rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 9653

# **Abstract**

This paper provides novel evidence on the economic impact of industrial automation in a large developing economy. It combines labor force survey and manufacturing plantlevel data from Indonesia over 2008–15, when the country experienced a rapid increase in imports of robots. The findings show a positive impact of robots on various measures of plants’ performance and integration into global value chains. In contrast to existing evidence on advanced and emerging economies, these plant-level impacts result in an increase in manufacturing and services employment at the local level. Such employment effects are consistent with evidence of positive employment spillovers from downstream robot-adopting plants, which help extend the benefits of automation to non-adopting plants. The spillover effects may provide a rationale to incentivize manufacturing firms to adopt industrial robots. The results also suggest that the gains from automation are not equally shared: adoption of robots is associated with a reduction in the labor share in value added and an increase in skill wage premia.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at mcali@worldbank.org and giopresidente@gmail.com.

_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
Figure 4: Trust dividends of better government performance <!-- Start of picture text --> Satisfaction with social services and institutional trust Satisfaction with economic performance and institutional trust<br>1 - Not at all 2 3 4 - Very 1 - Not at all 2 3 4 - Very<br>Satisfaction with social services (index) Satisfaction with economic performance (index)<br>(a) Social services (b) Economic performance<br>80 65<br>70<br>60<br>60<br>55<br>Probability of trust (%) Probability of trust (%)<br>50<br>40 50<br><!-- End of picture text --> Data source: World Bank survey on trust (2021). Note: the estimated predictions are derived from a logit model regressing trust in the executive (coded as a binary variable) on the range of independent variables presented in our model and controlling for relevant geographic and socio-demographic variables.

Satisfaction with the quality of public processes – and in particular with public integrity and policy credibility – is associated with a higher trust dividend than satisfaction with policy outcomes. As shown in Figure 5, integrity and credibility matter substantially for institutional trust – arguably more than government performance. Public integrity has a large positive pay-o↵: individuals who have a very positive perception of public integrity are 43.6 percentage points more likely to trust the government than individuals who have very negative perceptions (respectively 82.3 percent vs 38.7 percent report trusting government). Individuals who believe that policy announcements are often or always followed by actions are more likely to trust legislative and executive institutions by 12.5 to 18.4 percentage points, compared to those who do not believe in the credibility of policy announcements (67.6 percent of those who think that announcement are always followed by actions trust executive institutions, compared to 55.6 percent of those who think it is often the case and 49.2 percent, of those who think it is never the case). In addition, equitable treatment by the public administration comes out as a critical driver of trust in Morocco: individuals who perceive to have always been treated equitably by the public administration are more likely to trust institutions by 8.4 percentage points compared to those who perceive to have been treated inequitably (respectively 57.6 and 49.4 percent report trusting executive institutions).

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migration. Even though the OLS regressions control for wealth and other pre-determined characteristics, migrant-sending households might correlate with some unobservables such as connectedness, which also affects investment in education.

Interaction terms with female migrants indicate that households with more female migrants show almost no difference in explaining the fraction of all children enrolled in school, as shown in columns 5-10. This finding holds for the indicator for households with only female migrants, and the female share among migrants. Even though the sign is oftentimes negative, the point estimates are small and insignificant.

Second, as presented in Table 7, the dependent variable is the fraction of children 6- 18 years old in the household that are working in the last 12 months of the 2007 survey. The sample average indicates that 17% of children work, probably in family business or informal jobs. Children in urban households with Islamic and educated head are less likely to work in the labor market.

The OLS regression for this outcome variable suggests that children in migrantsending households are significantly less likely to work, whether or not controlling for community variables. As shown in column 1, the share of children in migrant households who work is 7 percentage points less than that in non-migrant households. This finding becomes more nuanced when we use the instrumental variable approach. The coefficients on the ―migrant households‖ variable, reported under columns 3 and 4, are not distinguishable from zero.

Columns 5-10 of Table 7 show that the coefficient of the gender interaction term is robustly negative. We find that children work less when it comes to households with more female migrants. This finding holds for the indicator for households with only female migrants, as well as the female share among migrants. To interpret column 6, for example, households with only female migrants reduce the share of children working by 32 percentage points more than the impact of households with at least some male migrants. The coefficients of ―Migrant Households‖ variable, reported in the IV regressions (Columns 9 and 10), are positive but indistinguishable from zero. It is inferred that migration has no statistically significant effect on child labor supply in families with only male migrants. However, households with only female migrants reduce the share of children working by 26.8 – 43.8 = 17 percentage points, as indicated 18
# **REFERENCE:**

- Alderman, H., P. F. Orazem, et al. (2001). "School quality, school cost and the public/private school choices of low-income households in Pakistan." Journal of Human Resources **36** (2): 304–326.

