rafmacalaba/datause-encoder-data huggingface dataset:
also some evidence that treated firms reduced the employment of regular (non-contract) workers. Overall, the amendment increased the share of contract workers.

We also find that firms exempted from IDA Chapter V-B behaved as if they now faced a lower implicit regulatory tax on labor. We infer this implicit cost using a method based on the firm’s optimal cost minimization conditions which makes minimal assumptions on the production function and market demand (Chaurey, Manghnani, Perego and Sharma 2019).<sup>10</sup> We estimate a 13 percent reduction in the implicit labor tax, driven by a lower implicit tax on contract labor specifically.

We present a variety of robustness checks on these results regarding the contract labor share and the implicit labor tax. A dynamic event study specification confirms that the results are not due to differential pre-treatment trends across factories of different sizes. The results are robust to using only firms from neighboring states or states with similar per capita GDP in the control group. Speaking to the concern that firms may have strategically under-reported their employment in 2014-15 in anticipation of the policy change, we show that the main results are robust to redefining treatment status based on the firm’s average employment in the three most recent pre-treatment years.

The absence of an impact on total employment and output per worker is unexpected in light of the prevailing message in the literature that the IDA has stunted Indian manufacturing firms. However, it is worth noting that theory is ambiguous about the aggregate employment effects of reducing employment protection, at least in the short run. When labor adjustment becomes easier, labor flows should increase but the change in net equilibrium employment is not necessarily positive (Bentolila and Bertola (1999) and Hopenhayn and Rogerson (1993)).

The positive impact on contract hiring is more puzzling, since the IDA amendment should have reduced the relative appeal of contract workers. The obvious suspect behind this finding is the simultaneous easing of the threshold at which the CLA applies, since this law imposes certain restrictions on how contract labor may be used. But as we show, the estimated positive impact of the IDA amendment on contract hiring is larger among treated firms that still remained subject to the CLA, as compared to those newly exempted from it. Hence, the CLA amendment cannot explain our findings.

It could be that among firms with 100-300 workers, the repeal of IDA Chapter V-B was interpreted as a general relaxation in the enforcement of labor regulations, including those restrictive

> 10Our conceptualization of the implicit labor tax is similar to that in Hsieh and Klenow (2009).

3
**Table 6. Summary of other food and non-food items included in the national consumption**

||||**ag**|**gregat**|**es**||||
|---|---|---|---|---|---|---|---|---|
||**Afghanistan**|**Bangladesh**|**Bhutan**|**India**|**Maldives**|**Nepal**|**Pakistan**|**Sri Lanka**|
||||Food|expendi|tures||||
|Is self-<br>|||||||||
|production<br>accounted for?<sup>**(1)**</sup><br>Are meals<br>|Yes<sup>(1)</sup>|Yes<sup>(1)</sup>|Yes<sup>(2)</sup>|Yes|No<sup>1)</sup>|Yes|Yes|Yes|
|outside the<br>household<br>accounted for?|Yes|No<sup>(3)</sup>|Yes<sup>(4)</sup>|Yes<sup>(5)</sup>|Yes<sup>(6)</sup>|Yes<sup>(7)</sup>|No|No|
||||Non-fo|od expen|ditures||||
|Number of items|||||||||
|<br>included||193|85|286|401|62|74|47|
|Number of items<br>excluded||10|26|0|28|35|31|35|
|Are consumer|||||||||
|durables<br>accounted for?|Yes|Yes<sup>(8)</sup>|Yes<sup>(8)</sup>|Yes<sup>(8)</sup>|No|Yes|No|Yes<sup>(8)</sup>|
|||||Housing|||||
|Actual rent|||||||||
|<br>included?|Yes|Yes|Yes|Yes|No|Yes|Yes|Yes|
|Imputed rent<br>included?|Yes|Yes<sup>(9)</sup>|Yes<sup>(9)</sup>|No|No|Yes<sup>(10)</sup>|Yes<sup>(9)</sup>|Yes<sup>(9)</sup>|
||||Healt|h and edu|cation||||
|Health|||||||||
|<br>expenditures?<br>|No|Yes|Yes|Yes|Yes|No|Yes|Yes|
|Education<br>exenditures?|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|p||||Others|Occasional||||
|Main Items<br>Excluded|Weddings,<br>celebrations,<br>donations,<br>talisman and<br>other<br>miscellaneous<br>expenditures|Occasional<br>expenditures,<br>income tax,<br>interest<br>charges and<br>insurance.|Agricultu<br>ral input,<br>tax and<br>insurance<br>and other.|<br>None|expenditures<br>, durables,<br>rent,<br>mortgage<br>and loan<br>brokerage<br>services|Occasional<br>expenses<sup>(11)</sup>|Occasional<br>expenditures,<br>durables, tax,<br>annual items<sup>(12)</sup>|Occasional<br>expenditures,<br>tax/insurance/<br>contribution to|

Source: Authors’ own estimates based on South Asia Harmonized Micro Dataset (SARMD).

