rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: Recent CGE Models related to water issues

|**Model**|**Background**|**Further Analysis**|**Aim**<br>**Modeling approach**|**Production Function**|**Utility Function**|
|---|---|---|---|---|---|
|**Seung****_et. al.,_2000**|||Comparing the effects of water<br>transfer<br>from<br>agriculture<br>to<br>recreative areas<br>County level dynamic CGE<br>model<br>with<br>a<br>recreation<br>demand module|Cobb-Douglas with fixed ratio<br>intermediate inputs|<br>CES|
|**Goodman, 2000**|||Comparing the effects of water<br>storage<br>increase<br>and<br>water<br>transfers<br>County level dynamic CGE<br>model|CES|CES|
|**Peterson****_et. al.,_ 2004**|ORANI (Dixon_et. al._. 1982)<br>;TERM Model (Horridge_et. al.,_<br>2003)|Dwyer,2005;|the long run effects of trade<br>under<br>reductions<br>in<br>water<br>availability<br>and<br>short<br>run<br>reductions based on observed<br>allocations<br>Large Scale,standard CGE|Nested CES,3 Level|demand follows a linear<br>system of expenditure|
|**Dwyer****_et. al.,_2005**|extends the analysis of Peterson<br>et al. (2004) to investigate the<br>effects of expanding trade to<br>include both irrigators and urban<br>water users|||||
|**Tirado ****_et. al., _2004 **||Tirado_et. al._. 2005<br>and 2006|analyze<br>the<br>welfare<br>gains<br>associated with an improvement<br>in the allocation of water rights<br>through<br>voluntary<br>water<br>exchanges (mainly between the<br>agriculture and urban sectors).|nested<br>CES,<br>5<br>level<br>for<br>agriculture and 4 level for<br>others|<br>Stone-Gearyfunction|
|**Tirado****_et. al.,_2005**|Tirado_et. al., _(2004)|Tirado_et. al., _2006|provide information on water<br>management<br>options<br>under<br>Water Framework Directive.|||

38
A close reading of the time use literature, however, fails to uncover sufficient justification for this claim. In terms of feasibility, the type of data needed to evaluate the cost-effectiveness of survey-based time diaries versus other methods (e.g., survey costs, interview length) are rarely shared by organizations. Equally rare are follow-up surveys (e.g., qualitative interviews, cognitive testing) aimed at evaluating respondents’ experience with different methods—for instance, how easy was it for the respondent to recall a certain activity? Where comparative data on the feasibility of time use methods does not seem to exist at all, data abound on the comparative accuracy of time use methods, particularly the question of stylized questions versus time diaries (Juster, Ono, and Stafford 2003; Kan and Pudney 2008; Frazis and Stewart 2012). This research generally tends to favor time diaries, noting the presence of greater systematic error in estimates based on stylized questions. However, these studies almost entirely rely on data from developed countries, mostly from the United States or United Kingdom. Virtually no rigorous empirical research exists comparing these methods in development settings.

# **3. COGNITIVE ASPECTS OF TIME USE METHODS**

To understand the specific challenges involved in collecting time use data in development settings we draw on insights into the cognitive aspects of survey methods from the psychology literature.<sup>8</sup> Consider, for example, the following question from the WEAI 2.0 pilot survey: “In the last seven days, how much time did you spend farming?” To accurately answer this question respondents must: (1) properly identify the relevant reference period (“the last seven days”) and the specific activities in question (“farming”); (2) search their memory of the past seven days to retrieve all instances of farming activities, and (3) correctly aggregate all of these into a frequency estimate. Each one of these steps presents unique challenges to respondents.

Perhaps the most common of these challenges relates to the ability to correctly identify the activity in question. This problem often has to do with how questions are phrased. Broad terms, such as

> 8 Although this evidence comes almost entirely from subjects in developed countries, we believe many of the insights are likely to be relevant in developing countries.

7
are still large with respect to self-report (for example, about 10 percentage point difference in male participation in employment between what reported by themselves and what reported by a female proxy – 87 vs. 78 percent).

