rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 7410

# **Abstract**

Using three waves of survey data from fishing villages in Aceh, Indonesia for 2005–09, the paper examines the determinants of local volunteer labor after the tsunami. Volunteer labor is the village public sector labor force for maintenance, clean-up and renovation of public capital. While also examining the effects on volunteerism of village destruction and trauma, pre-existing social capital, diversity, and aid delivery, the papers focuses on the effects of democratization. The tsunami and massive international aid effort prompted the settlement of the insurgency movement in Aceh, which had led to suspension of local elections over the prior twenty or more years. Until 2006, village heads who call volunteer days were effectively selected by village elites, who may highly value the public facilities maintained by volunteer labor. With elections, volunteer days fall under the new regime, with democratically elected village heads calling fewer volunteer days, which may appeal more to the typical villager. Identification comes from pseudo-randomized differential timing of elections.

This paper is a product of the Operations and Strategy Team, Development Economics Vice Presidency. 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 arik@cbn.net.id.

_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
Table 13. Smartphone data: average daily time (mins), and share of daily time, spent in passive care across non-care and non-sleep activities

|||Po|p-up data: activity|conducted with|:||
|---|---|---|---|---|---|---|
||Any ch<br>(aged<br>|ildren<br>0-17)<br>|Children a<br>|ged 0-9<br>|Children a<br>|ged 10-17<br>|
||Women|Men|Women|Men|Women|Men|
|Average daily minutes:|||||||
|Agriculture|8.8|7.0|3.1|1.8|6.2|5.5|
|Nonfarm employment|5.5***|16.3***|3.2|5.4|3.0***|13.0***|
|Unpaid work:|||||||
|Obtaining services/resources for HH|5.9***|2.4***|3.9***|0.8***|2.3|1.9|
|Cooking, cleaning in house|42.1***|5.1***|29.6***|1.6***|16.9***|3.7***|
|Transport|21.5|19.1|14.8***|5.4***|8.1***|14.7***|
|School/studying|3.8|5.8|0.02|0.5|3.7|5.3|
|Leisure|100.9|107.3|67.1***|48.2***|57.4**|73.3**|
|Community activities|1.7|2.7|0.6|0.2|1.2|2.7|
|Personal care|6.1**|1.2**|4.8**|0.6**|3.2|0.6|
|Eating/drinking|22.2***|13.9***|16.4***|7.5***|10.3***|7.6***|
|All non-care and non-sleep activities|218.4***|180.8***|143.5***|72.0***|112.3*|128.4*|
|Share of daily time (within activity<br>categories; among those involved):|||||||
|Agriculture|0.17**|0.12**|0.07***|0.03***|0.10|0.09|
|Nonfarm employment|0.13|0.11|0.08***|0.03***|0.05*|0.09*|
|Unpaid work:|||||||
|Obtaining services/resources for HH|0.13**|0.09**|0.09***|0.04***|0.04|0.06|
|Cooking, cleaning in house|0.18**|0.14**|0.13***|0.06***|0.07**|0.10**|
|Transport|0.19***|0.10***|0.14***|0.03***|0.07|0.08|
|School/studying|0.27|0.25|0.08|0.04|0.19|0.21|
|Leisure|0.30***|0.25***|0.21***|0.12***|0.15|0.17|
|Community activities|0.20|0.17|0.09**|0.02**|0.12|0.16|
|Personal care|0.07***|0.04***|0.05***|0.02***|0.02|0.03|
|Eating/drinking|0.50***|0.31***|0.36***|0.16***|0.22***|0.18***|
|All non-care and non-sleep activities|0.24***|0.19***|0.16***|0.07***|0.12*|0.13*|

# <u>Notes:</u>

(1) Based on 1,026 and 1,035 observations for women and men respondents, respectively, for Days 5, 8 and 10 of the survey period.

(2) T-tests of equality of means conducted across men and women. ***p<0.01, **p<0.05, *p<0.10 (3) After each recorded activity in the _Time Tracker_ app, respondents were asked whom the activity was conducted with.

30
and appealing, though they are not without limitations. Such models emphasized the importance of capital, labor, education, and innovation in driving growth, but overlook the differences and intricate inter-linkages between sectors. Consequently, single-sector models fall short in elucidating how an economy’s compositional structure relates to its growth trajectory. The economic complexity theory (Hidalgo and Hausmann 2009) shows that a country’s industrial composition and specialization patterns strongly influence its growth prospects. Understanding the complex interplay between growth and structural transformation is essential for formulating policies that enable sustainable development at scale (Gollin and Kaboski 2023).

