rafmacalaba/datause-encoder-data huggingface dataset:
Table 3. GDP Estimates of Different Compilers, 1987 and 1990 (IECSE **=** 100)

||C|IR|**OFF**|**ICIAL**|<br>**Pla**|**nEcon**|
|---|---|---|---|---|---|---|
|**Country**|**1987**|**1990**|**1987**|**1990**|**1987**|**1990**|
|FSU|96|101|..|..|93|95|
|Armenia|98|99|98|97|99|96|
|Azerbaijan|101|112|100|99|103|98|
|Belarus|96|103|..|99|105|100|
|Estonia|99|82|95|95|91|89|
|Georgia|100|109|104|101|99|101|
|Kazakhstan|96|82|..|82|91|83|
|Kyrgyzstan|97|98|94|95|94|93|
|Latvia|104|96|122|100|96|98|
|Lithuania|97|93|96|90|100|93|
|Moldova|101|97|95|97|104|98|
|Russia|99|102|..|97|95|95|
|Tajikistan|100|107|97|94|95|99|
|Turkmenistan|98|96|..|93|95|92|
|Ukraine|103|102|99|100|99|98|
|Uzbekistan|96|95|..|..|93|92|

## Sources: CR: US Census' Center for International Research.

Official: Reports to World Bank as reprinted in the Statistical Handbook of the Former USSR. PlanEcon: Planlcon Report, Vol. VIII, March 27, 1992.

The use of two independent estimation procedure adopted for this study (together with estimates reported by the private concern, PlanEcon, for ease of reference) is regarded as an adequate indication of the range of uncertainty surrounding the nominal ruble accounts. There is less certainty, however, about price indicators. Since constant price national accounts are usually computed by deflating nominal values, distortions in price indices can affect estimates of growth rates. This seems to have become an increasing problem after 1989 and will add a major element of uncertainty to estimates of $GNP per capita by 1992.

.

# c. Treatment of Cross-Border Transactions

What had been a nation is now 15 distinct economies bound together, at least in the nearterm, by complex webs of interdependence spun by decades of central planning. After the comparatively simple task of apportioning extra-Union trade among the 15, trade among what were subnational units must be reclassified as cross-border transactions. But the value assigned to such transactions depends heavily on how one interprets, for each of the 15, past FSU 18
|PB index (male)|1.617***|1.535***|1.729***|1.576***|1.465|0.777|
|---|---|---|---|---|---|---|
||(0.192)|(0.193)|(0.257)|(0.215)|(0.324)|(0.345)|
|IH index (male)|0.893|1.079|0.965|1.115|0.692|1.906|
||(0.104)|(0.122)|(0.145)|(0.137)|(0.150)|(0.937)|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|
|# of households|825|573|621|468|199|103|

Note: *** denotes significance at 1%, ** at 5%, and * at 10% level. Standard errors are in parentheses. All columns report results from Logistic regressions. Control variables include age, years of education, marital status, number of children, dummy variables for Amman and Zarqa and if male counterpart works. The standardized indices include all items asked for each component after factor analysis, as specified in Annex 2, Table 2.8, and separated for men and women from the same household.

The overall finding for married women is that the expectations of their husband’s views, and the husbands’ actual views, seem to be correlated with decision making about women’s work, and especially by perceptions of acceptability of woman’s work and traditional gender roles. Husbands are also uncomfortable with women working with other men (the publicness and mixing index). Interventions targeting married women need to include their husbands as well. It may be the “breadwinner and protector norm,” rather than social empirical expectations of women working per se, that drive the choices of married women.

For unmarried women, these results suggest that social empirical expectations, i.e. expectations of what other women do, are likely to be important perceptions for women’s labor force participation. The influence of male counterparts (fathers and brothers) may not be as binding. This prescribes a different set of policy tools messages when targeting unmarried women.

# **What explains liberal social norms?**

Table 7 presents OLS results on the revised social norms index consisting of social empirical and social normative items after factor analysis.<sup>9</sup> We regress the index on a number of observables in the survey data as shown in Columns 1-3. Results are presented for all respondents before we show separate results for women and men.

