rafmacalaba/data-use-mentions-extended huggingface dataset:
private sector dynamics, as we are able to assess whether the regime change that accompanied the crisis differentially impacted firms connected with the Suharto regime.<sup>1</sup> The main findings can be summarized as follows: the data do not unequivocally support the cleansing paradigm. The crisis led to a spike in exit, a slowdown in entry, and excessive employment reallocation. Productive firms on average experienced lower employment losses, but the correlation between productivity and employment growth did not strengthen. Rather than raising the productivity threshold for survival, the crisis was more indiscriminate in terms of the productivity of firms driven out of business. Firms more vulnerable to changing credit market conditions were much more likely to exit during the crisis, but the attenuation of the link between productivity and survival did not increase with vulnerability to changing loan conditions. The attenuation was also not an artifact of reduced entry rates or driven by political transition. The results are robust to excluding firms with connection to the Suharto regime. Moreover, if anything, among the politically connected firms, productivity became a more important determinant of firm survival. By contrast, the attenuation was particularly pronounced in provinces with comparatively high minimum wages, suggesting that labor regulations obstructed efficiency enhancing reallocation. The effects are not all negative, however. The protective power of productivity against exit was restored post-crisis, and the crisis appears to have weeded out the weakest potential entrants; while entry rates were lower during the crisis, those firms that entered were on average much more productive, which helped mitigate the loss in aggregate productivity.

The remainder of this paper is organized as follows. The next section reviews related literature and describes the context. Section 3 summarizes our hypotheses and explains our approach. Section 4 describes the data, while section 5 presents descriptive statistics on job flows and decompositions of aggregate productivity growth. Section 6, which constitutes the core of the paper, presents firm survival and employment growth models. A final section concludes.

> 1 We use the data on political connections collected
In contrast, in Henan and Sichuan, a good proportion of in-migrants worked as technician. For example, in Henan, about 20 percent of in-migrants worked as technician, higher than the percentage of town local population, which had only 12.55 percent of them working as technicians. The pattern also holds true in Sichuan. In addition, there were 28 percent and 34 percent of in-migrants working in service sector in towns in Henan and Sichuan provinces respectively, which were also higher than that for town general population.

Furthermore, among the town in-migrants in Henan and Sichuan, data of Table 12 do not suggest a severe labor market segregation, which exists between those inmigrants from same province and those from outside provinces as we observed in Zhejiang data. For example, generally there are more in-migrants from other provinces working in the blue-collar production sector than in-migrants of same province in Henan (37.89% vs. 22.60%) and Sichuan (23.13% vs. 26.53%); however the difference was much smaller than that we observed in Zhejiang (83.31% vs. 43.24%) For the same token, although the percentage of workers engaging in the whitecollar occupations such as manager, technician, and sale-related jobs was lower among inter-province in-migrants than among the intra-province in-migrants, the gap is much smaller. All of this suggests that labor market is more flexible in Henan and Sichuan, whereas it is more segregated in Zhejiang.

Table 11 Occupation composition of towns in-migrants (15 years old or above) by migration status: 2000

|__________<br>province|__________________<br>type of population|_______<br>1|______<br>2|______<br>3|______<br>4|______<br>5|______<br>6|_______<br>7|
|---|---|---|---|---|---|--
related with the endogenous regressors and yet be orthogonal to the error term. I present the formal tests of instrument validity in Section 5.4 but I would like to provide some intuition here. Capital share is expected to be negatively related to the output/imports ratio (Qit/Mit) given that Colombia is more likely to produce products with smaller capital content and import those rich in capital based on a comparative advantage argument. The materials prices affect the domestic output prices, hence Qit/Mitεit but not the tariffs of a given sector i, conditional on Qit, Mit, and εit. Scale is positively correlated with productivity and it is an inherent characteristic of a sector. The productivity of a sector is also expected to be affected from the embodied upstream productivity which is likely to be independent of the sector’s own tariffs.

Since the model is quite parsimonious, it is also prone to an omitted variable bias. The use of the fixed industry effects in equation (18) and its different versions, and the first differencing in equation (19) and its different versions should alleviate this potential problem along with the Instrumental Variables (IV) estimation.

In the rest of this section I present and discuss the data, results, and robustness of my estimations. In Section 6, I test how accounting for the endogeneity of tariffs with respect to productivity affects the regressions that analyze the impact of trade reform on productivity.