(1) Total consumption, including self-production, is reported in one question per item. In other countries, market and self-production are reported separately for each item.

(2) Self production is accounted for also among non-food items (Block 10 of the questionnaire).

(3) Questionnaire: Section 9A2 item 14.

(4) Questionnaire: variables at the end of Block 8 (5) Questionnaire: Block 12, served processed food and packaged processed food.

(6) Coicop: 1111.

(7) Section 5: coicop 131.

(8) No consumption flow estimated, expenditure items included at purchase price.

(9) Questionnaire reports market equivalent values.

(10) Questionnaire reports market equivalent values; outliers and missing imputed using prediction from hedonic regressions.

(11) Some of the infrequent expenses have been kept, most notably legal expenses and insurance.

(12) Annual license fees (TV / VCR / dish antina etc.); Annual license fee for arms etc.

Accounting for meals eaten outside of the household can have huge implications on poverty rates. A study from Peru showed that including the consumption of food eaten outside home raises the extreme poverty rate by 18 percent (Farfan et al., 2015). Furthermore, most household surveys 10
<!-- Start of picture text --> 2<br>uw<br>NS<br>om<br>a<br>=<br>2<br>ra<br>a<br>Eg<br>fai] TT<br>><br>£<br>Ke)<br>=<br>=o<br>$e<br>fo)<br>ite}<br>Without feedback With Feedback<br>Nolop [NN lop. Low value zip code «= I) 10. High value zip code<br><!-- End of picture text -->
8

1. Wealth portfolios include capital goods, financial assets and other assets, notably real estate and land. Large real inc'eases in property prices--spurred perhaps by repressing returns on other assets--may indeed impact adversely on domestic savings rates, essentially because of consumption out of capital gains; see Tobin, 1966.14 This introduces a relationship between interest rates, savings and investment (as measured in the national accounts) and growth. But another "stylized fact" of development is that, over the long term, real prices of fixed factors such as land also tend to increase at least as rapidly as the rate of economic growth and sometimes more rapidly. Such a process can be represented in a Cobb-Douglas or CES growth model with land entering into production and fixed in total quantity: land rent then represents a constant or rising percentage of GDP. This suggests that, in a rapidly growing economy, interest rates would need to be higher for financial assets to compete wit land and real estate in wealth portfolios than in one which grew slowly for otner reasons.

2. Growth models provide further arguments for the long-run endogeneity of the interest rate. Abstracting from the additional liquidity and convenience attributes of financial assets (and from distress borrowing), their return should be closely related to the rate of profit from owning physical capital. Over a range of growth models, the steady-state growth model which maximizes consumption per head (the "golden rule") is characterized by the identity of the rates of growth and profit; for discussion, see Hahn and Matthews, 1969. This holds regardless of the number of commodities in the model and the savings assumptions made, and a similar feature is characteristic of more recent optimum growth models. Higher growth would therefore be expected to be reflected in higher interest rates and, given the extra convenience and liquidity yields of financial assets, a situation where interest rates on these exceeded growth rates would probably be unsustainable over the long run.

Economies with favorable preconditions for growth (including other aspects of the policy framework such as the trade regime) will therefore tend to have higher profit and interest rates. This, indeed, is the mechanism which would be expected to reallocate global fina *Na towards the more rapidly growing economies. Rapidly growing economies would, on this view, tend to run 14 For one example of the relationship between cheap credit availability and real land prices see World Bank, 1985, pp. 126-127. The impact of higher capital gains on residential property on falliT.g levels of household savings in the early I970s was a concern for the US in the 1970s. See also Summers, 1981.

