rafmacalaba/datause-encoder-data huggingface dataset:
the correlations is similar in the case of the wage series that nets out changes in the composition of the labor force, although the fall in the real wage cyclicality is more gradual.

# 5 Conclusions

This paper studies changes in the cyclicality of real wages in four Latin American countries during the past three decades. Our empirical approach relies on a vector autoregression model with both, time-varying coeffi cients and residuals volatilities, which properly accounts for the dynamic properties of the underlying data series and their comovement, as well as for changes in the underlying relationship between the different variables.

We find that real wages were highly procyclical during the 1980s and early 1990s in most of the countries studied, but the responses of wages to business cycle fluctuations declined in the second half of the 1990s and 2000s. This decline coincided with the disinflation process in Latin America, suggesting a new role for downward wage rigidities in the region. Consistent with the existence of downward wage rigidities, we find in most cases an excess cyclicality of wages: the response of the real wage to output was stronger in expansions than in recessions.

Our results are primarily based on aggregated data for the manufacturing sector, and hence present certain limitations: First, compositional effects due to ins and outs of workers with different characteristics along the business cycle have been shown to bias downward estimates wage cyclicality in developed economies. Second, higher volatility of manufacturing output than GDP may also offer a misleading picture of the behavior of the real wage in the whole economy.

We have assessed the importance of these two potential concerns using micro data in the case of Brazil. Perhaps surprisingly, we find that wages holding the composition of the labor force constant over the cycle behave very similarly to aggregate wages. This stands in contrast with previous findings for high-income countries.

One possible reason for the lack of important compositional effects in these countries is the role of informality as a buffer. To the extent that the relevant adjustment margin 20
Table F.3. Impacts of PEP on Labor Outcomes

||Labor Outcomes<br>(Index)|Formal<br>Job|Hours Worked<br>(log)|Reservation<br>Wage (log)|Job<br>Satisfaction|Self<br>Employed|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|1[PEP_i_ = 1]|1.753|0.118|0.060|-0.081|0.096|-0.514|
||(0.957)|(0.077)|(0.325)|(0.152)|(0.258)|(0.230)|
|q-values|[0.202]|[0.746]|[0.744]|[0.744]|[0.185]|[0.252]|
|Obs. left|335|280|102|78|194|265|
|Obs. right|676|662|233|85|416|629|
|Observations|2,672|2,019|1,210|654|2,019|2,006|
|Mean values (Non-RAMV refugees)|0.000|0.001|3.937|0.564|0.324|0.430|
|Outcome Level|Individual|Individual|Individual|Individual|Individual|Individual|

_Notes:_ Dependent variables: (i) Labor Outcomes (Index) is constructed using the outcome variables of columns (ii)–(vi) using the methodology of Kling et al. (2007); (ii) Formal Job is an indicator variable [=1] if the refugee is employed, reports a pension fund, and has a job with a written contract; (iii) Hours worked (log) is the logarithm of weekly hours worked; (iv) Reservation Wage is the logarithm; (v) Job Satisfaction is an indicator [=1] if the respondent wants to change the job they currently have; and (vi) Self-Employed is an indicator variable [=1] if main occupation is independent or self-employed. All columns include department (Antioquia, Atl ́antico, Bogot ́a, and Norte de Santander) and sampling-city fixed effects. Individual controls include: age, gender, and years of education before migration. Labor history in Venezuela controls include: ever worked [=1], type of job, had a written contract [=1], and gap between last job and migration. Household controls in Venezuela include: number of children; household size; if had energy, water, and sewage [=1]; owner of dwelling [=1]; and had smartphone [=1]. Migration decisions controls include: had family/friends in Colombia before migrating [=1], knew of job opportunities before migrating [=1], if the head migrated for health reasons [=1], and time of settlement in Colombia. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.

65
of primary schools, curricular reform, teacher upgrading, book provision at the basic education level. At the secondary level, the program contributes to the expansion of secondary school facilities, curriculum revision, teacher upgrading, and instructional materials and equipment. For technical and vocational education and training, the program supports employer and market surveys and the encouragement of private participation. For teacher training the program contributes to upgrading and expansion of teacher training facilities, curriculum revision, distance education, head teacher training, and establishment of national standards. The fifth component assists in the expansion of the tertiary sector. Finally, an institutional development component supports planning, financial management, implementation, and monitoring and evaluation capacities of the Ministry of Education. A comprehensive CBA of such a program would not be feasible given issues pointed out in the earlier sections of estimating social returns, the distributive effects and attribution. Moreover, the data in Ethiopia are sparse and of low quality.