Overall, women have more liberal social norms towards women’s work, although this is driven largely by working women. Being married leads to increasingly negative perceptions towards women working, compared to men. Women in Amman appear to also have more negative perceptions of women working and consequences. Age squared is positively correlated with liberal social norms, and somewhat counter intuitively, having a younger child is also positively correlated for women. As expected, education and household income are positively and significantly correlated for both men and women. These variables, together, explain between 9% to 14% of the variation in social norms.

> 9 The results are robust to using an index of all of the original social empirical and social normative items asked to respondents.

21
withdraw support from the ruling party coalition, leading to immediate dissolution of the government.

In Table 3 we include each of these additional variables whose omission may have potentially biased the coefficients on affiliation and its interactions in the results reported in the previous table. Coalition politics at the state level and the state election cycle are not significantly correlated with state deficits, and including them in the regression does not affect the size or significance of the coefficients of interest. This result is interesting in its own right because it seems to indicate that state-level political variables have no statistically discernable effect on deficits, although they might on the composition of spending and revenues (as reported by Khemani 2000). Instead, it is the state’s political relation with the center that accounts for large variation in its deficit. The results of including an interaction variable for the effect of affiliation after 1990 indicates that political bargaining by unaffiliated states did not emerge even after the political weakening of single-party strength in the national legislature. We tried several specifications to test for the impact of emerging national coalition politics but found no significant change in subnational deficits during and after 1990.

**Table 3. Effect of political affiliation on state fiscal deficit**

<u>(t-statistic in parenthesis)</u>

|_Variable_|_(1)_|_(2)_|
|---|---|---|
|Political affiliation|78.03|56.24|
|(=1 if center and state govt. belong to same political party)|(2.32)|(2.13)|
||–82.54|–57.42|
|Affiliation * Seats|(–2.54)|(–2.09)|
||–11.69|–1.69|
|(1- Affiliation) * Seats|(–0.51)|(–0.09)|
|Coalition government|–10.01||
|(=1 if state executive consists of a coalition govt.)|(–0.60)||
|State election year|–10.60||
|(=1 in the year preceding a state election)|(–0.92)<br>10.37||
|(1- Affiliation) * Years1990-1995|(0.43)|0.33|
|Real fiscal deficit per capita (lag 1)||(3.58)<br>0.13|
|Real fiscal deficit per capita (lag 2)||(1.54)|
||0.01|0.002|
|Real state income per capita|(1.68)<br>–0.002|(0.45)<br>–0.001|
|Total population|(–3.00)|(–1.88)|
||N=360|N=360|
||R-sq=0.71|R-sq=0.76|

_Note:_ State fixed effects and year effects included; OLS regressions with robust standard errors; dependent variable is real fiscal deficit per capita 18
# **Group Incentives for the Public Good: A Field Experiment on Improving the Urban Environment**

Carol Newman<sup>a</sup> , Tara Mitchell<sup>a</sup> , Marcus Holmlund<sup>b</sup> , and Chloë Fernandez<sup>b</sup> a Department of Economics and Trinity Impact Evaluation unit (TIME), Trinity College Dublin, Ireland b Development Impact Evaluation (DIME), World Bank

