rafmacalaba/datause-encoder-data huggingface dataset:
**Table 4. Reasons for avoiding base wage cuts – ranking of responses**

||**Tot**|**al**|**EU-**|**15**|**C**|**EE**|
|---|---|---|---|---|---|---|
||Share|Rank|Share|Rank|Share|Rank|
|Most productive workers leave|0.86|1|0.84|3|0.90|1|
|Lower worker morale/ less effort|0.86|2|0.87|1|0.82|2|
|Labour regulations/ collective|||||||
|bargaining|0.74|3|0.85|2|0.45|8|
|Difficult to attract new workers|0.72|4|0.70|4|0.76|3|
|Labour turnover costs increase|0.70|5|0.69|5|0.73|4|
|External wages matter|0.67|6|0.68|6|0.66|6|
|Reputation suffers|0.60|7|0.58|7|0.65|7|
|Implicit contract|0.59|8|0.54|8|0.71|5|

Note: Share of firms which replied “very relevant” or “relevant” and the corresponding rank.

The greatest cross-country variation is found in the importance attached to labour regulations and collective bargaining, which ranges from 36% of firms in Poland to 93% of firms in Spain. Labour regulation was the lowest ranked reason by firms in CEE countries; whereas it was ranked second by firms in EU-15 countries (see Table 4). The percentage of firms suggesting that regulation is behind the absence of wage cuts was almost twice as high in the EU-15 as in CEE countries (85% and 45% respectively). This is likely to be related to the substantial differences in the institutional structure of the wage-setting process across the European Union member states. As was indicated in Section 3.2, the percentage of workers covered by collective agreements tends to be much higher in EU-15 than in CEE. The difference stems mostly from the reach of collective agreements negotiated outside the firm at the sectoral or regional level (see Table 1). We will examine in more detail in the next Section the effect of the type and intensity of collective bargaining agreements on firms’ perception of this as a reason for avoiding wage reductions.

A tabulation of the importance of each of these factors across sectors shows that effort and reputation are again consistently amongst the major inhibitors of wage reductions (see Table 5). Regulation and collective agreements vary less in their relevance across sectors than they do across countries, although construction stands out as having a particularly low percentage of firms classifying this reason as relevant, perhaps 18
62 Table A2: Fertilizer Use and Intensity in Sri Lanka, by crop

|||**Fertiliz**|**er Type**||||
|---|---|---|---|---|---|---|
|**Crop**|**Urea**|**TSP**|**MOP**|**Others**|**Total**|**Fertilizer Inten-**<br>**sity (MT/ha)**|
|Chili pepper|3800|3800|3800|0|11400|0.300|
|Cinnamon|15114||7558|0|22672|0.662|
|Cloves|1359||2039|226|3624|0.454|
|Cocoa|178||222|44|444|0.243|
|Coconut|20147|2865|40295|15000|78307|0.173|
|Cowpea|1008|1550|1203|0|3761|0.243|
|Fruits|48359|35353|80067|0|163779|1.279|
|Green gram|1495|2300|1725|0|5520|0.240|
|<br>Horse gram|3|6|2|0|11|0.073|
|<br>Kurakkan|2345|605|908|0|3858|0.351|
|Maize|32300|14000|7000|0|53300|0.381|
|Nutmeg|484||727|80|1291|0.454|
|Onion|2926|1500|1126|0|5552|0.370|
|Paddy|247000|61000|74000|0|382000|0.294|
|Peanuts|1203|1850|1388|0|4441|0.240|
|Pepper|14562||10922|3640|29124|0.710|
|Potatoes|2310|1890|1750|0|5950|0.850|
|Rubber|6767||7151|4391|18309|0.146|
|Sesame|1595|1740|870|0|4205|0.290|
|Soybeans|.|.|.|.|.|0.067|
|Tea|96050||36864|59206|192120|0.959|
|Undu|1755|2700|2025|0|6480|0.240|
|Vegetables|22744|21297|14920|0|58961|0.640|

_Notes_ : for each crop, the table displays total fertilizer use in Sri Lanka (in MT) for four types of fertilizer: urea, triple superphosphate (TSP), muriate of potash (MOP), and other fertilizers. The last column shows fertilizer intensity (in MT/ha), which is computed by dividing the crop’s total fertilizer use (in MT) by its cultivated area (in ha). The data comes from Sri Lanka’s NFS, except for soybeans, whose fertilizer intensity data comes from the International Fertilizer Development Center and the International Fertilizer Association (specifically, from their ‘Asia’ estimates).