The economic analysis of this project was guided by the questions listed in Box 1. The work included fiscal impact analysis, institutional risk analysis, analysis of alternatives, and poverty analysis, all supported by broader country and economic work. A crucial part of the work also includes cost-benefit analysis of one part of the project – which could have led to different design alternatives had it not been undertaken.

One of the main activities is the building, upgrading and renovation of primary schools. In fact, there will be 2,423 new first cycle primary schools built within walking distance of communities. The massive civil works campaign requires the coordinated effort of planners, 28
- 23 -

many farmers produce a particular crop, the local market price would decline. This raises an issue of endogeneity in the model.

To address this issue, an instrumental variable is constructed from the previous LSMS data in 2008. Using the same method, the average local market prices are calculated at each location. For export crops, the regional market prices are also lagged for 2 years. With the current prices replaced with the predicted values based on the first-stage regression, the conditional logit model is re-estimated.

The results turned out to be broadly the same as the above (Table 3). The price variable still has a negative coefficient: The lower prices, the more attractive crop choice. There remains concern that the time lag may not be enough to instrument the market prices. But the conventional exogeneity test cannot be rejected at the 5 percent significance level. The test statistic is 3.47, suggesting that the prices are likely to be exogenous. Therefore, the results can be understood to imply that the market prices may decline as too many farmers grow the same crops, or that farmers simply do not know market prices. In either case, farmers are not really effectively using the market prices to guide their crop choices.
# **Appendix 9: Sharpened q-values**

Appendix Table 9.1 shows which main outcomes have sharpened q-values below 0.10, taking as a family the set of 26 outcome-survey round combinations for a given treatment reported in Tables 4 and 5.

|**Appendix Tab**|**le 9.1: Sharpenedq-values below 0.10 f**|**or main effects**||||
|---|---|---|---|---|---|
|**Table**|**Outcome**|**Treatment**|**Round**|**p-value**|**q-value**|
|4|Finance & Accounting Practices|Insourcing|1|0.0040|0.015|
|4|Marketing & Sales Practices|Insourcing|1|0.0263|0.071|
|4|Marketing & Sales Practices|Insourcing|2|0.0309|0.074|
|4|Verified Traditional Practices|Insourcing|1|0.0002|0.003|
|4|Verified Traditional Practices|Insourcing|2|0.0000|0.001|
|4|Verified Digital Practices|Insourcing|1|0.0010|0.005|
|4|Verified Digital Practices|Insourcing|2|0.0009|0.005|
|4|Overall Index of Business Practices|Insourcing|1|0.0008|0.005|
|4|Overall Index of Business Practices|Insourcing|2|0.0213|0.065|
|4|Finance & Accounting Practices|Outsourcing|1|0.0514|0.066|
|4|Finance & Accounting Practices|Outsourcing|2|0.1091|0.098|
|4|Marketing & Sales Practices|Outsourcing|1|0.0280|0.062|
|4|Marketing & Sales Practices|Outsourcing|2|0.0063|0.019|
|4|Operations & HR Practices|Outsourcing|2|0.0484|0.066|
|4|Verified Traditional Practices|Outsourcing|1|0.0004|0.003|
|4|Verified Traditional Practices|Outsourcing|2|0.0002|0.003|
|4|Verified Digital Practices|Outsourcing|1|0.0001|0.003|
|4|Verified Digital Practices|Outsourcing|2|0.0007|0.005|
|4|Overall Index of Business Practices|Outsourcing|1|0.0024|0.011|
|4|Overall Index of Business Practices|Outsourcing|2|0.0033|0.013|
|5|I.H.S. Monthly Sales|Outsourcing|2|0.0454|0.066|
|5|I.H.S. Yearly Sales|Outsourcing|1|0.0412|0.066|
|5|I.H.S. Yearly Sales|Outsourcing|2|0.0306|0.062|
|5|I.H.S. Yearly Profits|Outsourcing|2|0.0184|0.046|
|5|Sales & Profits Index|Outsourcing|2|0.0382|0.066|
|5|I.H.S. Employment|Outsourcing|2|0.0918|0.098|
|4|Finance & Accounting Practices|Consulting|1|0.0227|0.099|
|4|Finance & Accounting Practices|Consulting|2|0.0138|0.087|
|4|Operations & HR Practices|Consulting|2|0.0242|0.099|
|4|Verified Traditional Practices|Consulting|1|0.0049|0.066|
|4|Verified Traditional Practices|Consulting|2|0.0000|0.001|
|4<br>Notes: no outc<br>Sharpened q-v|Overall Index of Business Practices<br>ome for training had a standardized q-val<br>alues control false discovery rate across|Consulting<br>ue below 0.10<br>26 outcome-round|2<br>estimates f|0.0086<br>or a treatm|0.074<br>ent.|

