rafmacalaba/datause-encoder-data huggingface dataset:
productivity growth only during recessionary periods, while it tends to have a negative effect on productivity growth during economic expansionary periods. Hahn (2000) also finds that net entry accounts for a greater proportion of aggregate productivity growth during economic contractions. In terms of the other components of productivity growth, Baily et al. (1992) and Hahn (2000) find that the _within effect_ tends to be larger during cyclical upturns. To investigate the sensitivity of our decomposition results to the business cycle, we include real growth rate data for the manufacturing sector over the period of analysis. Consistent with these previous studies, the _within effect_ appears to vary pro-cyclically (the effect is considerably stronger using annual decompositions given the short duration of the economic fluctations.). In contrast, there is little evidence of cyclical variation in the between-firm component, and weak pro-cyclical effects of net entry. The combination of the pro-cyclical movements of the _within effect_ and net entry, net entry does not account for a greater proportion of aggregate productivity growth during economic contractions.

This manufacturing-wide analysis may be obscuring important differences in the drivers of productivity changes between industries. To investigate this possibility, in Figure 4c, we present the Baily et al. (2002) TFP decomposition for seven sectors.<sup>24</sup> There are considerable disparities between sectors with the net entry effect being positive in 4, but negative or insignificant in 3. In contrast, the _within effect_ appears to have a consistently negative influence across the sectors but food & tobacco.

The findings from the job creation and destruction indicated that there were more entrants than exiters, but that they were smaller, leaving net job creation relatively flat.

> 24 The labour productivity decomposition showed consistent results to the TFP decomposition and thus are not included in this paper, but are available from the authors upon request.

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Figure 2: Timeline: Baseline and Endline Data Collection <!-- Start of picture text --> Baseline<br>Movies<br>Movies + M−Learning<br>Follow−up<br>After COVID Follow−up<br>Dec 2018 Apr 2019 Jul 2019Sep 2019 05 Mar 202021 Mar 2020 05 Sep 2020 05 Dec 2020<br><!-- End of picture text --> have worked through, we also measured parental aspirations and expectations for their children’s futures, parental self-efficacy beliefs, perception of social norms and parental time allocation for home-learning activities. Finally, to study potential spillovers on siblings and other household members, the baseline and follow-up surveys collected data on school enrollment, learning outcomes, early parenthood, and early insertion in the labor market.

To measure the proportion of out-of-school children, we use two questions depending on whether follow-up data was collected before or after COVID-19 closures. Prior to March 2020, we asked parents if the target child was attending formal schooling (this represented 21% of the overall sample). For the remaining participants, and once the follow-up data collection resumed in September 2020, we asked a retrospective question about the child attending formal education between January-March 2020. See Section 5.5 for more information about the implications of COVID-19 and our robustness checks.<sup>9</sup>

> 9Because our main outcome on out-of-school children is based on parents’ responses, our measure might be subject to social desirability biases or experimenter-demand effects. Data checks of parental responses with school registry records were not possible in our study due to implementation and cost constraints. To address this potential issue, we collected a ‘wisdom of crowd’ measure that does not rely on direct information about the target child, and therefore it is less sensitive to such biases. The ‘wisdom of crowds’ measure aggregated the responses to the following question: “Now I would like to ask you about girls/boys like [Child’s name]. Think now about 10 children who are the same age, gender and intelligence as [Child’s name]. These 10 children have parents with the same level of literacy, education and socio-demographic characteristics as you and your spouse/partner. How many of these 10 children do you think will attend school in January 2021? If you do not know the answer, try to give your best guess.” This question was adjusted for September 2020 if the household was interviewed in March 2020 and for January 2021 if the household was interviewed in September-December 2020, as a result of the pause in data collec-

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Whether employers take the external wage level into account depends to a large extent on the availability of information about the wages of similar jobs in that sector or region. In the US, external wages appear to be of little relevance for downward nominal wage rigidity, perhaps because unionization is low and information about external pay is scarce (Bewley, 2004). In contrast, Agell and Lundborg (2003) found substantial support to the external pay grade hypothesis in highly unionized Sweden.

