rafmacalaba/datause-encoder-data huggingface dataset:
**Table 7: Comparing Actual to Model-Generated Prices**

||_2005_|_2006_|_2007_|_2008_|_2009_|_2010_|_2011_|_2012_|
|---|---|---|---|---|---|---|---|---|
|_Maize_|||||||||
|Actual ($/mt)|99|122|164|223|166|186|292|280|
|Fitted ($/mt), 1960-2012|146|150|167|184|158|173|212|207|
|Difference (%), 1960-2012|48|23|2|-17|-5|-7|-27|-26|
|Fitted ($/mt), 1960-2004|120|127|140|138|112|120|142|138|
|Difference (%), 1960-2004|22|5|-15|-38|-32|-36|-51|-51|
|_Wheat_|||||||||
|Actual ($/mt)|152|192|255|326|224|224|316|280|
|Fitted ($/mt), 1960-2012|214|227|256|288|219|213|238|245|
|Difference (%), 1960-2012|40|18|0|-12|-2|-5|-25|-12|
|Fitted ($/mt), 1960-2004|193|209|237|249|182|173|192|197|
|Difference (%), 1960-2004|27|9|-7|-24|-19|-22|-39|-30|
|_Rice_|||||||||
|Actual ($/mt)|286|305|326|650|555|489|453|550|
|Fitted ($/mt), 1960-2012|420|462|512|497|361|366|415|412|
|Difference (%), 1960-2012|47|52|57|-24|-35|-25|-24|-25|
|Fitted ($/mt), 1960-2004|352|417|467|366|236|231|256|254|
|Difference (%), 1960-2004|23|37|43|-44|-58|-53|-53|-54|
|_Soybeans_|||||||||
|Actual ($/mt)|275|269|384|523|437|450|541|540|
|Fitted ($/mt), 1965-2012|351|361|373|444|390|392|432|459|
|Difference (%), 1965-2012|28|34|-3|-15|-11|-13|-20|-15|
|Fitted ($/mt), 1965-2004|293|314|322|343|286|280|302|324|
|Difference (%), 1965-2004|7|17|-16|-33|-34|-38|-44|-40|
|_Palm oil_|||||||||
|Actual ($/mt)|422|478|780|949|683|901|1,125|1,125|
|Fitted ($/mt), 1965-2012|649|670|687|889|699|739|871|883|
|Difference (%), 1965-2012|54|40|-12|-6|2|-18|-23|-18|
|Fitted ($/mt), 1965-2004|585|629|650|732|552|576|661|667|
|Difference(%), 1965-2004|39|32|-17|-23|-19|-36|-41|-38|

**_Source:_** Authors’ calculations based on table 2 and 4 estimates.

**_Note_ :** ‘mt’ refers to metric tons. The first row of each panel denotes the price of the commodity, the logarithm of which is the dependent variable. The second row denotes the fitted price–based full sample model, and the third row denotes the percentage difference between the actual and fitted prices (based on the 1960–2012 sample, generated from the estimates reported in table 2). The fourth and fifth rows report fitted prices along with their percentage differences from actual prices based on the pre-2005 sample model (1960–2004, generated from the estimates reported in table 4).

— 26 —
COST RECOVERY, EQUITY, AND EFFICIENCY IN WATER TARIFFS IN AFRICA

|Utility|2001|2002|2003|2004|2005|Average<br>2001–05|
|---|---|---|---|---|---|---|
|SWSC|—|—|—|—|0.49|0.49|
|LWSC|—|—|—|—|0.73|0.73|
|NWSC|—|—|—|—|—|—|
|**Average**|**0.61**|**0.44**|**0.49**|**0.55**|**0.61**||
|**MIC**|**0.78**|**0.72**|**0.95**|**1.11**|**1.31**||
|**LIC**|**0.55**|**0.39**|**0.37**|**0.42**|**0.46**||

_Source:_ AICD WSS Survey Database, 2007.

MIC = middle-income countries; LIC = low-income countries.

