rafmacalaba/datause-encoder-data huggingface dataset:
- Maccini, Sharon and Dean Yang (2009) “Under the weather: Health, schooling, and economic consequences of early-life rainfall,” _American Economic Review_ , 99 (3), 1006– 26.

- Morikawa, Robert (2014) “Remote Sensing Tools for Evaluating Poverty Alleviation Projects: A Case Study in Tanzania,” _Procedia Engineering_ , 78, 178–187, https: //doi.org/10.1016/j.proeng.2014.07.055, Humanitarian Technology: Science, Systems and Global Impact 2014, HumTech2014.

- NEPA and UN Environment (2016) “Afghanistan: Climate Change Science Perspectives.”

- NSIA (2020) “Income and Expenditure Labor Force Survey Report,” _National Statistics and Information Authority, Afghanistan_ , `https://fscluster.org/afghanistan/ document/income-and-expenditure-labor` .

- Pape, Utz Johann and Philip Randolph Wollburg (2019) “Impact of Drought on Poverty in Somalia,” _World Bank Policy Research Working Paper_ (8698).

- Ravallion, Martin (1998) “Poverty Lines in Theory and Practice,” _LSMS Working Paper_ (133).

- Reardon, Thomas, Peter Matlon, and Christopher Delgado (1988) “Coping with household-level food insecurity in drought-affected areas of Burkina Faso,” _World Development_ , 16 (9), 1065–1074.

- Rousta, Iman, Haraldur Olafsson, Md Moniruzzaman, Hao Zhang, Yuei-An Liou, Terence Darlington Mushore, and Amitesh Gupta (2020) “Impacts of drought on vegetation assessed by vegetation indices and meteorological factors in Afghanistan,” _Remote Sensing_ , 12 (15), 2433.

- Schlenker, Wolfram and David B Lobell (2010) “Robust negative impacts of climate change on African agriculture,” _Environmental Research Letters_ , 5 (1), 014010.

- Skoufias, Emmanuel, Katja Vinha, and Berhe Mekonnen Beyene (2021) “Quantifying Vulnerability to Poverty in the Drought-Prone Lowlands of Ethiopia.”

- Trenberth, Kevin E, Aiguo Dai, Gerard Van Der Schrier, Philip D Jones, Jonathan Barichivich, Keith R Briffa, and Justin Sheffield (2014) “Global warming and changes in drought,” _Nature Climate Change_ , 4 (1), 17–22.

- Yatchew, A. (1998) “Nonparametric regression techniques in economics,” _Journal of Economic Literature_ , 36, 669–721.

23
# **Table 15: Deposit penetration and financial inclusion agenda**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|GDP per capita (log)|0.643***|0.653***|0.681***|0.660***|0.673***|0.675***|0.692***|0.664***|0.619***|
||(0.061)|(0.057)|(0.056)|(0.057)|(0.056)|(0.055)|(0.056)|(0.055)|(0.061)|
|FI index|-0.077*<br>(0.044)|||||||||
|FI index (effective)||-0.075<br>(0.046)||||||||
|Resource gap|||-0.031<br>(0.058)|||||||
|FI deposits index||||-0.091<br>(0.059)||||||
|FI deposits index (effective)|||||-0.075<br>(0.060)|||||
|Responsible for consumer protection||||||-0.430***<br>(0.154)||||
|Responsible for financial literacy|||||||0.141<br>(0.166)|||
|Responsible for promoting savings||||||||-0.243<br>(0.159)||
|Responsible for promoting rural access to|||||||||-0.424**|
|finance|||||||||(0.179)|
|Constant|1.298**|1.140**|0.774|1.116**|0.925*|1.114**|0.574|0.998**|1.450**|
||(0.584)|(0.533)|(0.496)|(0.541)|(0.518)|(0.499)|(0.522)|(0.485)|(0.560)|
|N|100|100|100|100|100|96|96|96|96|
|Adjusted R<sup>2</sup>|0.640|0.637|0.627|0.635|0.631|0.654|0.636|0.641|0.651|
|Robust standard errors in parentheses, *** p|<0.01, ** p<0|.05, * p<0.1||||||||
|Dependent variable: # commercial bank depo|sits/1000 adu|lts(log)||||||||

41
- 31 -

and (ii) whether Japan imports less manufactures than it should. This review leads to some conclusions.