Producer Prices). We proxy the national prices of maize, onions, potato, rice, and tea with their (national) unit costs of production.<sup>102</sup> Since unit costs of production are not available for cinnamon and cloves, we use their (national) FOB export prices instead.

# **A.E Fertilizer Use and Subsidies**

To compute chemical fertilizer use in the production of various agricultural crops, we use data provided by Sri Lanka’s NFS. Fertilizer requirements for 2022, displayed in Appendix Table A2, are reported as MT of fertilizer required per ha of cultivated land for each of 23 crops. As explained in Section IV.C, this fertilizer intensity metric is used in our main analysis to build a firm-level fertil102Due to our model’s perfect competition assumption, a crop’s unit cost of production equals its (farm-gate) producer price, which justifies using the former (which is available in the Economic and Social Statistics report) as a proxy for the latter. For tea and rice, unit costs are reported for the whole island, but for maize, onions, and potatoes they are reported for individual districts. We use data from Puttlam for onions and from Badulla for maize and potatoes, as these districts are major producers of their respective crops.
**Table 8: Counterfactual PPPs (CPPP2) based on Household level Data: (Base Country: India)**

|**Country**|**2011 ICP**<br>**PPP***|**Overall**|**Quintile 1**|**Quintile 2**|**Quintile 3**|**Quintile 4**|**Quintile 5**|
|---|---|---|---|---|---|---|---|
|Iraq|35.882|41.932|45.060|45.705|40.995|37.678|29.124|
|Malawi|5.195|7.166|5.718|7.244|7.116|7.461|6.811|
|Tanzania|38.494|30.080|23.887|29.605|29.047|31.053|30.117|
|Vietnam|479.060|639.906|562.399|663.861|635.605|628.115|542.026|

***** Base country: India

**Table 9: Gini Expenditure Inequality Index of households pooled over the 5 Countries at Different PPPs (Base Country: India)**

||**Gini Index (in**|**%) based on**||
|---|---|---|---|
|**2011 ICP PPP**|**CPPP1***|**CPPP2**|**Quintile wise**|
||||**PPPs**|
|38.634|38.585|38.541|38.572|

*Excluding Malawi

46
# I. Synthetic Atlas-type Conversion Factor (SACF)

It is Bank practice to seek an alternative to official rates when such rates differ egregiously from effective transactions rates. Until now, altemate conversion factors have only been used by the Bank to deal with temporarily over-valued currencies defended by increasing trade and exchange restrictions. The normal method for deriving such alternates depends on there being some earlier period when the exchange rate was accepted as reasonable, and exchange and trade restriction were lighter. Under these conditions, a fairly objective alternate can be computed by moving the reasonable historical rate forward based on the difference in inflation rates between the country and the United States. This approach cannot work for the FSU since there is no earlier period of rational exchange rates and lighter restrictions.

Having found exchange rates wanting even in this historical sense, there seems no alternative to some use of PPPs in deducing _Atlas-type_ estimates for the FSU economies. This requires some correction for the difference in "scale' between PPPs and Atlas-type conversion factors. The simplest way is to reverse the regression equation used to infer PPP-based from Atlas-type estimates in the 1992 edition of the Bank's _World Development Indicators_ (rable 30), by adding a PPP for the FSU based on its preliminary 1990 submissions to the global International Comparisons Programme (ICP).

However, this method did not give us plausible results (see Ahmad 1992) that led this study to an approach that links PPP data from the CMEA and the global International Comparison Programme (ICP) to obtain a ruble/dollar cross-rate in PPP terms. This is then adjusted from a PPP to an Atlas-type measure by the relation of corresponding measures for available comparator countries.