# **3. Survey Design and Data Description**

# **_3.1 Survey Overview_**

The analysis in the current paper is based on a survey of firms that was conducted between the second half of 2007 and the first quarter of 2008 in 16 European Union countries, 14 of which included the questions analysed here on the reasons for avoiding wage cuts. The 14 countries covered are Austria, Belgium, the Czech Republic, Estonia, France, Hungary, Ireland, Italy, the Netherlands, Lithuania, Poland, Portugal, Slovenia and Spain.<sup>5</sup> The survey was carried out by the national central bank of each country and all countries based the survey on a harmonised questionnaire, which was developed in the context of the Eurosystem Wage Dynamics Network, a research network analysing wage and labour cost dynamics. The harmonised questionnaire contained a core set of questions on the firms’ wage setting strategies, which were asked in all countries and thus gives us a detailed and comparable picture of these issues across all countries. The harmonised questionnaire was further adapted by some countries to account for specific country characteristics and differences in the institutional frameworks. As a result, some countries opted for shorter versions of this questionnaire, while others extended it in several dimensions.

The sampling frame in each country was based on firms with at least five employees. The sectors covered are manufacturing, energy, construction, market services, non-market services, trade and financial intermediation.<sup>6</sup> The sample covers 14,975 firms representing around 47.3 million

> 5 The survey was conducted either by traditional mail, phone and face to face interviews or over the internet. The survey was addressed to the company’s CEO or senior-level human resources manager(s). Germany and Greece also conducted the survey, but with different questions on wage cuts and so they are not included in this paper.

> 6 There are however some differences in the sectoral coverage of individual countries – see the online appendix to Babecky et al. (2012) for full details.

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The storage model representing the world market is solved using this algorithm and a 50-node spline approximation. This spline approximation defines the continuous Markov chain (11) representing the world price dynamics. It is subsequently used in the storage-trade model as a transition equation, which means that it is a part of equation (66).

For the optimal trade policy, the method requires a few modifications, since equation (54) includes a derivative of _ψ_ , _ψ_ being an approximation of the behavior of price with respect to state variables. The calculation of this approximation is actually done in the above method in step 3. Since derivatives of _ψ_ appears only in expectations terms, to apply the method we have to make sure that the definition of the function _h_ includes these terms, which leads to a new equation (69): where Φ _s_ ( _s, θ_ ) is the derivative of Φ with respect to state variables.

This is only a sketch of the solution method. In fact, several methods are used in this paper to solve the models, depending on which one is the most efficient. For example, instead of using the simple updating rule in step 3, a Newton, or inexact Newton, updating is used when feasible. For more precisions, see the `RECS` solver ( `https://github.com/christophe-gouel/recs` ), with which the models are solved.

# **References**

- Ambler, S. and Pelgrin, F. (2010). Time-consistent control in non-linear models. _Journal of Economic Dynamics and Control_ , 34(10), 2215–2228.

- Anderson, J. E. and Riley, J. G. (1976). International trade with fluctuating prices. _International Economic Review_ , 17(1), 76–97.

- Anderson, K. and Nelgen, S. (2012). Trade barrier volatility and agricultural price stabilization. _World Development_ , 40(1), 36–48.

- Asian Development Bank (2008). _Food Prices and Inflation in Developing Asia: Is Poverty Reduction Coming to an End?_ Special report, ADB, Economics and Research Department, Manila.

- Babcock, B. A. and Hennessy, D. A. (1996). Input demand under yield and revenue insurance. _American Journal of Agricultural Economics_ , 78(2), 416–427.

- Bagwell, K. and Sykes, A. O. (2004). Chile – price band system and safeguard measures relating to certain agricultural products. _World Trade Review_ , 3(03), 507–528.

- Batra, R. N. and Russell, W. R. (1974). Gains from trade under uncertainty. _The American Economic Review_ , 64(6), 1040–1048.

- Bigman, D. and Reutlinger, S. (1979). Food price and supply stabilization: National buffer stocks and trade policies. _American Journal of Agricultural Economics_ , 61(4), 657–667.

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# **1. Introduction**

Measuring work accurately is crucial for policy making, especially in low- and middle-income countries where the employment gaps between women and men as well as youth and adults are particularly large.<sup>7</sup> Even though the gender- and age-based employment gaps can be explained by a variety of reasons, including structural changes exacerbated by the COVID-19 pandemic and persistent gender norms (Klasen, 2019; Goldin & Mitchell, 2017; Goldin et al., 2017); these gaps might also reflect, in part, the quality and composition of the underlying data, specifically the possible undermeasurement of women’s and youths’ labor market outcomes. In fact, existing evidence suggests that standard survey methods traditionally used to collect labor data may not adequately elicit accurate responses from some groups (Ambler et al., 2021; Bardasi et al., 2011; Arthi et al., 2018; Dillon, 2012; Discenza et al., 2021). For example, as evidence shows that women tend to underreport their work activities—due to social norms (Franck and Olsson, 2014), misunderstanding of housework and outside employment (Muller and Sousa, 2020), or the structure of labor modules (Discenza et al., 2021)—official labor data may reflect inaccurate measurements of their work.