# 5.2 Data

The base data for the estimations span 1982 through 1998 but given the lack of tariff and production information for certain years the sample reduces to 1983, 1985, and 1988-1998. The tariff and effective rate of protection (ERP) figures are obtained from DNP (National Planning Department) of Colombia at the 8-digit product level,<sup>39</sup> which are then aggregated to the 4-digit ISIC sectors by using simple averages.<sup>40</sup> The 4-digit ISIC level import data come from the COMTRADE dataset, United Nations Statistics
the Indian context. We also consider the share of the district population in a scheduled caste/tribe and the female labor force participation rate (e.g., Iyer et al. 2011, Klapper and Parker 2011).

Education and infrastructure are two factors consistently linked to India’s regional development.<sup>5</sup> We measure education level as the district’s percentage of adults with a graduate (post-secondary) degree and through literacy rates. Our infrastructure measures are the share of villages in a district with electricity access or paved roads. Finally, spatial locations relative to major population centers are frequently found to be important.<sup>6</sup> We thus include a measure from Lall et al. (2011) of the driving time from the central node of a district to the nearest of India’s ten largest cities<sup>7</sup> as a measure of physical connectivity and across-district infrastructure. This is calculated based on data on India’s road networks gathered using GIS software. We finally model the strength of the household banking sector for each district.

The univariate correlations in Table 4a are weak. The upper panel considers changes in the unorganized sector share. These basic traits do not connect systematically with unorganized share adjustments—the correlations except three are all less than 0.1 and statistically insignificant. The bottom panel considers growth in unorganized sector employment. Some of the correlations are stronger, with growth drivers identified in past work like education and infrastructure being again linked to unorganized sector growth. The comparison of the two panels reveals, however, that this is a general district growth effect that is not decreasing the unorganized sector’s share. Table 4b finds similar results when looking at industry traits and changes in the level of unorganized activity by industry.

The stability of these patterns suggests that the persistence of India’s unorganized sector is not due to a particular subset of regions or industries. For example, it is not the case
# **3.3 Model validation and AI impact channels**

Most of the six model propositions are intuitive and have been observed for an extended period. Rigorous empirical analysis, presented in Appendix A, strongly supports these propositions. The analysis draws upon multiple datasets covering the past seven decades, providing robust evidence for the model’s validity.

The model demonstrates how technology influences relative prices, nominal and real outputs, sectoral employment shares, as well as real income levels and growth trajectories. It also highlights the distinct effects of technology on large and small open economies through trade. Given that generative AI primarily enhances productivity in high-skill services, the model predicts the following:

- Impact on real income: If AI’s productivity gains are confined to the high-skill service sector, the aggregate growth effect will be limited, as this sector represents a relatively small share of total output and employment. However, AI’s growth impact will be significantly greater if it drives productivity increases across all sectors.

- Impact on sectoral composition: AI’s effect on sectoral employment shares is ambiguous, contingent on its impact on real income and sectoral productivity. In general, AI tends to accelerate the shift toward low-skill services. If its productivity impact is most pronounced in high-skill services, as evidence suggests, it will result in an earlier and lower peak in high-skill services employment at a lower real income level. Similar to the phenomenon of premature deindustrialization, generative AI could lead to ”premature de-professionalization” in many developing countries.

- Impact on comparative advantage and trade: In large open economies, AI is unlikely to alter comparative advantage, as productivity changes are offset by corresponding shifts in relative prices. Over time, AI is expected to reduce employment in high-skill services and drive down global relative prices in this sector. For small open economies, AI presents an opportunity to gain comparative advantage in high-skill services, increasing production, exports, and employment in this sector.

The analysis above assumes that AI does not alter consumer preferences.
, E., Pinchasik, D. R., Amundsen, A. H., & Hagman, R. (2021). Facilitating adoption of electric buses through policy: Learnings from a trial in Norway. _Energy Policy_ , 155, 112310. https://doi.org/10.1016/j.enpol.2021.112310 Tiefenbeck, V., Staake, T., Roth, K., & Sachs, O. (2013). For better or for worse? Empirical evidence of moral licensing in a behavioral energy conservation campaign. _Energy Policy_ , 57, 160-171. https://doi.org/https://doi.org/10.1016/j.enpol.2013.01.021 Timilsina, G.R. (2020). _Demystifying the Costs of Electricity Generation Technologies_ . World Bank Policy Research Working Paper, WPS9303. Washington, DC: World Bank.

Timilsina, G.R. (2021). _Economics of Distributed Photovoltaics: An Illustration from Bangladesh_ . World Bank Policy Research Working Paper, WPS9699. Washington, DC: World Bank.

