rafmacalaba/datause-encoder-data huggingface dataset:
**Marshallian surplus from and equivalent variation bounds for high interest rate treatment and commitment savings** I apply Equations 14 and 15 for Marshallian surplus and equivalent variation, respectively, to calculate the welfare gains from the high match rate treatment and from retirement savings.

The high match rate treatment, expressed per $100 of control group deposits, generated Marshallian saver surplus of $107.4, with a standard error of $7.3. As the high match rate treatment generated a large increase in deposits through a large decrease in prices, its associated surplus is closely tied to the estimated demand elasticity.

Marshallian saver surplus from retirement savings is simply _−_ 1 _/ε_ , implying that retirement savings generates saver surplus of $23.7 per $100 of deposits, with a standard error of $1.5. Bounds on equivalent variation are of similar width to the 95% confidence interval for the estimate of Marshallian saver surplus. Although estimates of the demand elasticity for retirement savings are the most precise that I analyze in this paper, due to the large sample and large variation in match rates, statistical uncertainty in the point estimate remains similar in magnitude to bias in Marshallian saver surplus as a measure of equivalent variation due to income

# **4.2 Commitment savings (Karlan & Zinman, 2018)**

## **4.2.1 Context and experimental design**

I leverage experimental variation in interest rates on commitment savings accounts studied by Karlan & Zinman (2018) to estimate the saver surplus from commitment savings. The experiment was implemented with First Valley Bank in the Philippines, for a commitment savings product, Gihandom Savings.

**Commitment savings product** I briefly summarize the description of the commitment savings product from Karlan & Zinman (2018), and incorporate it into the model in Section 2. Households were recruited for the experiment in a door-to-door marketing campaign, and randomized into three commitment savings account offers, two of which we focus on which vary the annual interest rate _r_ – one with _r_ = 1 _._ 5%, and the other with _r_ = 3 _._ 0%. Following the offer, households could open the account from the bank with a minimum deposit of 100 PHP; households would set a goal amount of at least 2,000 PHP, and a goal term of between 3 months and 2 years, and could withdraw only after both the goal term and amount were met.

I therefore represent the commitment savings product in the household problem in Equa27
MLYTICAL UMY OF THIRTY ECElt SIDIES <!-- Start of picture text --> IN1EPI0 WAVLJES<br>ard (133) SPTA ISm nd OF SD intenal cpftat inf ta Iquct of C6 W=eU an unrq Umu*) GNP (lorld F1X= T ME YPE MNo period cored ORIAIES 40 or 52 DC., pliop $mut CMME- ISTICS OF I n md FP5I 9 196a-7I PEt MRLSIS CAWUTICS 10 M Per cqita proiet (Srvirg to M BMVIIITMML POLICY WR RNAUIAL VMm1mLS E6N SETC.rg C C- mom Sawir MIS PUULTIW MARIARES + ITlU rate FACTGIM WRWBESS=CIOPOLITICL 0.4" c 2-* 0.53<br>- Alto sheple: - br bq amntm regin um tnaom svldidiarily 1950-60 (11 yes) varf,l es) asigt other * or ns. ft Ohr Direct freign Infl Fwrig aid rate frNtumu + or n.s. + cqpitsl rat rate growd, rate re"ts Ato usa dmM*with futimn Last wng aqAe rot of bit rate<br>sfu,ffient<br>Lbor fae o rAdh<br>.............................................................................<br>..........................................................................................................................<br><!-- End of picture text --> ("a) ElEIE* q miterior is nrt a slunificant Show that to 0P (WorLd () m*) 2 ample l1ina# 25 Av _rag 21 as 1980 rate Inbbitity of Variation in fport wiws ert rate (aw 1ffO adIn _eatm ret., countries _lowInm_ nor_ African cmutriies bt* t iqcit-tet iratbfiity _rath ftcor_ t ir an In in 4680 (b) 24 c tries with flo (4410 193) _Afrfa_ n.s. in of posWr or 1rstdbility beween 1960 1960)i - in alte _aport_ no (a) uple ,iwort Varition In P*pAL tf on _ai 19O Qr,_ n.r.rae rate **(a)** (a) r.s. + or (b) n.s.or (b). rath **uafeveL** (b) factor in Africa _din_ to _tak_ of ltqidity .................................................................................................................................................................