39
# **_I. Competing Objectives_**

The returns from improvements in health are estimated to be very large. Whether it is an increase in life expectancy, health during early childhood, or health during peak earning years, or health in the twilight years, the benefits to individuals and to society are strongly positive and according to some researchers, overshadow the gains from most other investments.<sup>1</sup> For example, Murphy and Topel (2005) have calculated that between 1970 and 2000, the increase in longevity added $3.2 trillion annually to the national wealth of the United States.<sup>2</sup> The cumulative welfare cost of the AIDS epidemic in SubSaharan Africa (SSA) is estimated to have risen to $800 billion by 2000, equal to the output of the whole SSA region (Philipson and Soares 2005).<sup>3</sup> Hence, it is not surprising that improving the health of the poor—particularly of the most disadvantaged—is a key Millennium Development Goal.<sup>4</sup> A typical solution advocated by some is increased public spending on health targeted to favor the poor and backed by foreign assistance, as well as by an international effort to perfect drugs and vaccines to ameliorate infectious diseases bedeviling the developing nations (Commission on Macroeconomics and Health 2001). The Commission on Macroeconomics and Health proposed that per capita spending on health be raised to between $30 and $40 per annum from the current level of $24 in low income countries and $13 in the least developed countries, only $7 of which comes from budgetary outlays (Commission on Macroeconomics and Health 2001, p. 16). The proposed solution, even if it could be substantially implemented is not by any means unproblematic. Better health is one of several major objectives of policymakers in developing economies and it is an objective that is closely intertwined with the increase

> 1 This is a part of the reason why close to 7 percent of global GDP was spent on health related expenditures in 2001, with high income counties averaging close to 8 percent and low income countries, a little under 5 percent of GDP (Schieber and others 2006).  Partly also, because the relative costs of health services and products are also rising (Bloom and Canning 2003).

> 2 According to their calculations, reducing the cancer mortality by 1% could be worth $500 billion (Murphy and Topel 2005).

> 3 The valuation of life was calculated through the marginal willingness-to-pay approach (Philipson and Soares 2005).

> 4 In fact, three out of eight goals specified in the MDGs are directly related to health.  These are: reduction in child morality; improvement in maternal health; and combating HIV/AIDS, malaria, and other diseases. In addition, eight of the 18 targets are health-related.  When Bjorn Lomborg recently asked 10 ambassadors to the UN as to their priorities if they had $50 billion to spend, their top three choices were scaled up health services to protect against communicable diseases; improved water supply and sanitation; and increased education ("Lomborg, Bjorn" 2006).

1
clients, supporting the findings of Barrios et al. (2006) who find evidence that foreign firms collocating in the same sector and region significantly increase productivity and employment. A larger size (ln _emp_ ) seems to be negatively associated with FDI linkages, while exporting ( _exp_ ) seems to have a positive impact, although both narrowly miss the 10 percent threshold of statistical significance. Including all absorptive capacities simultaneously (column 9) confirms the findings only for firm size (ln _emp_ ) and distance to the foreign firm (ln _dist_ ).

**Table 10: The Effect of Suppliers’ Absorptive Capacity on Output Share to Foreign Firms, OLS**

|Dependent variable:|_outpisc_|||||||||
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|_gapisc_|-1.6276<br>(0.609)||||||||-1.0695<br>(0.800)|
|_sophisc_|5.9014*<br>(0.094)||||||||6.4544<br>(0.120)|
|_emp_terisc_||-0.1314|||||||-0.2208|
|||(0.317)|||||||(0.234)|
|_emp_secisc_|||0.1005||||||0.0037|
||||(0.396)||||||(0.980)|
|ln_experisc_||||1.4755|||||4.7960|
|||||(0.744)|||||(0.450)|
|_man_educisc_|||||-10.0299||||-6.3287|
||||||(0.142)||||(0.412)|
|_man_experisc_|||||6.0535||||9.7105|
||||||(0.419)||||(0.283)|
|ln_empisc_||||||-3.4974|||-6.7818*|
|||||||(0.106)|||(0.051)|
|_exportisc_|||||||9.8261||10.2026|
||||||||(0.121)||(0.296)|
|ln_distisc_||||||||-4.0871**<br>(0.014)|-2.9573*<br>(0.069)|
|_constantisc_|48.7270**|63.7402***|54.2755***|54.3062**|83.3056***|70.1656***|56.2935***|69.4351***|80.0081***|
||(0.013)|(0.001)|(0.002)|(0.013)|(0.000)|(0.000)|(0.001)|(0.000)|(0.003)|
|Country- sector FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|R2|0.31|0.32|0.31|0.29|0.33|0.33|0.30|0.34|0.48|
|Observations|109|107|107|109|112|107|110|105|93|