83
A comprehensive review of the behavior of savings in Colombia is presented in Tables 1 to 3. These Tables show 5 year averages of adjusted private, public and national saving rates, quantify its movements as a percentage of GNP and identify the causes behind those changes<sup>8</sup> . The explanations of the change in savings are divided among movements in consumption, capital gains and disposable income. The latter is divided among changes in gross operating surplus (GOS), taxes and other components (i.e. subsidies, transfers from the external to the private and public sectors, transfers from the private to the public sector and ONFP). As can be seen in Table 1, private disposable income also include labor income. Several important conclusions can be derived from those tables regarding the recent behavior of savings.

**Table 1 Accounting for the Change in Private Savings**

|**Period**|**Adjusted**<br>**Saving**<br>**Rate**<br>**(% of**<br>GNP)|**Change**||**Movements**<br>**(%**|**in Pri**<br>**of GN**<br>**Expl**|**vate Savi**<br>**P)**<br>**anations**|**ng**|||
|---|---|---|---|---|---|---|---|---|---|
||||**Consumption**|**Capital**<br>**Gains**||**Unadjust**|**ed Disp**|**osable Inco**|**me**|
||||||**Tot.**|GOS|TAX|**Salaries**|**Other**|
|56-60|14.0|4.3|-3.3|0.6|0.4|0.7|0.1|-0.1|-0.3|
|61-65|12.0|-2.0|2.3|-1.1|1.4|-2.7|0.6|3.6|-0.**1**|
|66-70|13.0|1.0|-1.5|3.0|-3.5|-3.2|-3.1|0.7|2.1|
|71-75|13.4|0.4|-1.9|-0.6|-0.9|-1.1|-0.5|1.0|-0.3|
|76-80|14.5|1.1|-2.4|0.5|-1.8|-2.9|-1.7|0.7|2.1|
|81-85|13.6|-0.9|2.0|-0.8|1.9|-4.4|1.9|3.8|0.6|
|86-90|16.7|3.1|-5.2|0.2|-2.3|0.6|-2.2|-4.8|4.1|
|91-93|14.2|-2.5|0.7|1.3|-3.1|-2.4|-1.2|0.5|0.0|

Source: National Accounts, Data Appendix and Authors Calculations. Between 1950 and 1969 the measure of private saving constructed by El Banco de la Rep6blica was a measure of net saving. In order to be consistent with the measure of gross saving constructed by DANE since 1970, the depreciation of physical capital was added. The interests on external public debt were subtracted from the unadjusted measure of disposable income presented in the National Accounts.

s It could be possible to argue that Table I would be more illustrative if the private saving rate is defined as a percentage of private income before taxes and its changes are explained only by movements in taxes and consumption. However, this would not show the causes behind the changes in disposable income, an issue relevant for policy makers.

12
<!-- Start of picture text --> vt 7<br>— KP Children — Adults — DR<br>oO ——-<br>N<br>°<br>20 25 30 35 40 45 50 55<br>age<br><!-- End of picture text --> <!-- Start of picture text -->
*<br>~<br>wa<br>oO<br>20 25 30 35 40 45 50 55<br>age<br><!-- End of picture text -->

<!-- Start of picture text --> vt<br>oO<br>N<br>S<br>20 25 30 35 40 45 50 55<br>age<br><!-- End of picture text --> <!-- Start of picture text --> N<br>-—Cc C/KP —| VA<br>r<br>Lo<br>ned<br>20 25 30 35 40 45 50 55<br>age<br><!-- End of picture text -->

<!-- Start of picture text --> =<br>=<br>N;<br>o<br>20 25 30 35 40 45 50 55<br>age<br><!-- End of picture text --> <!-- Start of picture text --> fe)<br>2)<br>oO<br>wo<br>N<br>N<br>- aeZ<br>20 25 30 35 40 45 50 55<br>age<br><!-- End of picture text -->
The estimates of θ are high, but well in the range of values reported in figure 1. Importantly, the PPML elasticity estimate is much lower and more plausible than the values obtained with the other estimators. Santos Silva and Tenreyro (2006) explain this systematic difference in the estimated value between the OLS and the PPML estimators by the heteroskedasticity rather than by the censorship bias.<sup>28</sup> The explanatory power of all the models reported in Table 1 is quite high and increase over time, suggesting that the gravity model is a better representation of bilateral trade in later years.<sup>29</sup> One reason for this better fit would be better data, especially for developing countries. Another, would be a change in the trade structure of developing countries as development leads to a shift towards trade in differentiated rather than homogenous products, hence towards a situation closer to that depicted by the gravity model (see e.g. the evidence in Feenstra, 2004 and Evenett and Keller, 2002).