.......... **.** ............ ................ ............
their household’s food expenditure. Similarly, Table 3 presents the alignment and misalignment of food expenditure for a household and for an individual, against the median food expenditure.

_Table 2: Average individual monthly food expenditure (in Philippine pesos), by household per AME food expenditure quantile_

||Hou<br>Bottom|sehold per A|ME food expenditure qua|ntile<br>Top||
|---|---|---|---|---|---|
||quintile|Second|Third<br>Fourth|quintile|N|
|Philippines|1599|2022|2280<br>2591|3556|18,290|
||(34)|(36)|(39)<br>(50)|(84)||
|Children 1-6 yr.|1808|2613|3152<br>3845|5915|2,295|
||(120)|(147)|(156)<br>(195)|(391)||
|Children 7-12 yr.|1668|2115|2428<br>2690|3858|2,684|
||(63)|(71)|(96)<br>(91)|(208)||
|Teen 13-19 yr.|1616|1947|2099<br>2318|3455|2,884|
||(57)|(61)|(72)<br>(74)|(184)||
|Adult 20-59 yr.|1421|1806|1929<br>2137|2727|7,802|
||(29)|(45)|(34)<br>(42)|(68)||
|Elderly 60+ yr.|1543|1801|2260<br>2368|3109|1,920|
||(54)|(46)|(89)<br>(106)|(118)||
|Pregnant &||||||
|Lactating Adult|1353|1886|1908<br>2290|2336|532|
||(54)|(124)|(143)<br>(138)|(110)||

_Note_ : Food expenditures are calculated by multiplying food consumption information from NNS 2013 (household per AME food consumption and individual food consumption are obtained from household diet and individual diet module separately) with unit price information derived from FIES 2015. Expenditures are in 2015 Philippine pesos. Standard errors are shown in parentheses.

Considering Table 2, we find that average individual monthly food expenditure increases with the level of household per AME expenditures, as gauged by household food expenditure quintiles. This is as we would expect. Generally, expenditure also increases, based on individual age: typically, adults have greater food expenditure, relative to children. Notable exceptions to this trend are that teenagers (between 13 and 19 years old) tend to have greater expenditure than adults 13
_Table 23_

|Dependent variable: Fiscal adjustm|ent when|needed b|ased on Es|colano et|al.(2014)||||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid CE minus structural as % GDP|-0.039|-0.035|-0.030|-0.055|-0.037|-0.035|-0.044|-0.051|
||(0.068)|(0.052)|(0.064)|(0.063)|(0.066)|(0.056)|(0.065)|(0.060)|
|Rigid SS minus structural as % GDP-|0.031***|-0.030***|-0.033***|-0.032***|-0.020***|0.107*|-0.110**|-0.077**|
||(0.012)|(0.009)|(0.012)|(0.011)|(0.006)|(0.056)|(0.047)|(0.029)|
|**Economic factors**|||||||||
|GDP growth (t-1)||0.053||0.143|0.424|0.332|0.195|0.129|
|||(1.340)||(1.527)|(1.573)|(1.544)|(1.536)|(1.520)|
|Inflation (t-1)||2.747***||3.224***|3.758***|3.453***|3.731***|3.265***|
|||(0.808)||(1.166)|(1.108)|(1.120)|(1.069)|(1.200)|
|U.S. Interest rate||-0.136**||-0.151**|-0.162**|-0.240***|-0.144**|-0.147**|
|||(0.051)||(0.066)|(0.067)|(0.059)|(0.068)|(0.058)|
|**Political factors**|||||||||
|Gini|||0.006|0.004|0.008|0.058**|0.003|-0.003|
||||(0.010)|(0.013)|(0.013)|(0.027)|(0.014)|(0.013)|
|Rule of law|||-0.208***|0.115|0.131|0.120|-0.041|0.132|
||||(0.068)|(0.141)|(0.136)|(0.119)|(0.168)|(0.147)|
|Margin of Majority|||0.386|0.143|0.115|0.313|0.120|-0.982*|
||||(0.385)|(0.373)|(0.376)|(0.355)|(0.381)|(0.515)|
|Election's year|||-0.111|-0.081|-0.071|-0.069|-0.069|-0.065|
||||(0.079)|(0.077)|(0.071)|(0.072)|(0.075)|(0.082)|
|**Interactions**|||||||||
|EMEs * Rigidity|||||-0.061<br>(0.037)||||
|Gini * Rigidity||||||-0.004**<br>(0.002)|||
|Rule of law * Rigidity|||||||0.016*<br>(0.008)||
|Majority * Rigidity||||||||0.089<br>(0.057)|
|Constant|0.599**|0.907***|0.956|0.354|0.437|-0.483|0.486|0.779|
||(0.231)|(0.253)|(0.718)|(0.940)|(0.905)|(0.982)|(0.937)|(0.897)|
|Observations|182|180|164|162|162|162|162|162|
|R-squared|0.035|0.157|0.100|0.181|0.196|0.225|0.201|0.202|
|Number of ifscode|60|60|53|53|53|53|53|53|
|CountryFE<br>Robust standard errors clustered by<br>*** p<0.01, ** p<0.05, * p<0.1|Yes<br>country|Yes<br>in parenth|Yes<br>eses|Yes|Yes|Yes|Yes|Yes|