- Attanasio, O., E. Fitzsimons, et al. (2006). Child Education and Work Choices in the Presence of a Conditional Cash Transfer Programme in Rural Colombia, C.E.P.R. Discussion Papers, CEPR Discussion Papers: 5792.

- Basu, K. and P. Van (1998). "The economics of child labor." American Economic Review **88** (412-427).

- Bhalotra, S. (2007). "Is Child Work Necessary?" Oxford Bulletin of Economics and Statistics **69** (1): 29-55.

- Bhalotra, S. and C. Heady (2003). "Child farm labor: the wealth paradox." The World Bank <u>Economic Review</u> **17** (2): 197-227.

- Bourguignon, F., F. H. G. Ferreira, et al. (2003). "Conditional Cash Transfers, Schooling, and Child Labor: Micro-simulating Brazil's Bolsa Escola Program." World Bank Economic <u>Review</u> **17** (2): 229-254.

- Cockburn, J. and B. Dostie (2007). "Child work and schooling: the role of household asset profiles and poverty in rural Ethiopia." Journal of African Economies **16** (4): 519-63.

- de Janvry, A., F. Finan, et al. (2004). Can Conditional Cash Transfers Serve as Safety Nets to Keep Children at School and Out of the Labor Market?, Department of Agricultural & Resource Economics, UC Berkeley, Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series: 999.

- Edmonds, E. (2005). "Does child labor decline with improving economic status." Journal of <u>Human Resources.</u>

- Edmonds, E. (2007). "Child labor." NBER working paper 12926. National Bureau of Economic <u>Research.</u>

Edmonds, E. and N. Schady (2008). "Poverty alleviation and child labor." Policy Research <u>Working Paper 4702. the World Bank Group. Washington DC.</u>

- Ersado, L. (2005). "Child labor adn schooling decision in urban adn rural areas: comparative evidence from Nepal, Peru and Zimbabwe." World Development **33** (3): 455-480.

- Kambhampati, U. S. and R. Rajan (2006). "Economic growth: A panacea for child labor?" World <u>Development</u> **34** (3): 426-255.

35
- 17-

**Table 11** Results for Real Consumption, Error Correction Version

|Dependent Variable|Change in Private<br>Consumption|Change in Public<br>Consumption||
|---|---|---|---|
|Constant|936|_665_||
||(1.23)|(0.94)||
|Real GDP(-1)|-0.05|-0.03||
||(-1.33)|(-0.82)||
|Real Interest Rate(- 1)|2288|-1118||
||(1.26)|(-0.67)||
|Capital Account(-1)|0.08|0.038||
||(1.28)|(0.61)||
|AReal GDP|0.47|0.019||
||(15.8**)|(0.69)||
|AReal GDP(-1)|0.09<br>(4.37**)|0.09<br>(4.58**)||
|ACapital Account|0.17<br>(3.06**)|0.03<br>(0.59)||
|**RL**|0.98|||
|0.62||||
|Durbin-Watson Statistic|2.39||3.15|
|F-Statistic|292||8.44|
|p-value of F-Statistic|0.00||0.00|

Note: T-Statistics in parentheses. * :significant at 10% level, **:significant at 5% level

# **IV.2 Investment**

Our results for investment seem less robust to specification. We still use the three monthly deposit rate as a proxy of the opportunity cost of funds, due to the lack of reliable lending rate data. Although the levels of lending and deposit rates are different, the variation in the deposit rate would be expected to track the variation in the lending rate reasonably well. The results of the basic equation estimation for private fixed capital formation are in the first two columns of Table 12. Though the capital account does not significantly affect private fixed capital formation whether real M2 is included or not, the real interest rate becomes significantly negative only when it is included. But the sign of the coefficient of real M2 is negative, which is not plausible. The results for fixed capital formation by the public sector, as shown in the last two columns of Table 11 are not different. Capital flows do not significantly enter any of the two equations, while the implausible sign on real M2 persists.