On the other hand, the relationship between property prices and financial resource mobilization may itself depend on financial market structure. If mortgage finance is not easily available, it is sometimes argued (as in the case of Japan) that increasing property prices may stimulate savings towards downpayments for the purchase of property.
channel). Second, reforms to interbank payment systems enable banks to economize on their holdings of excess reserves (reserves channel).

Overall, we ...nd strong evidence that the ...rst of these channels is at work. We ...nd payment systems reforms to be associated with a trend increase in credit. We also ...nd that payment systems reforms decrease the amount of cash used in the economy, relative to bank deposits. Moreover, further evidence suggests that these two ...ndings are unlikely to be coincidental: negative shocks to the currency ratio result more generally in an increase in credit. As regards the reserves channel, we ...nd less compelling evidence. We do not ...nd payment systems reforms to be associated with a reduction in excess reserves. Moreover, we do not ...nd that changes in the excess reserves ratio results more generally in changes in credit.

Taken together, these ...ndings may have broader implications for our understanding of ...nancial intermediation, highlighting the nexus between the role of banks in the provision of payment services and their role in the creation of credit. By contrast, our ...ndings shed doubt on the relevance of central bank reserves in determining the amount of credit extended by banks, questioning traditional accounts of the "bank lending channel" of monetary policy.

# References

- [1] Agenor, P. R., Aizenmann, J., Ho¤maister, A., 2004. The credit crunch in East Asia: what can bank excess liquid assets tell us?. Journal of International Money and Finance 23, 27-49.

- [2] Balino, T., Dhawan, J., Sundararadjan ,V., 1994. Payments systems reforms and monetary policy in emerging market economies in Central and Eastern Europe. IMF Sta¤ Papers 41, 383-410.

- [3] Barth, J.R., Caprio, G., Levine, R., 2006. Rethinking bank regulation - till angels govern. Cambridge: Cambridge University Press.

- [4] Berlin, M., Mester, L., 1999. Deposits and relationship lending. Review of Financial Studies 12, 579-607.

- [5] Bernanke, B., Blinder, A., 1988. Credit, money, and aggregate demand. American Economic Review papers and proceedings 78, 435-439.

- [6] Bertrand, M., Schoar, A.,Thesmar, D., 2007. Banking deregulation and industry structure: evidence from the french banking reforms of 1985. MIT, mimeo.

- [7] Boyd, J., Levine, R., Smith, D. B., 2001. The impact of in‡ation on ...nancial sector performance. Journal of Monetary Economics 47, 221-48.

18
be regressive as the poor pay a higher share of their income as bribes. On the other hand, Hunt (2010) reports evidence suggesting that corruption in health care in Uganda is progressive both at the intensive and extensive margins. Hunt and Laszlo (2012) ...nd that bribery is not regressive in Uganda and Peru. Hunt (2008) shows that the distributional e¤ects of bribes in Peru depend on the public service one considers. She ...nds that bribery is regressive for users of police service, but it is progressive for users of the judiciary. Mocan (2008), using household data from a number of countries, shows that the higher income households are more likely to face a demand for a bribe in developing countries, but the e¤ect is not signi...cant in developed countries.<sup>18</sup> In an important paper on corruption faced by ...rms, Svensson (2003) carefully considers the identi...cation issues, and ...nds that the bribe amount paid by ...rms in Uganda increases with its pro...t (“ability to pay"). However, the slope of the bribe function, although positive, is not steep.

With the exception of Hunt and Laszlo (2012) and Svensson (2003), much of the evidence on the relationship between the bribe amount and household income (or ...rm pro...t) is based on OLS regressions; they do not address the biases due to unobserved heterogeneity and measurement errors. In the context of corruption faced by households, Hunt and Laszlo (2012) take a step forward and correct for the biases due to measurement error by using household wealth indicators as instruments, but their strategy is not designed to tackle the omitted variables bias arising from unobserved heterogeneity in preference and ability.

# (3) Conceptual Framework

To guide the empirical work, we use a simple model of bribery for admission into school. The focus of our analysis is on two things: (i) to understand under what conditions we can expect bribes to be progressive (or conversely, regressive), and (ii) to sort out the implications of alternative assumptions regarding bargaining power and information structure for the empirical analysis. We discuss progressivity both at the extensive and intensive margins. If bribes are progressive at the extensive margin, the probability that a household pays bribes for education of its children should be lower for the low-income households. At the intensive margin, the standard

> 18 It has been noted in the literature that the rich may be more likely to pay bribes, because they enjoy more public services (Hunt and Laszlo (2012), Mocan (2008)). For example, most of the poor never face the possibility of paying bribes for a passport, because they do not travel internationally. It is thus important to focus on a given service that is used by both rich and poor (schooling in our case) to better understand the distributional consequences of bribes.