Source: Own calculations. p*<0.1, p**<0.05, p***<0.01 (p-values in parentheses). Note: All variables refer to FY 2012. All regressions control for country-sector fixed effects. Standard errors are robust to heteroscedasticity.

# **_Results for Established Suppliers_**

It is likely that firms having a longer supplier experience have different absorptive capacities compared to firms that just started supplying to a foreign client, especially as structural changes (such as changes in the supplier’s capacity, sophistication of production processes or skill levels) may happen early on during their relationship. We therefore rerun the regressions for supplier firms that have a supplier relationship of at least three years (see Table 11).

While the positive impact of a more sophisticated production process ( _soph_ ) and the negative impact of a larger distance to the foreign firm (ln _dist_ ) can be confirmed, we also find a significantly 24
- Veld, J. (2016). _Public investment stimulus in surplus countries and their euro area spillovers_ . Luxembourg: Publications Office.

- Volz, U. (2020). Investing in a Green Recovery. Finance and Development. September 2020. <u>https://www.imf.org/external/pubs/ft/fandd/2020/09/investing-in-a-green-recovery-volz.htm</u> (last accessed February 10, 2021)

- Whalen, C.J. & Reichling, F. (2015). The Fiscal Multiplier and Economic Policy Analysis in the United States, Congressional Budget Office Working Paper Series, February 2015.

- Woodford, M. (2011). Simple analytics of the government expenditure multiplier. _American Economic Journal: Macroeconomics_ 3(1): 1–35.

World Bank. (2000). _<mark>East Asia: recovery and beyond.</mark>_ <mark>Washington, D.C.: World Bank Group.</mark>

- World Bank. (2007). _Spending for Development: Making the Most of Indonesia's New Opportunities, Indonesia Public Expenditure Review 2007_ . Washington, DC.

World Bank. (2020). _Private Participation in Infrastructure (PPI) 2020 Half Year Report_ .

Xiang, D., Sharma, P., & Zhang, Y. (2019). The Global Financial Crisis, Fiscal Stimulus Package and the Chinese Banking Sector — A Pre- and Post-Efficiency Analysis. _Review of Pacific Basin Financial Markets and Policies_ , 22(2).

- Zandi, M. (2008), A Second Quick Boost From Government Could Spark Recovery, edited excerpts from July 24, 2008, testimony by Mark Zandi, chief economist of Moody’s Economy.com, before the U.S. House of Representatives Committee on Small Business.

