rafmacalaba/datause-encoder-data huggingface dataset:
the Rosstat’s official estimates are slightly higher), the trends are quite similar as shown in the next section.

Using the LiTS, informal workers are identified as those without a written contract in their main job. The 2016 survey contains a question that asks whether the individual had a written contract for the primary job, with possible responses including permanent with/without contract, temporary with/without contract, seasonal with/without contract, daily laborer and other. In previous survey rounds, the relevant question asked only whether an individual had a written contract in her main job or not. We define a worker as formal if she has a contract in her primary job, whether permanent, temporary or seasonal, and informal otherwise.

Using the ESS, informal workers are identified as those without a contract in their main job. Among employment-related questions, the survey asks whether the individual held a contract in the main job, and whether it was of unlimited or limited duration. We define a worker as formal if she held a contract in her main job, regardless of duration of contract, and informal otherwise.

# **Recent evolution of informal employment in Russia**

In this section, we proceed to estimate levels of informal employment and examine trends across surveys. We are able to extend the trend using recent estimates of informal employment as fortunately all data sources are updated through 2016. This allows a close comparison of trends over time across different surveys. For each survey, we focus on informal employment in the main job as described in the previous section. We observe a long-term increase in informality from the early 2000s (and even before that based on RLMS data, though not shown here) through 2016, with a short period of stagnation or even decrease in the second half of the 2000s. Encouragingly, this trend appears to be consistent across different data sources, including the official measure of informal employment monitored by Rosstat, which is presented alongside our estimates for comparison purposes. This result serves as a robust starting point for the rest of the analysis.

A longer-term trend from Rosstat’s official measure confirms a rapid increase in informality in the first decade since 2000, with a notable but temporary dip observed in 2010. The reasons behind this seemingly temporary decline are not immediately clear and appears to be an outlier against the long-term trend. In any case, there was a brief stagnation or decrease in informal employment in the years around the financial crisis, which was followed by another period of increase. The official Rosstat definition of informal employment is based on the LFS, and defines as informal those workers who do not work for an enterprise in their primary or secondary job. Notably, it does not capture informal workers in the corporate sector, i.e., those “working at an enterprise” but without a contract. It may also exclude some workers employed by unregistered small businesses, if respondents answer that they are working for “a firm”.

10/67 Informal Employment and Worker’s Wellbeing in Russia
more effect in some cases than others, and the controls do change the regression function on expenditure; in a number of cases, the controls attenuate the gains at high expenditure levels, even producing an inverted-U relationship.

How well does _SAH_ reflect objective health circumstances? We give in Table 3 the cross-sectional OP for 2002 in which we regress _SAH_ on all measures of specific abilities and ailments reported in the survey. We included all the variables in the RLMS 2002 round that we felt could reasonably be treated as exogeneous to overall health status. These assumptions can always be questioned, and we cannot rule out the possibility that errors in the self-reporting of even truly exogeneous predictor can introduce a correlation with the error term. We decided not to include two variables that are likely to be correlated with _SAH_ , namely whether the respondent is a smoker and alcohol consumption, on the grounds that the concern about endogeneity was too severe in these cases.

For both men and women, roughly half the variance in _SAH_ is explicable in terms of age and the specific health attributes identified in the data set; for women a slightly higher share of the variance is explained by these variables. The signs are generally what one would expect. _SAH_ tends to fall for both men and women over the bulk of the data.<sup>15</sup> Amongst the physical activity variables, the strongest predictors of _SAH_ are ability to run, climb stairs, crouch and eat. The effects of the physical activity variables are similar for men and women, though with generally larger coefficients for men. Body-mass index does not have a strong effect, though it does exhibit the expected inverted U shape. Most of the reported ailments and diseases have the expected signs. Particularly strong and significant effects are evident for lung problems, liver

> 15 The turning point for men is at age 50, while for women it is over 100 years.  The relatively low turning point for men (implying that men over 50 start to perceive that their health as improving with age) could well stem from selective mortality (recalling that male life expectancy is about 60, more than 10 years lower than for women). Adaptive behavior amongst older men may also be playing a role, along the lines of Groot (2000).