8
-4.

productivity effects on the opportunity cost of the mother's time and thus affects desired family size. It also improves the chances of child survival and so indirectly affects desired family size. Finally, education builds ability as well as willingness to use the available modern methods of contraception effectively and thus helps people achieve their desired family size. Family planning services enable couples more readily to achieve (lower) desired family size and over time may influence desired family size itself by affecting infant deaths (and perhaps also social norms).

2.6 From a policy perspective, it would be valuable to know more about the relative importance of the many pathways through which female education and family planning program services influence fertility. But such a structural model is extremely difficult to estimate because of data and econometric limitations. First and most important, DFS caanot be observed objectively. It is essentially an opinion, and people's responses may be influenced by the number of children they already have. Many a couple may be reluctant to say they did not want the children they already have. Second, country-level data sources are often not suitable for comparison as the type of canvassing and questions asked of the respondents do not always match. Therefore, most analysts feel much more comfortable banking on objectively observed fertility. Moreover, the task of estimating a structural model with crossnational data is even more difficult because the (admittedly unsatisfactory) data on DFS is available only for a small set of 37 countries which have had World Fertility Surveys. Finally, even if more satisfactory data on DFS were available for a wider sample of countries, it is difficult to find identifying exogenous variables, especially in cross-national data sets, that permit distinguishing the influences on desired family size, child survival, and ability to achieve desired family size. Female education clearly affects all three. Many analysts therefore rely on reduced form estimation of the TFR, as do we. Thus, we can determine the net impact of female secondary education and other exogenous variables but not precise pathways.
effect than automating plants from the increased demand for inputs, although in this case the backward linkage effect on employment is positive also for automating plants. The coefficient of exposure to automation is again positive and significant.

As the sample in columns (2) and (3) is smaller than that in table 5 (given the availability of plant level input data), to check that this is not driving the change in the _ETR_ coefficient, in column (4) we replicate the result of table 5 (column 1) but over the smaller sample. The coefficient of exposure to automation is negative and significant and even larger in absolute term than in table 5, thus lending further support to the hypothesis that the negative _ETR_ coefficient on employment is driven by the positive vertical linkages. The results are robust also to using directly the exogenous instruments in an OLS regression so as to avoid having to instrument two endogenous variables (column 5).

The improved performance of plants induced by automation does not only result in increased demand for goods, but also of services. This is documented in column (6) which uses services’ expenditure as dependent variable. The positive and significant coefficient suggests that plants adopting robots spend additional resources on professional services, which include R&D and marketing consulting.

Table 7: Plant level impact of robots on domestic inputs’ expenditures and employment: vertical linkages.

|VARIABLES|(1)<br>Domestic<br>inputs|(2)<br>Employment|(3)<br>Employment|(4)<br>Employment|(5)<br>Employment|(6)<br>Services|
|---|---|---|---|---|---|---|
|ETR _×_ secondary|0.064***|0.028**|0.018**|-0.014**|-0.003|0.064*|
||(0.010)|(0.008)|(0.006)|(0.005)|(0.004)|(0.033)|
|Downstream ETR||0.020|0.036**||||
|||(0.017)|(0.012)||||
|Downstream ETR _×_ ETR _×_ secondary||-0.018***<br>(0.001)|-0.014***<br>(0.003)||||
|Downstream ETR (oecd)|||||0.002**<br>(0.000)||
|Downstream ETR (oecd) _×_ ETR _×_ secondary|||||-0.001***<br>(0.000)||
|Observations|53,517|40,958|40,958|40,958|40,958|53,517|
|R-squared|0.867|0.940||0.940|0.940|0.710|
|Plant FE|yes|yes|yes|yes|yes|yes|
|Industry-year FE|yes|yes|yes|yes|yes|yes|
|Other technologies|yes|yes|yes|yes|yes|yes|
|First stage F-stat|||13.78||||

The table presents OLS and 2SLS estimates of the relationship between plants’ exposure to robots / downstream exposure, log real domestic inputs’ expenditures and employment. For the 2SLS estimates, exposure to robots is instrumented with the average exposure in the OECD region. Secondary is the 2006 share of plants’ employment with secondary education. Standard errors are clustered at the 2-digit industryand year-level. The coefficients with<sup>_⋆⋆⋆_</sup> are significant at the 1% level, with<sup>_⋆⋆_</sup> are significant at the 5% level, and with<sup>_⋆_</sup> are significant at the 10% level.