I construct a simple multi-sector growth model to conceptualize the distinct channels through which AI influences growth and structural transformation. The model builds on recent structural transformation frameworks (Rodrik 2016; Comin, Lashkari, and Mestieri 2021; Matsuyama 2019) and incorporates nonhomothetic preferences, where income elasticities of demand vary across sectors. The economy has four sectors: agriculture (A), manufacturing (M), high-skill services (Sh), and low-skill services (Sl). Labor is the only factor of production. The model initially assumes a closed economy, later extending to an open economy context. It also explores various model extensions, transitioning from exogenous to endogenous growth and incorporating inter-sectoral productivity linkages. The model’s propositions are strongly supported by empirical evidence.

High-skill services include information and communication technology (ICT) services, finance and insurance, and professional, scientific, and technical services. The term ”high-skill” serves as a shorthand for sectors characterized by high levels of skill, income, tradability, and digitalization. These industries significantly differ from other service sectors across all four dimensions. Based on data from the U.S. and China, these three industries exhibit the highest average earnings, ICT service input intensity, trade intensity, and the largest share of employees with advanced degrees (master’s or higher). Other services are classified as low-skill services. Although education and healthcare also have a high proportion of employees with advanced degrees, they are considered low-skill due to their low tradability and digitalization.

AI affects growth and structural transformation through three distinct channels:

1. Demand: AI creates radically new products and shifts consumer preferences. It reshapes utility functions by altering income elasticities for certain sectors and changing the elasticity of substitution across sectors.

2. Supply: AI affects relative prices across sectors. While previous technologies primarily affected the goods-producing sector and routine tasks, generative AI targets high-skill services, particularly 2
260 USD) per month in 2015 to 1300 TL (about 350 USD) in net terms.<sup>10</sup> With an inflation rate of 8.8 percent in 2015, the increase represented a rise of about 24 percent in real terms. By contrast, the minimum-wage increases in previous years had ranged from 5 percent to 8 percent annually in nominal terms, levels that were roughly aligned with consumer expectations and realizations about inflation (Figure 2). To alleviate possible negative employment effects of the 2016 minimum-wage increase, the government introduced a subsidy of 100TL of employers’ social security contributions for employees whose wages were at a level below twice the minimum wage of 2015. As a result, the cost of minimum wage to employers increased by about 26 percent in January 2016.

The increase in the minimum wage was largely politically motivated and driven by <mark>the electoral competition in 2015 (Akgunduz et al., 2019). A</mark> ll political parties promised a sharp increase in the minimum wage in their campaigns before the November 2015 elections, and they competed with one another over the magnitude of the raise promised. The ruling Justice and Development Party (AK Party), which was re-elected, promised a net minimum wage of 1300 TL. Consequently, the new minimum wage was set at this level on January 1<sup>st</sup> , 2016. The political process that led to the new minimum wage suggests that the change was largely <mark>exogenous to the internal dynamics of the economy, and instead was mainly driven by the exogenous political competition.</mark> The 2016 minimum wage thus appears to constitute a valid experiment to study the causal effects of the minimum-wage increase on firms and employment.<sup>11</sup>

# **3. Data**

## 3.1. The Turkish Enterprise Information System

The primary data source used in this paper is the Turkish Enterprise Information System (EIS), an exceptionally rich, linked, employer-employee dataset covering the universe of registered nonfinancial firms in the economy. The records of registered employees of a given registered firm are linked to the records of that firm. The firms and their employees are observed every quarter from

> 10 The Turkish minimum wage was of 950 TL in the first half of 2015, and 1000 TL in the second half.

> 11 We provide further evidence on the validity of the identification strategy in the remainder of the paper.

8
- Feinstein, L. 2003. “Inequality in the Early Cognitive Development of British Children in the 1970 Cohort.” _Economica_ , 70: 73-97.

- Ferreira, Francisco H. G. and Norbert Schady. 2009. “Aggregate Economic Shocks, Child Schooling, and Child Health.” The World Bank Research Observer, 24(2): 147-181.

- Guantai, F. and Kijima, Y., 2020. Ethnic violence and birth outcomes: evidence from exposure to the 1992 conflict in Kenya. _Demography_ , pp.1-22.

