rafmacalaba/datause-encoder-data huggingface dataset:
where _P,d,_ Pa, and **_Pr_** are the price of each type of domestically produced rice, rice imported from APEC country, and rice imported from the rest of the world, respectively. In the equation ta, and t, are the tariff rate imposed on the import of rice from APEC and the rest of the world, respectively, and Y is the amount of income available to spend on rice purchase.

When we identify the parameters _n,_ **_na, nr_** q, _Pd P,,, P,, Y,_ **_ta_** and t, we can solve the model for the welfare maximizing amount of consumption of each type of rice _(Cd,_ **_Ce,_** and **_CQ)._** Then, to examine the magnitude of the impact of the APEC free trade agreement on the Japanese rice market, all we have to do is obtain, by simulation, the values of _C,* C",_ and **_C,_** for reduced values of t,,, and compare them with corresponding values with no tariff concessions. Needless to say, when the rice imports from APEC countries are totally liberalized, **_ta_** becomes zero.<sup>6</sup>

# **_(ii)_** _Identification of Parameter Values_

Using data from Food and Agriculture Organization (FAO), we obtained P,,-0.38 (dollar price per kilogram of rice) and P,=0.38. For the price of the domestic rice, we adjusted the price of domestic rice in the year 2000 by taking into consideration the recent slight decline in buying price by the government, and we obtained **_Pd=_**<sup>**_1_**</sup> .92. In other words, the producer price of domestic rice is five times higher than foreign rice even in the year 2000. Using statistics from the Japanese Ministry of Agriculture and Fishery, we obtained a total buying price for domestic rice of Y=19,479 (million dollars). While actual data are available for _Pd,_ **_Pa, P,,_** and Y, other

**6** Since total liberalization of such a sensitive agricultural product in Japan is unlikely, the effects of various levels of tariff reductions, as well as the case of zero tariff, are simulated below.

50
Policy Research Working Paper 6855

# **Abstract**

This paper re-examines the roles of changes in income and inequality in poverty reduction. The study provides estimates of the relative effects of inequality reduction versus growth promotion in reducing poverty for countries with different levels of initial poverty. The analysis uses country panel-data for 1980–2010. The results indicate that, as countries become less poor, inequality-reducing policies are likely to become relatively more effective for poverty reduction than growthpromoting policies. The results indicate that the growth elasticity of poverty reduction either increases or remains constant with the level of initial poverty. Nevertheless, the results also strongly indicate that, as poverty declines, the inequality elasticity of poverty reduction increases faster. Therefore, if the marginal cost of reducing inequality relative to the marginal cost of increasing growth does not increase with lower poverty levels, to accelerate poverty reduction, greater emphasis should be given to equity rather than growth as countries attain higher levels of development.

This paper is a product of the Poverty Reduction and Equity Unit, Poverty Reduction and Economic Management Network. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The authors may be contacted at polinto@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<!-- Start of picture text --> % population<br>without access to<br>Under 5 mortality 69 without% population accessto<br>rate (per 1,000) ae ” piped water on<br>eer rarenee60 at premises<br>ihoti i so i\}itHa<br>. seH iH aa!iti s7 % population7<br>pe SONS a0 i? defecation<br>agTMaeMeSuet23. wt39"‘af49<br>a44<br>% working age % children under<br>adults illiterate 5 stunted<br>9% children under<br>5 underweight<br>sene-== 2005/06 ==-e--= 2012/15<br><!-- End of picture text --> <!-- Start of picture text --> Proportion of population (%)<br>80% f2e5~00_<br>10% + 80%aaeae “enaeWeen,n% === improvedWithout access to sanitation<br>71% “s. 69% 3. ae<br>60% ~ weea 60%~ ~-#-- ‘waterWithout on access premisesto piped<br>“aay --©-~ Practicing open Defecation<br>40% 44%<br>1994 2005 2015<br><!-- End of picture text --> <!-- Start of picture text --> 105 im<br>. ..<br>100 - —<br>say<br>“7S .<br>80 - Lon ‘. --©-- Infant Mortality Rate<br>80 ~/., ~n.<br>60 + “sn, mse ms“=. 50<br>56 wee Oe --0-- Under S MortalityRate<br>40 - Ried “7<br>39<br>20 + T r 1<br>194 2005 2014<br><!-- End of picture text -->
Blau, D., and J. Currie. 2007. “Who's Minding the Kids? Preschool, Day Care, and After School Care.” In F. Welch and E. Hanushek, eds., _The Handbook of Education Economics_ . Amsterdam: North Holland.