61. First, it should be noted that only two studies investigated both questions, while others dealt with only one or the other of the two questions. Saxonhouse (1985), Bergsten/Cline (1985) and Noland (1987) focused on Japan's trade in all products, while Sato (1986), Carliner (1985) and Lawrence (1987) concentrated on the manufactured imports. Balassa (1986b) and Barbone (1988) were the only ones which dealt with both total and manufactured imports.

62. Second, it is important to be clear on the precise definition of the norm against which Japan's performance is compared. Should Japan be compared with all other countries, or with industrial countries only? If we prefer to compare Japan only with industrial countries, then some of the results of the statistical investigations where Japan was compared with a mixed group of industrial and developing countries would become less relevant. The cases in point are the exercises of Saxonhouse (1985) and Carliner (1985) and a part of Noland's study (1987) where some developing countries were included in the samples. However, Noland's (1987) statistical investigations on his industrial country subsample remain relevant.

63. Third, the definition of several variables chosen as determinants of "normal" import behavior varied substantially among the researchers. The level of per capita income would represent the stage of the country's economic development. Balassa hypothesized that the per capita income would negatively affect the ratio of imports to GDP. However, he found that this was not so clear in the case of imports from developing countries. The explanation offered by Balassa was that
**Figur 1**

<!-- Start of picture text --> Composition of World Bank Lending<br>for General Education, FYB3-90<br>Pereentage of all education lending<br>40-<br>80- ___ _<br>20- 0 1 1<br>1968-60 1970-74 1976-50 181-86 1980<br>-11PrIRsy Me oSndry EMPosmeodrIy GOTeWw1w trelnrg<br><!-- End of picture text -->

# * Data do not include nonformal basic education ard technical education.

In terms of the regional distribution of Bank lending for primary and nonformal education, the region of Europe, the Middle East, and North Africa (EMBNA) has the largest share (42%) of lending amounts during FYS5-90, mainly because of a large average size of loan (US$44 million) rather than a large number of projects (See Table 2). Asia, with a smaller average project size of US$21 million, accounted for 24% of the total number of primary education projects for the same period. Although Africa's share of 20% of total Bank lending for primary education remained stable during the 1980., the region's primary education's share of total regional education lending has increased steadily from 20% in FY80-84 to over 33% in FYS-89. The number of primary projects in the region accounted for half of all primary projacs financed during FY85-90.

Table 3 shows a gradual increase in all regions, except Latln America and the Caribbean (LAC), in the distribution of primary education projects over time.

**5**
The average elderly respondent lived in a household with 4 adults aged 15-50, 2 teens aged 7-14 and one child aged 6 or younger. During the first wave of interviews, nearly three-quarters of adults over 50 (73%) lived in households with all three generations and 10% lived in households with prime-aged adults only (Ainsworth and Dayton 2000). The relation of the respondent to the household head is likely to affect decision-making in resource allocation within the household. Nearly two-thirds were the head of their household, including virtually all of the elderly men (96%) and a third of the elderly women (35%). A third of the women were the spouse of the head (34%) and one in five was the mother of the head. The head, on average, was 61 years old and had 3.4 years of schooling.

**_Household sanitation._** Potable water and adequate sanitation facilities are key health inputs that should lower morbidity, including diarrhea, and improve BMI. Only 19% of the elderly lived in dwellings with piped water or a covered well; 4% lived in a dwelling with no toilet or latrine.

**_Community health and infrastructure._** The comnmunity adult mortality rate (age 15 and older) per 1000 population in that age group is based on the results of a house-to-house enumeration conducted by the research team for sampling purposes in 1991. The overall adult mortality rate of 16/1000 is roughly three times higher than we would have expected in the absence of AIDS, although it is somewhat inflated by the inclusion of people over 50. About 22% of respondents were in communities reporting a recent epidemic. Community infrastructure variables include whether the road is passable all year and whether there is a health facility within 5 kilometers of the community. Almost half (46%) of the elderly lived in a community where the road was impassable for at least part of the year, three-quarters lived within _5_ km of a health facility and about a quarter lived in urban areas.