# a. Role of Purchasing Power Parities (PPPs)

There is a rich literature on "short-cut" methods to deduce PPP-based estimates of per capita income from Atls-type estimates. This section considers how these led to the synthetic _Atlas-type_ conversion factor (SACF) proposed for the FSU, and perhaps other CMEA economies. The common characteristic is that all use the known relationship between the two types of conversion factors _(Atlas_ and PPPs) for some country or countries, plus one of the two for an additional country, to deduce the other for the additional country.

Perhaps not coincidentally, the FSU and other historically planned economies had a tradition of PPP comparisons, within the CMEA. Thus, the practical constraints usually found in relating PPPs to Atlas-type conversion factors are reversed. National compilers and decisionmakers in CMEA countries are used to PPP-based comparisons but not those based on exchange rates.

10
Clemens (2010) provides a final example of the use of a lottery provided by migration policy. He studies the H1-B visa, which is an admission channel for high skilled workers who wish to work in the United States. There is a cap on the number of people who can annually enter through this category, and while applications are processed on a first-come, first-served basis, in 2007 and 2008 so many applications were received on the opening days, that a lottery was used to select which applications to process. Rather than trying to get administrative data from the United States Citizenship and Immigration Services, which would be incredibly difficult, Clemens obtained personnel records from a large Indian information technology/software firm that supplied a large number of applicants to this lottery. He is then able to use these records to determine the impact of migrating on the migration, job title, and earnings of the applicants in this Indian firm.

An underlying assumption of the experimental estimates is that the migration lottery influences the outcome of interest for an individual only through that individual’s migration decision. In particular, we require the Stable Unit Treatment Value Assumption (Rubin, 1986), which means that the outcome of one individual should not be affected by the lottery outcome of another individual. Thus when we consider the case of measuring the income gain from migration, we would require that the income of lottery losers is not affected by whether other people in the sample win or lose the lottery. One potential way this assumption could be violated would be if the lottery winners send remittances to the lottery loser households. This is unlikely to be much of an issue in cases where the number of winners is small relative to the overall population, and can be directly checked through surveys. A more problematic concern would be if the employment prospects of the lottery losers change as a result of the winners migrating. Whilst there is some evidence to suggest that large-scale migration can increase the wages earned by non-migrants through less competition for jobs (Mishra, 2007), this is again likely to be at most a second-order concern when the number of winners is small relative to the overall population. But it is more of a concern in cases like that studied by Clemens (2010), where the entrants studied are all from the same company, and it is therefore harder to imagine that the job opportunities available to the non-migrants are not affected by having some company members abroad.

6
this way because of the fundamental dierences in the adjustments occurring at the micro-level between these episodes, dierences that we want to carefully account for in the theory that we develop later.

# 3.1 Aggregate and Firm-Level Features of Accelerations and PostCommunist Transitions

Consider rst the dynamics of aggregate variables. Figure 3.1 shows the average behavior of TFP and investment rates in our selection of growth accelerations and post-communist transitions. The left panel plots the average dynamics of TFP. In the vertical axis, units are measured relative to the average value of TFP in the 5 years preceding the take-o.<sup>3</sup> For post-communist countries, we assume that all transitions start in 1990, so the corresponding line illustrates the ratio between the average of TFP across countries relative to the average value between 1985 and 1990.

Despite the initial slump in the case of post-communist transitions, both TFP and investment rate increase over time. This pattern of behavior has been noted before in the literature as a limitation of the standard neoclassical growth model, which is silent about TFP dynamics and predicts a decreasing path in the investment rate when converging towards an equilibrium with higher capital stock. In this context, one of the goals of our paper is to attempt to reconcile theory and data, by developing a quantitative model of transitions with endogenous TFP and investment rate dynamics.<sup>4</sup> While exhibiting similar characteristics in the aggregate, acceleration episodes and post-communist transitions dier notably in the adjustments taking place at the micro-level, in particular regarding the size distribution of rms. To see this, gure 3.2 reproduces the dynamics of the average size of manufacturing rms, in following a growth acceleration (years _t_ + 7 through _t_ + 16 ) is above 2 percent. We update the identication of growth accelerations applying the methodology to the most recent data in Penn World Tables 10.0 Zeileis (2021). The complete list of post-communist countries and the list of acceleration episodes picked up by the methodology is presented in Appendix A.

