rafmacalaba/data-use-mentions-extended huggingface dataset:
considering whether it supplemented subsidies when toilet costs exceed the subsidy amount. We also discuss potential reasons behind the observed low loan-to-new-toilet conversion rate for this group of households.

# **Provision of bridge-funding to subsidy eligibles**

Given that the bulk of the SBM subsidy amount is disbursed only after a toilet has been successfully constructed, households need funding up-front to cover toilet construction costs. The extent of this is visualised when we examine data on the time lag between toilet construction and subsidy receipt. Figure 5 plots the timing of subsidy disbursement reported in the endline survey and that reported for toilet construction. It shows that over 50% of households who had received the subsidy by the time of the endline survey received the subsidy after toilet construction, with the remaining households report receiving the subsidy either before, or at the same time as toilet construction.<sup>34</sup> Households that reported receiving the subsidy after toilet construction waited an average of 10 months after toilet construction to receive the subsidy, with over 10% waiting for over a year.

This finding can be cross-validated using SBM officials’ report of delays in SBM disbursement, captured during a survey of GP SBM officials in December 2017: Consistent with the client reporting, half of 77 GP officials reported that the time between subsidy application and disbursement was at least 3 months.<sup>35</sup> Overall, these descriptive results confirm that a significant percentage of households experienced a quite substantial time gap between toilet construction and subsidy receipt. This feature of government subsidies could possibly make sanitation loans, which are generally disbursed prior to toilet construction, attractive for financing the upfront costs of a toilet. They could allow households to alleviate short-run liquidity constraints to construct a toilet, and thereafter receive the subsidy. However, if the delay in subsidy disbursement is very long, households will face higher costs of investing in a toilet since they will need to pay higher interest charges since they cannot use the subsidy to repay the loan.<sup>36
Policy Research Working Paper 5891

# **Abstract**

This paper investigates whether the agglomeration of economic activity in regional clusters affects long-run manufacturing total factor productivity growth in an emerging market context. It explores a large firm-level panel dataset for Chile during a period characterized by high growth rates and rising regional income inequality (1992–2004). The findings are clear-cut. Locations with greater concentration of a particular sector did _not_ experience faster growth in total factor productivity during this period. Rather, local sector diversity was associated with higher long-run growth in total factor productivity. However, there is no evidence that the diversity effect was driven by the local interaction with a set of suppliers and/or clients. The authors interpret this as evidence that agglomeration economies are driven by other factors, such as the sharing of access to specialized inputs not provided solely by a single sector, such as skills or financing.

This paper is a product of the Trade and Integration Team, Development Research Group. 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 author may be contacted at afernandes@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
# **1 Introduction**

Are automation technologies an opportunity or a threat to developing countries? The literature has examined this question by studying automation in advanced economies - which have been early adopters - focusing on the impact on imports from developing countries (Kugler et al. (2020); Artuc et al. (2019); Faber (2018); Artuc et al. (2018)).<sup>1</sup> Much less is known about the impact of automation in developing countries on their own economies. This is an important gap for at least two reasons. First, given the differences in the structure of production and the type of skills in the labor force, it is not clear that the evidence on automation in high-income countries may provide useful guidance for developing countries. Second, firms in low and middle income countries have begun to invest in automation technologies, whose penetration is expected to grow over the next decades (Hallward-Driemeier and Nayyar (2017)).

To help fill the gap, this paper focuses on industrial robots, an important class of automation technology. It examines empirically the impact of robots on firms and local labor markets in Indonesia, which is a suitable context for this analysis. The number of robots in the country was very limited before the beginning of our sample in 2008 and accelerated stiffly thereafter. By the end of the sample in 2015, the penetration of robots in the most automated industries was similar to advanced economies. Therefore, the experience of Indonesia - an early adopter among developing countries - should be informative about other large developing economies, in which adoption rates are still limited today and are expected to grow. Indonesia provides a rich set of high quality data including a large panel of manufacturing plants and labor force surveys, which our analysis can leverage along with data on imports of robots.

