rafmacalaba/datause-encoder-data huggingface dataset:
percent of cultivated areas were mechanically ploughed in 1979 (Figure 1, panel a).<sup>8</sup> New irrigated areas were mostly opened under powered irrigation instead of the traditional gravity system (NSBC, 2013; Ji et al., 2012; Wang et al., 2007).

The pattern of mechanical farm operations in Chinese agricultural production changed completely during the early period of rural reform. In the implementation of the Household Responsibility System, small-sized machines and draft animals were distributed to households on an egalitarian basis. However, large and medium-sized machines, such as riding tractors which used to be shared by a production team composed of generally 20 to 30 households or managed by the committee of village leaders, were not amenable to distribution to individual households. The use of mechanical operations in ploughing declined rapidly because households sought to save operational costs and preferred to use draft animals for timely cultivation (Figure 1). From 1979 to 1983, about 8 million ha of cultivated land were not mechanically ploughed any more as it turned out not to be cost effective. The share of areas under which sowing and reaping were mechanically operated stayed constant at 10 and 4 percent, respectively. Furthermore, the small size of cultivated land divided by several fragmented plots is another constraint that inhibits mechanical farm operations (Fleisher and Liu, 1992). The irrigation systems in some areas became wasted or abandoned because of lack of maintenance and the decreased investment in irrigation facilities.

Experience in many developed countries shows that the process of mechanization is driven by changes in relative prices, particularly the rising wage rate of off-farm labor, and China is not an exception (Wang, et al., 2014). The empirical studies by Cai et al. (2008), Wang et al. (2011) and Li et al. (2012) confirmed that migrant wages increased rapidly, along with wages available to other types of workers, since the late 1990s. The cost analysis in agricultural production also indicates that the annual growth rate of average on-farm labor cost (RMB/day) was 8 percent between 1997 and 2008, and

> 8 The jump observed in the share from 1999 to 2000 is due to the change of the land registration statistics on cultivated land after the land census in 1996.
yihdt = μYoungChildh + ωYoungChildhPreschooldt+ΓXidt + γdt + εidt (6) where YoungChildh is equal to 1 if there is a child under 10 in the household _h_ of woman _i._ The remainder of the notation is the same as in equation (5). Similarly, we find a significant negative association between the presence of young children in the household and the likelihood that a woman works, which is mitigated by preschool availability (Table 10, Columns 4-6).

# 5.2. Reduction in worker turnover

In addition to enabling labor market entry, preschool availability may increase firm productivity by increasing job stability, reducing worker turnover among women. Although the majority of studies which evaluate the impact of childcare services on maternal labor market outcomes do not analyze turnover, those that do suggest that access to childcare strengthens maternal attachment to jobs. Angeles et al. (2011) find that _Programa de Estancias Infantiles_ in Mexico, which provides access to childcare for working mothers, increased job stability among its beneficiaries. Ranganathan and Pedulla (2021) find that availability of employer-sponsored childcare increases daily work attendance, which may contribute to a reduction in turnover.

Several studies, in turn, suggest that higher turnover is likely to result in productivity losses: high turnover deteriorates firm-specific stock of knowledge and expertise directly through the loss of trained workers and through lowering incentives for the firms to invest in training of their employees. This effect is captured by TFP, as this form of human capital cannot be fully accounted for in wages (Brown and Medoff, 1978; Shaw, 2011; Strober, 1990; Yanadori and Kato, 2007; Giesing and Laurentsyeva, 2017).

We explore the likelihood of this mechanism by examining the association between preschool availability and worker turnover using SAKERNAS data. We estimate regressions (5) and (6) with dummies equal to 1 if worker _i_ changed job in year _t_ . We carry out similar analysis for the likelihood of job turnover in manufacturing and services. Table 11 shows that women experience higher job turnover in manufacturing but lower job turnover in services and overall. Notably, a relatively low fraction of Indonesians is employed in manufacturing: about 10 percent of the full SAKERNAS sample. However, the availability of preschools in the district is significantly associated with a reduction in women’s probability of changing manufacturing jobs within a year.

