, unless there are rules to guide cooperation, it becomes moot.

7
**Table 1**

# **Aid and banking crises 1977‐2007**

|Equation|**1**|**2**|**3**|**4**|
|---|---|---|---|---|
|Dependent variable|Net disb.|Gross disb.|Net<br>transfers|Net disb.|
|Number of years from crisis start|‐0.044**|‐0.039**|‐0.042*||
||(0.018)|(0.018)|(0.024)||
|Number of years from crisis start squared|0.002***|0.002***|0.002**||
||(0.0005)|(0.0005)|(0.001)||
|Dummy for first six years from crisis start|<br>|<br>|<br>|0.015<br>(0.126)|
|Dummy for years 7‐13 from crisis start|<br>|<br>|<br>|‐0.300*<br>(0.158)|
|Dummy for years 14 and higher from crisis start|<br>|<br>|<br>|‐0.029<br>(0.227)|
|Log per capita income|3.143***|3.200***|3.166***|3.200***|
||(0.698)|(0.162)|(0.703)|(0.717)|
|Log population|‐0.350|‐0.612|0.629|‐0.279|
||(1.271)|(1.329)|(1.413)|(1.280)|
|R<sup>2</sup>|.96|.96|.96|.96|
|No. of donors|24|24|24|24|
|No. of observations|692|676|686|692|

Note *p< .1, **p<0.05, ***p<0.01. All regressions control for country and year effects, not shown for space reasons. Standard errors are adjusted for clustering at the country level. Banking crises dates are from L&V.

26
# **1. INTRODUCTION**

Time is increasingly recognized as a basic resource required for reducing poverty and improving well-being (Williams, Masuda, and Tallis 2015). A 2010 United Nations (UN) report finds that in all regions of the world: (1) women spend at least twice as much time as men on unpaid (household) work and (2) women’s total work burden is higher than men’s when both paid and unpaid work are taken into account (UNDESA 2010). Such patterns demonstrate the global persistence, particularly in less developed countries, of a traditional gender division of labor, in which women specialize in domestic responsibilities, such as the care of children and other family members (reproductive labor), and men specialize in activities that are more closely tied to income generation (productive labor). The unequal sharing of household work between the sexes imposes several costs on women’s well-being and livelihoods. Notably, time spent on domestic work restricts women’s access to paid employment and reduces their time available for education, leisure, self-care and social activities.

Designing effective policies or programs to address gender inequalities in time use is complex and requires considering the social context of men’s and women’s roles and responsibilities within the household among other factors. An important but often overlooked prerequisite to this process is the ability of researchers to accurately measure men’s and women’s time burdens and gender disparities in both productive and reproductive work. Various survey-based methods for measuring time use currently exist, such as stylized questions and time diaries, which have been applied in both developed and developing country settings. However, a lack of consistency across space and time in how these methods are used makes it difficult to compare results across countries, to capture changes in time use patterns over time, and to monitor progress in the unequal distribution of care work between men and women.

To advance our understanding of gendered time use patterns and trends in developing countries, this paper critically reviews methods used to measure time use. We begin by outlining common methods for measuring time use in developing countries. We follow this with a discussion of the challenges associated with measuring time use and a brief empirical analysis using data from Women’s Empowerment in Agriculture Index (WEAI) surveys in Bangladesh and Uganda, which contain stylized 2
Determinants of market integration and price transmission in Indonesia Gonzalo Varela<sup>+</sup> , Enrique Aldaz-Carroll<sup>*</sup> & Leonardo Iacovone<sup>*</sup> Sector Board: POV Keywords: Spatial Integration, Commodity Prices JEL: Q11, Q18

> + World Bank, University of Sussex, Department of Economics, CARIS, e-mail: <u>gvarela@worldbank.org</u>

> * World Bank, e-mail: ealdazcarroll@worldbank.org

> * World Bank, e-mail: liacovone@worldbank.org
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- World Health Organization (WHO). 2016a. Ambient air pollution:  A global assessment of exposure and burden of disease. WHO. Geneva.

- World Health Organization (WHO). 2016b. Burning opportunity: clean household energy for health, sustainable development, and wellbeing of women and children. WHO. Geneva.

32
Bowen, H. P., Leamer, E. E. and Sveikauskas, L. (1987) “Multicountry, Multifactor Tests of the Factor Abundance”, _The American Economic Review_ , Vol. 77, pp. 791-809.