16
economy as a benchmark, given that the United States has a relatively low intensity of bribing.<sup>16</sup> Third, to evaluate the quantitative effects of rent-seeking in the form of bribes, I calibrated the rest of the parameters to match relevant features of bribing in Argentina. All data moments and targets are based on the WBES, unless otherwise stated. One year in the model is equivalent to one year in the data.

# **4.1. Moment Selection and Calibration**

**Standard parameters** . Values for the model’s standard parameters can be found in table 2. The discount factor _β_ is calibrated to match a real interest rate _r_ of 4 percent. This rate implies a calibrated impatience rate of 0.05. The depreciation rate Ω is set to imply an average investment to capital ratio of approximately 6 percent, which corresponds to the average value for the private capital stock in the United States. Following the literature, I set the share of capital in value added at _α_ = 1 ⁄ 3. Finally, I set the elasticity of substitution at _ρ_ = 3, as in Hsieh and Klenow (2014).<sup>17</sup>

**Table 2. Standard parameters**

|**Parameter**|**Value**|**Target/source**|
|---|---|---|
|Share of income to capital|_α_= 0.33|Literature|
|Elasticity of substitution|_ρ_= 3|Broda and Weinstein (2006)|
|Depreciation rate|Ω = 0.06|Literature|
|Discount factor|_β_= 0.96|Interest rate of 4%|

**Technological and innovation parameters** . Six parameters need to be jointly calibrated to match six relevant moments for the U.S. economy. For technical details of the computational procedure used to calibrate them, see appendix B.4. The moments and parameters are described in table 3.

> 16 What matters for this analysis is that the U.S. economy is relatively undistorted by bribing behavior. There could be other ways by which firms in the United States try to influence their profits, like lobbying, but those ways are outside the scope of this paper, which focuses on illegal activities.

> 17  This number is consistent with estimates found in the trade and industrial organization literature. See, for example, Broda and Weinstein (2006), which provides estimations of elasticity of substitution for U.S. imports at a disaggregated level.

21
The administrative data show that SACCO deposits are balanced across the three experimental arms in both baseline periods. Survey data show that 46% of our respondents are male, 56% have completed at least primary education, are 46 years of age on average, and have an average household size of 5 members. All these attributes are balanced across the three experimental arms, as represented by the insignificant p-values in columns (6) and (7) of Table 1.

For the household surveys, the attrition rate between the two endline rounds was less than 4% and uncorrelated with treatment status.

We analyze take-up at both the aggregate level and the household survey level. On aggregate, more than 68,000 SACCO members received training under the program, with an average attendance rate of 3.5 sessions pooled across both treatment arms. Weighted analysis using household survey data show that 27% of SACCO community members sought financial advice in the past year, and 21-23% attended at least one financial education training session at their respective SACCO. Both these variables are balanced across the two treatment arms.

Take-up of the program is, of course, endogenous but the low rates of program attendance could also be partly due to the sheer scope of the overall program and the large SACCO membership size – on average there were 5,526 community members per SACCO in our sample prior to program implementation. Hence, reaching wider program coverage within the SACCO community could take longer than a year. For this reason, we present TOT results for all outcome variables alongside the main ITT analysis below.

Further on take-up, Table 2 shows how fixed respondent characteristics influenced take-up and we find that while gender did not vary, those who have completed primary school, are younger, and come from larger sized households were significantly more likely to attend financial education training at their SACCO. Analyzing attendance records in Table 3, we find that the frequency of attendance is significantly different across the two treatment arms. Conditional on attendance, 63 percent of community members in T1 attended more than two sessions of the financial education program, while this figure is only 54 percent in T2. The difference between these attendance rates is statistically significant at the 5% level, as shown in column (3) of Table 3. Likewise, column (4) 12
**TABLE 2. DISTRIBUTION OF FIRMS’** **<u>CHARACTERISTICS</u>**