> 3Since accelerations occur at dierent dates in each country, we construct a measure of average TFP dynamics as follows. For each country, we construct the time series of TFP during the acceleration years and we express them relative to the average value of TFP in the 5 years preceding the start of the acceleration; and then we average across countries.

> 4Christiano (1989), King and Rebelo (1993), Chen et.al. (2006), and Buera and Shin (2013) are salient examples of papers that have noted the conict between the neoclassical growth model and macroeconomic data on transitions and developed extensions of the neoclassical model to bridge the gap between the two. See the literature review for a more thorough explanation of how our paper relates to this literature.

8
**Table 2.1b. Accounts in Macro SAM for archetype low‐income country in 2015**

|**Account**|**Explanation**|
|---|---|
|act‐prv|activity‐privateproduction|
|act‐gov|activity‐governmentproduction|
|com‐prv|commodity‐privateproduction|
|com‐gov|commodity‐governmentproduction|
|f‐lab|factor ‐ labor|
|f‐cap|factor ‐private capital|
|hhd|household|
|gov|government|
|row|rest of world|
|tax‐act|taxes ‐ activities|
|tax‐com|taxes ‐ commodities|
|tax‐imp|taxes ‐ tariffs on imports|
|tax‐exp|taxes ‐ exports|
|tax‐dir|taxes ‐ income|
|cssoc|social securitycontributions|
|cap‐hhd|capital account ‐ household|
|cap‐gov|capital account ‐government|
|cap‐row|capital account ‐ rest of world|
|inv‐prv|investment ‐private capital|
|inv‐gov|investment ‐government capital|
|dstk|stock change|

Source: Cicowiez and Lofgren (2017a).

# **Model structure**

In any single year, GEM‐Core has the structure summarized in Figure 2.1. As indicated by the figure, which serves as the reference point for this model overview, the major building blocks of the model are activities (the entities that carry out production), commodities (activity outputs and/or imports; linked to markets), factors (also linked to markets), and institutions (households, enterprises, the government, and the rest of the world). Given the relatively detailed treatment of the financing of private investment (compared to most other CGE models), the private (non‐government) capital account also has its own box. In the following model presentation, we assume that the different blocks have the disaggregation presented in the above SAM (Table 2.1).

‐5‐
# Table 4: **The Dynamic Effect of Blackouts Owner and Worker Labor**

All specifications include time fixed effects and firm fixed effects. Day pairs for which one or both blackout responses are missing are dropped. Standard errors are clustered at the neighborhood level. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

||(1)<br>Owner<br>Worked|(2)<br>Owner<br>Hours|(3)<br>#<br>Workers|(4)<br>Worker<br>Hours|
|---|---|---|---|---|
|**Panel A: One-Person Firms**|||||
|Blackout Today Only|-0.04**|-0.59***|x|x|
||(0.02)|(0.16)|x|x|
|Lagged Blackout Only|-0.01|-0.12|x|x|
||(0.01)|(0.15)|x|x|
|Blackout Both Today and Lagged|-0.08***|-0.89***|x|x|
||(0.03)|(0.24)|x|x|
|Outcome Variable Average|0.68|5.80|x|x|
|Observations|5,226|5,226|x|x|
|**Panel B: Firms With Workers**|||||
|Blackout Today Only|-0.01|-0.20|0.07**|0.72*|
||(0.02)|(0.17)|(0.03)|(0.36)|
|Lagged Blackout Only|0.04***|0.35***|0.09***|0.98**|
||(0.01)|(0.11)|(0.03)|(0.37)|
|Blackout Both Today and Lagged|-0.03|-0.39*|0.08**|0.73*|
||(0.03)|(0.21)|(0.04)|(0.40)|
|Outcome Variable Average|0.84|7.76|1.67|15.52|
|Observations|4,711|4,711|4,711|4,711|