23
but this likely depends on a firm’s legal status (Dewaelheyns and Cynthia Van Hulle (2008)). One of the advantages of the broad approach we utilize here is to leverage other global, comparable data, such as the World Bank’s Doing Business database, while also considering several macro-level controls.

# **III. Data and Methodology**

# **<u>The Data and Measuring Exit</u>**

We use data taken from the World Bank’s Enterprise Surveys (ES) project <u>(www.enterprisesurveys.org). Since 2006, the World Bank has consolidated – under one centralized</u> methodology – the collection of these firm-level data. The surveys are conducted on a semi-regular basis, usually with a cycle of 4 to 5 years between rounds, and they cover a wide range of topics affecting the business environment and firm performance. One key facet of this harmonized methodology is that the ES maintain a consistent and clearly defined universe of inference, defined as formal,<sup>5</sup> private-sector establishments with at least 5 employees, operating in manufacturing or select service sectors.<sup>6</sup> The ES data, moreover, take advantage of this single and comparable universe to be representative of the national private-sector economy; accordingly, all estimates here are surveyweighted unless otherwise stated.

A key priority in the collection of the ES data is the maintenance of panel data, allowing researchers to investigate changes in the business environment over time. While the construction of these panels is the key focus of data collection, meticulous effort is simultaneously taken to gather information on the operating (or eligibility) status of all firms previously interviewed, including those that have ceased operations. Firms’ so-called eligibility status is determined and assigned in an initial screening phase, which occurs prior to the full ES interview, independent of the current-round survey design or a firm’s willingness to participate. This ensures that information on the representative sample of firms from the previous round is captured, allowing for an indicator of the firm survival/exit dynamics over the elapsed time between the two survey rounds.

In practice, though, determining firms’ eligibility status can be challenging. Firms operate within the tectonics of often-shifting business environments, and their exact status can be difficult to

> 5 Though several disparate measures of formality have been used in the literature, the ES methodology define “formal” enterprises as those registered with the relevant authority, often the tax authority or relevant business registry.

> 6 Sectors of activity are defined by their ISIC Rev. 3.1 classification and include all manufacturing under Group D as well as service sectors comprised of Group F (construction), Group G (wholesale and retail trade), Group H (hotels and restaurants), Group I (transportation), as well as IT firms (ISIC code 72).

9
**Table 1.4: Effects of Natural Disasters on Macroeconomic Variables**

|**MacroeconomicIndicators**|**Expected Post-DisasterEffect**|
|---|---|
|Physical Stock|Destructionofcapitalstock followedbyreconstruction.|
|1.Production(2+3+4)|Decreasefollowedbyincrease.|
|1.1Agriculture|Significantdrop**in**production(ifhurrcane,floodordrought).|
|1.2Industry|Decrease duetodisruptionoftransportation,reducedcapitalstock and production<br>capacities.|
|1.3Service|Decreaseduetodisruptionoftransportationandpaymentsystem.|
|2.Consumption|Decreasedueto declineinproduction|
|2.1Private|Decrease duetodeclineinproduction.|
|2.2Public|Decreasesomewhatmitigatedbyex-post intemationalfinancingand counter-<br>cyclical fiscalpolicy.|
|3.Investment|Decreaseininvestmentsintheaftermathofthedisaster.|
|4.Net ExportsofGoodsand<br>Services|Decreaseinexports duetodisruptionofproduction process includingtransportation<br>infrastructure.<br>Increaseinimportsaspartofthereconstructionprocess.|
|PublicFinances|Increaseinfiscaldeficit duetoadeclineintaxrevenueslinkedtothedeclinein<br>productionand theincreaseinpublic expenditures for reconstruction.|
|Inflation Rate|Increasecausedbythedisruptionofproductionanddistributionandincreasing<br>transportationcosts.|

Source. Adapted from Albala-Betrand (1993) and Downing, Holstoom and Tol (1999). As reported in Charveriat (2000), Table 1.2 p. 16.

18