18
**TableA5. Factors affecting the non-poor household's probability to live in the Kafue Park System GMA and, once in the GMA, the probability to participate in the** **<u>VAG/CRB</u>**

||Kafue|Probitmode|lsfortheh|ousehold's probability to<br>Participate in the|
|---|---|---|---|---|
|Variable|Variable description|Liveinthe|GMA|VAG/CRB<sup>_a_</sup>|
|||(1)||(2)|
|Hage|Age of the household head in years|0.011|**|0.030|
|||(0.005)||(0.022)|
|Fhhh|Female-headed household dummy|-0.440|**|-0.361<br>**|
|||(0.176)||(0.150)|
|Maxedu|Education of the most educated household|-0.003||0.062|
||member in years|(0.033)||(0.058)|
|c0to14|Number of children below 15 years|0.106|***|0.019|
|||(0.031)||(0.058)|
|f15to60|Number of female members 15-60 years old|-0.034||-0.119|
|||(0.087)||(0.140)|
|m15to60|Number of male members 15-60 years old|-0.054||-0.150|
|||(0.064)||(0.201)|
|m61plus|Number of members older than 60 years|-0.099||-0.828|
|||(0.136)||(0.804)|
|Kroad|Distance to the nearest main road in km|0.013|**|-0.004|
|||(0.005)||(0.006)|
|Kbsch|Distance to the nearest basic school in km|0.018||0.009|
|||(0.031)||(0.038)|
|Kheal|Distance to the nearets health centre in km|-0.029|**|-0.018|
|||(0.014)||(0.029)|
|Vacast|Value of consumer durable assets in ZMK|-0.432|*|0.358|
|||(0.243)||(0.425)|
|Dcoop|Participation in cooperatives dummy|-0.699|**|0.392|
|||(0.325)||(0.609)|
|Nproj|Number of projects in the community|-0.256|***|-0.171|
|||(0.082)||(0.114)|
|Dcfnd|CRB funded dummy variable|<br>0.013||<br>0.120|
|||(0.249)||(0.477)|
|Npart|Number of participants in the CRB/VAG|0.205|*|0.228<br>***|
|||(0.109)||(0.022)|
|Cons|Intercept|-0.716|*|-3.320<br>***|
|||(0.434)||(0.908)|

> _a_ Based only on the sub-sample of households that are located in the GMAs Level of significance: *=Significant at 10%; **=Significant at 5%; ***=Significant at 1% Values in parentheses are standard errors

20
# **What are the purposes and benefits of having an account?**

People have myriad reasons for maintaining an account at a formal financial institution. Some use their account to do little more than receive wage payments. Others see their account as an essential tool for transferring financial support to or from relatives living elsewhere. And still others are interested mainly in having a safe place to save. The purposes and benefits of having an account vary just as much across regions and income groups as do other aspects of account use.

### **Personal and business use of accounts**

Adults with a formal account by purpose of its use (%) <!-- Start of picture text --> 2 1 3 BUSINESS<br>ONLY<br>15 8<br>28 BOTH<br>PERSONAL<br>88 ONLY<br>82<br>70<br>37<br>ALL HIGH- DEVELOPING<br>ECONOMIES INCOME ECONOMIES<br>ECONOMIES<br><!-- End of picture text --> _Note:_ Data exclude adults who have an account at a post office only. _Source:_ Demirguc-Kunt and Klapper 2012.

## BUSINESS OR PERSONAL USE

Worldwide, the vast majority of adults with a formal account use it for personal rather than business purposes (figure 1.18). In high-income economies, however, 25 percent of adults—and nearly a third of account holders—report using an account for business purposes. In developing economies only 4 percent of adults—and 11 percent of account holders—report doing so. There are a few notable exceptions, however: in Chad, Morocco, Togo, and Uganda, for example, more than 35 percent of account holders report using their account for business purposes. These exceptions aside, the contrast between high-income and developing economies is consistent with the overall lower account penetration and the smaller number of formally registered businesses in developing economies.<sup>22</sup>

## RECEIVING WAGES AND GOVERNMENT PAYMENTS

Using a formal account to receive wages is most common in high-income economies, Europe and Central Asia, and Latin America and the Caribbean (figure 1.19). In Europe and Central Asia 27 percent of all adults (and 61 percent of account holders) report having used an account to receive money or payments for work or from selling goods in the past 12 months. Relying on an account to receive money or payments from the government is most common in high-income economies, where 42 percent of all adults (and 47 percent of account holders) report having used an account for this type of transaction in the past year.

Using accounts to receive either wages or government payments is least common in South Asia. In Sri Lanka, for example, fewer than 10 percent of adults use an account to receive wages or government payments, even though the country has relatively high account penetration (69 percent) for its region and income level.