Bordón, P. 2007. “El efecto de los jardines infantiles en la oferta laboral femenina. Análisis del caso chileno.” Working Paper, CEA. Santiago, Chile.

Bravo, D., D. Contreras, and E. Puentes. 2008. “Female Labor Supply and Child Care Supply in Chile.” Working Paper, Department of Economics, University of Chile. Santiago, Chile.

Card, D., and A. Krueger. 1992. “Does School Quality Matter? Returns to Education and the Characteristics of Public Schools in the United States.” _Journal of Political Economy_ 100: 1–40.

Cascio, E. 2009. “Maternal Labor Supply and the Introduction of Kindergartens into American Public Schools.” _Journal of Human Resources_ 44: 140–70.

Contreras, D., and G. Plaza. 2010. “Female Labor Force Participation in Chile: How Important Are the Cultural Factors?” _Feminist Economics_ 16 (2): 27–46.

Choné, P., D. Le Blanc, and I. Robert-Bobée. 2004. “Female Labor Supply and Child Care.” _Économie and Prévision_ 162: 23–50.

Currie, J. 2001. “Early Childhood Education Programs.” _Journal of Economic Perspectives_ 15 (2): 213–38.

Del Boca, D. 2002. “The Effect of Childcare and Part-Time Opportunities on Participation and 25
in reporting (for any given year, a number of countries fail to report their tariff schedules and/or imports), we take averages of both trade flows and tariff duties over the three-year period, so that a country is excluded only if it reports no trade or tariff data in all three years. The resulting database consists of 3,412,606 import flows between 56 reporter and 192 partner countries<sup>24</sup> (see Table 2), and covers approximately 69 percent of world trade over the relevant time period.

<u>Table 2: Number of countries in the trade and tarif database</u>

||East|East|Latin|Middle|South|Sub-Sah.|North|
|---|---|---|---|---|---|---|---|
||Asia|Europe|America|East|Asia|Africa||
|Importer|7|11|19|2|2|6|9|
|Exporter|32|35|39|20|8|48|10|

_Source:_ UN COMTRADE and UN TRAINS databases.

In order to estimate the share of world trade that takes place between PTA partners, we merge the PTA database with the above matrix of world trade flows. We only consider PTAs that have come into force prior to 2002—thus the trade flows between sample countries are covered by 141 PTAs. In order to obtain the value of trade between PTA partners, we classify imports as intra- or extra-PTA at the tariff line level and calculate the regional and global shares by summing the values in each category. This is equivalent to estimating the shares as import-weighted averages, therefore allowing countries with large trade volumes to dominate the results. By contrast, calculating the relevant shares as unweighted averages of individual country intra-PTA trade ratios would give equal weights to countries of different sizes. Therefore, this approach would produce average rather than total shares of world trade between PTA partners. Since the primary goal of this section is to estimate the relative magnitude of preferential flows in world trade, the weighted average is the better vehicle for our explorations. Thus, unless explicitly specified, the following discussion as well as subsequent tables and figures will refer to import-weighted averages.

The main results are shown in Figure 2, while Table 11 with full regional detail is relegated to Appendix C. The height of the left stacked column for each region represents imports between PTA partners as a share of total intra-regional imports. Therefore, approximately 12 percent of intra-EAP trade takes place between countries who have signed at least one PTA with each other, while the comparable number for South Asia is over 98 percent. At the global level, trade flows

> 24UN definitions of “partner” and “reporter” correspond in this case to importer and exporter, respectively.