**_Regional and seasonal control variables._** Not shown in Table 2 are regional and seasonal control variables: the district of residence, the month of the interview, the year of the interview (or 'passage', to measure secular trends), the order of the interview (or 'wave', to account for improvements in interviewer and respondent performance), and the latitude and longitude of the center of the cluster (GPS coordinates). We also include dummy variables to control for censoring in our ability to observe future deaths because of the end of the survey or sample attrition. Results for these variables are not shown to conserve space and can be obtained from the authors.

**_Household transfer income._** Private or public transfers can be used to mitigate the impact of adult deaths. The theoretical model includes a term for 'exogenous' income in the reduced form. We use two measures: (a) receipt of transfers from individuals to everyone in the household _except_ the elderly respondent; and (b) receipt at the household level assistance from

_12_
# **Table 6.   Recommendations by Income Level**

|||**OF**|||**RF**||
|---|---|---|---|---|---|---|
||**HIC**|**MIC**|**LIC**|**HIC**|**MIC**|**LIC**|
|**A. INFRASTRUCTURE**|**14.5**|**21.0**|**22.7**|**14.5**|**21.0**|**22.7**|
|**_a. Contractual_**|**2.2**|**5.3**|**8.1**|**15.1**|**25.3**|**35.8**|
|i. Creditor Rights|0.7|1.2|1.5|30.8|22.0|18.1|
|ii. Insolvency|1.0|1.0|0.8|46.2|19.7|9.7|
|iii. Judiciary|0.5|3.1|5.9|23.1|58.3|72.2|
|**_b. Informational_**|**4.0**|**7.9**|**7.2**|**27.9**|**37.3**|**31.8**|
|i. Accounting|0.8|1.8|1.8|20.8|22.5|25.0|
|ii. Auditing|0.5|1.0|0.8|12.5|12.8|10.9|
|iii. Disclosure & Reporting|1.0|0.9|0.1|25.0|11.8|1.6|
|iv. Rating|0.2|0.1|0.0|4.2|1.1|0.0|
|v. Registries|1.5|4.1|4.5|37.5|51.9|62.5|
|**_c. Transactional_**|**6.6**|**6.3**|**6.3**|**45.3**|**30.1**|**27.9**|
|i. Trading|0.3|1.1|0.7|5.1|17.2|10.7|
|ii. Paying|6.2|5.2|5.6|94.9|82.8|89.3|
|**_d. Corporate Governance_**|**1.7**|**1.5**|**1.0**|**11.6**|**7.2**|**4.5**|
|**B. OVERSIGHT**|**72.5**|**57.2**|**45.2**|**72.5**|**57.2**|**45.2**|
|**_a. Prudential_**|**67.1**|**52.1**|**40.5**|**92.6**|**91.0**|**89.5**|
|i. Legal & organizational framework|23.4|18.4|17.4|34.9|35.3|42.9|
|ii. Regulation|15.7|12.3|11.4|23.4|23.7|28.1|
|iii. Supervision|21.8|14.2|8.9|32.4|27.2|22.0|
|iv. Crisis management|6.2|7.2|2.8|9.3|13.8|7.0|
|**_b. Market performance & integrity_**|**5.4**|**5.2**|**4.7**|**7.4**|**9.0**|**10.5**|
|i. Market competition|1.7|2.6|1.8|31.3|51.2|38.1|
|ii. Consumer protection|0.8|0.9|1.5|15.6|17.9|31.0|
|iii. Anti-Money Laundering|2.9|1.6|1.5|53.1|30.9|31.0|
|**C. PUBLIC POLICY**|**13.0**|**21.7**|**32.1**|**13.0**|**21.7**|**32.1**|
|**_a. General enabling policies_**|**8.1**|**14.0**|**16.8**|**62.3**|**64.6**|**52.3**|
|i. Monetary & Foreign Exchange Management|3.9|8.1|11.2|47.9|57.5|66.4|
|ii. Debt Management|2.7|3.4|2.7|33.3|24.3|16.1|
|iii. Tax Management|1.2|2.0|2.5|14.6|14.1|14.8|
|iv. Fiscal & public sector management|0.3|0.6|0.5|4.2|4.2|2.7|
|**_b. Financial Development Policy_**|**4.9**|**7.7**|**15.3**|**37.7**|**35.4**|**47.7**|
|i. Direct intervention|4.2|6.3|13.0|86.2|82.0|84.6|
|ii. Indirect intervention|0.3|0.7|0.9|6.9|9.3|5.9|
|iii. Pro-market Activism|0.3|0.7|1.5|6.9|8.7|9.6|