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22
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Table 4. Growth of total output in selected countries, 1830-1913 average annual percentage changes

|_________________________________________<br>France|____________________________________<br>1830s and<br>1840s<br>1.6|_<br>1850s and<br>1860s<br>1.9|1870s and<br>1880s<br>0.9|1890s to<br>1913<br>1.9|
|---|---|---|---|---|
|Germany|1.8|2.6|2.0|3.2|
|Italy|..|1.0|0.5|2.8|
|United Kingdom|2.0|2.4|1.9|1.7|
|UnitedStates|..|4.2|4.6|3.6|

Note: Exact periods for each country are: France, 1832-52, 1852-69, 1869-96, 1896-1913; Germany, 1831-57, 1857-74, 1874-93, 1893-1913; Italy, 186175, 1875-96, 1896-1913; United Kingdom, 1831-51, 1851-71, 1871-96, 18961913; United States, 1850-73, 1873-92, 1892-1913.

Source: Maddison, 1982.
**IX REFERENCES .................................................**

**40**

# **List of Tables**

|Table 1: Contractual savings ratio to GDP (percent) ..........................................|........... 2|
|---|---|
|Table 2: Portfolio Composition .....................................................|9|
|Table 3: Granger causality tests: summary ....................................................|25|
|Table 4: Granger causality (one way) from institutions to markets only ............|.............. 26|
|Table 5: Granger causality (two ways) between institutions and markets ...........|............. 27|
|Table 6: No Granger causality between institutions and markets .......................|.............. 27|
|Table 7: Shares of Stocks in Investment Portfolios: Selected Countries .............|............. 28|
|Table 8: Contractual savings - Granger causality tests ......................................|.............. 31|
|Table 9: Pension funds - Granger causality tests ...............................................|.....<br>33|
|Table 10: Life insurance - Granger causality tests<br>...................................|...<br>35|
|Table 11: Non-life insurance - Granger causality tests<br>.............................|.....<br>37|
|Table 12: List of countries ....................................................|39|

# **List of Figures**

|Figure 1: Households' Asset Portfolio ....................................................<br>9|
|---|
|Figure 2: Payoff Tree ...................................................<br>13|
|Figure 3: Contractual savings in system financial assets (%, 1996) .14|
|Figure 4: Contractual Savings and Market Capitalization, 1996 .15|
|Figure 5: Contractual Savings and Value Traded, 1996 .15|
|Figure 6: Changes in Contractual Savings and Market Capitalization, 1990 - 1996 .<br>16|
|Figure 7: Changes in Contractual Savings and Value Traded, 1990 - 1996 .16|
|Figure 8: United States Financial Institutions Portfolios .......................................<br>17|
|Figure 9: Institutional Investors' Portfolio .......................................<br>19|
<!-- Start of picture text --> A. Comparison of NFHS-2 and NFHS-3 with NFHS-1 B. Comparison of NFHS-3 with NFHS-2<br>wo wo .<br>~~<br>° ° oe, a” pee,<br>2 eee ae al wes 2 ee .<br>1978 1980 1982 1984 1986 1988 1990 1992 1985 1987 1989 1991 1993 1995 1997<br>Year Year<br>————_ NFHS 2 - NFHS 1 wemm= NFHS 3 - NFHS 1 ———__ NFHS 3 - NFHS 2<br><!-- End of picture text -->
38 With no investment, given supply functions, borrowing (lending) will occur for ~r ( ~r) because there is a fall (a rise) in consumption of tradablea and nontradablea in t~e second period. With positive investment the ease relevant to the CGE applications -- Glick and Kharas show that borrowing not only affects production and consumption through relative prices, but also through factor intensities in each sector. Only for the ease of b • o (that is when consumption is perfectly substitutable across time periods) are relative· price movements independent of inveataent. Otherwise, the relative factor intensity in production will influence the outcome. Thus While it ie poaaible to isolate the factors that determine whether borrowing (lending) will take place, it is difficult to derive analytical results, even in the eiaple ease where all investment goods are .produeed in the tradable sector. Also the outeoae is quite sensitive to the value assumed by the interteaporal elasticity of substitution, a parameter difficult to give an intuitively eo.torting 9alue to. 23/ 6.1. Optimal Foreign Borrowing Strategies: A Case Study of Thailand (1985-92) Aa the recent debt repayaent difficulties of developing countries have ahown, it is critical to conduct external borrowing within overall eon•istent .. eroeeonomde policies, lest capital flight occur when credibility wanes. The debt crisis of the early eighties also taught creditors the fraaility of sovereign borrowing and debtors the realities o~ credit rationing. For example, the eapirieal evidence shows that lenders~ willingness to lend is positively related to the borrowers~ fiscal positions (lharae and Shishido, 1984). This suggests that borrowers will find it

-~ ' ..
model assumes) as student numbers fall (e.g. see Sondergaard et al. 2012). For the countries with the need to hire more teachers, the government may face challenging in hiring as many additional teachers as our simulations suggest (because of limited government finance, lack of interested candidates, etc.).