To consider the effect of differences in schooling, we compare the reports by proxies and subjects of the education gap classified as having any schooling (S) or no schooling (N). The results are reported in Table 11c, where S-N reflects a schooled proxy reporting on non-schooled subject, and so on. For LFP, non-schooled proxy respondents report higher LFP than schooled counterparts, but this difference is statistically significant only for LFP of schooled subjects (although the gap is also there for non-schooled subjects). We do not find differences in hours reporting. Daily earnings are reported as higher by schooled proxies for schooled subjects by 434 TZ shillings than the non-schooled proxy reporting on a schooled subject. We see no statistically significant differences in labor statistics for non-school subjects by the schooling of the proxy respondent. Schooling differences between the proxy and subject matter most for schooled subjects.

# **6. Conclusions**

Despite the importance of household survey instruments as a source for labor statistics, there is a dearth of evidence on the best practices for collecting these statistics in developing countries. The differences in survey design for national labor statistics over time within a country and across countries has serious implications for both measuring labor markets and for research on labor activities. This paper presents a survey experiment focusing on two key aspects of survey design to estimate their 30
# **Chapter 1. Introduction**

Banking sectors in transition<sup>economies of Eastem Europe and FormerSoviet Union</sup> have experienced major transformations<sup>throughout the1990's. In the pre- and early-transition</sup> periods, state policies generally<sup>distorted resourceallocation,as credit (subject to a variety of</sup> controls) was directed toward<sup>sustaining existingindustries and maintainingliving standards</sup> through explicit and implicit<sup>subsidies to enterprisesand households. Since the primary role of</sup> the banking system was to<sup>channelfunds to the realsector,efficiency and profitability were not</sup> among the top priorities. The<sup>banks were notengaged in evaluatingthecredit conditions of their</sup> borrowers, and therefore no<sup>risk management techniques werein use.Old statisticalstandards</sup> were designed to serve the<sup>objectiveof easy planningas opposed to disclosure of the true</sup> financial state of banks.

While some countries in the<sup>regionhave been successfulin eliminatingunderlying</sup> distortions and restructuring<sup>their financialsectors, in some casesfinancial sectors remain</sup> underdeveloped and the<sup>rates of financial intermediation continue to be quitelow (Table1).This</sup> in<sup>light of mounting evidenceof the effectof financial sector development</sup> is especially alarming on economic growth (see,<sup>forinstance, Levine and Renelt (1992), King and Levine (1993),etc.).and</sup> Although part of the success<sup>acrosscountries could be attributed to better initial conditions1</sup> achievements of early macroeconomic<sup>stabilization programs, the importanceof structural</sup> reforms were the dominant<sup>force behind the successin performance(e.g., Berg</sup><sup>_et al.,_1999).</sup> Progress in legal and regulatory<sup>reforms,enterprise privatization, priceand external trade</sup> liberalization were partially<sup>responsiblefor the changesoccurringin some transition countries.</sup> These changes manifested<sup>themselves not only in the financialsoundness of banking institutions,</sup> but also in a wider range and<sup>sophisticationof financial services provided by the latter.A few</sup> countries in the region were<sup>able to transform their banking sectorsfrom astate where dozens of</sup> small banks were set up to serve<sup>individual enterprises(or worse, tooperate as scam machines!),</sup> to a state where putting all<sup>their eggs in one basketis considered suicidal, establishing</sup> correspondent relationships<sup>with major Western banks is a matter of lifeand death, and internet</sup> banldng is no longer a luxury.<sup>Reducedavailability of centralizedfinancing encouraged banksin</sup> I Yet de Melo _et al._ (1997)<sup>and, subsequently,Havrylyshyn and van Rooden (2000) show that</sup> although the initial conditions<sup>wereimportantin defining the differencein performanceacross</sup> countries, their significance<sup>diminished over time.</sup> 3
the brackets of the PIT: if at least one member of the household earns more than $1.06/day (2005 PPP) in the formal sector (either wage or self-employed) that member pays a PIT of 13 percent. The threshold for paying a 23 percent PIT tax rate for the waged employed is $4.58/day (2005 PPP). Similarly, a person earning more than $0.80/day pays social and health insurance. As a result, around 9 percent of the population in Albania were impoverished (became poor or fell deeper into poverty at $4/day) after paying direct taxes and contributions, i.e. when moving from market income (plus pensions) to net market income (1.8 percent were impoverished at the $2.5/day poverty line).