Figure 6. Cross-Section Estimates of the Elasticity of Trade to Distance θ <!-- Start of picture text --> OLS - ln(M) [-1 .07 ]<br>1.30<br>OLS - ln(1 +M) [-1 .1 2]<br>1.20<br>HT - ln(1 +M) [-0.97 ]<br>1.10<br>ET tobit - ln(a+M) [-1 .23]<br>PPML - M [-0.67 ]<br>1.00<br>0.90<br>1970 1975 1980 1985 1990 1995 2000 2005<br><!-- End of picture text --> Note: 5-years periods over 1970-2004 and a 2-years period 2005-2006 Corresponding Trade elasticity to distance in 1970 reported into brackets. _Source: authors’ calculations_ 28 See the discussion in Santos Silva and Tenreyro (2006). Since the coefficient on the PPML represents the marginal estimate of a change in distance on bilateral trade, the coefficients also represent trade elasticities to distance. Note that the OLS on ln(1+M)) and ET tobit estimates give very close results on this sample.

29 R2 are not directly comparable to pseudo-R2 as the number of observations is not always the same and more importantly, the R2 are based on sums of squares whereas the peudo-R2 are based on ratios of log-likelihoods.

23
imported inputs. We therefore augment the model with two measures of the intensity to which exporting firms import upstream products that are likely to enter as inputs into their production destined for export. As this intensity is not easily captured in the data, we use two measures, a broad one (“intermediate imports (1)”) and a narrow one (“intermediate imports (2)”).<sup>8</sup> The first measure is the share of imported products in total exports of a firm, considering only imports whose processing level is inferior to the level of processing of the main exported product. The second measure is more refined. In addition to the level of processing we rely on broad product categories presented in Appendix Table 1 to check if the underlying broad product categories of an imported product and the main exported product can be vertically linked, so that products of the first can be used as inputs for the second.<sup>9</sup> The narrow measure is likely to be more precise, whereas the broad one is available for a larger number of observations.

As can be seen in Table 20, it turns out to make no qualitative difference which measure of imported inputs one uses, nor whether one looks at all exports or only at non-traditional exports: in all cases, imported input intensity is found to increase export hazard rates statistically significantly. This means that, other things equal, the more a firm relies on imported inputs, the more unstable its export spells become. According to our estimated coefficients, a 10% increase in the share of intermediate imports would increase a hazard rate of nontraditional exports by 0.3-0.4%.

> between input costs and export prices, and they may reduce fixed costs to exporting by providing the quality needed for demanding export markets.

> 8 We rely on the classification prepared by the International Trade Centre, based on HS6 product codes. The  level of processing is a categorical variable taking the value of 1 for raw materials, 2 for semi processed goods and 3 for processed goods

> 9 Specifically, if an imported product belongs to the same broad product category as the main exported product, we rely on the level of processing. If an imported product and the main exported product belong to different broad product categories, we check whether the products of the imported product broad category can serve as inputs for the broad category of the main exported product. Finally, for the products from seemingly unrelated broad product categories, we performed manual checks to see if an imported product can serve as an input for production of the main exported product.