**JEL Codes** : O12, O13, O18, **Key Words** : Infrastructure, Public Goods, Community-Based Organizations

Chloë Fernandez (email: cfernandez2@worldbank.org) and Marcus Holmlund (email: mholmlund@worldbank.org) are at Development Impact Evaluation (DIME), World Bank. Tara Mitchell (email: mitchet@tcd.ie) and Carol Newman (corresponding author: cnewman@tcd.ie) are at Trinity College Dublin and TIME (Trinity Impact Evaluation Unit). This research is part of the World Bank-assisted Senegal Stormwater Management and Climate Change Adaptation Project (P122845) PROGEP) project which was funded by World Bank, the Government of Senegal and the Nordic Development Fund. We are thankful to our partners at the Municipal Development Agency (Agence de Développement Municipal, ADM), Pierre Coly and Mouhamadou Ba. We are also grateful to a large number of people for their valuable comments and insights. These include Andrea Guariso, Denis Jordy, Isabelle Kane, Arianna Legovini, Gabriele Rechbauer, Fatim Seck, Eric Lancelot, Amos Abu, Farouk Banna, an anonymous DIME reviewer, and participants at conferences including the NEUDC at Tufts University, the CSAE conference Oxford and the Irish Economics Association Annual Conference, and participants at seminars in Queens University Belfast, Groningen University, University of Copenhagen, Graduate Institute Geneva, and Copenhagen Business School. We thank Molly OfferWestort, Violaine Pierre, Felipe Dunsch, and Ababacar Gueye for invaluable assistance with project coordination and data collection. Our thanks also to our research assistants Margaryta Klymak, Michell Dong, and Qiao Wang. This work benefitted from generous funding from the UK/DfID through the i2i Trust Fund, the Bank-Netherlands Partnership Program, the Nordic Development Fund, and the World Bank. The RCT was registered on the AEA RCT Registry on July 7, 2015 (AEARCTR-0000754). Computational reproducibility was confirmed by DIME Analytics.
**<u>Vignette 4</u>**

|(1)|(2)<br>PCE +<br>household||
|---|---|---|
|PCE|characteristics||
|Coef.<br>se|Coef.|se|
|Log expenditure per capita<br>0.119***<br>0.033|0.083**|0.037|
|_Household Demographics_|||
|Female headed household<br>|0.043<br>|0.053<br>|
|Age of household head<br>|0.002<br>|0.011<br>|
|Age of household head squared|-0.000|0.000|
|Household size|-0.016|0.013|
|Number of children|0.027|0.018|
|Number of older adults|0.064|0.052|
|Number of migrants|0.012|0.028|
|_Ethnicity (Reference: Tajik)_|||
|Uzbek|0.078*|0.047|
|Russian|0.099|0.154|
|Other|-0.162|0.162|
|_Education (Reference: No Education)_<br>|||
|Primary|0.089|0.122|
|Basic|0.163|0.119|
|General Secondary|0.194|0.126|
|Special Secondary|0.200|0.129|
|Technical Secondary|0.123|0.125|
|Higher Education|-0.325|0.452|
|Graduate School|0.043|0.053|
|_Employment Characteristics of Household, Head’s Occupa_|_tion_||
|<br>_(Reference for Occupation: Unemployed)_|||
|Number of employed|0.079***|0.020|
|Agriculture / Fishing / Forestry|-0.118*|0.066|
|Manufacture / Mining|0.032|0.087|
|Services|-0.191|0.152|
|Construction|-0.006|0.082|
|Public Administration / Education / Health|0.021|0.002|
|Sales and Services|-0.047|0.062|
|Other|-0.002|0.117|
|_Agriculture (Reference for holdings: No Land)_|||
|<br>Small Holding|-0.156**|0.067|
|Medium Holding|-0.165**|0.082|
|Large Holding|-0.031|0.082|
|Anylivestock, poultry, etc|0.030|0.059|
|_Geography (Reference: Dushanbe)_|||
|<br>Sogd Urban|-0.301***|0.068|
|<br>Sogd Rural|-0.384***|0.079|
|<br>Khatlon Urban|0.203*|0.104|
|Khatlon Rural|-0.219***|0.077|
|RRP Urban|-0.247**|0.112|
|RRP Rural|-0.340***|0.072|
|Gbao Urban|0.140|0.131|
|Gbao Rural|0.140*|0.084|
|Number of observations<br>4,771|4,771||
|Pseudo R2<br>0.002|0.019||