# **Indirect taxes, on the other hand, are regressive and slightly unequalizing, and they result in sizable**

**poverty increases** . This effect is largely led by the VAT, given its larger size as a fiscal intervention. Specifically, the VAT has a negative Kakwani (Figure 13), and while its effects on increasing inequality are small (0.13 Gini points), it has a marginal contribution to poverty of 2.9 and 6.8 percent, with the $2.5/day and $4/day poverty lines, respectively (Figure 14). In other words, the VAT pushed a share of households into poverty to an extent that direct transfers were not able to offset. Excises, although progressive, also have a negative impact on poverty. Around 29 percent of households were fiscally impoverished when moving from disposable to consumable income (i.e. after paying indirect taxes) at the $4/day poverty line (8 percent at the $2.5/day line). Compared to other countries in the region (see Annex 3) such as Armenia, Croatia, Georgia and Russia, indirect taxes were less regressive in Albania.

**Figure 13. Progressivity of Indirect Taxes in Figure 14. Marginal Contributions of Indirect Taxes Albania (Kakwani Index) in Albania**

<!-- Start of picture text --> 0.25 0.035<br>0.025<br>0.2<br>0.015<br>0.15<br>0.005<br>0.1 -0.005 VAT Excise All indirect taxes<br>-0.015<br>0.05<br>-0.025<br>0<br>-0.035<br>VAT Excise All indirect<br>-0.05 taxes Redistributive Effect<br>-0.1 Poverty reduction effect ( $ 2.5 PPP )<br>Poverty reduction effect ( $ 4 PPP )<br><!-- End of picture text --> Source: Own estimates using the Albania HBS 2015.

Source: Own estimates using the Albania HBS 2015.

24
by the SAPF but, as already noted above, no such regulation has been issued to date. The only reserve that the AFPs still have to keep to protect affiliates in the case of fraud or mismanagement is the _encaje._ This mandatory reserve not only depends on the size of the fund under management (as in Chile) but also on the risk of the fund's investments.

**Table 4 Investment Limits (11/1996)**

|**Instruments**|% of assets|
|---|---|
|**Individual limits**||
|a) Government securities|30|
|b) Central Bank securities|30|
|c) Fixed-term deposits|30|
|d) Financial sector bonds|25|
|e) Short term debt|10|
|f) Corporate bonds|35|
|g) Common shares|20|
|h) Workers' shares|20|
|i) Preferred share certificates|10|
|j) Short term margin loans|10|
|k) Derivatives|10|
|1) Mutual funds|10|
|m) Primary issues for new ventures|10|
|n) Foreign securities|_5_|
|**Combined limits**||
|a+b|40|
|c+d|40|
|g+h+i<br>jk+l|35|

Source: SAFP, Decreto Ley No. 25897

**Table 5 AFP Investments (% of total assets)**

||**12/93**|**12/94**|**12/95**|**12/96**|
|---|---|---|---|---|
|**Government bonds**|13.0|6.0|1.7|0.6|
|**BCR deposits**|18.9|20.0|20.5|0.0|
|**Bank deposits**|61.4|34.2|27.0|25.0|
|**Bank bonds**|6.7|20.2|21.7|18.2|
|**Corporate bonds**|**0**|3.3|9.5|22.1|
|**Equities**|**0**|14.2|18.1|31.9|
|**Other**|**0**|1.9|1.4|2.3|

Source: SAFP 17
**Table 2: Jordan: Evolution of Insurance Premiums, 1997-2002**

||1997|1998<br>Million|1999<br>JD|2000|2001|2002|
|---|---|---|---|---|---|---|
|Life Premiums|20.27|14.57|15.31|16.28|17.40|18.47|
|General Premiums|69.94|85.11|85.05|87.90|103.04|128.40|
|Total Premiums|90.21|99.68|100.36|104.18|120.44|146.87|
|||% GD|P||||
|Life Premiums|0.39|0.26|0.27|0.27|0.27|0.28|
|General Premiums|1.36|1.52|1.47|1.46|1.62|1.95|
|Total Premiums|1.76|1.78|1.74|1.74|1.89|2.23|
|||% Tot|al||||
|Life Premiums|22.5|14.6|15.3|15.6|14.4|12.6|
|General Premiums|77.5|85.4|84.7|84.4|85.6|87.4|
|Total Premiums|100.0|100.0|100.0|100.0|100.0|100.0|
|||% Grow|th||||
|Life Premiums||-28.1|5.1|6.3|6.9|6.1|
|General Premiums||21.7|-0.1|3.4|17.2|24.6|
|Total Premiums||10.5|0.7|3.8|15.6|21.9|
|Real Growth||7.2|0.1|3.1|13.6|19.8|

Data for 2002 are preliminary Source: Insurance Commission

# **B. Institutional Structure**

There are 26 licensed insurers in Jordan, divided between 18 composites (engaging in both life and general business), 7 specializing in general insurance, and 1 life insurer. Foreign companies may operate with branches. The life subsidiary of a leading American group has branch operations in Jordan and is by far the local leader in life business.