Our key contribution is to document a positive impact of automation on employment in Indonesia (at least until 2015). Consistently with the predictions of a task-based model (
In both Moldova and North Macedonia, covering the pre-shock poor either through perfect means testing or using a PMT indicator (columns 2 and 5) becomes a suboptimal response (again, using the poverty gap squared as a metric). If the post-shock poor could be perfectly identified (column 3) covering them would still remain an optimal response, but now the use of a PMT delivers poor impacts because the PMT cannot identify well the new poor who fell into poverty as a consequence of the shock. In contrast, in both countries the use of an indicator of who suffered losses (either perfect or imperfect, columns 7 and 8) provides now greater poverty impacts than the use of a PMT; in fact, mixing the use of a loss indicator with the PMT does provide slightly lower impacts than using just the loss indicator, reflecting the fact that households affected by the random shock loss lose all their labor income and fall into deeper poverty than the average for the ex-ante poor. Using a flat, universal transfer remains, again, among the options delivering the lowest impacts.

Results for Albania, while overall similar, present some subtle differences. Using a loss indicator delivers slightly better impacts than using the PMT, but differences remain small. This is consistent with the fact that, if one would distribute the emergency budget to the exante poor (column 2), one would still achieve the second-highest impacts among all design alternatives – something that does not hold for Moldova and North Macedonia. A contributing factor is that labor income as reported in the survey is only one source of income for many Albanian households, and despite many households losing all their labor income from the random shock, there are still many households in extreme poverty that remain poorer than the average household affected by the shock.

25
central point of a grid cell is defined as the mean of the absolute differences in elevation of the central point between the central grid cell and its eight adjacent grid cells, i.e., grid cells in the north, northeast, east, southeast, south, southwest, west, and northwest of the central grid cell. That is, TRI� �∑�����E� �E��⁄8, where _TRIi_ denotes the ruggedness in the central grid cell _i_ , _Ei_ and _Ej_ represent the values of elevation in the central grid cell _i_ and neighboring grid cell _j_ , respectively (Wilson _et al._ , 2007). Using our elevation data, we ran the TRI command from a Python library (GDAL: https://docs.qgis.org/2.8/en/docs/user_manual/processing_algs/gdalogr/gdal_analysis.html) and generated a single-band output raster with the index values. The resultant raster was subsequently used as an input value layer for the zonal statistics to obtain the mean terrain ruggedness for Indonesian district.

33 All tables and figures in this section, as well as the tables showing various robustness checks to our main results (see Appendix A), only report the estimated agglomeration wage premium, _β_ in equation (1). All regressions always include the full set of controls discussed in Section 4.1.1. Table A1 in Appendix A shows the estimated effect of the 19
4 The productivity equation can then be derived from equations (1) as (6) _dy; = He[PsLidl, + sK,dk, + SMddmiI +_ **_UJ_** _[SK, - SK,]dki + FT dt, F'_ where _dy,_ is output growth and _F' dt,_ measures the technology change or TFP growth. The _F'_ second term on the right hand side can be simplified to (g; - jti)dk1<sup>using (5). Equation (6) is</sup> estimated in log-differences, using actual enterprise-level data to construct the first right-hand side term. There are two terms to estimate for each industry (gi and **gt,** the scale and markup parameters), as well as the TFP parameter for each enterprise. We use the reported book value of fixed assets to construct the capital revenue share.

To take into account the likelihood that foreign investment choices are not randomly distributed-the descriptive statistics reported in Table 1 suggest that the firms attracting FDI and joint ventures have above average initial performance-we correct for the possible endogeneity of foreign investment choices by using the generalized Heckman two-step procedure for correcting sample selection bias as developed by Amemiya (1984). This involves separate estimation of the foreign investment decision and the subsequent firm productivity growth performance. The first step uses a probit model to determine the probability of foreign investment based on initial efficiency (proxied by the share of variable costs in total revenues), firm size, and type of industry. The second step involves an estimation, using only observations on firms with foreign linkages. This results in an omitted variable sample selection bias. The Amemiya procedure provides for a specification of the omitted variable that can be used in the full sample to alleviate sample selection. An additional variable estimated in the first step is included in the second-step regression.

14
higher preference for unstructured work environments and shifts than men.  Women disproportionately cite “the need to care for the household” as their reason for being self employed, contract workers, or informal wage earners; where the flexible work shifts and work locations allow them to more easily fulfill their gender roles as primary caregivers of the households (Cunningham and Gomez 2004). Further, informal work also permits the easy and relatively frequent entry and exit from the labor market, which is characteristic of women’s labor histories. Partly due to household responsibilities, partly due to their role as safety nets when the household experiences a negative income shock (Cunningham 2001, World Bank 2001), female turnover is much higher than men’s.