17
<!-- Start of picture text --> Underlying gender : ae<br>: Interventions & policies<br>constraints<br>&<br>ow<br>a<br>28<br>on<br>¥wa vc<br>£<br><!-- End of picture text --> <!-- Start of picture text --> 2ZF c<br>a<br>Land tition propa :<br>inomaion ‘a ae ee Livestock training, extension &<br><!-- End of picture text --> <!-- Start of picture text --> z= ————- So Commitmentall<br>savings products |<br>Financialliteracy training = J<br>Bolded text in black = IE gender evidence from Sub-Saharan Africa<br>Bolded text in brown = IE gender evidence from other developing countries<br><!-- End of picture text -->
ber 2019). Such variation in treatment time also applies in Gorontalo.

We performed DiD estimations separately for RPOS, TMD, and WS (West Manggarai) and RPOS, RPOS-Grab, and TMD (Gorontalo) to get a picture of POS impact on restaurant tax receipts. Likewise, we run separate estimation for WS (West Manggarai) and PAC Hotel and TMD (Gorontalo) on our hotel observations to get a sense of POS’ impact on hotel tax receipts.

We opted to run the estimation routine separately for each type of POS devices instead of a single estimation on multiple treatments sample set due to a number of fundamental reasons. Firstly, pooling all the treatment types into a single estimation apparently requires a stricter assumption, that is, the effect of at least one type of treatment needs to be homogenous across observations (Fricke, 2017).

In our case, the assumption requires, for example, the expected impact of RPOS distribution on supposedly TMD-targeted restaurants should be identical to RPOS’ impact on RPOS-targeted restaurants. Likewise, the expected effect of WS distribution among supposedly RPOS-targeted restaurants should be about similar to that of originally WStargeted restaurants, and so on. That is not a plausible assumption given the nonrandom treatment assignment in most policy interventions, including in our case. More often than not, choice of treatments and targeted treated observations are usually fixed considering the varying efficacy of different treatments on different treated observation groups. Furthermore, we also detected a strong tendency of behavioral variation among POS recipients of different sizes, scale, and technological mastery which again signals the weak foothold for such an identifying assumption.

Secondly, we do not have a clear _a priori_ view on the relative intensity among different POS types discussed in the paper. In other words, the POS types are not clearly ordered in terms of effect strength on the outcome–they are distinct, and each works differently. This prevents us to confidently infer from a single estimation with multiple treatments as a lower bound for ATETs magnitude, as has been laid out in Fricke (2017).

Throughout our estimation routine, control groups in each estimation excluded hotel and restaurants which received other treatments (for instance, when estimating RPOS treatment, the control group has excluded those which received TMD or Web Service). This was done in anticipation of potential underestimation that would arise if restaurants receiving different POS from the one being examined remain in the control group.

We attempt to compare treated hotels and restaurants (re: those that received POS) to the control ones—pre-treatment to post-treatment period. For each POS type, we concretely estimate: where ln _Ri,t_ is the log of our outcome variable, recorded amount of hotel and restaurant tax receipt of hotel/restaurant _i_ in a month _t_ ; _α_ is a constant, _μi_ are restaurant/hotel fixed effects, and _λt_ are time dummies. The variable _Treati_ = 1 if restaurant _i_ received the POS 13
or education level, and by age. Compared to Figure 3, Figure 4 suggests that the unemployment rate among females is higher than among males, across age groups and skill levels. In addition, while unemployment falls with age, skilled women do not have perceptibly lower unemployment rates than less skilled women. In the case of women in the labor force, then, there is some evidence that education does not confer as much advantage as it does to men.