- 52 -
If we believe that marketing is effective only if one has a good product to sell, we would expect to see that IPAs located in countries with a better business climate are more effective. As it is impossible to find convincing time-varying proxies for the business climate for the whole period under study, we make a strong assumption that the business climate throughout the period considered was equal to the average outcome during 1996-2005.<sup>34</sup> We then interact business climate proxies compiled by Kaufman et al. (2006) with the IPA dummy.<sup>35</sup> Results presented in Table 21 confirm our priors. We find a positive and statistically significant interaction for the proxy for government effectiveness, regulatory quality, control of corruption, voice and accountability as well as for the summary measure. Somewhat surprisingly, the interaction terms with political stability and the rule of law do not appear to be statistically significant.

If agencies are effective at building the country’s image, we would expect their activities to affect foreign perceptions of the host country. In an exercise not reported here, we employed the AT Kearney’s FDI confidence index (ATKFDICI), which measures high ranking managers’ perceptions of around 60 countries as FDI destinations. The managers are employed in the 1000 largest global firms. We tested whether the number of (or spending on) advertisements in the Financial Times (FT) was correlated with the index, after controlling for GDP, GDP per capita, GDP growth, inflation, regional dummies and a dummy for year 2003.<sup>36</sup> We found a positive and statistically significant correlation. Then we showed that this correlation is robust to additionally controlling for (one at a time) political risk, political stability and government effectiveness. We repeated the exercise for the average number of general advertisements in foreign press and domestic press placed by IPA during the 2003-2005 period, as reported in the Census. We found a significant positive correlation between the marketing efforts in foreign press and investor’s perceptions. The correlation between advertisements in domestic press and investors’ perceptions was insignificant, as expected. While far from perfect, this exercise is suggestive of IPAs being effective at image building.

> 34 More specifically, we use the average of all index values available: 1996, 1998, 2000, 2002-2005.

> 35 Note that we do not include the business climate measure by itself as its effect is absorbed by the country fixed effect.

> 36 The ATKFDICI data were available to us for the years 2003, 2004 and 2005 and the FT data for 2002 and 2005. We therefore matched FT data for 2002 to ATKFDICI 2003. We are grateful to the _Financial Times_ for providing us with the data.

28
quality relative to domestic capital equipment goods produced in India since most of the imports of capital goods come from developed economies.

Our sample contains information for around 3,744 firms in organized industrial activities from manufacturing sector for the period 1990-1997. Since we lagged control variables, our estimating sample starts in 1990 and the total number of observations firm-year pairs is 14,425. In order to keep a constant sample throughout the paper and to establish the stability of the point estimates, we keep firms that report information on all the firm and industry level control variables. Although our panel of firms is unbalanced, there is no statistical difference in the average firm characteristics between the initial year and the final year of our sample.