This view of largely home responsibility-induced informality is borne out when the patterns of single and married women are broken out. Unmarried women without children, who have few domestic responsibilities are even more represented in the formal sector than are men while married women are over-represented in the unpaid labor sector.<sup>40</sup> Men’s sector of employment does not differ by marital or parental status suggesting the importance of child care responsibilities (Cunningham 2001). Further, single, childless women’s transition matrices are statistically indistinguishable from those of men. This said, employers also know female turnover patterns, and may discriminate for statistical reasons against young women in filling formal jobs. In some countries (eg., Bolivia), labor regulations still contain provisions such as a shorter work week, prohibition of night work, weak regulations of domestic work and maternity benefits paid by employers and non-transferable to husbands, all of which hinder better quality employment for women.

**_Occupational segregation by gender remains a defining characteristics of Latin American labor markets._** The Duncan index (proportion of men and women who would have to trade occupations to

> 40 In Mexico, for example, 10 percent of married women are unpaid workers while 87 percent of unmarried women are formal sector employees.
18 section 5. In particular, the SAE prediction techniques are validated and applied in four country settings—validated in Vietnam and rural China and applied in Russia and Kenya. Separate consumption models are estimated for different geographic areas within each country (national, rural, urban, province). The settings further differ in the evolution of poverty observed during the spell (decline, stagnation, increase) and sample size. Classified by the direction of poverty change and the geographic area, a total of 25 different settings can be considered. Table 1 provides a summary (including the level of the poverty headcount in the base year and the observed poverty change).

# **3 Testing Poverty Predictions Using Two Surveys of Comparable Design**

We start with an assessment of the performance of the adapted SAE techniques in Vietnam and China, two different settings with highly comparable data and each encompassing periods of substantial structural change. We then move to the case of Russia and Kenya to demonstrate that the poverty prediction technique based on model specifications that eschew regressors requiring adjustment for cost of living variations can help to adjudicate questions about the appropriate choice of price deflators.

Following the introduction of the Doi Moi reforms in 1987, Vietnam experienced strong, broad-based economic growth throughout the 1990s resulting in an estimated drop in poverty from 60.6% in 1992/1993 to 37% in 1997/1998. These estimates are based on the well respected and highly comparable Vietnam Living Standards Surveys (VLSS) of 1992/1993 and 1997/1998 and are widely judged as presenting an accurate picture of the evolution of living standards (Agarwal, Dollar and Glewwe, 2004). Both surveys are representative at the national and regional levels. The 1997/1998 survey contains panel
<mark>corporate debt. For the use-of-funds analysis, we merge the SDC data with Worldscope data, which provide information on the financial statements of firms. Those data include important information on firms’ assets, cash holdings, and sales (reported in balance sheets, income statements, and cash flow statements). Worldscope data are available for 44% of the firms in the SDC database, resulting in a merged data set of 2,190 firms.</mark>

# **<mark>4. Corporate Bond Issuances</mark>**

# _<mark>4.1. New Findings on Yields and Issuance Behavior</mark>_

<mark>As discussed in Section 2, we conjecture that part of the surge in investor interest in emerging market corporate debt after the GFC reflected a change in the investor base. We hypothesize that this compositional shift, together with the existence of the CEMBI Narrow index, with a $500 million minimum cutoff, produced an increase in the interest of international investors for large ($500 million and greater) emerging market corporate bonds.</mark> <mark>To study how the shifts in size-dependent investor interest affected market yields, we begin with simple comparisons. In Figure 2, we plot the evolution of the yield to maturity during 2000-2016 for bonds issued by emerging market corporates with face value below $300 million, between $300 million and below $500 million, and equal to or above $500 million. We observe that yields for all issuance sizes declined after the GFC, but the effect is particularly pronounced for $500 million bonds.</mark> <mark>In Figure 3, Panel A, we aggregate within the pre- and post-crisis periods and compare the average yield to maturity of bonds of different issuance sizes for the two time periods. We observe that, on average, yield to maturity decreases with issuance size. More importantly, consistent with Hypothesis 1, after 2008, we observe a sharp
We therefore replicate our baseline regression as presented in equation (3) and use, again, the Ackerman, plus the Hsieh and Klenow specification of TFP to see whether any difference appears. Again we use both types of input‐output coefficients to check the stability of our results. Table 8 shows the results and confirm the fact that conduct barriers in services are in large part the most important issue that inhibits downstream firms from reaping higher economic benefits in terms of productivity. Yet, this results does not hold up all the time. In fact, using Ackerberg this variable only becomes marginally significant in the left‐hand panel when employing country‐specific input‐output tables while EU‐wide tables do not bring any significant results on this conduct variable at all. Instead, FDI restrictions comes out as significantly strong. In contrast, the Hsieh and Klenow measure of TFP provides robust evidence that conduct barriers are indeed the main regulatory index variable that affects TFP in downstream sectors which therefore substantiates our earlier findings.