Graham, C. 2015. “The Pursuit Of Happiness In The US: Inequality In Agency, Optimism, And Life Chances.”

- Grantham-McGregor, S., Yin Bun Cheung, Santiago Cueto, Paul Glewwe, Linda Richter, Barbara Strupp, and the International Child Development Steering Group. 2007. “Developmental potential in the first 5 years for children in developing countries.” The Lancet, 369:60–70

Greene, W.H. (2005). Econometric Analysis. 6th ed., Prentice Hall, Upper Saddle River.

- Greenough, W. and J. Black. 1992. Induction of brain structure by experience: Substrate for cognitive development. In M. R. .Gunnar & C. A. Nelson (Ed.), Minnesota symposia on child psychology 24: Developmental behavioral neuroscience (pp. 155-200. Hillsdale, NJ: Lawrence Erlbaum.

- Gunnar, M., Quevedo, K. 2007. The neurobiology of stress and development. _Annual Review of Psychology_ , 58:145–173.

- Harpham, T, M. Reichenheim, R. Oser, E. Thomas, N. Hamid, S. Jaswal, A. Ludermir, M. Aidoo. (2003). Measuring mental health in a cost-effective manner. _Health Policy Plan._ September; 18(3): 344-9.

- Hurley K., M. Black, M. Papas, L. Caulfield. (2008). Maternal symptoms of stress, depression, and anxiety are related to nonresponsive feeding styles in a statewide sample of WIC participants. _Journal of Nutrition_ ; 138: 799-805.

- Hay, D.F., Pawlby, S., Sharp, D., Asten, P., Mills, A., Kumar, R. 2001. Intellectual problems shown 11-year-old children whose mothers had postnatal depression. _Journal of Child Psychology and Psychiatry_ ; 42, 871–890.

Kurstjens, S. and Wolke, D. 2001. Maternal depression on cognitive development of children over the first seven years of life. _Journal of Child Psychology and Psychiatry_ , 43(5), 623-636.

- Lovejoy, M., P. Graczyk, E. O’Hare, and G. Neuman. 2000. Maternal depression and parenting behavior: a meta-analysic review. _Clinical Psychology Review_ , 20(5), 561-592.

22
8

**10. As a result, liquidity in dollar and euro funding markets tightened sharply, raising borrowing costs significantly** (figure 7d). Interbank market lending came to a halt, as Euro Area banks refrained from lending to one another and preferred to park their excess cash at the ECB, forcing weaker banks to borrow at penalty rates (figure 7e). European banks also faced dollar shortages, as illustrated by a sharp retrenchment of U.S. money market funds in the latter half of 2011 and elevated dollar-euro swap costs (figures 7d and 8). The increase in the risk aversion of money market funds‘ toward European banks has been reflected in the growing share of repos in their total euro-zone exposure. This has put pressure on dollar-denominated operations of European banks, including trade finance, which account for an average of around 15 percent of bank assets, and are often very short term (and hence susceptible to funding shocks).

**Figure 8. Declining U.S. Money Market Fund Exposure to European Banks**<sup>**a**</sup>

<!-- Start of picture text --> (In percent)<br>60<br>50<br>Exposure to all<br>40 European banks<br>Exposure to Euro<br>Area Banks<br>30<br>20 Share of repos of European<br>bank exposure (as part of a<br>broader shift to secured<br>10<br>funding)<br>0<br>2H06 1H07 2H07 1H08 2H08 1H09 2H09 1H10 2H10 2/11 5/11 6/11 7/11 8/11 9/11 10/11 11/11 12/11 1/12 2/12<br><!-- End of picture text --> Source: Fitch Ratings, January 26, 2012. a Exposure measured in terms of money market funds‘ exposure to bank CDs, CPs, repos, and other instruments as percent of the funds‘ total assets under management.