|Size||Region|
|---|---|---|
|Micro (less than 16 employees)|90.2%|North<br>19.3%|
|Small (16-100 employees)|7.8%|Center<br>49.2%|
|Medium (100-250 employees)|1.2%|South<br>21.0%|
|Large (more than 250 employees)|0.7%|Mexico City<br>10.6%|
|Divisionofactivity||Firm's exports aremore than50% ofoutput|
|Food, beverages, and tobacco|35.6%|No<br>99.1%|
|Textiles, clothing and leather|14.3%|Yes<br>0.9%|
|Wood and wood products|12.9%|Thefirm investsin research& development|
|Paper, paper products, printing, and publishing|6.8%|No<br>92.8%|
|Chemicals, oil derivatives, and coal|3.2%|Yes<br>7.2%|
|Non-metallic mineral products|8.1%|Thefirm has quality controlcertifications|
|Basic metallic industries|0.1%|No<br>96.8%|
|Metallic products, machinery, and equipment.|17.3%|Yes<br>3.3%|
|Other manufacturingindustries|1.8%|Thefirmadoptednew technology since1999|
|Foreigncapital is over50%||No<br>61.6%|
|No|99.5%|Yes<br>38.4%|
|Yes|0.5%||

Source: Calculations based on ENESTYC 2001.

With respect to firm size, 90 percent are micro in size, 8 percent are considered small, 1 percent medium, and almost 1 percent large, which is consistent with economic census data. Of these, the majority of manufacturing firms seem to be most concentrated in Central Mexico, accounting for 49 percent of the total, with the remainder evenly distributed between the Northern and Southern regions, and Mexico City. According to the division of activity, food, beverages and tobacco account for the largest share of firms with 36 percent; metallic products, machinery and equipment come second with 17 percent. Basic metallic industry ranks last, with only 0.1 percent. With respect to foreign openness, the percentage of firms with more than 50 percent foreign capital is only 0.5 percent and only 1 percent of firms export at least half of their output. The share of firms that invest in research and development (R&D) is 7 percent, whereas the percentage of firms that adopted new technologies is 38 percent. Only 3 percent of the firms have some quality control certification.

# **IV. FRAMEWORK AND METHODOLOGY**

We follow Hellerstein, Neumark, and Troske’s (1999) model to compare the relative marginal products and relative wages of workers. Consider a simple economy 7
<u>Table 2: Estimated Elasticities of Substitution</u>

||**Country**||**Statistic**|**s**||**Nobs of HS**|**Average**|
|---|---|---|---|---|---|---|---|
|||**Percentile 90**|**Percentile 50**|**Percentile 10**|**Mean**|**products**|**variety**|
|1|Bhutan|20.7|3.7|1.8|21.6|1548|12.4|
|2|Brunei Darussalam|14.4|4.0|2.1|18.1|4213|17.3|
|3|Cambodia|12.3|4.1|2.1|8.5|4302|17.6|
|4|China|12.6|4.7|2.8|11.1|5368|23.7|
|5|Congo, Rep.|12.5|4.2|2.3|10.0|3829|16.4|
|6|Fiji|11.0|3.6|1.9|8.7|4228|18.5|
|7|Hong Kong SAR, China|12.9|4.4|2.4|10.6|5317|22.7|
|8|Indonesia|10.2|4.0|2.4|7.5|5327|22.9|
|9|Japan|15.2|4.7|2.5|23.1|5368|23.4|
|10|Kenya|15.2|4.5|2.4|13.6|4864|18.5|
|11|Korea, Rep.|12.5|4.6|2.7|11.9|5355|23.5|
|12|Lao PDR|15.0|3.7|1.9|10.9|3394|18.1|
|13|Macao SAR, China|12.9|4.2|2.2|9.6|3879|19.0|
|14|Malawi|13.0|3.8|1.9|8.5|3950|17.6|
|15|Malaysia|9.0|3.4|2.1|11.2|5334|23.4|
|16|Mauritius|11.8|4.1|2.3|13.3|4806|20.7|
|17|Mongolia|12.3|4.0|2.2|13.8|3708|17.2|
|18|Mozambique|11.9|3.4|1.9|26.4|4465|18.1|
|19|Myanmar|14.7|3.9|2.1|12.1|4470|18.5|
|20|Philippines|8.7|3.6|2.3|6.4|5234|22.6|
|21|Rwanda|17.0|4.8|2.4|16.8|3841|13.3|
|22|Singapore|15.6|4.7|2.5|12.3|5303|22.5|
|23|Tanzania|19.3|4.9|2.5|13.9|4754|15.2|
|24|Thailand|12.2|4.3|2.5|10.5|5333|22.7|
|25|Uganda|15.1|4.3|2.2|16.1|4502|16.4|
|26|Viet Nam|13.2|4.3|2.4|10.1|5240|21.3|
|27|Zambia|12.8|3.8|2.0|12.6|4503|19.9|
|28|Zimbabwe|14.7|3.9|2.0|14.9|4561|18.4|
||**Average**||**4.1**||**13.0**|||

Note: Authors’ calculation. See text for explanation.

technique. As a result, there are fewer lambda ratios than product groups or sigmas. Some lambda ratios cannot be defined at the HS-6 level since there is no common variety. We then follow Broda and Weinstein (2006) and define the lambda ratio at the HS-4 level.