On the other hand, our model may be _overestimating_ the importance of the stock. We assumed that the teacher attrition rate is constant for the coming decades in each country. However, counterparts in several Pacific Islands countries report that many of their teachers are leaving the profession, migrating to other countries. Data from one Pacific Island country shows a higher teacher attrition (up to nearly 10 percent) right after the onset of COVID-19, and thereafter, a decline to below 2 percent, which is within the assumption we used in this analysis. Another country presented a relatively modest increase (below 4 percent) after the onset of COVID-19. However, unfortunately, many of the counterparts were unable to quantify how many teachers were leaving the teaching profession annually. If, indeed, attrition rates in Pacific Island countries are higher than assumed (and on an increasing trend), these countries would need to hire more teachers, which, in turn, would imply that their stock of teachers already in the system would be smaller.

The reality is that the higher the rate of attrition, the higher the importance of recruiting and training new teachers (to replace those teachers that leave the system).

We recommend that countries try to fill this knowledge gap, either by analyzing teacher payroll data (to identify the rate at which teachers are leaving the profession) or through surveys.

# V. Conclusions

This paper presents a simple approach to estimate the stock and flow of primary school teachers in the future across 22 middle-income countries in the East Asia and Pacific region. It uses selected indicators already in place to monitor progress against the SDG4 target – the net enrollment rate, the pupil-to-teacher ratio, and the teacher attrition rate. To our knowledge, the simulation across all 22 MICs in the EAP region is the first of its kind. We hope it provides useful input for policy makers’ and development partners’ deliberations on how to improve teaching quality, with applicability in regions other than EAP.

The simulation reveals that in every EAP country, teachers who are already “in the system” are expected to constitute the majority of those still employed in 2030, despite significant heterogeneity across the 22 countries regarding projected changes in required teacher positions based on estimated school-age populations and student enrollments in the coming years.

Our finding has an important policy implication, namely: if countries want to improve the quality of teaching in schools, their primary focus in the next 10 years should be on improving the stock – i.e. the quality of their current teacher workforce (through more and better teacher professional development) (Figure 3).

8
Figure 3.5: Concentration of Loan and Lease Market (2006) <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Net Loans & Leases Commercial Loans & SME Loans & Leases Net Leases SME Leases<br>Leases<br>Largest Top 3 Top 5<br>Market Share (%)<br><!-- End of picture text --> _Source: Authors’ analysis based on figures provided by SF. Note: The legal (as opposed to the business) definition of SMEs is used. The largest, top 3 and top 5 (by market share) credit institutions for each loan market segment – not necessarily the same entities across all segments – are shown in the above Figure._ A favorable macroeconomic conjecture has led to relatively low non-performing loan (NPL) levels for SME exposures until now. After a long period of shrinkage due to the 1999 crisis, financial intermediation begun to recover in 2004 and has accelerated since then across most market segments. According to the SF, the proportion of riskiest SME loans and leases (i.e. those classified C, D and E) was only around 3.2 percent as of end2006, compared to 2.6 percent for large firms. This represents a substantial improvement in loan quality since 2000, when the commercial loan portfolio’s NPL rate had exceeded 14 percent. As will be discussed later in the paper, the combination of benign external and strong domestic macroeconomic conditions has boosted liquidity and helped to maintain SME credit growth in spite of important institutional deficiencies. However, this favorable NPL performance might not continue going forward as the macroeconomic cycle turns and as credit institutions increasingly move down the credit risk curve in search of new SME clients.

18
in 2011). Other results in Table 2 include reductions in information deprivations and mixed improvement in the dimensions that are more relevant to the wellbeing of girls and women.

The reductions in deprivations presented in Table 2 are also illustrated by the movement of graphs from 2000 to 2011 and 2005 to 2011 (Figure 1). Panel A and Panel B illustrate movements of the indicators in urban and rural areas respectively. Panel C combines both and takes only indicators for which information is available in both rural and urban areas. The graphs show reductions in all dimensions. This Figure also shows that the reductions in rural areas have been much larger than in urban areas (Figure 1: A1 and A2 vs. B1 and B2). However, the rates of deprivation in rural areas are still higher. In some cases, the 2011 rates in rural areas are close to the rates in urban areas that were observed in 2000 (Figure 1: C1 and C2).

**Figure 1. A single deprivation analysis in urban and rural areas, 2000 -11 and 2005-11**

<!-- Start of picture text --> A1. Urban (2000-2011) A2. Urban (2005-2011)<br>2000 2011 2005 2011<br>EDU_ALL EDU_ALL<br>60% 80%<br>BPL 40% EDU_GIRLS BPL 60% EDU_GIRLS<br>40%<br>20%<br>20%<br>0% SANIT 0% HEALTH_QUAL<br>SANIT ASSET<br>INFO BIRTH<br>WATER INFO<br>ASSET<br><!-- End of picture text --> 9