**11. The European firewall, a key instrument to prevent contagion and dampen the adverse feedback loops, has been unable to calm the markets.** After months of debate, in March 2012, euro leaders increased the capacity of European bailout fund to about €800 billion, with the ability to provide €500 billion in fresh capital that would be available to support sovereign debt markets or bank recapitalizations.<sup>3</sup> However, the increased amount has been viewed as insufficient for the refinancing needs of large peripheral countries such as Spain and Italy, if market conditions in the periphery were to deteriorate and trigger investor anxiety.<sup>4</sup>

> 3 The source of contention has been the disagreement between creditor and debtor states over the trade-off between stability considerations and retaining reform incentives in the periphery. The ―fiscal compact,‖ as a first step toward fiscal convergence, agreed on December 2011, imposes budget discipline and provides some reassurance to creditor countries. As such, the compact was likely a necessary condition for an increase in the bailout fund. 4 For example, the uncertainty regarding private sector intervention (PSI) in Greece to contribute to debt sustainability brought about anxiety among market participations facing coercive haircuts on their holdings of sovereign debt and raised expectations that similar events could take place in other troubled economies.
<!-- Start of picture text --> Ratio between MNE and Nat. Average TFP minus 1 TFP (Manufacturing)<br>-1 -0.5 0 0.5 1 15 v0) 0.5 1 LS. 2 25 3 3.5 4 45 5<br>hn Colombia}; -—________0—<br>|__| China -——_o——_<br>[| Indonesia Ho<br>es -——_—_——_e<br>|| Ethiopia tHe—o<br>a Philippines o—_____1_e<br>Uganda o——_“H<br>Poland -——e<br>Uzbekistan to—e<br>Chile eo<br>Oo Madagascar||Romania Hoere<br>DRC}; te—o<br>Kenya e—t—_o<br>Venezuela He<br>Nigeria}; He———o<br>Céte d'Ivoire Heo<br>U Malaysia e—-o0<br>Morocco}; ote<br>Peru Heo<br>Russia e—o—_‘H1<br>India oi<br>LJ Egypt et—o<br>[| GuatemalaMalawi toee—_t+—_o<br>I Argentina °<br>| Mali e—a<br>LL Ghana oe oH<br>i Vietnam eo—H1<br>MNE 10 MNE 50 MNE 50 MNE 10 All firms<br><!-- End of picture text -->
Research Components of Wortd Bank Education Projects. since 1982.

|**Area**|Country and LoanJCredit|<br>l<br>l<br>hNumber|Approved|Research Components|Current Status of Studies,<br>including<br>Completed Documents/Data|
|---|---|---|---|---|
||||(iv)<br>analyte student performance in Grade 10<br>examination;<br>(v)impact of textbooks;<br>tvi)<br>use and impact of new teachers;<br>(vii)<br>progression<br>rates from Grade 8 to Grade 9<br>(Amex 9)|Also:<br>M.Olsen, J. Wasilewski and E. Ape(is(1966).<br>mThecoordination,<br>development and implementation<br>of non-formal<br>education<br>in PNG."|
|ASIA|Philippines<br>LH2200-PH|1983|Vocational<br>Training.<br>No Research component.|Confirmed.|
|ASIA|Solomon Islands<br>CR 1212-SOL<br>l|1982|Primary Education.<br>Conduct studies on issues in<br>the education sector (para.3.15 p.20).<br>(ii)<br>Evaluation surveys to consider ir:reased<br>enrc-Iments;<br>(iii) increase in futly trained teachers;<br>(iv) methods of distribution<br>and effectiveness<br>of textbooks;<br>(v) nrumbers<br>of in-service<br>courses conducted<br>(3.14 20).|C. Currin (1986).<sup>"Curricular</sup><br>development and<br>teacher training: A review." (doc. U 433-126)|
|ASIA|Solomon Islands<br>CR 1686-SOL|1986|Secondary Education.<br>Studies on(i) teacher<br>utilization<br>andincentives;<br>(ii)<br>in financing of education (due June 1987)<br>(para.<br>2.09 p.12).|No information<br>available.|
|ASIA|Sri Lanka CR 1698-CE|1986|Second Vocational<br>Training Proiect.(i)<br>Study<br>economicandfinancial<br>costsand benefits<br>of<br>publicly finaxced construction industry<br>training(due Sept1987)(para2.03 p.13).<br>(ii) Four tracer studies to cover demographic<br>details of graduates (para2.23p.20).|(i)Coststudy carried out during 1967 -reviewed<br>during_12/88_supervision<br>mission.<br>People's Bank<br>Research Department (1987)."Study<br>on financing<br>of ICTAD supported training<br>programes: Part10<br>(doc. U F13102)|

47
the economy by including several variables at time _t_ (i.e., the year of the first-wave survey) as they relate to the probability of exit by time _t + n_ , as well as a proxy for shocks and the business cycle to each economy in the form of GDP growth over the interim period. Such shocks are also controlled for somewhat by our inclusion of year controls as well as our controls for the number of interim years between survey rounds, _n_year_ . Further, the latter two variables help capture the effect of excluded variables of regulation and its enforcements on firm survival, a unique point of analysis enabled by our repeated cross-sectional data.