Table 3 shows the summary statistics for the lambda ratios for each country. The median lambda ratio of an average country is 0.99980146, expressing that the typical imported product category in the importing country experienced a positive variety growth of about 1 percent<sup>11</sup> . Using the lambda ratios as a measure of variety growth is more sophisticated than just counting new and disappearing varieties. Due to the large number of new varieties with small market shares, just counting the new varieties can be misleading. Thus, this 11 Calculated as 1/0.9997837=1%.

17
different ages, and localities that historically had different numbers of recruiting stations.

Recent work by Callaway et al. (2021) has highlighted that difference-in-differences with continuous treatments or different doses of a treatment results in additional complications to consider relative to the binary treatment case. In particular, a stronger parallel trends assumption needs to be employed, and if treatment effects are heterogeneous across units receiving different doses of the treatment, two-way fixed effects specifications can be hard to interpret, even when treatment is not staggered over time. They discuss several possible solutions, and migration work that relies on this differential exposure across areas will need to discuss these issues more in the future.

A final set of issues that can arise occurs when policies can get reversed or “turned off”. While this can raise further complications in the presence of dynamic effects and treatment heterogeneity, it may also increase the credibility of causal claims. For example, observing an effect in one direction when a policy is introduced, and then that the estimated effects go (as would be predicted) in the opposite direction after a policy is reversed, may serve as further proof that the policy is responsible for the changes in outcomes observed. Studies that take advantage of such reversals include Dinkelman and Mariotti (2016) and Abarcar and Theoharides (2022).

# **4.3 Instrumental Variables**

The instrumental variables (IV) approach exploits variation in the real world to establish causal relationships. When implemented credibly, IV deals with concerns about omitted variable bias and reverse causation.

Consider the following regression equation estimated using a cross-section of data on households, indexed by _i_ . The outcome of interest is _Yi_ , for example the level of household per capita consumption. We are interested in estimating the impact of an independent variable _Xi_ , such as whether the household has a member earning income overseas as a migrant worker.

In general, we worry that the coefficient _β_ on _Xi_ is biased due to omitted variables and/or reverse causation. When using the IV approach, a researcher uses an _instrument Zi_ that helps isolate exogenous variation in the independent variable _Xi_ in determining the impact of _Xi_ on outcome _Yi_ . The following “first stage” regression captures the impact of the instrument _Zi_ on _Xi_ : There are key assumptions underlying IV. First, the instrument must be _relevant_ : _Zi_ 23
# **5.4 Robustness tests**

To analyze the robustness of our results, we estimated the same OLS regressions in levels but without fixed effects to evaluate the sensitivity of our results to unobserved heterogeneity. Tables 18, 19 and 20 show these results for the manufacturing sector. The pooled regressions exhibit similar results to the ones of the initial OLS regressions and yield the same conclusions as the IV estimates except for the case of the ratio of white/blue-collar wages in which once again, when we do not instrument our ICT use variable, it appears to have positive effects over the wage gap and once we use our instrument these positive effects are not observed anymore.

Also, considering the criticism posed by Ciccone and Papaioannou (2010) regarding the use of benchmark industry characteristics interacted with regional characteristics, though it is directed to crosscountry models which is not our case<sup>7</sup> , we estimated the same models with a different set of instrumental variables in which we used information from Mexico’s 1999 Economic Census to calculate ICT sectoral intensity. The results (not shown here) do not change much.

## **5.4.1 Heterogeneity of results by sales-per-worker increases**

We estimate split regressions by change in sales-per-worker quartile over the share of labor that uses Internet. We choose this variable as it is the one that should be better instrumented using our IV. The purpose of this analysis is to test whether the effects of ICT adoption over our main outcome variables vary according to labor productivity growth. That is, we analyze whether higher-growth firms have different effects.