37
# **Table 11: Full Logit regression of poverty exit and entry (1993-2000)**

||`(1)`<br>|`(2)`<br>|
|---|---|---|
||`exit`|`entry`|
|`Log real income 1993`|`0.155`|<br>`-0.295`|
||`(1.88)`|`(4.75)**`|
|`Age`|`0.098`|`-0.128`|
||`(1.38)`|`(2.45)*`|
|`Age squared`|`-0.001`|`0.002`|
||`(1.61)`|`(2.68)**`|
|`Sex`|`-0.056`<br>|`0.088`<br>|
||`(0.31)`|`(0.64)`|
|`Working hours per month`|<br>`0.001`|<br>`-0.001`|
||`(0.81)`|`(0.79)`|
|`Wage income dummy`|`-0.153`|`0.299`|
||`(0.95)`|`(2.30)*`|
|`Years of schooling`|`0.132`|`-0.136`|
||`(5.03)**`|`(7.84)**`|
|`Household size`|`-0.180`|`0.118`|
||`(3.03)**`|`(2.30)*`|
|`Number of Children < 5`|`-0.004`|`0.196`|
||`(0.04)`|`(2.23)*`|
|`Number of Men`|`0.044`|`0.191`|
||`(0.57)`|`(2.67)**`|
|`Urban Dummy`|`-0.777`|`0.535`|
||`(4.13)**`|`(3.53)**`|
|`North Sumatra`|`-0.938`|`-0.128`|
||`(1.29)`|`(0.34)`|
|`West Sumatra`|`0.019`|`1.315`|
||`(0.02)`|`(3.68)**`|
|`South Sumatra`|`-0.418`|`0.913`|
||`(0.58)`|`(2.63)**`|
|`Lampung`|`-0.954`|`0.471`|
||`(1.27)`|`(1.18)`|
|`West Java`|`-0.322`|`0.818`|
||`(0.50)`|`(2.78)**`|
|`Central Java`|`0.128`<br>|`0.227`<br>|
||`(0.20)`|`(0.70)`|
|`Di Yogyakarta`|`-0.166`|`0.686`|
||`(0.24)`|`(1.94)`|
|`East Java`|`0.428`|`0.850`|
||`(0.67)`|`(2.67)**`|
|`Bali`|`0.158`|`0.672`|
||`(0.23)`|`(1.94)`|
|`West NusaTenggara`|`0.277`|`1.311`|
||`(0.41)`|`(3.74)**`|
|`South Kalimantan`|`-0.071`|`0.679`|
||`(0.10)`|`(1.98)*`|
|`South Sulawesi`|`0.721`|`-0.028`|
||`(1.03)`|`(0.07)`|
|`Moved Province`|`3.830`|`0.018`|
||`(3.40)**`|`(0.03)`|
|`Moved Kabupaten`|`0.938`|`-0.203`|
||`(2.62)**`|`(0.94)`|
|`Moved Kecamatan`|<br>`-0.105`|<br>`-0.024`|

42
Table 1. Historical inequality extraction ratios for England/United Kingdom and the United States, selected years

# **England/United Kingdom**

|Year|Estimated<br>Gini|Estimated α<br>(GDP per<br>capita/s)|Maxim|um Gini|Inequalit<br>ratio (i|y extraction<br>n percent)|
|---|---|---|---|---|---|---|
||||With s only|with b=0.5|With s<br>only|with<br>b=0.5|
|1209|36.7|2.1|53.1|31.5|69|117|
|1688|45.0|4.7|78.8|54.0|57|83|
|1759|45.9|5.9|82.9|58.7|55|78|
|1801|51.5|6.7|85.0|61.3|61|84|
|1867|53.0|9.9|89.9|68.2|59|78|
|1964|33.6|31.9|96.9|82.2|35|41|
|1979|28.8|43.9|97.7|84.9|29|34|
|1991|35.9|53.9|98.1|86.4|37|42|
|2002|37.4|69.8|98.6|88.0|38|42|
|2007|37.1|78.8|98.7|88.7|38|42|
|2010|37.4|76.2|98.7|88.5|38|42|

# **United States**

|Country/year|Estimated<br>Gini|Estimated α<br>(GDP per<br>capita/s)|Maxim|um Gini|Inequalit<br>ratio (i|y extraction<br>n percent)|
|---|---|---|---|---|---|---|
||||With s only|with b=0.5|With s<br>only|with<br>b=0.5|
|1774|45.6|5.3|81.1|56.5|56|81|
|1860|53.0|7.3|86.2|62.9|62|84|
|1929|48.1|23.0|95.7|79.1|50|61|
|1935|47.2|18.2|94.5|76.6|50|62|
|1950|40.5|31.9|96.9|82.3|42|49|
|1967|39.7|47.8|98.0|85.5|41|46|
|1979|40.4|62.6|98.4|87.4|41|46|
|1991|42.8|76.2|98.7|88.5|43|48|
|2002|46.2|95.3|99.0|89.8|47|51|
|2007|46.3|104.5|99.0|90.2|47|51|
|2009|46.8|99.4|99.0|89.7|47|52|