Tugcu, C. T., & Tiwari, A. K. (2016). Does renewable and/or non-renewable energy consumption matter for total factor productivity (TFP) growth? Evidence from the BRICS. _Renewable and Sustainable Energy Reviews_ , _65_ , 610-616. https://doi.org/10.1016/j.rser.2016.07.016 Ugur, M., Trushin, E., Solomon, E., & Guidi, F. (2016). R&D and productivity in OECD firms and industries: A hierarchical meta-regression analysis. _Research Policy_ , 45(10), 20692086. <u>https://doi.org/10.1016/j.respol.2016.08.001</u> 54
are likely to act as a barrier to the ability of citizens to migrate. The question which then arises is why they are so high. One possible explanation is that passport costs are high relative to income in poorly governed countries. This may be due to poor capacity to efficiently carry out bureaucratic procedures, a repressive desire to stop citizens traveling, or to the inability to collect government revenue through standard taxation procedures. To investigate the relationship between passport costs and government, we employ the Governance IV indicators for the

> 8 Note that in the Democratic Republic of Congo, a passport has an official price of US$80, but the going price for an ordinary citizen is US$150 (Source: World Bank resident mission).

- 8 -
static poverty are quite different from those for long-term chronic poverty. Yet, while these dynamics, by definition, requires analysis that must be based on panel data, the data used in the existing literature to investigate the effects of scaling on poverty measurement typically come from cross-sectional surveys (e.g., Newhouse _et al._ , 2017).<sup>2</sup> Such data do not provide a good understanding of how household demographics impact transient or chronic poverty, or to put it differently, how employing different scaling parameters affects household poverty dynamic patterns. To our knowledge, we are among the first to investigate the impacts of scale adjustments on poverty dynamics. As discussed later, we employ several different definitions of poverty dynamics for more robust analysis.

Furthermore, the RLMS offers panel data with longer time intervals than most existing studies. Longer-run panel data allow us to extend our analysis to broader definitions of households— including multigenerational households—and to better capture demographic changes caused by the formation of complex extended families.<sup>3</sup> Finally, the richer countries examined in existing studies, such as Germany, Switzerland or the United Kingdom, have a smaller household size on average than that of the Russian Federation. This different demographic structure implies that findings on the former countries may not necessarily apply to Russia. Furthermore, our study is especially relevant for Russia for two other reasons. Firstly, the equivalence scale currently embedded in the official poverty lines allows for unequal consumption needs but completely ignores the economies of scale in household size. A direct policy implication of no scale adjustment is that the official poverty lines would oftentimes

> 2 But see Dang, Jolliffe, and Carletto (2019) for a review of alternative poverty measurement methods in contexts where no panel data exist.

> 3 Only Borah _et al._ (2018) used longer panel data to analyze equivalence scales but their analysis was restricted to “classical households”, which consist of either a single adult or two partnered
|0.555<br>[0.497]|‐0.015<br>(0.038)|‐0.027<br>(0.024)|‐0.033<br>(0.021)|3,037|0.776|0.642|0.816|0.454|
|Amount of taxes paid in the|19,779|‐4,285**|‐657|‐1,413|2,976|0.131|0.184|0.575|0.227|
|Notes: Baseline survey data (<br>Standard deviations presente<br>OLS regression of the firm <br>(dummies for each triplet). ***<br>previous year|[28,779]<br>March 2014)<br>d in bracket<br>owner/firm <br>, **, * indic|(2,160)<br>. Only bus<br>s. Column<br> characteri<br>ate statisti|(1,369)<br>inesses s<br>s 2‐4: coef<br>stic on t<br>cal signif|(1,214)<br>urveyed a<br>ficients a<br>reatment <br>icance at|t seco<br>nd sta<br> dumm<br>1, 5 and|nd follo<br>ndard er<br>ies, con<br>10%.|w‐up are <br>rors (in p<br>trolling f|included<br>arenthes<br>or strat|. Column 1:<br>es) from an<br>a dummies|

45
incorrect estimates for TFP (Wang and Schmidt 2002; Moyo 2011). A common approach is to therefore include the determinants of TFP and output into one equation. We adopt this approach and estimate the following equation assuming a translog production function<sup>4</sup> :

> 4 The water infrastructure and firm productivity literature has adopted different functional forms. A few studies have estimated Cobb-Douglas production functions (Bogetic and Olusi, 2013; Arnold et al., 2008; Moyo, 2011) while others have estimated translog production functions or translog cost functions (Escribano et al., 2010; Iimi, 2011). As it is a more flexible functional form relative to the Cobb-Douglas, we opt-for a translog specification for the production function. The estimation of a translog cost function has also been used for power infrastructure and firm productivity estimations (Fisher-Vanden et al., 2015). Due to revisions in the cost section of the Enterprise Surveys over time, estimating a cost function would be challenging. Thus, we estimate a production function.