Error correction versions of the estimation are summarized in Table 13. It can seen that public investment still is not related to capital flows, but capital flows, in past level, or past changes, do effect private fixed capital formation. Based on the error correction
<!-- Start of picture text --> B Upward mobility gap ECA B Upward mobility gap HIC<br>PIP =1 PIP = .0247<br>15.0 a ee | 1 40.0 —----tSSeS eae ee SsSsSSeSSeSeSSeS====F1 ee<br>|<br>§ | 30.0<br>3 10.0 | 2<br>= Ibs 20.0 53<br>2ao55.0 i| , “3ao<br>| 10.0<br>|<br>0.0 =-------- ------ 0 0.0 0<br>-0.3 -0.2 -0.1 0.0 -0.0 0.0 0.0 0.0 04<br>B Upward mobility gap LAC B Upward mobility gap OTH<br>PIP =1 PIP =1<br>15.0 jroo TTT ttc ttc csoccnno 1 15.0 neneieneieeneteneneieneneieneiananes 1<br>| |<br>§||<br>S210.0 I10.0 I>z<br><<br>. “2<br>P= | 5 | 58<br>2 504! E<br>°5 504) 5.0 |<br>| |<br>woy------- @=—-—------~—}f0 (XD os atieastonstiantiantiantententiontiontentententantintien ——Ffo<br>0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.4 05<br>Range of estimated coefficient Range of estimated coefficient<br>B Probability of higher education ECA B Probability of higher education HIC<br>PIP = 1 PIP = .0956<br>a oe —--- LLL,<br>10 264 ZO NL<br>§o5 | 2.015 =5><br>aSos 5 5 8o<br>3 1.0 a<br>8 |<br>| 0.5<br>|<br>00+4-—-—-—--—-—--—-—-—-“--------+0 0.0 0<br>0.0 2.0 4.0 6.0 8.0 -0.6 -04 -0.2 0.0 0.2<br>B Probability of higher education LAC B Probability of higher education OTH<br>PIP =1 PIP =1<br>0.6 1| —— ee —-—-—-4; 0.6)1 —- ee Ee 1<br>| |<br>s||<br>22044]| 5 044 || 5 g3<br>g |i . | 5388<br>aG 02+ | 0.24 | a<br>||<br>||<br>oy ------ <-------- 0 ONS os denetinetitieesiaetentientionenteentientientetestentiontantentententn 5)<br>0.0 2.0 4.0 6.0 8.0 0.0 5.0 10.0 15.0<br>Range of estimated coefficient Range of estimated coefficient<br><!-- End of picture text -->
<!-- Start of picture text --> 22<br>2<br>z<br>2<br>1995 2000 Year 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> 22<br>g<br><=<br>8<br>2<br>1995 2000 Year 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> ae a<br>Melee eor a<br>° oN<br>©<br>NN<br>cS<br>1995 2000 Year 2005 2010<br>———=- Total N. Employees ———— White Collar<br>——— Blue Collar<br><!-- End of picture text --> <!-- Start of picture text --> <<br>©<br>2<br>qio<br>1995 2000 Year 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>2<br>©2<br>e<br>1995 2000 Year 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>? _.<br>98E wet vet—<br>5 a“<br>rs ae<br>Zozoz€5 “ wo<br>1995 2000 Year 2005 2010<br>~~ Total Wages waite Collar<br>—— Blue Collar<br><!-- End of picture text --> <!-- Start of picture text --> 6<br>2<br>=<br>a=<br>1995 2000 Year 2005 20101<br><!-- End of picture text --> <!-- Start of picture text --> ©<br>rye<br>2<br>2 1995 2000 2005 2010<br><!-- End of picture text -->
# **TABLES AND FIGURES**

Figure 2.1 – Mexico’s degree of Openness <!-- Start of picture text --> Degree of Openness Degree of Openess<br>0.7 35<br>GATT NAFTA NEG NAFT A<br>0.6 30<br>0.5 25<br>0.4 20<br>0.3 15<br>0.2 10<br>0.1 5<br>0 0<br>Openess Tariff (Import w eighted)<br>1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000<br>(I+E)/GDP Tariff %<br><!-- End of picture text --> Source: World Development Indicators, World Bank.

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> <sup>7</sup>

Ferree (2006) summarizes three common explanations for ethnic voting. Ethnic voting could be a simple expression of ethnic identity, unprompted by politicians’ commitments to serve ethnic interests in any tangible way. It could also be coincidental: policy cleavages simply overlap with ethnic differences. Finally, as Chandra (2004), Posner (2005), and Ferree argue, ethnic voting could emerge because uninformed voters use ethnicity as a cue about politician preferences. The second and third differ only with respect to voter information. The effects of both, however, depend on whether voters also believe that politicians have incentives to avoid shirking on commitments they make to co-ethnics.