12
|) - Manufacturing Sample<br>Exit<br>Informal<br>(4)<br>0.00878***<br>(0.00251)<br>0.240<br>71,656<br>0.00193<br>(0.00208)<br>0.240<br>71,656<br>0.0187***<br>(0.00563)<br>0.239<br>**p in Working Age)**<br>0.468***<br>(0.0291)<br>259.05<br>71,656<br>Yes<br>Yes<br>Yes<br>s and i) armed attacks by illegal armed groups<br>nment transfers to health, education, and other<br>2000 GDP share in agriculture, services, and<br>reported in parentheses. _∗∗∗_signifcant at the|
|---|
|Sectors (cont’d<br>Formal<br>(3)<br>0.0122***<br>(0.00427)<br>0.264<br>47,273<br>0.00586**<br>(0.00297)<br>0.264<br>47,273<br>0.0277***<br>(0.00976)<br>0.262<br>**IDPs (% Po**<br>0.441***<br>(0.0374)<br>139.57<br>47,273<br>Yes<br>Yes<br>Yes<br>s of year dummie<br>v) central gover<br>ces in 1995, viii)<br>rrors by frm are|
|**Table (IX)**<br>Impacts of IDP infows on Firms in the Informal and Formal<br>**Dependent Variables (in logs)**<br>Entry<br>Formal<br>Informal<br>(1)<br>(2)<br>**Panel A. Reduced Form**<br>Predicted IDP Infows<br>-0.00271<br>-0.00486**<br>(0.00361)<br>(0.00239)<br>R-squared<br>0.531<br>0.671<br>Observations<br>47,273<br>71,656<br>**Panel B. OLS**<br>Share of IDP (%Pop in Working Age)<br>-0.00309<br>-0.00331*<br>(0.00215)<br>(0.00185)<br>R-squared<br>0.531<br>0.671<br>Observations<br>47,273<br>71,656<br>**Panel C. 2SLS**<br>Share of IDP (%Pop in Working Age)<br>-0.00615<br>-0.0104**<br>(0.00819)<br>(0.00510)<br>Observations<br>0.531<br>0.671<br>**Panel D. First Stage**<br>**Dep Variable: Share of**<br>Predicted Infows 1993<br>0.441***<br>0.468***<br>(0.0374)<br>(0.0291)<br>First Stage F-statistic<br>139.57<br>259.05<br>Observations<br>47,273<br>71,656<br>**Controls for all Panels**<br>Year FE<br>Yes<br>Yes<br>Firm FE<br>Yes<br>Yes<br>Additional Controls<br>Yes<br>Yes<br>_Notes:_ Each coeffcient corresponds to a separate regression. Additional controls include interaction<br>in 1995, ii) fatal victims of attacks in 1995, iii) homicide rates 1995, iv) public expenditures in 1995,<br>expenditures in 1995, vi) number of fnancial institutions in 1995, vii) number of tax collection off<br>industry, ix) municipal tax income in 1995, and x) night light density in 1995. Clustered standard e<br>1%, _∗∗_signifcant at the 5%, _∗_signifcant at the 10%.|

40
10, the shock to the petroleum sector is a price increase of 11.15% which is an average of the price shocks applied to diesel and gasoline. The assumption here is that gasoline and diesel have a similar weight in the I-O oil refining sector and that they represent the almost totality of the sector. The price shocks applied to electricity is 2.1%, which is an average price increase across tariffs blocks weighted by the number of households in each block.<sup>9</sup> The most accurate estimates for direct effects remain those provided in the previous section and we will disregard estimates of direct effects using I/O data. What is of interest here is the relative share of indirect effects over total effects. Using this share, one can then derive a better approximation of the real value of indirect effects using the direct effects values of the previous section.

**Table 10 - Reforms 2014: Baseline Data (Indirect Effects)**

||Unit|Price<br>January<br>1st,<br>2014|Price<br>October<br>1st,<br>2014|Price<br>increase<br>(%)|Average<br>price<br>increase|HBS<br>Sector|Corresponding I/O<br>Sector|
|---|---|---|---|---|---|---|---|
|Gasoline|L|12.02|12.80|6.49|11.15|Petroleum|D23-Oil Refining|
|Diesel|L|8.54|9.89|15.81|11.15|Petroleum|D23-Oil Refining|
|Electricity|kWh|1.02|1.04|2.10|2.10||E001 Electric energy|

Source: Official Bulletins No. 6222, 16 January 2014 and No. 6288, 4 September 2014 and authors’ estimations based on average prices for electricity and water.

Results of the simulations show that the relation between direct and indirect effects varies significantly across products and across quintiles. If we simulate shocks for the two sectors independently we find indirect effects to be 87.79 of the total for petroleum products and 36.55% for electricity. The relative weight of indirect effects is also different across quintiles. Indirect effects on petroleum products are the quasi-totality of effects for the first (poorest) quintile while they become 81.33% for the upper quintile. This is understandable because the poor consume very little gasoline and diesel. Instead, for electricity, indirect effects represent 30.1% of total effects for the first quintile and this share increases to 42.17% for the fifth quintile. That is because the coverage of electricity is very large in Morocco and many if not most of the poor consume electricity.

**Table 11 - Reforms 2014 – Indirect effects (% of total effects)**

||**Petroleum**|**Electricity**|
|---|---|---|
|Quintile 1|99.55|30.10|
|Quintile 2|98.87|30.31|
|Quintile 3|96.48|30.52|
|Quintile 4|93.43|33.89|
|Quintile 5|81.33|42.17|
|**Total**|**87.79**|**36.55**|

Source: Authors’ estimations using SUBSIM.

> 9 Note that the share of consumption in each block could also be used for weighting.

16