<!-- Start of picture text --> Figure 3. Unemployment by Education and by Age Group: Men, Figure 4. Unemployment by Education and by Age Group:<br>2004 Women, 2004<br>Primary education or lower Secondary education or higher Primary education or lower Secondary education or higher<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>Age 15-19 Age 20-24 Age 25-34 Age 35-44 Age 45-54 Age 55+ Age 15-19 Age 20-24 Age 25-34 Age 35-44 Age 45-54 Age 55+<br>Source: LSMS and authors' calculations. Source: LSMS and authors' calculations.<br><!-- End of picture text --> To properly account for observable individual characteristics, we report the results of a multivariate model. Appendix Table 2, reproduced from Tiongson and Yemtsov (2006) reports the results of a multinomial logit model of labor market transitions between 2001 and 2002, and between 2001 and 2004. This specification follows those of previous models of transition probabilities (see for example Lauerova and Terrell 2002; Bukowskiy and Lewandowski, 2005). The results broadly confirm that there are significant differences in labor market transitions by gender, age, education, and geographic location. In particular, when compared to women, men are less likely to leave employment into inactivity, and those unemployed are more likely to access employment, while those out of the labor force are also more like to enter the labor force.

The results also suggest that education facilitates transitions into employment, reduce the likelihood of becoming unemployed, and lower the exit out of the labor force. These estimates are significant for year to year transitions, as well as over the 3 years period. The age differences remain consistent with earlier estimates. In particular, the results suggest that younger workers are more likely to go into inactivity or unemployment, holding other variables constant. They are also less likely to move out of inactivity into either employment or unemployment both in the short and medium term.

# **B.** **_The effect of early labor market experience on later outcomes_**

What are the effects of experiencing jobless spells in 2001 and 2002 on employment outcomes in 2004? What are the effects of experiencing unemployment spells on earnings in 2004? Does the effect vary with among different groups? We use a 11
# **Appendix I.A.17 Historical Demographic Data, India 1881-1971**

|Year|1881|1891|1901|1911|
|---|---|---|---|---|
|Population aged 15 to 64/<br>population over 65 years old|-|-|-|25.14|
|Population aged 20 to 59/<br>population over 60 years old|8.99|8.88|9.45|9.30|
|Percentage of population<br>over60 years old|5.33|5.24|5.06|5.16|
|Percentage of population<br>under 15 years oid|38.85|39.77|38.64|38.48|
|Women over 60 years old/|||||
|Men over 60 years old|1.19|1.19|1.15|1.09|

|**Year**|**1921**|**1931**|**1951**|**1961**|**1971**|
|---|---|---|---|---|---|
|Population aged 15 to 64/<br>population over 65 years old|23.70|26.64|16.45|18.18|16.30|
|Population aged 20 to 59/<br>population over 60 years old|8.95|11.62|8.28|8.08|7.26|
|Percentage of population<br>over 60 years old|5.29|4.03|5.70|5.60|5.97|
|Percentageofpopulation<br>under 15 years old|39.19|40.02|37.50|41.06|42.02|
|Women over 60 years old/<br>Men over 60 years old|1.04|0.99|1.01|0.98|0.94|

Ratios calculated using data found in Mitchell, B. R. 1982 "International Historical Statistics: Asia and Africa"
localc omponents as well.1<sup>2</sup> For example, country productivity shocks are known to have significant global components (e.g. Glick and Rogoff **1995,** and Iscan 2000). Thus, we may write InAt **=** _aolnA +aalnAf_ where **_AG_** and **AL** stand for the global and local components of domestic productivity respectively. Below we use these ideas in constructing our globalization measure which captures the size of the global exposure in foreign direct investment.