**24**

MEASURING FINANCIAL INCLUSION
Table 1: Extreme weather events can have substantial negative effects on household welfare, immediately and through adaptation over time, with adaptation and disaster relief often mitigating the immediate losses

|Dependent variable:<br>log expenditure/capita|OLS with Hu|bert‐White heteroskedasticity and cluster design<br>correction|ML corrected for spatial<br>correlation, cluster design and<br>heteroskedasticity|
|---|---|---|---|
|(excl. health expenditure)||||
||(1)|(2)<br>(3)|(4)|
||paramete<br>|paramet<br> <br>parameter/|parameter/|
||r/|er/<br>t‐value|t‐value|
||t‐value|t‐value||
|**_Extreme weather event in_**<br>**_community_**||||
|**Droughts**||||
|Drought this year|0.0309*<br>**|0.0016<br>‐0.0671|0.0245|
||2.92|0.06<br>‐1.04|0.58|
|Drought this year*drought<br>frequency||0.3023<br>0.2918|0.0704|
|||1.48<br>1.39|0.46|
|Drought this year*ln(hours) from<br>500k city||0.0177|‐0.0006|
|||1.23|‐0.06|
|Drought last year|0.0118|0.004<br>0.1159|‐0.0008|
||0.44|0.06<br>0.45|‐0.01|
|Drought last year*drought||0.1253<br>0.0179|‐0.1257|
|frequency||||
|||0.35<br>0.05|‐0.45|
|Drought last year*ln(hours) from||‐0.0212|0.0111|
|500k city||||
|||‐0.39|0.31|
|Drought frequency (proportion)||‐<br>‐2.5466***|‐1.2609***|
|||0.5241*<br>**||
|||‐4.74<br>‐5.54|‐4.25|
|Drought frequency*ln(hours)<br>from 500k city||0.5486***|0.2845***|
Quintile 5: Comparison of Tax Regimes to the Baseline

|Quintile 5|2013|2014|2015|2016|2017|2018|2019|2020|
|---|---|---|---|---|---|---|---|---|
|Baseline|0|0|0|0|0|0|0|0|
|Sales Tax Increase|-0.08|-0.13|-0.08|0.00|-0.09|-0.23|-0.04|0.23|
|Corporate Tax Increase|-0.79|-0.58|-0.62|-0.46|-0.66|-0.51|-0.63|-0.11|
|Tariff Increase|0.03|0.01|-0.03|0.04|-0.07|-0.08|-0.07|0.27|

# Quintile 5: Comparison of tax regimes to the baseline

<!-- Start of picture text --> 1<br>0<br>2013 2014 2015 2016 2017 2018 2019 2020<br>-1<br>Baseline Sales Tax Increase<br>Corporate Tax Increase Tariff Increase<br><!-- End of picture text --> Change in Total from the Baseline

|Change in total from base|Baseline|Sales Tax Increase|Corporate Tax Increase|Tariff Increase|
|---|---|---|---|---|
|Quintile 1|0|-1.93|-2.39|-1.88|
|Quintile 2|0|-1.85|-2.27|-1.86|
|Quintile 3|0|-1.97|-2.54|-1.90|
|Quintile 4|0|-1.98|-2.48|-1.92|
|Quintile 5|0|-2.01|-2.49|-1.94|

Change in total from the baseline <!-- Start of picture text --> 1<br>0<br>Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5<br>-1<br>-2<br>-3<br>Baseline Sales Tax Increase<br>Corporate Tax Increase Tariff Increase<br><!-- End of picture text --> 37
**Table   IV:   Enrollment   &   Switching   -­‐   Impact   on   Market   Outcomes**

||<br>**Villag**|**e Enrollment(Yea**|**r 2)**|**Village Average Test**<br>|
|---|---|---|---|---|
||**Primary**<br>**Enrollment Rate**|**Switching Rate**|**Dropout Rate**|**Scores - Same Kids, No**<br>**Switchers(Year 2)**|
||(1)|(2)|(3)|(4)|
|**_Panel A: No Controls_**<br>|||||
|Report Card|0.0390|0.009|0.009|0.129**|
||(0.0263)|(0.007)|(0.006)|(0.0608)|
|Observations<br>|112|112|112|112|
|R-Squared|0.473|0.0561|0.377|0.397|
|**_Panel B: Baseline Control Only_**<br>|||||
|Report Card|0.0351**|||0.107***|
||(0.0140)|||(0.0402)|
|Baseline|0.973***|||0.711***|
||(0.0470)|||(0.0595)|
|Observations<br>|112|||112|
|R-Squared|0.851|||0.742|
|**_Panel C: Baseline and Village Controls_**<br>|||||
|Report Card|0.0324**|0.009|0.007|0.113***|
||(0.0137)|(0.0074)|(0.0056)|(0.0408)|
|Baseline|1.037***|||0.711***|
||(0.0690)|||(0.0587)|
|Observations|112|112|112|112|
|R-Squared|0.853|0.083|0.429|0.745|
|Baseline Depvar(mean)|0.71|-|-|-0.012|

