rafmacalaba/datause-encoder-data huggingface dataset:
MINIMUM WAGES IN CAMBODIA 25 with a garment sector worker, the impact of minimum wage hike is close to zero throughout the entire distribution (left plot, Figure 8). For households without a garment sector worker, the estimates are statistically insignificant throughout the distribution except at the top quarter of the quantiles where the impacts are negative (right plot, Figure 8).

# 7. Conclusion

This study highlights a few things about minimum wage hikes that can be informative to future minimum wage policy in Cambodia and elsewhere. First, the direct impact on employment is nil and on earnings is small relative to the magnitude of the hikes. The concerns for large dis-employment effect does not, at least for the period studied, hold true. Furthermore, the overall wage earnings by households do not seem to change following a hike in minimum wage. Second, the minimum wage hike affects earnings throughout the distribution and not just at the bottom where workers are earning wages close to the level of minimum wage. This suggests that the nature of compensation schemes, or the wagenegotiation process matter for the distributional consequences of minimum wage policy. Third, the minimum wage hike in a particular sector may have unintended negative impacts on workers in the unaffected sectors unlike a positive _lighthouse effect_ often described in the literature.

However, it is important to be cautious in extrapolating these results for future hikes in minimum wages. For instance, the impact might be starker (more negative) as the level of the minimum wage keeps increasing relative to the distribution of garment sector wages. Second, and more importantly in the context of Cambodia, it is important to understand how the changes in minimum wages factor into the production function of the foreign enterprises that employ most of the wage employment in the garment sector. If foreign enterprises are elastic on their Cambodian garment labor demand at higher levels of minimum wages, the impacts of future hikes could lower garment employment. The evidence in this study shows that, at least at the current levels of minimum wages, the elasticity does not seem to be too high.

# References

- **Addison, John T, McKinley L Blackburn, and Chad D Cotti.** 2012. “The effect of minimum wages on labour market outcomes: County-level estimates from the restaurantand-bar sector.” _British Journal of Industrial Relations_ , 50(3): 412–435.

- **Bell, Linda A.** 1997. “The impact of minimum wages in Mexico and Colombia.” _Journal of Labor Economics_ , 15(S3): S102–S135.

- **Boeri, Tito, Pietro Garibaldi, and Marta Ribeiro.** 2011. “The lighthouse effect and beyond.” _Review of income and Wealth_ , 57: S54–S78.
African countries in the same way and making necessary adjustments to account for the commodity price crises. Lastly, we also adjust the estimated impacts for Sierra Leone according to the Epidemic Preparedness index of Chad and Sierra Leone.

Table 1: Estimated impact of the COVID-19 pandemic on main impact channels

|Impact channel|Shock Size (%)|
|---|---|
|Labor supply|-2.2|
|Labor productivity|-6.2|
|Capital productivity|-2.1|
|Trade and transport margins|2.7|
|F/x flows|-33.2|
|Commodity prices||
|Oil|-30.0|
|Minerals|-10.0|
|Tourism|-25.0|
|Source: Authors’ calculation followin<br>(2020)|g Djiofack et al.|

The model is set up for a short-run analysis by eliminating various substitution possibilities in production. The standard set of model parameters allows various production factors to be substituted with each other and adjust the export supply based only on the difference between domestic and international prices. In the version used for this paper, such substitutions are either not possible or very limited. These changes reflect the possible disruptions and difficulties many sectors are likely to face while adjusting to the pandemic conditions. In this short-term setting:

- Substitution between capital and skilled labor is not possible.

- Substitution between capital vintages (i.e., new and old capital) is not possible.

- Substitution between land, labor, and capital is not possible at the top production nest.

- Substitution of export and domestic supply is significantly reduced.

# 2.2. Econometric estimation using data from Chad’s household surveys

We leverage data from the Chad High-Frequency Phone Survey (HFPS)<sup>8</sup> conducted in May-June 2020 by INSEED (with support from the World Bank) to assess the actual impact on female-headed households in Chad. The main objective is to identify the type of households directly or indirectly affected by the pandemic and identify its main impact channels. The survey is a 30-minute Computer Assisted Telephone Interview (CATI) conducted at the household level and contains limited information at the individual level. As such, we are unable to assess the gendered impact on women in general through the survey. Since we know the gender of the household head, we can assess the impact on female-headed households.

The Chad HFPS 2020 sample was drawn from ECOSIT 4 (conducted in 2018-2019), which is the same survey that we used to engender the Chad SAM. The final survey sample consists of 1,748 households

> 8 Accessible at <u>https://microdata.worldbank.org/index.php/catalog/3792.</u>

5
**Figure 3: Evolution of inequality in the Russian Federation, 1995-2012**

<!-- Start of picture text --> Gini index<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>Gini index (Income per capita) 95% CI<br>Gini index (monthly wages) 95% CI<br>1995 1996 1998 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text --> _Source:_ RLMS (Rounds VI to XXI) _Notes:_ The Gini index for wages is calculated on the basis of currently employed wage workers in the 18-60 age group, reporting at least 140 worked hours in the last 30 days. Per capita income is calculated for the entire population using the variable ‘ _tincm_n’_ of the RLMS. Wages and income for rounds VI and VII were divided by 1,000 to account for the nominal revaluation of the ruble in January 1998 (1,000 old rubles = 1 new ruble). The RLMS was not administered in 1997 and 1999.