The results below point to a complementary explanation for ethnic voting. In an environment in which political competitors cannot make credible commitments to broad groups of voters, they resort to clientelist appeals to members of their clientelist networks – networks defined precisely by the ability of members to make mutually credible commitments (Keefer and Vlaicu 2008). This argument is consistent with a large literature arguing that politics in Africa is largely clientelist and that political appeals are rooted in patron-client ties. Bratton and van de Walle (1997) emphasize clientelism and the dominant role of the “big man” in African politics. Van de Walle (2001) concludes that in many African countries “democratization has had little impact on economic decision making, because the new democratic regimes remain governed by neo-patrimonial logic (p. 18).” Broadly credible appeals to favor an entire ethnic group are generally inconsistent with such neo-patrimonial logic, though the clients of “big men” may primarily be co-ethnics.

Specific country studies support the argument that the clients of “big men” are more likely to be members – though only a subset – of the same ethnic group. Stroh’s (2009) field research in Burkina Faso emphasizes that parties look for candidates who are _fils du terroir_ , sons of the earth, who are deeply rooted in their local communities – that is, who
**Figure A2. Relative Price Level, Selected Poorer Export Destinations**

<!-- Start of picture text --> Brazil Angola Turkey<br>1997 1999 2001 2003 2005 1997 1999 2001 2003 2005 1997 1999 2001 2003 2005<br>year year year<br>Morocco Hungary Russia<br>1997 1999 2001 2003 2005 1997 1999 2001 2003 2005 1997 1999 2001 2003 2005<br>year year year<br>200 200 200<br>150 150 150<br>100 100 100<br>relative price level (1997=100) 50 relative price level (1997=100) 50 relative price level (1997=100) 50<br>0 0 0<br>200 200 200<br>150 150 150<br>100 100 100<br>relative price level (1997=100) 50 relative price level (1997=100) 50 relative price level (1997=100) 50<br>0 0 0<br><!-- End of picture text --> Notes: Relative price level calculated as (1 _/e_ ) _∗ CP ICP Iportujtgal,t_ where _e_ is the nominal exchange rate; this is the reciprocal of the real exchange rate as conventionally defined. Relative price level normalized to 100 in 1997.
5 offecta.X Because virtually all passenger demand studies report ordinary demand elasticities, that is the appropriate interpretation of the results in this survey.

The own-price elasticity is distinguihed from cross-price elasticities. The latter is the percentage change in qnwa:.;ity demanded, say rail traffic, in response to a percentage change in the price of another service such as trucking. For substitutes, the cross-price elasticity for compensated demand is positive. If two products were unrelated to one another in the minds of consumers, the cross-price elasticity of the compensated demand would be zero, and cross-price elasticities are negative for complementary goods and s".vices.

t is important to distinguish between the overall market elasticity of de_ l for transportation and the demand facing individual modes of transport. The market demand refers to the demand for transportation relative to other (non-transport) sectors of the economy. The price elasticity of demand for individual modes is related to but different from the market elasticity of demand. Under the usual aggregation condition (i.e., conditions for existence of a consistent aggregate), the linkage between mode-specific elasticities (ownprice elasticity Fii and cross-price elasticities Fij) and the own-price elasticity for aggregate transportation demand (F) can be written as 41:

-

- (1) F EiSi (Ej Fij)

where Si refers to the volume share of mode i.

- 3 If one derives passenger demand by minimizing consumer's expenditure function for achieving a given utility level (or simply apply Hotelling's lemma to the expk<sup>4</sup> iture function to derive the demand functions), then the resulting des function would be a compensated one (i.e., changes along an indifference frontier).

- 4 In a simple two mode case, it becomes: F - S1*(F11 + F12) + S2*(F21 + F22).
Figure 6: Second Order Marginal Dominance Comparison by Household Composition <!-- Start of picture text --> 1989<br>, 200<br>~!150-<br>* ~~~~~~~~~~~~~~~~~~~~~~~~0-5<br>100 ......... ~~~~elders<br>0. 1-0- -- All households<br>E<br>.a 50<br>0<br>Lo 0<br>U.<br>0.25 0.5 0.75 1 1.25 1.5<br>Alternative Poverty Lines<br>1990<br>70<br>- 60.. . . . . . . . . . . .<br>................ .. . el<br>350 - =_h<br>40<br>!U 150-.-..- - =e elders<br>> 30 I-<br>a. -- = - = - All households<br>lo<br>10<br>00 00<br>0.25 0.5 0.75 1 1.25 1.5<br>0* 250 ___ Alternative Poverty Lines<br>1991<br>~ 250<br>200.. . . . . . . . . . . . . . . . . . . . . . . .<br>~150 .. ____0-5<br>ii I -. ~~~~~~~~~~~~~~~~~~~~~~elder<br>>. 10 - - .--- - .--- ------<br>5 0 .. All hmouseholds~<br>50<br>0.25 0.5 0.75 1 1.25 1.5<br>Alternative Povert Lines<br><!-- End of picture text --> Note: Alternative poverty lines represent fractions of J$6 198 (in 1991 dollars, which is the third decile of 1989 percapita expenditure).