8
In some countries, volumetric charges may be complemented by fixed load charges that differentiate according to the maximum capacity that the user draws from the system. These are relevant because capacity rather than energy is the major driver of fixed costs on the power system, making it relevant to structure tariffs around the amount of load that customers draw from the system. In almost all countries in which demand charges exist, these are linear load charges per kV. They are most prevalent in industrial tariff schedules, exist in approximately half of commercial tariff schedules, but are rare among residential tariffs.

Finally, countries are increasingly considering time-of-use charges that apply multipliers to standard charges depending on whether consumption takes place during peak or off-peak periods. In reality, it is not so much total load that drives investment costs on the system, as the peak-hour load. Time-of-use tariffs are a means of reflecting this reality in customer charges. In most countries time-of-use tariffs act as linear rates that vary by time block. In the majority of countries, time-of-use blocks consist of peak and off-peak hour blocks. Less common are broader divisions into day and night times, followed by seasonal variation. Timeof-use tariffs are prevalent in 40 percent of industrial tariff schedules and can be found in a third of all commercial tariff structures. Residential users are rarely subject to time-of-use tariffs.

For each customer category, the paper reports an average effective electricity tariff per kilowatt-hour, which allows for standardized price comparisons across a wide diversity of tariff designs. The average tariff is the price per kilowatt-hour of electricity consumed for a representative consumer once all charges — fixed, volumetric, load-based and time-of-use — have been fully incorporated (Box 1) (Briceno-Garmendia and Shkaratan, 2011). This requires assumptions to be made regarding the average monthly electricity bill. For residential customers, the average electricity consumption is derived from the IEA energy balances by taking the total residential electricity consumption and dividing by the number of connected households. Since there are limited data on the distribution of electricity consumption within a given country,
The harvest quantity on each plot was then valued using a set of constant crop prices. Specifically, each price corresponds to the median crop sale price calculated in one survey round in each country. Applying constant prices for each crop-country combination eliminates the effect of relative price fluctuations over time. This allows us to isolate the impact of disasters on harvest quantity rather than measuring their impact on quantity _and_ prices. An alternative approach was used to calculate and report aggregate losses, such as in Table 4 and Figure 3. In these cases, country and wave specific prices were calculated, by estimating median farmer sale prices for different crop types. This was done to reflect losses as they were perceived by farmers in the year of the shock. Whether current or constant, harvest values are initially calculated in local currency units and then converted and adjusted to 2020 USD using exchange rates and a CPI drawn from a library of the World Bank World Development Indicators.<sup>53</sup> Yields, defined as the value of production per hectare, were obtained by dividing the total harvest value on the plot by GPS-measured plot area. Finally, yields are winsorized at the 1<sup>st</sup> and 99<sup>th</sup> percentiles (while allowing full losses to be equal to 0).

# _Identification of disaster events and loss size_

Identification of disaster events is based on farmers reporting crop production losses before harvest for each crop on each of their plots. Specifically, for each crop cultivated on each plot, farmers are asked whether the area harvested was less than the area planted, i.e. if some of their crop has been lost, along with the cause of the loss in harvested area. In Ethiopia, farmers are further asked whether the crops they harvested had any damage on them and what the cause of damage was. We define disaster crop losses as any loss in crop area or any damage on the crops harvested due to climatological (drought, irregular rain,
Policy Research Working Paper 9065

# **Abstract**

After the tragic factory collapse of Rana Plaza in 2013, both the direct reforms and indirect responses of retailers have plausibly affected workers in the Ready-Made Garment (RMG) sector in Bangladesh. These responses included a minimum wage increase, high profile but voluntary audits, and an increased reluctance to subcontract to smaller factories. This paper estimates the net impact of these responses using six rounds of the Labor Force Survey and a triple difference approach that compares garment workers to non-garment workers, in districts containing the vast majority of export garment factories versus other districts, pre versus post Rana Plaza. As intended by the reforms, we find that increased international scrutiny improved working conditions by 0.80 standard deviations. In contrast with - what the theory of compensating differentials would sug gest, we do not find that workers’ wages were negatively impacted: instead, the post-Rana Plaza responses increased wages by about 10%.

This paper is a product of the Social Protection and Jobs 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 lbossavie@worldbank.org or ycho1@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._