34
Saibene 2019).<sup>4</sup> Some studies report that variations in R&D investment needs and corporate profits are important in explaining within-country movements in corporate savings, yet, given common global trends in these factors, they are unlikely to have been key drivers of the persistent cross-country differences in corporate savings.<sup>5</sup> In this paper, we analyze the determinants of time-series and cross-country variations in corporate savings, with a focus on corporate income taxes. The global average corporate tax rate has been consistently falling since 1980, in what has been referred to as an international tax competition, but the declines have varied from country to country (International Monetary Fund 2019a; Tax Foundation 2018). For example, Italy, Japan, and the United Kingdom have gradually reduced their statutory tax rates on corporate profits by over 10 percentage points over the last two decades; the United States did not reduce its rate until 2018, when it did so by 13 percentage points. Though there has been little published research exploring the empirical relationship between corporate tax rates and corporate savings, given the large volume of studies suggesting that corporate taxes influence firms’ balance sheets—for example, through corporate leverage (Modigliani and Miller 1963; Faccio and Xu 2015; Fan, Titman, and Twite 2012) and firms’ cash holdings (Riddick and Whited 2009)—corporate taxes may be expected to have significant effects on corporate savings.

> 4 Studies based on national samples from advanced economies attribute the rise in net lending/borrowing to increases in profits (International Monetary Fund 2006), the output gap (OECD 2006), and the weakness in investment (Gruber and Kamin 2016).

> 5 Also, as documented frequently in the literature, R&D expenditure has been substantially larger in the United States than in other countries, while US corporate saving rates have been at the low end of the spectrum.

3
Table 6: Effect of savings default on expenditures

||(1)<br>Total|(2)<br>Food|(3)<br>Non-<br>durables|(4)<br>Durables|(5)<br>Transfers<br>and fees|
|---|---|---|---|---|---|
||Panel A: O|ne week afte|r transfer|||
|Savings Default|110.674<br>(1125.239)|743.611*<br>(435.474)|-262.969<br>(227.998)|-414.721<br>(766.860)|-83.273<br>(240.874)|
|Observations|472|472|472|472|472|
|Mean of dependent variable in Cash group|15149.95|5003.48|2414.48|6213.47|1233.89|
|SD of dependent variable in Cash group<br>R-squared|12765.54<br>0.25|5204.55<br>0.24|2929.28<br>0.22|8295.42<br>0.10|2836.18<br>0.12|
||Panel B: T|wo weeks aft|er transfer|||
|Savings Default|770.108|163.045|337.003|22.935|265.871|
||(1111.960)|(427.687)|(239.114)|(697.530)|(228.800)|
|Observations|312|312|312|312|312|
|Mean of dependent variable in Cash group|8751.50|3920.65|1378.50|2830.26|640.00|
|SD of dependent variable in Cash group|10331.99|4660.08|1997.26|5699.57|1835.09|
|R-squared|0.30|0.35|0.25|0.20|0.07|

‘Non-durables’ is the sum of spending on non-food non-durables. ‘Durables’ is the sum of spending on durable goods, assets, livestock and farm inputs. ‘Transfers and fees’ is the sum of spending on ceremonies, funerals, school fees, loans given, formal insurance, fines and fees. Panel A shows regressions with outcome variables measured one week after the transfer, Panel B uses the same outcomes measured two weeks after the transfer. The sample for Panel B is smaller since two-week follow-up data were only collected for respondents who received transfers immediately or with one day delay. All specifications include village and week-of-first-survey fixed effects, and the value of the outcome measured at survey 1. Exchange rate: MK 420 <u>perUSD.</u> Robust standard errors in <u>parentheses.</u> * _<u>p <</u>_ 0 _._ 1, ** _<u>p <</u>_ 0 _._ 05, *** _<u>p <</u>_ 0 _._ 01.

34
Local Knowledge, Formal Evidence, and Policy Decisions Eva Vivalt<sup>∗</sup> Aidan Coville<sup>†</sup> Sampada KC<sup>‡</sup> _JEL Codes:_ D04, O21, O22.

_Keywords:_ Evidence-based policy; discrete choice experiment; external validity.