_35_
relationship-more stable economies may promote greater financial sector development."<sup>7</sup> Clearly, the legal origin of a country, which has been linked to financial development, is a predetermined and exogenous variable in the above estimations.'<sup>8</sup> Hence, we are able to use it to instrument for financial development in our cross-country data set. Our cross-country data set also has some important disadvantages compared to the panel we used for the main results. Not the least of which is the small size of our data set; data availability restricts our analysis to only 27 countries. In addition, while we believe that the additional variables we add are important components of the country-specific fixed effect, we do not believe they include all important country characteristics. As a result of these disadvantages, we are cautious in our interpretation of the cross-country results and present them only as supplementary to our main results. Table 6 presents the results of this estimation for the variability of per capita consumption growth.1<sup>9</sup>

# [See Table 6.]

Our results for the per capita consumption growth regressions are generally supportive of the conclusions we drew above. Higher levels of _BANK, PRIVATE_ and _PRIVY_ are associated with lower volatility of per capita consumption. Per capita GDP and investment regressions, not reported here, generate negative but insignificant coefficients in three of the four GDP regressions and all of the investment regressions.

The coefficients on the additional country characteristics we were able to include in the cross-country regressions also suggest some interesting conclusions. Stronger accounting standards are associated with lower variability of consumption in two regressions, while exchange rate volatility is robustly and positively related to consumption variability in three out of four specifications. Surprisingly, less corrupt governments are

> 1<sup>7</sup> While our use of initial financial development and subsequent volatility somewhat addresses this issue, it is interesting to reverse this estimation and examine the effect of initial volatility and subsequent financial development. When we use the two non-overlapping periods of financial development and volatility in the panel data, however, we find no evidence for this reverse causality. '<sup>8</sup> For more detailed discussions, see La Porta et al (1998) and Beck, Levine and Loayza (2000a, 2000b).

19We were not able to obtain statistically significant results for the financial development variables in the GDP and investment estimations, and we do not report them here.

16
**<u>Appendix table 2: Extent of double registration for agricultural leases by lease validity</u>**

||**Number**|**% of**|**overlap >20**|**% with**|**Area under**|**% of o**|**verlap >20**|**% with**|
|---|---|---|---|---|---|---|---|---|
||**of leases**|**Valid**|**Expired**|**Indet.**|**leases (1000 ha)**|**Valid**|**Expired**|**Indet.**|
|**Total Malawi**|||||||||
|Valid leases|2,847|6.25|14.01|6.32|300|2.80|3.65|1.30|
|Expired leases|2,4474|2.93|22.02|6.51|644|1.73|9.93|3.05|
|Indeterminate leases|7,819|2.66|18.75|6.46|405|0.96|5.25|2.28|
|**North**|||||||||
|Valid leases|394|3.30|5.58|1.52|32|0.39|0.83|0.18|
|Expired leases|3,902|0.90|14.81|4.25|117|0.31|7.99|1.80|
|Indeterminate leases|1,885|0.74|10.77|4.88|82|0.09|2.99|1.15|
|**Center**|||||||||
|Valid leases|1,652|6.78|20.94|8.54|176|4.33|5.63|1.32|
|Expired leases|19,077|3.31|24.90|7.13|467|2.09|11.53|3.41|
|Indeterminate leases|4,831|3.35|25.05|6.89|229|1.00|7.48|2.41|
|**South**|||||||||
|Valid leases|801|6.62|3.87|4.12|92|0.73|0.86|1.66|
|Expired leases|1,495|3.28|4.14|4.48|61|1.71|1.31|2.71|
|Indeterminate leases|1,103|2.90|4.81|7.25|94|1.64|1.75|2.95|

_Source:_ Own computation from the National Geographical Estates Database.

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