Table 3 provides the list of all variables used in the regression; Table 4 reports the summary statistics for all the variables included in the regression, pooled across the 47 countries in the regression sample. The cumulative exit rate is about 16 percent based on our conservative measure of exit, the _strict_exit_ , and 23% based on our less conservative measure of exit, the _weak_exit_ . Real annual sales per worker, our measure of productivity, average at about 57,000 in 2005 US$, which is by definition close to the estimated labor productivity of competitors in a firm’s country-sector of $58,000. Most of the firms in the analysis are relatively young, with an average age of just under 12 years, a fact that helps mitigate somewhat against incumbency bias concerns. Over a third of the firms reports having a female among their owners, while just over one in four firms reports having used a bank loan to finance fixed investment or working capital. Nine percent of the firms have foreign ownership, defined as firms with 10% or more owned by foreign firms or individuals. Exporting firms, defined as those with direct exports accounting for 10% or more of total annual sales, constitute about 11% of the total sample. In terms of legal structure, over 60% of the firms are of limited liability type, broadly defined to encompass shareholding (publically traded or not) and limited partnership companies; at first blush, this rate appears high but could be accounted for by the legal idiosyncrasies in certain countries, a fact controlled for by country-level fixed effects. About 38% of the firms in the sample are in the manufacturing sector, the remaining 62% being in service sector broadly defined (29% being in retail).

19
ECCAS’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

# **Figure 2.14 . Age distribution of airline fleet in the ECCAS region**

<!-- Start of picture text --> 2001 2007<br>15%<br>2%<br>26%<br>15% 28% 50%<br>44%<br>20%<br>Western recent Western recent<br>Western somewhat recent Western somewhat recent<br>Western old<br>Western old<br>Western age unknown<br>Western age unkown<br><!-- End of picture text --> _Source:_ Bofinger 2009.

32
inputs were therefore available to farmers during the pre-planting period, instead of physically being locked away at the bank. This result is much more consistent with the commitment accounts helping to solve an “other control” problem rather than a self-control problem. It is likely that the offer of the commitment accounts simply allowed farmers to credibly claim to others in their social network that their funds were locked away. Even though most of farmers’ funds were in ordinary accounts, this could have been a credible claim because the division of an individual’s funds between ordinary and commitment accounts was not directly observable to others.<sup>15</sup> Columns 3, 4 and 5 indicate that the larger input use caused by the commitment treatment resulted in higher total crop output in the 2010 harvest. The coefficient on the commitment treatment is large and statistically significantly different from zero at the 10% level for proceeds from crop sales (column 3) and for the value of crop not sold (column 4). The coefficient on the commitment (no raffle) treatment on the value of sold and unsold output (column 5, the sum of the dependent variables in the previous two columns) is statistically significantly different from zero at the 1% level. The increase in total value of crop output (MK 33,418) amounts to 22% of mean crop value in the control group. The coefficient on the ordinary (no raffle) treatment in column 5 is also positive but its magnitude is only around 20% of that on the commitment treatment, and is not statistically significantly different from zero. The difference between the ordinary (no raffle) and commitment (no raffle) coefficients in column 5 is statistically different from zero at the 10% level (p-value 0.074).

Column 6 of Table 5 shows the impact of the treatments on farm profits, defined as the difference between the total value of crop output (dependent variable of column 5) and the total value of inputs used (dependent variable of column 2). The coefficient on the commitment treatment is large in economic terms and marginally statistically significant (p-value 0.11). The coefficient for the ordinary account is small and not statistically significant, and the difference vis-a-vis the commitment account is marginally significant (p-value 0.11).

Column 7 examines the impact of the treatments on total household expenditures in the endline (post-harvest) survey. The commitment (no raffle) treatment coefficient is positive and statistically significantly different from zero at the 5% level, while the coefficient on the ordinary (no raffle) treatment is substantially smaller and not statistically significantly different from zero.

> 15 To be clear, the public raffle treatments provided a signal of only an individual’s _total_ balances at OIBM, not how those savings were split between ordinary and commitment accounts.

16