> ∗Department of Economics, University of Toronto, eva.vivalt@utoronto.ca

> †Development Impact (DIME), World Bank, acoville@worldbank.org

> ‡University of British Columbia, samkc.student@ubc.ca. We thank Arianna Legovini for support and helpful feedback and Oscar Mitnik, Sebastian Martinez, and Silvia Velez Caroco for enabling the surveys to be run. Antonio Ortiz-Mena and Young Ju Nam provided excellent research assistance. Computational reproducibility for the results presented in this paper was verified by DIME Analytics. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Third, to improve transparency and accountability to taxpayers, the post-project evaluation system needs to be strengthened and be made credible.

The Japanese policymakers are well aware of these criticisms and issues. In June 1998, for example, the "Council on ODA Reforms for the 21st Century," a government advisory body, presented to the Foreign Minister a report emphasizing the need to (a) formulate country assistance programs, (b) emphasize poverty alleviation and social development with a focus on people-oriented development, (c) increase assistance for human resources development, (d) give broader intellectual support, and (e) form global partnerships and cooperation with other countries, multilateral institutions and private sectors, among others (MOFA 1999). Implementing these new initiatives would, in turn, require Japan to build its own capacity to think strategically about the sector-wide and economy-wide impacts of its projects in the recipient countries.

# V. **THE CHINA CHALLENGE**

Following the normalization of diplomatic relations between the two countries in 1978, Japan became the first non-socialist country to provide concessional assistance to the People's Republic of China (hereafter, simply China). In May 1979, the Japanese government decided to provide a loan of Y420 billion to help jointly extract natural resources and energies (Zhao 1993). In August 1979, the Chinese government followed by approaching the Japanese government with a request for a loan to finance major infrastructure projects. Over the years 1979-99, Japan extended Y2.7 trillion of ODA and Y3.4 trillion of untied loans (OOF) to China, making China the second largest recipient of Japan's development assistance after Indonesia.

**Table 9. Net Flows of ODA to China in Selected Recent Years (Millions of US dollars; percent of grand total in parentheses)**

||1990|1995|1998|1999|
|---|---|---|---|---|
|**Japan**|723.0|1,380.2|1,158.2|1,226.0|
||(34.6)|(39.6)|(48.8)|(52.5)|
|**United States**|||**0.4**|38.3|
|||----|(0.0)|(1.6)|
|**European Union**|674.2|1,020.1|478.0|477.1|
||(32.2)|(29.2)|(20.1)|(20.4)|
|**France**|88.0|91.2|29.8|46.2|
||(4.2)|(2.6)|(1.3)|(2.0)|
|**Germany**|228.9|684.1|321.3|304.6|
||(10.9)|(19.6)|(13.6)|(13.1)|
|**United Kingdom**|33.3|47.8|55.4|59.3|
||(1.6)|**(1.4)**|(0.2)|(2.5)|
|**Total Bilaterals**|1511.7|2,531.3|1,731.7|1,821.6|
||(72.4)|(72.5)|(73.0)|(78.1)|
|**Total Multilaterals**|577.4|958.0|639.5|512.2|
||(27.6)|(27.5)|(27.0)|(21.9)|
|**Grand Total**|2,089.1|3,489.3|2,371.2|2,333.8|
||(100.0)|(100.0)|(100.0)|(100.0)|

_Note:_ ODA includes grants and concessional loans only. _Source:_ OECD DAC.