# _Input-tariff data_

To identify the impact of input-trade liberalization on firms’ foreign technology choice, we use input tariffs at the 3-digit-NIC industry level. Tariffs data is provided by WITS (World Bank) and corresponds to India’s effectively applied most favorite nation (MFN) import tariffs with respect to the Rest of The World at the industry level ISIC (rev 2).<sup>25</sup> In order to identify the effect of input tariff changes on firms’ decision to import capital goods, we construct different tariffs measures for capital goods and for variable intermediate goods. In this sense, we depart from previous studies on input-trade liberalization to consider both variable inputs and capital goods in the construction of input tariffs.

This methodology allows us to disentangle the indirect effects of tariffs on intermediate goods on firms’ decision to import capital goods from the direct effects of tariffs on capital goods. For each 3- digit industry, _s_ , we generate a capital goods tariff as the weighted average of tariffs on the capital goods used in the production of final goods of that 3-digit industry, where the weights reflect the share of capital goods of the final goods industry on total expenditures in capital goods using India’s input-output matrix

> 25We use correspondence tables to convert tariffs into ISIC rev 3.1. that match almost perfectly with NIC 3-digit classification. This dataset is available at http://wits.worldbank.org/wits/.

26
(Filipino) and 4.09 (hekasi). The total score increased by 4.21, which is equivalent to about 33% of the standard deviation of NAT 2006 of non-SBM schools.<sup>16</sup> Table 4 controls school factors such as changes in total enrollment, pupil classroom ratio, pupil teacher ratio, and total amount of teachers’ salary by their position. If SBM changes school conditions through investments in school facilities etc., the inclusion of such variables as controls may bias the impact estimate. However, it is still important to check robustness of the main results (Table 3) by including changes in school conditions.<sup>17</sup> Table 4 confirms that the main findings remain robust, without any significant changes in the estimated impacts. The above results in Tables 3 and 4 show that (i) the SBM impacts on NAT are significant and relatively large, and (ii) whether or not controlling for school conditions does not affect the impact estimates.

It is, however, still possible that other factors contributed to the above findings. For example, NAT might have become easier in recent years for some reason. If so, low performing schools that also introduced SBM earlier were able to improve their test scores, regardless of SBM. Consistently it is also shown that the variation of NAT scores across schools has decreased in the same period (Table A1), while variations in other conventional school and teacher resource inputs have increased during the same period (Paradekar, Sipahimalani-Rao, and Yamauchi, 2012). This is consistent with the observations in Table 2 as well as the findings in Tables 3 and 4, since the average NAT score of the SBM/2006 schools caught up to that of the SBM/2009 schools, which can be attributed at least partially to the introduction of SBM.

> 16 Preliminary analyses show that, contrary to the initial conjecture, the change in test score is relatively large in 2006-2007, not in later periods, which implies that SBM had a rather immediate effect.

> 17 Double differencing without propensity score matching also gives qualitatively similar results. The impacts are estimated slightly smaller than those using propensity score matching, which implies that SBM grants were allocated to schools that performed relatively worse (despite that they developed school improvement plans earlier than others).