There are at least two reasons that may explain why standard survey methods used to collect data on labor indicators potentially undermeasure women’s and youths’ labor market outcomes. First, standard labor modules included in household surveys are often better at collecting information on formal or regular activities and may fail to properly capture the informal and casual activities that are classified as work under current international guidelines. Respondents may not consider their informal activities, such as preparing food to sell or helping in a family-owned business, as work. Second, standard data collection protocols for household surveys allow for proxy respondents to provide responses on behalf of other household members. This practice may result in biased reports of labor indicators, particularly when the absent household member works in the informal sector and the proxy respondent is not aware of the household member’s labor activities. This would be the case, for example, for a male proxy

> 7 For example, in 2022, only 43.8% of women compared to 67.9% of men were employed (ILO, 2022). Similarly, only 43.7% of youth between the ages of 15 to 24 years in low-income countries are recorded as employed compared to 71.3% of adults (ILO, 2022).

2
pervisors are different from the non-exposed lines on observable characteristics measured before the start of the promotion program.<sup>43</sup> I use daily line-level production data we collected and harmonized from the factories for four important production outcomes: efficiency, alteration rates, absenteeism and product complexity. Before the start of the promotion program, the lines exposed to the new female supervisors had very similar production statistics as the non-exposed lines. These groups did not differ in their efficiency, their complexity of garments produced and their alteration rates. The exposed lines have only slightly lower absenteeism rates, about 0.34 percentage points lower than the non-exposed mean of 4.23%.

The main specification for the role model analysis is similar to the one I employ for the promotion analysis. For outcome _Y_ of each exposed and non-exposed subordinate operator _s_ in factory _f_ , _exposed_ is the covariate of interest and defined as a dummy variable indicating whether the individual was working on a line to which a selected participant of the promotion program was assigned, where the individual’s line code was collected at the factory follow-up for the promotion program. _exposed_ is equal to zero if the individual was working on a line unrelated to the promotion program.<sup>44</sup> Note that this comparison is likely to be a conservative estimate, since the female supervisor can be expected to have been visible to operators working on neighboring non-exposed lines, too. _X_ is a vector of potential control variables, which consists of all 15 variables for which I tested balance on in Table 1 and their squared terms, in addition to factory, enumerator and month dummy variables. The set of control variables to be included are selected using the PDS Lasso algorithm as explained in Section 3.5.1. Standard errors are clustered at the production-line level, following Abadie et al. (2017). I again limit the sample to those women who report having another adult decision-maker in the household.

# **4 The effects of a promotion**

## **4.1 Promotions and income**

I now present the impacts of the promotion program on selected women, estimated for the outcomes defined in Section 3.3 using Equation 1. The first step of the analysis confirms that the

> 43Again, I only use lines of operators who are actually included in the analysis, to account for attrition. See Table C.8 in the Online Appendix for these tests, and Online Appendix Table C.4 for definitions.

> 44This means that no selected participant for the promotion program was assigned to that line and no selected participant was found to be working on that line at the survey follow-up for the promotion program.

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U.S., the U.K. and Canada (e.g., Stafford, 1985; Moffitt, 1992; Blundell and MaCurdy, 1999; and Widerquist, 2005). In developing countries, however, the evidence regarding the labor supply responses of adults to transfer programs is quite scarce. The study of Sahn and Alderman (1996), one of the rare studies in this topic, suggests that the labor supply effect of a rice subsidy program in Sri Lanka is significantly large. Yet, in a recently published ex-ante microsimulation study of the impact of the Bolsa Escola program on poverty in Brazil, the income effect of the transfers on adult labor supply is assumed to be negligible (Bourguignon, Ferreira, and Leite, 2003).

Our paper sheds light on these issues using data from a large conditional cash transfer program in the poor rural areas of Mexico called PROGRESA (Health, Education and Nutrition Program). With a cash transfer of 20% of pre-program consumption, PROGRESA has the potential of affecting adult work incentives of both program participants as well as nonparticipants. For eligible households, the income effect of the cash transfer may be weakened by the direct and indirect time costs associated with adhering to the requirements of the program. In addition, the means testing associated with CCT programs may also affect the incentives of both eligible and non-eligible households. On the one hand, individuals who are not eligible for the program’s benefits may also have the incentive to work less or earn (or report) a lower income hoping to become eligible for the program in future rounds of expansion of the program. On the other hand, the possibility of future means-tests may impact on the labor supply and investment choices of currently eligible households.