> Importantly, the stochastic discount rate used **by** foreign investors to discount cash flows from local subsidiaries (,<sup>3</sup> t) is driven **by** global factors. In general, the discount factor varies with state variables or factors that impact the foreign investor's intertemporal rate of substitution. Some of these factors might also be local sources of uncertainty if markets are incomplete. If this country risk cannot be diversified away we may write

What global factors capture the variation in discount rates?T his question is a difficult one to answer as it presumes that we have a fully specified general equilibrium model to identify these factors. Often researchers have chosen to view the global factors as unobservable (e.g. Harvey **1991,** and Campbell and Hamao **1992).** This is also our **first** route. We estimate latent, or unobserved, factors from a set ofg lobal variables **Zt:"** The number of rows in matrix **b** indicates the number of relevant global factors acting through the discount factor. Under the assumption of a representative international investor, estimation of **(1)-(3)** across many countries yields a system'of equations with

> 1<sup>2</sup> This is a natural assumption, because foreign direct invetment is to some extent an outcome of transactions costs and capital market imperfections (see for example Caves **1996).** "The same identification issues apply to local factors, but because there is no good way of imposing restrictions on these we assume they are observed.

> 41n picking these global variables we constrain ourselves to use the instruments that have been shown to explain the cross section of international equity returns.

**8**
Therefore, _M i_ *<sup>(and the socially efficient level of</sup> _M i_ ) are higher: bureaucratic quality will be lower, but so will be the socially efficient level of bureaucratic quality.

Other simplifying assumptions could be relaxed. One could allow _W D_ to vary with the distribution of aid shares, with smaller donors taking the lead in bidding up the wages of top civil servants. Or, the productivity of managers could be allowed to vary continuously. In this case, donors would always hire the best managers. In contrast, when high-quality managers are homogeneous, supply responses might tend to equalize the quality of donor-employed and government-employed managers in the very long run.

# **3. Measurement**

Predictions from the model imply that bureaucratic quality will erode more in recipients with greater donor fragmentation, i.e. a larger number of donors each with a smaller share of the project market. Bureaucratic quality can be measured by a subjective index available for most countries from the International Country Risk Guide (ICRG), a commercial service providing information on political risks to overseas investors and lenders.<sup>10</sup> High scores on the ICRG bureaucratic quality variable indicate “autonomy from political pressure,” “strength and expertise to govern without drastic changes in policy or interruptions in government services” when governments change, and “established mechanisms for recruiting and training” (PRS Group 1998). Because the ICRG ratings are subjective, and the definition is not very detailed, it is instructive to validate it against a more detailed “Weberian scale” of bureaucratic development from Evans and Rauch (1999). Evans and Rauch surveyed several experts for each of 35 developing countries, inquiring for the period 1970–90 as a whole about various dimensions of bureaucratic structure, including meritocratic recruitment, internal promotion, career stability, and competitiveness of salaries. From the 126 total responses, they constructed country-level averages for each variable. Finally, they construct a “Weberian scale” from 10 items reflecting competitiveness of salaries and attractiveness of public sector careers, relative to the private sector, number of years high officials have been in the civil service, use of civil service exams, etc.

The “Weberian scale” is correlated at .62 with the ICRG bureaucratic quality index, averaged over all years through 1990 for which all data are available (see Figure

> 10 See www.prsgroup.com for more information. Among the various governance indicators used in the literature (see Kaufmann et al. 2003; Knack 2003),the ICRG data are unique in covering the majority of nations over a relatively long period of time. The ICRG data have been published since 1982. The bureaucratic quality rating was provided on a 0-6 scale through 1998, and on a 0-4 scale thereafter. In the analysis below, ratings from 1999 onward are multiplied by 1.5 to make them comparable with the 1982– 97 ratings.

12
**Figure 4:** Being economically active (any form of employment).

- **(a)** Predictions from interaction model.

<!-- Start of picture text --> .7<br>.6<br>.5<br>.4<br>.3<br>Men Women<br>Host IDP<br>E[Employed | gender, X]<br><!-- End of picture text -->

- **(b)** Average marginal effect of being an IDP.

<!-- Start of picture text --> .2<br>.15<br>.1<br>.05<br>0<br>-.05<br>Men Women<br>AME (IDP)<br><!-- End of picture text --> 24