# _Notes:_

*   significant   at   10   percent   level   **significant   at   5   percent   level   ***   significant   at   1   percent   level

The  outcome  variables  are:  Year  2  village  primary  enrollment  rate  (column  1);  switching  rate  and  drop  out  rate  at  the  village  level (columns  2  and  3);  and  Year  2  village  average  test  score  for  those  kids  who  did  not  switch  schools  between  years  1  and  2  (column  4). All  regressions  include  district  fixed  effects  and  display  robust  standard  errors  in  parantheses.  Panel  A  considers  no  additional controls;  Panel  B  includes  a  baseline  control  of  the  outcome  variable;  and  Panel  C  includes  baseline  of  the  outcome  variable  and additional  village  controls,  which  are  the  same  as  in  Table  II.  Baseline  Depvar  (mean)  displays  the  baseline  mean  for  the  sample  for all  outcome  variables.  Note  that  we  do  not  observe  baseline  rates  for  switching  and  dropout.  Columns  1-­‐4  are  run  on  all  112  sample villages.

37
Table 11: Judgement text regressions, additional controls, defendant win

||(1)<br>Times cited|(2)<br>Times cited|(3)<br>Words in judg.|(4)<br>Words in judg.|
|---|---|---|---|---|
|Judge maj. female|-0.0996<sup>_⇤_</sup>|-0.0748|89.96|100.3|
||(0.0539)|(0.0524)|(93.08)|(91.29)|
|Def. maj. female|-0.0209|0.0236|58.03|87.37<sup>_⇤_</sup>|
||(0.0849)|(0.0960)|(46.72)|(49.78)|
|Judge maj. fem. X def. maj. fem.|0.0407<br>(0.0998)|0.0250<br>(0.105)|-139.0<sup>_⇤_</sup><br>(73.73)|-152.7<sup>_⇤⇤_</sup><br>(73.13)|
|Judge-defendant same ethnicity=1|-0.137<sup>_⇤⇤_</sup>|-0.127<sup>_⇤_</sup>|-97.20|-70.46|
||(0.0612)|(0.0687)|(59.67)|(63.01)|
|DV mean|0.220|0.220|1505.1|1505.1|
|Court-year FE|Yes|Yes|Yes|Yes|
|Other controls|No|Yes|No|Yes|
|Observations|7963|7963|7963|7963|

The regressions test whether in-group bias is associated with significantly different aspects of judges’ written judgements. Standard errors, in parentheses, are clustered at the judge level. Num. citations refers to the number of citations in the judgement. Times cited refers to the number of times the case has been cited. Num. laws cited refers to the number of laws and and acts cited in the judgement. Words in judg. refers to the number of words in the written judgement. Other controls include binary variables indicating whether a given ethnicity is the plurality, one for each ethnicity, for defendants, plaintiffs, and judges; case type dummies; a dummy for an appeal case; and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Def. = defendant, maj. = majority, fem. = female.

24
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22 Table 2.

## ME A

# CHU 1974-32

QN QM Q. Z

|**PN**|PM|P.|PZ|
|---|---|---|---|
|0.38|0.04|0.32|-0.74|
|<br>0.0S|<br>0.66|<br>-0.36|-0.35|
|<br>0.28|<br>-0.37|<br>0.48|-0.39|
|0.26|0.61|-0.18|-0.69|

Table 2.A. NU PRICE ELASTICES CONSIDERI THE EFFECTS 3HROUGH THE PRICE OF NON-TRDABLES CHILE 1974-89 XPM P. PZ Q.),,, 0.71 _-0.35_ -0.36 {X -0.14 0.68 -0.54 Z 0.82 0.02 -0.84
# Should East Asia Go Regional? No, No and Maybe.