- Within prudential oversight, supervision is advanced while the legal framework and regulation (at least in relative terms) are basic. This suggests that, as income rises, watching how the game is being played gradually acquires more importance than making the rules. On the other hand, crisis management is intermediate, perhaps because middle income countries are more volatile than high income countries while having larger financial systems than low income countries, hence are the most crisis-prone of all.

15
And lastly, this paper might lay some foundation to develop a systematic and comprehensive approach to tracking country statistical performance regarding gender data. The recently developed Statistical Performance Indicators (SPI) provides a conceptual framework to track the progress of country data and statistical systems, with 5 pillars on data use, data services, data products, data sources, and data infrastructure (Dang et al. 2023). A “Gender SPI” could build from the SPI to focus on specific areas related to gender data production and dissemination, and serve as a means of identifying progress or stalls in national statistical systems with regard to gender data. Such an index would aid in tracking and prioritization of investments by countries and development partners to close gender data gaps.

29
an ex-ante elicitation of willingness-to-pay measures consumer surplus at some implicitly assumed and hence unknown marginal price, rather than at _any_ marginal price. However, the determination of optimal provision and two-part pricing requires, firstly, recovering the shortrun price elasticity of demand and, secondly, requires estimating the long-run price elasticity of demand when it differs from its short-run analog. Our approach does both of these things while highlighting a source of low elicited willingness-to-pay, complementary to but distinct from credit constraints (Devoto et al., 2012b; Berkouwer and Dean, 2021).

The rest of the paper is organized as follows: We describe the experiment, data and demand persistence results in section 2. In section 3, we set up a generic model of consumer demand with intertemporally dependent preferences and, in section 4, present the structural estimation of this model. With these results in hand, in Section 5, we assess optimal pricing schemes for new utility services, and specifically for piped water in our setting. We conclude the paper in Section 6.

# **2 Experiment and data**

Working with the Government of Vietnam, the overall objective of the experiment was to assess willingness-to-pay for piped water in a rural area where the service had recently been introduced and had begun supplanting the traditional mode of water supply, rainwater collection.

## **2.1 Experimental design**

The price experiment took place between January 2016-May 2018 in three villages of My Huong commune in the Red River Delta region of northern Vietnam. My Huong is relatively prosperous, with average per capita expenditures at about the 80th percentile for rural Vietnam. Since the arrival of piped water in 2012, My Huong has been served by a single provider, An Thinh utility, with whom we partnered over the course of the 3-year project. Among other things, An Thinh shared their customer billing records, which include monthly water usage (in m<sup>3</sup> ) and payment, from the month of connection onward, including throughout the price experiment.

**Household listing** An exhaustive door-to-door listing in the three villages was completed in mid-2015, yielding a total of 1660 households, which includes multi-family living arrangements