For the manufacturing sector (Table 21), we observe that for lower quartiles, there is an increase both in the number of white-collar workers and in the number of blue-collar workers. However, for higher quartiles, though we still observe some positive effects over white-collar workers, the number of blue-collar workers reduces, indicating a possible displacement of this type of workers due to increasing ICT adoption in this segment of firms. Accordingly, the ratio of white/blue-collar workers increases. Wages for both groups decrease for the lowest quartile, while increases are observed in the highest quartiles for both groups. However, similarly to our previous results, increases appear to be higher for blue-collar workers, leading to a reduction in the wage gap.

For services, increases in the use of ICT in the highest quartile are associated with a higher ratio of white/blue-collar workers, while negative or no significant effects are observed for lower quartiles of

> 7This paper indicates that the use of benchmark countries such as the U.S., to account for technology could be biased towards zero or away from zero depending on the technological similarity of the different countries included in the sample vs. the benchmark.

45
# **I. INTRODUCTION**

The oil sector is a critical sector of the Ecuadorian economy contributing about 17 % to the country's GDP. Ecuador began exporting crude oil in 1972 and over the last two and a half decades the oil sector has emerged as the country's most important sector. The oil sector is controlled by the government through the public sector enterprise, PETROECUADOR, which serves as the holding company for all state-owned petroleum operations. Movements in oil prices are of major concern to the government and forecasts of oil prices are built into the government budget. The performance of the oil sector critiically affects Ecuador's macroeconomic performance and shocks to this sector has economy wide repercussions.

The volatility of the world oil market since the OPEC oil price shocks of the 1970's has resulted in oil export dependent countries like Ecuador facing a considerable degree of macroeconomic risk. Between 1973-81 when Ecuador first emerged as an oil exporter, real GDP grew at an annual average rate of 6.1 %. During much of the 1980's, oil prices declined substantially. Over the 1985-86 period, Ecuador lost U$900 million equivalent to about 8 % of GDP. Declining oil revenues resulted in large public sector deficits and a worsening balance of payments situation. The effect of the oil price decline was that between 1981-91, GDP growth averaged about 2.1%, less than half the growth rate before the oil boom'. Ecuador's dependence on oil is such that even minor oil price declines have had a substantial cumulative adverse impact on Ecuador's macroeconomic performance.

Developing countries like Ecuador have sought to achieve export revenue stabilization through International Commodity Agreements with importing nations. These agreements have 1 The source of these figures are Ecuador: Policy Options **for the Rest of the 1990's,** Report No.11161-EC, Country Operations Dept. IV, World Bank.
||||_Annual_|_Gains from_|
|---|---|---|---|---|
||||_Import_|_Variety_|
|_Year_|_Bias_|(<br>1<br>_bias _<sup>_−_1)</sup> <sup>_×_ 100</sup>|_(% of GDP)_|_(% of GDP)_|
|2005|1.007|-0.69|47%|-0.33|
|2006|0.998|0.21|44%|0.09|
|2007|1.007|-0.70|50%|-0.35|
|2008|1.011|-1.13|64%|-0.73|
|2009|0.986|1.41|46%|0.65|
|2010|1.012|-1.23|46%|-0.56|
|2011|0.998|0.23|63%|0.15|
|2012|0.997|0.35|55%|0.19|
|2013|0.979|2.14|51%|1.08|
|2014|1.004|-0.41|43%|-0.18|
|2015|1.001|-0.11|33%|-0.03|
|2016|0.999|0.13|30%|0.04|
|2017|0.995|0.50|38%|0.19|
|2018|0.999|0.09|45%|0.04|
|2019|1.001|-0.07|43%|-0.03|
|2020|1.001|-0.11|40%|-0.05|
|2021|1.000|-0.03|45%|-0.02|
|**Average per-annum**|**0.986**|**1.95**|**46%**|**0.61**|
|**Total (1995-2021)**||**52.66**||**16.47**|

_Note:_ Authors’ calculation based on six-digit disaggregated data. See text for detailed explanation.

Table 6: _Import Price Bias and the Gains from Variety in Rwanda_ 22