Market concentration is very high in the life sector, where the largest company controls almost 60 percent of total premiums and the largest 3 companies have 71 percent of the market. The Herfindahl Index amounts to 3489, a very high level. In the non-life sector, concentration is much smaller with the largest 3 companies representing 25 percent of the market and a Herfindahl Index of 489. In fact, the non-life sector suffers from high fragmentation with nearly half the companies each having less than 3 percent of total premiums.

The expansion of the number of companies has taken place in spurts. 3 companies date from the 1950s and 1960s, 6 were established in the mid-1970s, 5 in the early 1980s, 3 in the late 1980s, and 10 in the mid-1990s. One company, established in 1980, was put under liquidation in 2001. The most recent waves of expansion took place despite the prevailing view that the market was already overcrowded.

7
# **Appendix Table 11: Balance of Controls in Cappelen et al. (2020)**

||Mean<br>among<br>Control|Preschool<br>vs Control<br>Difference|Robust<br>Standard<br>Errors|
|---|---|---|---|
|Female|0.46|0.08|0.07|
|Age|7.56|0.08|0.08|
|Black|0.19|0.04|0.06|
|Hispanic|0.76|-0.07|0.06|
|White|0.05|0.01|0.03|
|Time of Experiment|9.97|-0.09|0.29|

_Notes_ : The table investigates whether control children and children assigned to the preschool arm in Cappelen et al. (2020) are comparable in terms of predetermined characteristics. The first column shows means among the control children. The second column reports the difference in means between the control and preschool children. The third column shows robust standard errors. “Time of Experiment” is the time of day when the child took part in the experiment using a 24-hour clock. The table is comparable to Cappelen et al.’s (2020) Table 1. _N_ = 302.

46
**Table S9. Estimated total number of households that report pre-COVID-19 income and average household-level incidence of income receipt, by source**

|||_Panel A_|_: All Countries_|||
|---|---|---|---|---|---|
||Farm income|Business income|Wage income|Remittances|Other income sources|
|Total|62,095,944<br>(607,921)|35,097,532<br>(656,727)|25,267,792<br>(575,150)|103,765,224<br>(946,421)|18,852,056<br>(619,989)|
|Mean|0.730|0.447|0.284|0.652|0.253|
||(0.008)|(0.010)|(0.008)|(0.009)|(0.009)|
|Observations|9,155|9,155|9,155|9,155|9,155|
|||_Pane_|_l B: Ethiopia_|||
||Farm income|Business income|Wage income|Remittances|Other income sources|
|Total|12,545,457|4,207,898|4,740,819|0|3,865,377|
||(283,973)|(249,049)|(243,303)|(0)|(254,336)|
|Mean|0.634|0.213|0.240|0.000|0.195|
||(0.014)|(0.013)|(0.012)|(0.000)|(0.013)|
|Observations|3,249|3,249|3,249|3,249|3,249|
|||_Pane_|_l C: Malawi_|||
||Farm income|Business income|Wage income|Remittances|Other income sources|
|Total|3,243,256|1,584,694|1,210,010|3,600,947|1,054,078|
||(32,910)|(68,720)|(64,491)|(8,046)|(63,054)|
|Mean|0.893|0.436|0.333|0.991|0.290|
||(0.009)|(0.019)|(0.018)|(0.002)|(0.017)|
|Observations|1729|1729|1729|1729|1729|
||,|,<br>_Pane_|,<br>_l D: Nigeria_|,|,|
||Farm income|Business income|Wage income|Remittances|Other income sources|
|Total|21,167,000|16,985,522|8,020,662|26,334,226|9,433,393|
||(380,014)|(444,969)|(403,349)|(127,001)|(427,423)|
|Mean|0.785|0.630|0.298|0.977|0.350|
||(0.014)|(0.017)|(0.015)|(0.005)|(0.016)|
|Observations|1,950|1,950|1,950|1,950|1,950|
|||_Pane_|_l E: Uganda_|||
||Farm income|Business income|Wage income|Remittances|Other income sources|
|Total|6,025,480|3,533,057|2,773,255|8,462,181|547,760|
||(124,336)|(128,328)|(125,664)|(10,362)|(64,285)|
|Mean|0.709|0.416|0.326|0.995|0.064|
||(0.015)|(0.015)|(0.015)|(0.001)|(0.008)|
|Observations<br>_Note_: Each column<br>household-level in<br>country. Standard|2,227<br>reports the estimated<br>cidence of pre-COVI<br>errors on the estimates|2,227<br>total number of househol<br>D-19 income receipt fro<br>are in parenthesis.|2,227<br>ds that reported pre-CO<br>m each source. Data ar|2,227<br>VID-19 income from<br>e only from the first|2,227<br>each source and the average<br>phone survey round in each|