Sources: GDP per capita for both countries from Maddison (2007), expressed in 1990 PPPs. The subsistence minimum assumed $PPP 300 in 1990 international prices. UK Ginis: 1209-1801 from Milanovic, Lindert and Williamson (2011) based respectively on social tables drawn by Campbell (2007) for year 1209, Gregory King for 1688 [1696], Joseph Massie for 1759 [1760] and Patrick Colquhoun for 1801-3 [1806]. Data for 1867 calculated from Lindert and Williamson (1983) which are in turn based on social tables produced by Dudley Baxter [1869]. UK Ginis from 1967 onward from Luxembourg Income Study (income concept: disposable household per capita income across individuals). US Ginis: 1774 and 1860 from Lindert and Williamson (2011). Period 1929-50 from Goldsmith et al (1954, fn. 4, p. 7). Gini after 1967 from US Census Bureau (2010). The US concept is gross household income across households.

12
6

|4|The Health Zone provides training for 20 volunteers on maintenance of latrines, water supply systems, and<br>sanitation, conflict management, and petty cash management. The community elects a village WASH<br>committee.|
|---|---|
|5|The community spends ten hours over three days describing a community vision, analyzing the barriers to<br>reducing diarrheal diseases, choosing improvements to drinking water, sanitation, and hygiene, and<br>formulating an action plan. The community is asked to identify practical, low-cost solutions with a minimum<br>of outside assistance. New infrastructure is evaluated in terms of accessibility, technical feasibility, and<br>technical capacity.|
|6|The community builds new infrastructure over 90-180 days, supported by project funds. Key messages<br>about sanitation and hygiene are discussed during sensibilization meetings or during visits to families by<br>the WASH committee, community health workers, or other volunteers. Health Zone staff are expected to<br>visit the community monthly during this time; Health Area staff weekly.|
|7|The community self-evaluates again, to measure progress since Step 2. The Health Zone conducts<br>additional KAP surveys and hosts three hours of meetings to assess the findings and make a plan to<br>maintain progress.|
|8|The CMO spends one day in the community to assess whether or not the community has completed its<br>action plan and achieved the seven norms. If they have, a certification ceremony is held. The CMO and the<br>village WASH committee develop a Community Action Plan for Maintenance so that the changes achieved<br>through the program can be sustained over time.|

The IHfRA data collection team used electronic tablets and transmitted data to a cloud-based server, allowing the research team to conduct quality control measures in real-time, checking for consistency and errors. Additionally, IHfRA randomly selected 15% of villages for a second round of interviews, by different interviewers, with a shorter questionnaire, to check consistency across key variables. Separately, children from two households per village had their height and weight re-measured by an IHfRA supervisor, as a quality check.

# Outcomes

Primary outcomes were caregiver-reported diarrhea in the last seven days among all children who were under 5 years old at the time of the survey, length-for-age Z score for a randomly selected child in each household, and a WASH governance index. If the household had one child between age 2 and 5, we measured the length and weight of that child. If the household had more than one child between age 2 and 5, we randomly selected one child. If the household had no children between ages 2 and 5, but one or more children between ages 0 and 2, we measured
- 13 -

Price Deflators The NSS data on consumption expenditures are, of course, in current local prices. In order to make inter-regional comparisons and evaluate aggregate poverty, it is thus necessary to adjust the nominal data for spatial variations in the cost of living. We use the following two price indices to express all regional expenditures in 1983 all-India ruzal prices: i) The rural price index (RPI) for state j is defined where RPRs are the rural interstate price relatives (ratio of rural prices in state J to all-India rural prices) for 1973-74 and are based on the Fither regional price indices constructed by Bhattacharya et al (1980, Table 3a).<sup>9</sup> CPIALs are the Consumer Price Indices for Agricultural Labourers, as tabulated by the Labour Bureau, Ministry of Labour. It can be seen that the proposed price index simply updates the rural interstate price relatives for 1973-74 to 1983 ubing the CPIAL as the rural price deflator.