5
# **1.  Introduction**

There has been much debate over the value of industrial countries’ preferential trade programs which grant duty-free or reduced duty access to many developing countries’ exports. In 2005 non-agricultural market access discussions at the WTO, Oxfam International argued that preference erosion “is a serious loss for these countries, and one for which they should be compensated.<sup>1</sup> ” The Prime Minister of Mauritius, speaking to the G-90 Ministers’ Meeting in July 2004, stated that it is “crucial that modalities be developed to protect the preferential as well as favorable conditions of access of these countries. Otherwise, they run the risk of being chased out of the market with devastating socio-economic consequences...<sup>2</sup> ” In contrast, the IMF Managing Director told the WTO General Council in October, 2004, that according to an IMF study, “vulnerability to preference erosion is heavily concentrated in a sub-set of products, especially sugar and bananas,” and thus, “assistance to help countries cope with preference erosion can and should be closely targeted at the countries at risk.<sup>3</sup> ” Ostensibly, a country granted trade preferences would see demand for its exports grow, relative to other exporting countries still facing MFN tariffs. If the country receiving preferences is small, its exports would continue being sold in the importing country at the prevailing tariff-inclusive price, with the exporter earning the difference. Thus, the benefits of such preferences for the exporting country would be increased exports and a transfer of rent from the importing country. But how important are these trade preferences to developing countries? Are the tariff margins large? Do countries fully utilize

> 1 Oxfam International – Oxfam International contribution regarding NAMA Negotiations 25-29 April 2005 World Trade Organisation, Geneva. <u>http://www.oxfam.org.uk/what_we_do/issues/trade/sub_nama.htm,</u> downloaded July 12, 2005.

> 2 Speech of the Prime Minister, Hon. P. R. Bérenger, Republic of Mauritius, at the opening of the G-90 Ministers' Meeting 12 July 2004 <u>http://ncb.intnet.mu/mfa/speech/sppm.htm, downloaded July 12, 2005.</u>

1
||[0.015]***|[0.015]**|[0.013]|[0.033]***|[0.020]***|[0.032]|
|---|---|---|---|---|---|---|
|Total hectares ofdimba plots|0.073|0.044|0.049|0.037|0.066|0.101|
||[0.023]***|[0.023]*|[0.018]***|[0.029]|[0.027]**|[0.059]*|
|Total hectares of rain-fed plots|0.111|0.066|0.051|0.112|0.126|0.086|
||[0.007]***|[0.007]***|[0.008]***|[0.017]***|[0.010]***|[0.011]***|
|Regular bus service in<br>community|0.006|0.008|-0.020|-0.006|-0.027|0.011|
||[0.012]|[0.013]|[0.010]*|[0.032]|[0.018]|[0.018]|
|Healthclinicincommunity|0.061|0.030|0.051|0.069|0.099|0.032|
||[0.011]***|[0.012]**|[0.010]***|[0.029]**|[0.018]***|[0.016]**|
|EA is a Boma or Trading<br>center|0.203|0.169|0.046|0.331|0.242|0.137|
||[0.025]***|[0.026]***|[0.023]**|[0.068]***|[0.043]***|[0.036]***|
|Travel to nearest boma: >20-<br>30mins|-0.009|-0.030|0.021|0.007|0.073|-0.057|
||[0.016]|[0.018]*|[0.014]|[0.055]|[0.026]***|[0.023]**|
|Travel to nearest boma: >30-<br>45mins|-0.102|-0.058|-0.027|-0.267|-0.008|-0.103|
||[0.016]***|[0.018]***|[0.013]**|[0.050]***|[0.022]|[0.024]***|
|Travel to nearest boma: >45-<br>60mins|-0.039|-0.020|-0.025|-0.278|0.074|-0.044|
||[0.017]**|[0.019]|[0.015]*|[0.052]***|[0.024]***|[0.027]*|
|Travel to nearest boma:<br>>60mins|-0.018|-0.007|0.000|-0.128|0.103|-0.068|
||[0.017]|[0.019]|[0.014]|[0.052]**|[0.028]***|[0.024]***|
|ADMARC market in the<br>community|-0.050|-0.035|-0.032|0.032|-0.077|-0.049|
||[0.014]***|[0.016]**|[0.013]**|[0.044]|[0.024]***|[0.020]**|
|Bank incommunity|-0.014|-0.032|0.004|0.423|-0.042|-0.017|
||[0.022]|[0.025]|[0.020]|[0.096]***|[0.035]|[0.032]|
|Dailymarketincommunity|0.004|0.039|-0.004|-0.077|0.047|0.014|
||[0.013]|[0.015]***|[0.011]|[0.037]**|[0.024]**|[0.018]|
|Tarmac/asphalt road in<br>community|0.029|0.016|0.045|-0.194|0.194|-0.003|
||[0.017]*|[0.020]|[0.015]***|[0.044]***|[0.032]***|[0.025]|
|Biomass (m3/ha)|0.0010209|0.0000051|0.0015476|0.0016728|-0.00017|0.0025929|
||[0.001]**|[0.001]|[0.0004]***|[0.001]|[0.001]|[0.001]***|
|Biomass /Ha Squared|-0.0000194|-0.000004|-0.00002|-0.000018|-0.000008|-0.000035|
||[0.000005]*<br>**|[0.000007]|[0.000005]*<br>**|[0.00001]*|[0.000009]|[0.000008]*<br>**|
|North region|0.007||||||
||[0.029]||||||
|Central region|0.270||||||
||[0.022]***||||||
|ADD:Karonga||0.016|0.012|-0.097|||
|||[0.034]|[0.024]|[0.032]***|||
|ADD:Mzuzu|0.113|0.077|0.042||||
||[0.028]***|[0.029]***|[0.022]*||||
|ADD:Kasungu||0.080|0.215||0.133||
|||[0.025]***|[0.021]***||[0.027]***||
|ADD: Salima|-0.088|0.054|0.119||||
||[0.024]***|[0.030]*|[0.024]***||||
|ADD:Lilongwe|-0.043|0.128|0.080||0.092||
||[0.017]***|[0.024]***|[0.019]***||[0.026]***||