36
|**Table 1: Scen**<br>|**arios on Unilateral E**<br>**Target Em**|<br>**missions Reducti**<br>**issions Cuts**|**ons and Trade Ac**<br>**Basis for Border**|**tions(Border Tax A**<br> <br>**Product Coverage**<br>|<br>**djustments)**|
|---|---|---|---|---|---|
|**Scenario**|**High Income**|**Low and Middle**<br>**Income**|**Tax Adjustment**|**of Border Tax**<br>**Adjustment**|**Treatment of**<br>**Exports**|
|NBTA17|17% relative to<br>2005 emission<br>levels|0%|n.a.|n.a.|n.a.|
|BTAFU|17% relative to<br>2005 emission<br>levels|0%|Carbon content in<br>imports|All merchandise<br>imports|No rebate for<br>exports|
|BTADU|17% relative to<br>2005 emission<br>levels|0%|Carbon content in<br>domestic<br>production|All merchandise<br>imports|No rebate for<br>exports|
|BTADE|17% relative to<br>2005 emission<br>levels|0%|Carbon content in<br>domestic<br>production|All merchandise<br>imports|Exporters rebated<br>domestic carbon tax|
|BTAFR|17% relative to<br>2005 emission<br>levels|0%|Carbon content in<br>imports|Energy-intensive<br>merchandise imports|No rebate for<br>exports|
|BTADR|17% relative to<br>2005 emission<br>levels|0%|Carbon content in<br>domestic<br>production|Energy-intensive<br>merchandise imports|No rebate for<br>exports|

24
will adjust to fill the gap by allowing individuals to hold self-liquidating<sup>claims. As the financial markets</sup> develop one should expect to see increased use of longer-term<sup>claims such as long-term debt or equity. The</sup> analysis implies that there will be a small supply<sup>of long-term direct claims in economies where few</sup> participate in financial markets. The banking system will have<sup>a large role in the allocation of capital and</sup> the provision of liquidity. This analysis abstracts from<sup>important problems with enforcement of property</sup> rights over collateral and other bankruptcy/enforcement<sup>issues that are also present in many developing</sup> countries.

More liquid markets lead to less liquidity creation<sup>by banks, a smaller banking sector, and a longer</sup> average maturity of financial assets. More liquid markets<sup>also lead to longer maturity physical investment,</sup> and a smaller gap between the maturity of financial assets<sup>and physical investments. In addition, as more</sup> liquid markets force the banking system to shrink, the<sup>banks' holdings of long-term assets (term loans) will</sup> shrink more rapidly than it holdings of shorter-term loans.<sup>Hoshi, Kashyap, Scharfstein [1990] is an</sup> empirical study that documents the effects of market development<sup>on banks and banks' structure.</sup> Regulatory changes opened access to the Japanese<sup>bond market. The effects were broadly in line with the</sup> implications of this model. Banks' market share was<sup>reduced, and banks' holdings of long-term assets fell</sup> more rapidly than did holdings of short-tern assets.

The analysis also has implications for the effect of development<sup>of the banking sector on financial</sup> markets. The model can easily accommodate costs<sup>of financial intermediation (for example a proportional</sup> cost of banks holding assets), but the analysis is much more<sup>complicated with very few new insights. The</sup> effects of these costs can be seen by comparing the case where<sup>there are no intermediation costs, analyzed</sup> here, with that where the costs are so large that all assets are held<sup>directly. When intermediation costs are so</sup> high at all assets are held directly, each investor invests more<sup>in more liquid assets, yet the consumption of</sup> those who need liquidity (c,,1 ) is lower, and the secondary<sup>market price of long-term assets (b,) is lower than</sup> it is when banks face low intermediation costs. Reduced<sup>intermediation costs make financial markets more</sup> 27