ii) The urban price index (UPI) for state j is defined analogously as where UPRs are the urban interstate price relatives (analogous to RPRs) and URPR is the all-India urban-to-rural price relative for 1973-74. Both UPRs
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1. Introduction

Finding cost-effective ways to improve and expand access to formal financial services is a priority for many policymakers around the world.<sup>1</sup> In economics and finance, researchers typically model demand for financial services using variables such as interest rates, income, investment opportunities, risk, etc., but non-economic factors may also play an important role. One such non-economic factor is religious beliefs and practices which could limit utilization of financial services in certain contexts.

Indeed, about 25 percent of adults reported religious reasons as a barrier to having a bank account (DemirgüçKunt et al. 2015). This is common in Muslim-majority countries, which constitute over 1.4 billion people, due to the Islamic prohibition of dealing with interest<sup>2</sup> . Financial inclusion data support these survey results: in a crude crosscountry analysis, after controlling flexibly for GDP, Muslim-majority countries have a 24 percent lower rate of participation in active borrowing from banks (10.5% versus 7.9%) and a 29 percent lower rate of having a bank account (40.2% versus 28.6%)<sup>3</sup> .

However, surveys that merely ask individuals why they do not borrow or cross-country comparisons of financial sector participation may over- or under-attribute lack of borrowing to religion. There is a lack of non-survey

> 1 See Cull, Ehrbeck, and Holle (2014) for an overview of why financial inclusion is a priority for many policymakers and international organizations. This is often motivated both by micro-level evidence about the impact on households and communities from improved access and usage of financial services (Burgess and Pande 2005; Bruhn and Love 2014) as well as macro-level evidence about the role of a healthy banking sector in the development of the country (Jayaratne and Strahan 1996; Black and Strahan 2002; Levine 2005; Beck, Demirgüç-Kunt, and Levine 2007).

> 2 Many interpret Islamic law to prohibit dealing with _riba,_ often translated as usury. In practice this includes prohibiting receiving interest on savings, as well as paying interest on borrowing. Historically the Jewish and Christian faiths also prohibited usury ( _Bible, Exodus 22:25_ 2009; Moehlman 1934).

> 3 See Appendix Table A1. Data on religious populations around the world are taken from the World Religion Dataset (Maoz and Henderson 2013), while data about financial inclusion come from the World Bank’s global findex database (Demirgüç-Kunt et al. 2015). While both borrowing from and saving at banks is lower, borrowing from stores (which is more limited but also more likely to be Sharia-compliant) is higher in Muslim countries.

2
2 To our knowledge, all evidence on quality upgrading has relied on the analysis of data referring to imports in the constrained market. This has allowed for comparisons between constrained and unconstrained suppliers to the restricted market; but this, however, is not the issue addressed by the theory. Data on imports to the restricted market do not allow for comparisons between a particular exporter's sales to different markets, which is the phenomenon on which theory offers a prediction, namely that exports to a constrained market will be upgraded (presumably more rapidly than exports to unconstrained markets).

This paper measures quality and price effects of VERs using exporting country data. Oulr application is to Korean exports of footwear to various industrial countries over the period 1974-86, a period which offers both instances and removals of VERs. We approach the measurement of price and quality effects through the use of Tornqvist numbers previously applied by Aw and Roberts (1986, 1988) to US imports of footwear. Our results indicate the existence of binding VERs -- as reflected in price increases in the restricted markets when the VERs were in effect -- but quality downgrading. We also provide an interpretation for the observed downgrading.

The paper is organized as follows. In the remainder of the introduction, we summarize briefly why a VER may (or may not) be expected to provoke upgrading. In Section 2, we recall tne advantages of index numbers over unit values to measure price and quality shifts We also recall the assumptiQns under which index numbers are likely to be good indicators of price and quality shifts and describe the formulas upon which rests the evidence in the paper. Section 3 describes briefly the VERs into which Korea entered for its exports of footwear. Our evaluation of price