The empirical analysis uses panel data from households surveyed for the purpose of evaluating the impact of PROGRESA on basic indicators of household investment in human 2
4

**_5._** Such a matrix of conversion factors and corresponding weights could provide the mechanism for the following: (a) a systematic approach to the weighting of alternative rates; **(b)** estimating the effects of alternative weighting schemes, (c) determine what would be the effects of incorporating parallel or black market exchange rates; **(d)** provide the basis for less erratic and unpredictable fluctuations in the data shown in the Atlas, Worid Tables etc. and (e) improved and more transparent documentation of methods and "special cases."

# Scope **of Paper**

**6.** The focus is on conversion factors that are as representative as practicable of transaction or "effective" conversion factors. The choice of an appropriate conversion factor is based on the clear principle that the rate chosen must be that at which foreign transactions are effected. The Bank, like the Fund or any other international institution, would prefer to convert national currency data into a common numeraire at prevailing market exchange rate.<sup>4</sup> Determining the prevailing market rate, however, is a matter of judgment, even for international transactions and in the absence of exchange and trade restrictions. It must be stressed at the outset that much is beyond the scope of this paper.In particular, the search here is not for an "equilibrium" exchange rate or that which is sustainable and compatible with reasonable economic growth and a viable balance of payments situation in the medium term. Suffice to _-- v,_ countries are seldom in equilibrium and the labelling of an exchange rate as "being in equilibrium" is usually associated,

> **'** The technical issues of using the period average of the official exchange rate as a convenient and readily available conversion factor, and the primary requirements for moving from shorter period to longer period rates are discussed in Section VIII.
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With this reconstruction pathway, total _non-discounted_ output losses ̅̅̅̅ are equal to (see Figure 5): The parameter N is the reconstruction period and it can often be estimated by experts based on past experience. 9F<sup>10</sup> Note that the reconstruction time is not the time when the observed GDP or output returns to its pre-disaster value, but may be much longer. Indeed, GDP and output are affected by other mechanisms, including changes in labor productivity, trade effects, other investments, and possibly the “stimulus effect” of the disaster. In this framework, as mentioned in Section 2.2.5, the stimulus effect is not accounted for in disaster consequences since it corresponds to benefits that could have been captured in the absence of the disaster, through a classical stimulus policy.

The length of the reconstruction period will depend on many characteristics of the affected economy, including (1) the capacity of the sectors involved in the reconstruction process (especially the construction sector); (2) the flexibility of the economy and its ability to mobilize resources for reconstruction (e.g., the ability of workers to move to the construction sector, see Hallegatte, 2008); (3) the openness of the economy and its ability to access resources (e.g., skilled workers and materials for reconstruction); (4) the financial strength of private actors, households and firms, and their ability to access financial resources for reconstruction, through savings, insurance claims, or credit; and (5) the financial strength of the public sector and its ability to access financial resources to reconstruct (see the very thorough analysis of financing options in developing countries in Mechler, 2004). 10F<sup>11</sup> Taking an example of a disaster that makes capital losses equal to $500 million, in a country with a 10% interest rate, with a reconstruction period that is likely to span over 3 years would lead to output losses equal to $150 million (i.e. 30% of direct capital losses).

In the context of Figure 4 (hurricanes in Louisiana), this rule of thumb reproduces model results, assuming that losses less than $50 billion can be repaired in one year, and that losses amounting to $100, $150, $200, and $250 billion can be repaired in 5, 10, 12, and 15 years, respectively.

With discounting at a rate r, the net present value of output losses is F<sup>12</sup> :

# **2.4 Welfare losses and macroeconomic resilience**

Losses in economic output do not directly affect people’s welfare; for households, what matters most is consumption. 12F<sup>13</sup> It is thus important to investigate how output losses translate into consumption losses

> 10 Production losses also depend on the reconstruction pathway. Here we assume an exponential reconstruction; if 1 the reconstruction is linear in N years, then Eq.(11) becomes ̅̅̅̅ ~~.~~ μ

> 11 Specific instruments such as contingent credit lines help with reconstruction financing. See for instance on the World Bank’s Cat-DDO, http://treasury.worldbank.org/bdm/pdf/Handouts_Finance/CatDDO_Product_Note.pdf.

> 12 The variable noted with x̃ are the net present value of the future fluxes of x t .

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