Source: Author calculations from The Hohoe Garment Makers Project Data 19
**-** **<u>Table 2: Determinants of harvest diary non response and completion</u>**

||**Total**|**Response**|||**Completed**|**diary**||
|---|---|---|---|---|---|---|---|
||**Sample**|**No**|**Yes**|**Sig.**|**No**|**Yes**|**Sig.**|
|Household size|6.02|5.79|6.08|***|5.86|6.09||
|Head's age|43.73|42.40|44.06|***|42.99|43.98||
|Head's education|5.34|6.04|5.17|***|5.56|5.21|**|
|Female head|0.261|0.266|0.259||0.267|0.258||
|Main inc. agriculture|0.640|0.455|0.685|***|0.482|0.689||
|Iron roof|0.526|0.479|0.537|***|0.394|0.546|***|
|Cement floor|0.173|0.266|0.151|***|0.195|0.150||
|Assets (log)|5.611|5.503|5.638|***|5.187|5.663|***|
|Affected by flood|0.177|0.134|0.187|***|0.139|0.193|**|
|Affected by pest|0.131|0.125|0.132||0.136|0.132||
|Committee member|0.152|0.114|0.161|***|0.123|0.166|***|
|Area owned|5.804|8.334|5.182|*|5.606|5.260||
|Area cultivated|2.639|2.316|2.718|***|2.314|2.695||
|Value of output ($)|435.87|345.84|457.99||228.75|476.68|*|
|Log of yield|3.97|3.66|4.04|***|3.42|4.09|***|
|Owns cattle|0.305|0.255|0.317|***|0.291|0.307|***|
|No. of observations|5,506|1,055|4,451||433|4,018||

_Source:_ Own computation from 2005/06 UNHS and harvest diaries Note: Completion is conditional on having started filling in the diary, i.e. excludes cases of non-response.

17
_Ij*ec_ represent the interaction term between industry and our electricity measure. The coefficients of these interactions are also variables of interest and measure whether firm performance in certain industries is more affected by the process of connecting to electricity. _Ejc_ is the residual term.

Output (in this case proxied by sales) is a function of the existing production capacity (proxied by past sales), variable inputs (labor and capital) plus other factors that can affect productivity levels. Identifying these determining factors has been the focus of a vast literature. Here we try to test if the efficiency in the electricity connection process can be one of these factors. To do so we use similar specifications to the ones used in papers such as Seker (2009) and Hallward-Driemeier, Wallsten and Xu (2003).

Given that our variables of interest vary at the country and industry level, the more appropriate specification is at the industry level. The firm level regression is more likely to produce statistically significant results just because the number of observations is much larger. Therefore, it is important to verify if the results hold once the data are collapsed at the industry level.

_yjc_   0   1 _ec_   2 _Xjc_   3 _Ij_   5 _Cc_   6 _Ij_ * _ec_   _jc_ (2) Here _yjc_ represents either total sales on average in industry j and country c. The other variables remain as in (1) except for _Xjc_ which now represent average industry characteristics. These are as follows: average past sales, average labor input (measured by the number of employees), capital input (proxied by investment), average international trade engagement (measured by the percentage of firms that export directly at least 10% of their sales, import directly at least 10% of their inputs or both of these in industry j country c), average usage of bank finance (measured by the percentage of firms in industry j and country j that have a bank loan), and average number of power outages in a typical month.

Here we follow a variation of the specification used by Klapper, Laeven and Rajan (2004). These authors study the effect of the business environment on firm creation by interacting industry characteristics with country characteristics. We adapt their specification by changing the dependent variable from firm entry to firm performance and consequently also altering the industry and country level controls. We also add the past sales variable as a proxy of existing production capacity, which would not apply to the question of firm entry. In these specifications, all the data except for _e_ (percentile rank of getting an electricity connection) come from the Enterprise Surveys data set.

# _Firm Level Specifications Results_

The firm level regression results are presented in tables 16a and b. We first describe table 16a which is based on the full sample. Column 1 shows the results under the basic specification where the covariates are kept to a minimum. We control for country characteristics, industry characteristics and some firm level characteristics (past sales, labor and capital inputs and power 18