14
<!-- Start of picture text --> Percent Index<br>26“” vedian 400<br><Economic policy uncertainty (RHS<br>2.2 350<br>300<br>1.8 250<br>1.4 200<br>150<br>1.0<br>100<br>0.60 SS 50<br>ONT OWDOON TT ODOON<br>SSSSSggeeagR<br><!-- End of picture text --> <!-- Start of picture text --> Percent<br>6 mlnterquartile range —Median<br>5<br>4<br>3<br>2<br>1<br>°<br>ontee owmwaonwtQGFaMAaOoN<br>SRKRRKRARKRKAKRKRAAee 27, FT FFE TFTA nn<br><!-- End of picture text --> <!-- Start of picture text --> Percent Percent<br>1.8 —Advanced economies —EMDEs(RHS) 9<br>1.6 8<br>1.4 7<br>1.2 6<br>1.0 5<br>0.8 4<br>0.6 3<br>0.4 2<br>0.2 1<br>0.0 —________LLL—— _ 0<br>entrowmnonvtvoMaon<br>OeoOoomlUlcmMUrUmUCUCOUMUUCUCOOUCOOUCOOUCUCOOCOUCUCOOCUOOe Oe Oe ee<br>Nnwnnnnnnnanaaa<br><!-- End of picture text --> <!-- Start of picture text --> Percent Index<br>—Median<br>0.4 Economic policy uncertainty (RHS) 400<br>350<br>0.3 300<br>250<br>0.2<br>200<br>150<br>0.1<br>100<br>0.0. ———=—___—————— 50<br>On TOWDOONTO WOON<br>SSSSLeegRgRgRgR<br><!-- End of picture text --> <!-- Start of picture text --> Percent<br>1.8 mlnterquartile range<br>1.6 — .<br>14 Median<br>1.2<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>on TODON TO DON<br>eooooor<br>SssgsgsggegeggRgRRrrr raa<br><!-- End of picture text --> <!-- Start of picture text --> Percent Percent<br>0.3 —Advanced economies —EMDEs (RHS) 3.5<br>30<br>0.2 95.<br>2.0<br>1.5<br>0.1 1.0<br>0.5<br>0.0. —_____LL____—————— 0.0<br>entrowmontronoon<br>oo oooOrrrrrAna<br>Nnnwnnnnnnananaa<br>oOoOoooeonmlcODClUlLlUCUDULUCOOUCUCOOWCOCUCO<br><!-- End of picture text -->
The Rise of the Middle Class and Economic Growth in ASEAN by Markus Brueckner, Era Dabla-Norris, Mark Gradstein, and Daniel Lederman* _Key words:_ Income Inequality, Economic Growth, ASEAN _JEL codes:_ O1

> * Australian National University (Brueckner); International Monetary Fund (Dabla-Norris); Ben Gurion University (Gradstein); and World Bank (Lederman). Corresponding author's email: <u>markus.brueckner@anu.edu.au.</u> We gratefully acknowledge comments received at the Trade, Industrialization and Structural Reforms in ASEAN conference that was held in Ho Chi Minh City in January 2017; particular thanks to our discussant Willem Thorbecke. Comments provided by Bruce Chapman, Tim Hatton, and Rod Tyers are also much appreciated.
# **Table 13. HIES** **<u>to HIES imputation from 2006/07 and 2009/10 for rural areas</u>**