# **4.2.4 Manufacturing Firms**

So far, all firm‐level studies mentioned in our literature review have used manufacturing firms as the only downstream sector without taking downstream services into account. For some developing economies this is understandable as their manufacturing sector is relatively more important compared to their services activities in terms of value‐added. Yet, for developed economies this inter‐sectoral relationship changes into one where the services sector is generating more importance. Seen in this light, it is reasonable to expect that services sectors themselves are the main users of services as inputs. Indeed, our input‐output tables show that when taking stock of all services sectors in the economy, on average the manufacturing sector takes up only 21.6 percent of all services as inputs, while the services sector absorbs around 63 percent of this total input use from services. However, we are interested whether our results are stable when taking out services firms so that we are left with the sole situation in
were able to attract and retain customers and earn enough income to keep them afloat (Octavia, 2021).

As Indonesia recovers from the pandemic, the digital economy will play an increasingly important role for both informal enterprises and workers. While it has already benefited some groups, the key challenge now lies in ensuring that these benefits are shared more widely. Accomplishing this will require concerted effort on several fronts, including increasing internet penetration in rural areas where internet access is only half of that in urban areas (OECD, 2021). Moreover, Indonesians in the top 10 percent of the income distribution are 5 times more likely to be connected to the internet than those in the lowest 10 percent (Tiwari et. al 2021). There is also a large urban-rural divide in mobile phone usage for work: 33.6 percent in rural areas relative to 57.7 percent in urban areas (SAKERNAS, 2022).<sup>15</sup> In addition, many Indonesians lack the skills needed to navigate the digital economy effectively (OECD, 2021). Some informal workers, for example, may not know how to use certain platforms such as social media to promote their work (Octavia, 2021). Policy therefore has a critical role to play in bridging these digital inequalities.

Evidently, the pandemic not only exacerbated pre-existing inequalities between the formal and informal segments of the economy, but also within the informal economy itself. In other words, the pandemic contributed to the dualistic nature of informality in Indonesia – increasing both ‘bad’ informal jobs as well as ‘good’ informal jobs. Nonetheless, the pandemic has also reinforced some of the benefits that could be gained from formalization. For example, it is easier for the government to provide assistance to formal enterprises and workers who are registered in national databases. In contrast, individual-level data on informal workers is largely absent in Indonesia, which makes it extremely difficult for the government to assist informal workers in times of crisis (Wihardja & Cunningham, 2021
# Database

# Table A.3.1 List of countries

|Country group|Countries|
|---|---|
|Advanced economies<br>(24)|Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Ireland, Israel, Italy,<br>Japan, Korea, the Netherlands, New Zealand, Norway, Portugal, Singapore, Spain, Sweden, Switzerland,<br>the United Kingdom, the United States|
||Argentina, Bangladesh, Brazil, Chile, China, Colombia, the Arab Republic of Egypt, India, Indonesia, the|
|EMDEs (23)|Islamic Republic of Iran, Kuwait, Malaysia, Mexico, Pakistan, Peru, Poland, the Russian Federation,<br>Saudi Arabia, South Africa, Thailand, Tunisia, Turkey, Zambia|

Note: The numbers in parentheses are the number of countries in the sample. EMDEs = emerging market and developing economies.

# Table A.3.2 Description of the variables

|Variable|Description|Sources|
|---|---|---|
||Current year, one-year-ahead, five-year-ahead inflation forecasts based||
|Inflation expectations<br>(advanced economies:<br>1990H1-2018H1<br>EMDEs: 1995H1-2018H1)|<br>on surveys conducted biannually for 30 countries by Consensus<br>Economics, complemented by current year, one-year-ahead, and five-<br>year-ahead annual average headline CPI inflation forecasts produced<br>biannually for 17 countries in the IMFs World Economic Outlook<br>database.|Consensus Economics,<br>Consensus Forecast;<br>IMF, World Economic<br>Outlook database|
|Inflation targeting regime<br>(1995-2016)|<br>A dummy variable equal to 1 for inflation targeting regime and 0 for no<br>inflation targeting regime.|IMF, Annual Report on<br>Exchange Arrangements and<br