46
# **Table A2.IU.A: Intergenerational Persistence in Schooling in Urban India -** **<u>(18 35 Years Age Cohorts)</u>**

|||**LINEAR CEF**||**Q **|**UADRATIC C**|**EF**|
|---|---|---|---|---|---|---|
||**Sons (S)**|**Daughters**<br>**(D)**|D-S|**Sons (S)**|**Daughters**<br>**(D)**|D-S|
|Linear Coefft.|0.46|0.52|0.066|0.55|0.65|0.094|
||(0.0074)***|(0.0080)***|(0.011)***|(0.023)***|(0.025)***|(0.034)***|
||[0.012]***|[0.013]***|[0.013]***|[0.035]***|[0.036]***|[0.039]***|
||{0.024}***|{0.022}***|{0.017}***|{0.046}***|{0.037}***|{0.043}***|
|Quadratic Coefft.||||-0.0070|-0.0093|-0.0022|
|||||(0.0014)***|(0.0016)***|(0.0022)|
|||||[0.0022]***|[0.0022]***|[0.0025]|
|||||{0.0022}***|{0.0025}***|{0.0025}|
|Intercept|6.96|5.97|-0.99|6.81|5.80|-1.00|
||(0.071)***|(0.075)***|(0.10)***|(0.085)***|(0.086)***|(0.12)***|
||[0.14]***|[0.17]***|[0.13]***|[0.15]***|[0.17]***|[0.13]***|
||{0.34}***|{0.37}***|{0.17}***|{0.35}***|{0.35}***|{0.17}***|
|No. Observation|9449|9070|18519|9449|9070|18519|
|||**Mobil**|**ity Estimates f**|**romQuadratic**|**CEF**||
||**Absolute**|**Mobility**|||**Relative**|**Mobility**|
||**Sons**|**Daughters**|||**Sons**|**Daughters**|
|**_ES0_**|6.81|5.80||**_IGME0_**|0.55|0.65|
|**_ES5_**|9.39|8.82||**_IGME5_**|0.485|0.556|
|**_ES10_**|11.61|11.37||**_IGME10_**|0.414|0.464|
|**_ES16_**|13.82|13.82||**_IGME16_**|0.330|0.353|

Notes: (1) The data used are IHDS 2012 with children aged 18-35. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling. (3) ESK is expected schooling when father has K years of schooling. K=0,5,10,16. (4) Robust standard errors in parentheses; standard errors clustered at district-level in square brackets; standard errors clustered at state-level in braces; *** p<0.01, ** p<0.05, * p<0.1.