||||Mea|n of X in|
|---|---|---|---|---|
|Description|Coefficient|Std. Err.|2006/7|2009/10|
|Intercept|8.3354|0.0388|||
|Head’s age|-0.0024|0.0003|51.2|51.5|
|Bicycle|0.0768|0.0078|0.430|0.388|
|Boats|0.2085|0.0732|0.003|0.003|
|Bus lorry|0.3962|0.0452|0.018|0.021|
|Motor car van|0.3411|0.0245|0.053|0.056|
|Computers|0.1437|0.0204|0.059|0.120|
|Cookers|0.1631|0.0114|0.320|0.351|
|Dependency ratio (age -14, 64+ to HH size)|-0.0364|0.0173|0.318|0.323|
|Electric Fans|0.0924|0.0101|0.422|0.488|
|Flat house|0.0933|0.0376|0.014|0.015|
|Mud for floor|-0.0825|0.0126|0.121|0.093|
|Fridge|0.08|0.0115|0.338|0.403|
|Head’s major industry: Agriculture|-0.03|0.0099|0.236|0.226|
|Household size|-0.267|0.0098|4.0|3.9|
|Household size squared|0.0145|0.0009|18.5|17.8|
|Head’seducation completed prim|0.0227|0.0094|0.442|0.480|
|<br>Head’seducation passed OL|0.0492|0.015|0.150|0.145|
|Head’seducation passed AL|0.0956|0.0201|0.093|0.100|
|<br>Lighting-kerosene|-0.0476|0.0124|0.183|0.120|
|Telephone|0.1653|0.0108|0.304|0.495|
|Floor>750 sqft|0.1007|0.0086|0.386|0.408|
|Motor Bicycle|0.1475|0.0102|0.220|0.281|
|Maximumhousehold education completed primary|0.0881|0.0179|0.437|0.430|
|Maximumhousehold education passed OL|0.162|0.0203|0.245|0.245|
|Maximumhousehold education passed AL|0.2001|0.0219|0.257|0.272|
|Only paid income|-0.1908|0.0178|0.473|0.455|
|<br>Only self-income|-0.1588|0.0179|0.281|0.294|
|Source of drinking water within premises|0.0187|0.0085|0.691|0.760|
|<br>Paid and self-income|-0.209|0.0225|0.147|0.131|
|Per capita wage index|0.0079|0.0004|17.3|19.8|
|Roof: concrete|0.1784|0.0314|0.017|0.026|
|Sewing Machine|0.0396|0.0089|0.447|0.451|
|Pesticider|0.0816|0.0201|0.043|0.048|
|Three wheeler|0.2041|0.0212|0.048|0.074|
|Has own or shared toilet|0.0561|0.0242|0.976|0.983|
|Tractor 2 wheel|0.1571|0.024|0.031|0.038|
|Tractor 4 wheel|0.1487|0.0709|0.007|0.009|
|TV|0.067|0.011|0.777|0.818|
|Unemployed ratio to household size|-0.0784|0.0315|0.048|0.052|
|VCD|0.0691|0.0099|0.259|0.346|
|Wall: pressed soil blocks|-0.09|0.0162|0.051|0.041|
|Washingmachine<br>Source: Authors’ calculations usingHIES 2006/07 and 2009/10|0.0907|0.018|0.088|0.121|

20
**Training programs** can help enhance the productivity of labor and may be used to help workers adapt to a changing economic environment. Since human capital formation is a cumulative process, training benefits the most able workers foremost, making it a weak tool for protecting the most vulnerable. Training seems to be most effective when used in conjunction with other policies (Fares and Puerto, 2008) – providing further evidence for the contention that comprehensive policy packages are likely to be more effective than policies implemented in isolation.

While they have been utilized in a variety of forms during past crises, the fragmented evidence reviewed in Table 3 in the appendix suggests that, on average, the usefulness of training programs is limited. The net employment effect of training policies implemented in response to crises is in the range of 10% to 20% (See Table 4).

**Self-employment assistance programs** usually have high deadweight and displacement effects and only help a selected subset of the vulnerable population. During “normal” times, businesses created under self-employment policies have high failure rates, often exceeding 50% (see Abrahart and others 2000).  Evidence exists of positive impacts for older and better-educated workers, but take-up is highly concentrated amongst individuals with entrepreneurial skills, many of whom would have started up their own enterprise regardless of the introduction of self-employment support (Betcherman and others 2004; Abrahart and others 2000). For instance, Almeida and Galasso (2007) find that only a very small subset of former welfare beneficiaries – those who were younger, more educated, and with previous self-employment experience – were attracted to a selfemployment assistance program in Argentina. Subsidies for self-employment initiatives normally reach less than 5 percent of the unemployed but are somewhat more promising when targeted at particular groups – such as women and older individuals (Auer 2008; Abrahart and others 2000). Implementation of self-employment support may also entail a tradeoff between promoting the creation of new firms and protecting the profitability of incumbent firms (Auer 2008).

**Job-search assistance** services such as counseling, placement assistance and job matching typically have positive impacts on post-program employment and earnings and favorable costbenefit ratios in developed countries. However, it is not clear how they perform in developing countries, where many labor market transactions are informal, and in times of crisis, when the demand for labor is lower than usual.

# **3.2.3 Policies to Protect Losers**

_Unemployment Benefits_ According to the ILO survey 32% of countries have adopted some extension of unemployment benefits in response to the current financial crisis and 28% have used partial unemployment benefits to cover reduced hours and part-time training (ILO, 2009).

When the labor market transmission of shocks occurs primarily via a reduction in formal sector employment and an effective unemployment insurance system is in place, unemployment 11