# Arvind Panagariya*

Trade Policy Division Policy Research Department World Bank and Department of Economics University of Maryland

* I am indebted to Ramgopal Agarwala for many helpful discussions and to Sumana Dhar for superb research assistance and advance access to her ongoing work on East Asia's bilateral trade relationships. The paper is a part of the Regional Integration Initiative Study of the East Asia Region of the World Bank and of RPO 677-86.
# **Preferences, Purchasing Power Parity, and Inequality: Analytical Framework, Propositions, and Empirical Evidence**

## **1. Introduction**

## **Background**

The recent release of the final report of the 2011 International Comparison Program (ICP)<sup>1</sup> has generated renewed interest in the subject of Purchasing Power Parity (PPP). PPPs are required in a range of cross-country comparisons that include real expenditure, inequality and poverty comparisons. For example, the implementation of the international poverty line (IPL), typically denominated in US dollars, requires the use of PPPs to convert the IPL to local currencies. The manner of implementation of the PPPs in specifying country specific poverty lines by the World Bank has, however, been criticized by Reddy and Pogge (2007) on the grounds that it (a) “uses an arbitrary international poverty line that is not adequately anchored in any specification of the real requirements of human beings” and (b) “employs a concept of ‘purchasing power equivalence’ that is neither well defined nor appropriate for poverty assessment”. Reddy and Pogge’s (2007) view is at odds with the result of Deaton and Dupriez (2011) who have sought to justify the conventional use of PPPs in defining IPL by finding that that PPPs for the global poor are not much different from the economywide PPPs obtained from national accounts statistics.

The International Comparison Program (ICP) that is centrally directed from the global office of the World Bank is, by any measure, truly impressive in the scale of the exercise and highly ambitious in what it claims to achieve. As the volume [World Bank (2013)] that describes the objectives, methodology and the data sets of the ICP 2005 exercise states in its title, it seeks to measure the ‘real size’ of the world economy, even though it is not clear what the term ‘real’ really means. It does so by calculating ‘purchasing power parity’ (PPP) rates between the various countries’ currencies and using the PPPs rather than the market exchange rates in converting the national accounts of various countries and their components, denominated in local currencies, into a common currency, typically, the US dollar. As is well known, market

> 1 See World Bank (2015). The preliminary results were published in World Bank (2014).  See, also, Hamadeh and Mouyelo-Katoula (2014) for a lucid summary of the ICP, 2011 findings, and Diewert (2010) for a description of the new methodological developments in the earlier ICP, 2005 round.

2
patterns that inform their decisions whether to enter the labor market or to self-select into specific occupations. Self-selection has been found to have a significant impact on wage estimates and estimates of the pay gap (Belbo et al., 2003). Thus, we adopt Heckman’s (1979) method to correct for selection bias. However, the selection correction method is used only in the case of the mean decomposition analysis and not for the quantile decompositions discussed above. Selection correction within decomposition techniques, in general, can be problematic and is more complex in the context of quantile decompositions.<sup>8</sup> As a first step in applying the selection correction method proposed by Heckman, a probit model predicting labor force participation is estimated: In this equation, �� is a vector of instruments that includes number of children below age 6 in the household and number of shocks experienced in childhood. The predicted probabilities from this selection equation are then used to compute the selectivity term, which is added to the wage equation as an additional explanatory variable.

# **IV. Data**

The data for this study come from the <u>STEP Skill Measurement surveys. These data were</u> collected in 2012 and 2013 in seven countries—Armenia, Bolivia, Colombia, Georgia, Kenya, Ukraine, and Vietnam.<sup>9</sup> The household survey gathers information from urban adults between the ages of 15 and 64. The survey provides various measures of cognitive and noncognitive skills that are the focus of this paper (see Pierre et al. (2014) for a detailed description of the skills measures included in the STEP surveys). The STEP surveys provide a direct, objective measure of _reading proficiency skills,_<sup>10</sup> using an assessment scored on the same scale as the OECD’s PIAAC (Program for International Assessment of Adult Competencies). The scores on this assessment range from 0 to 500 and span six levels of proficiency.<sup>11</sup> In addition to the reading proficiency assessment, we use a self-reported measure of _complexity of computer use on the job_ as a second proxy for cognitive skills. Respondents report

> 8 Sensitivity to the choice of estimator (Belbo et al., 2003) or use of a semi-parametric approach using power series approximation for the selection term (Buchinsky, 1998) are some of the complexities encountered in correcting for selection within decomposition analysis.

> 9 Surveys were also conducted in Ghana, Lao PDR, Macedonia, Sri Lanka, and the Yunnan Province of China. Data for these countries do not meet the requirements for this paper, since the surveys did not include direct measurement of reading proficiency skills.

> 10 STEP also provides indirect measures of reading, writing, and mathematics skills, which are not used in this paper. 11 Interpretation of score levels can be found in Pierre et al., 2014, p.83.

10