17
Table 2. Descriptive statistics, by treatment arm

||24-hour<br>recall<br>S|martphone<br>diary||24-hour<br>recall|Smartphone<br>diary|
|---|---|---|---|---|---|
|Demographics|||<sup>Employment</sup>|||
|Household roster: HH head|0.41|0.41|Not employed|0.19*|0.16*|
|Household roster: spouse of HH head|0.38|0.37|Employed in agriculture|0.33|0.36|
|Age: 18-24|0.25|0.24|Employed in non-agriculture|0.48|0.49|
|Age: 25-34|0.26*|0.32*|Other employment characteristics:|||
|Age: 45-54*|0.12|0.11|Time (mins) to work|32.0**|27.4**|
|Age: 55+|0.06**|0.04**|Average monthly take-home profits in kwacha<br>(self-employed)|32,799.8|36,813.1|
|Married (monogamous), patrilineal|0.29|0.33|Average daily earnings in kwacha (wage)|2,350.8*|2,950.5*|
|Married (monogamous), matrilineal|0.41|0.37|Average daily earnings in kwacha (_ganyu_)|4,421.6|5,095.3|
|Separated/divorced|0.04|0.04||||
|Single|0.19|0.19|Ownership of assets|||
|Months resp. is away from HH in last year<br>|0.50<br>|0.49|Owns land, exclusively|0.25|0.23|
||||<sup>Owns land, jointly</sup>|0.80|0.78|
|Education|||<sup>Has an account: mobile money</sup>|0.55|0.58|
|Highest level of education: primary|0.61|0.59|Has an account: informal savings|0.13|0.15|
|Highest level of education: secondary or<br>above|0.32|0.34|Has an account: formal fin. institution|0.10|0.09|
||||<sup>Has a mobile phone</sup>|0.67|0.68|
|Household characteristics|||<sup>Owns a bicycle</sup>|0.28|0.28|
|Number of children <12 years|1.71|1.67|Owns a motorized vehicle|0.05|0.08|
|Household size|5.04|4.94||||
|HH has piped water|0.18|0.15|Number of respondents<br>|710|753|
|HH has electricity<br>Notes:|0.25*|0.29*||||

<u>Notes:</u> (1) T-tests of equality of means conducted across treatment groups; ***p<0.01, **p<0.05, *p<0.10.

# 3. Time Tracker Android smartphone app

## 3.1 Overview

The _Time Tracker_ app allows for a continuous, real-time recording of time use that someone carries out during the day. The main screen (Figure 2a) shows respondents easy-to-understand illustrations of typical daily activities, that as mentioned earlier, also correspond one-to-one with activities in the 24-hour recall instrument.<sup>11</sup> Separate versions of the app with images of men and women conducting activities were provided to men and women respondents, respectively, to ensure that certain activities often linked with gender norms (such as household work, driving, and certain forms of employment) were not necessarily under-counted by either women or men. Scrolling activity description in Chichewa were also included at the top of each image to aid in the correct identification of each activity.<sup>12</sup>

> 11 The illustrations were customized to different activities, and went through a process of editing and piloting before integrating in the _Time Tracker_ app. The app is structured in a way that the illustrations can be replaced easily in order to adapt to different country contexts.

> 12 Appendix Table A1 provides the activity list for the smartphone app, along with the pop-ups — as well as the Chichewa captions that accompanied the images.

9
<!-- Start of picture text --> Figure 15. Incidence curves with respect to baseline, by 2030<br><!-- End of picture text --> <!-- Start of picture text --> Growth Incidence Curve with respect to Baseline, 2030<br>EU-IDN FTAAP TPP-15<br>0 50 100 0 50 100 0 50 100<br>Percentiles of Per Capita Income<br>a: Growth b: a + Sectoral Reallocation c: b + Wage Premia d: c + Food prices<br>Growth Incidence Curve with respect to Baseline, 2030<br>TPP-11 RCEP<br>0 50 100 0 50 100<br>Percentiles of Per Capita Income<br>a: Growth b: a + Sectoral Reallocation c: b + Wage Premia d: c + Food prices<br>3<br>2<br>1<br>% change, w.r.t. Baseline<br>0<br>3<br>2<br>1<br>% change, w.r.t. Baseline<br>0<br><!-- End of picture text -->

# 6. Conclusions

With the flurry of new, more complex bilateral and regional PTAs, it is become more important to carefully assess their economic impacts. This paper has provided macro- and micro-estimates of the impacts of various important agreements currently negotiated or potentially negotiated by the largest economy in Southeast Asia – Indonesia. To that end, it has employed state-of-the-art simulation methods, including a dynamic multi-country global CGE model and a micro-simulation tool linking the macro-economic results to the household level.

The results suggest that of the PTAs under investigation, the EU- CEPA would yield the largest macro-economic gains for Indonesia in terms of income, output and exports. This large impact is mainly driven by the high expected reductions in trade barriers and the high share of international trade between Indonesia and the EU. Indonesia would gain substantially also if it decides to join 28