Inequality in wages has followed the income trend in the 2000s. Average and median monthly wages nearly doubled between 2002 and 2012, with the growth trend only interrupted during the 2008-09 international crisis (Figure 4). But more importantly, increases in wages grew faster at the bottom of the distribution, contributing to the decrease in inequality. Indeed, the Gini index for monthly wages decreased by 18 percent between 2002 and 2012 (Figure 5).<sup>3</sup> While the upwards jump in wage inequality during the first years of the economic transition was driven by augmented dispersion both in the upper tail and the lower tail of the earnings distribution (Flemming and Micklewright 1999; Brainerd, 1998), the decline in wage inequality of the 2000s was driven by a greater compression _at the bottom_ . Between 2000 and 2012, the 50-10<sup>th</sup> percentile ratio of hourly wages diminished by 32 percent while the 90-50<sup>th</sup> percentile ratio only declined by 24 percent (Figure 6). This, in turn, led to an increase in the share of wages out of total

> 3 The reduction in inequality found is robust to the selection of the indicator. Measured by the 90-10th percentile ratio, inequality dropped by 35 percent between 2002 and 2012, while it decreased by 25 percent in terms of the coefficient of variation (Figure 5).

4
crisis. Moreover, we find that CDS spreads are positively related to the fiscal cost relative to GDP of resolving any previous banking crisis. Thus, investors in bank liabilities appear to expect larger losses in countries that experienced costly banking crises before.

We also consider the pricing of bank risk, as measured by the volatility of weekly bank stock returns, into bank stock prices and CDS spreads, separately for systemically large and small banks. We find that the share prices of systemically large banks are more positively related to bank risk, while CDS spreads of systemically large banks are more negatively related to bank risk. This suggests that a marginal increase in bank risk increases the implicit subsidy from the financial safety net relatively more for systemically large banks – after controlling for systemic size and government finances. These results are in line with the view that systemically large banks are too large to fail.

Overall, we find that a systemically large bank may benefit relatively more from taking on more risk, while it can also lose relatively more from being in a country that runs large government deficits at a time of financial crisis. This makes the net benefit of systemic size ambiguous. It also suggests that some banks – particularly those with limited risk and located in high-deficit countries – may have grown beyond the size that maximizes their implicit subsidy from the financial safety net. Such banks can increase shareholder value by downsizing or splitting up. For our overall sample, estimated coefficients imply that the share prices of the average systemically important bank are discounted 22.3 percent on account of systemic size, providing strong incentives to reduce bank size relative to the national economy. Our data indicate that a smaller proportion of banks are systemically important - relative to GDP - in 2008 than in the two previous years, which could reflect private incentives to downsize.

This paper is related to several others that have considered the impact of bank size on bank stock returns and bank liabilities. In 1984, the US Comptroller of the Currency in testimony before Congress argued that a group of 11 large banks were ’too big to fail’ (TBTF) and that for those banks total deposit insurance would be provided. Using an event study methodology for a sample 63 US banks, O’Hara and Shaw (1990) find that there are positive wealth effects accruing to these TBTF banks, while there are negative wealth effects accruing to the smaller banks.

4
**Table 5: Average annual changes in Nighttime Light Luminosity**

||**2000-200**<br>Recessio|**2**<br>n|**2**<br>Re|**002-2004**<br>bound|**200**<br>Exp|**4-2006**<br>ansion|**2008-2009**<br>Recession||**20**<br>Re|**10-2011**<br>bound|
|---|---|---|---|---|---|---|---|---|---|---|
||**Core**|**Peri**|**Core**|**Peri**|**Core**|**Peri**|**Core**|**Peri**|**Core**|**Peri**|
||||Urban<br>|Agglomera<br>|tions in t<br>|he Least D<br>|eveloped P<br>|rovinces<br>|||
|Median|-3.1|-6.7|6.3|13.5|0.0|4.8|-7.5|-7.8|18.0|21.5|
|Mean|-3.8|-10.5|10.7|16.2|-0.8|3.9|-7.7|-7.0|18.8|21.7|
||||Ur<br>|ban Agglom<br>|erations i<br>|n the Tra<br>|nsition Prov<br>|inces<br>|||
|Median|-3.7|-4.1|2.9|7.3|2.2|5.3|-1.4|-1.1|8.4|13.9|
|Mean|-2.5|-5.5|3.2|7.9|1.5|5.4|-1.1|-0.5|9.9|14.5|
||||Ur<br>|ban Agglom<br>|erations i<br>|n the Dev<br>|eloped Prov<br>|inces<br>|||
|Median|-2.1|-7.6|2.7|8.9|2.4|12.6|-1.1|-1.4|2.3|10.8|
|Mean|-2.4|-6.9|2.4|8.4|3.4|13.0|-1.8|-1.4|2.4|11.0|
|||||Urban Agg|lomerati|ons in all t|he Provinc|es|||
|Median|-3.3|-5.6|4.1|10.2|2.2|5.6|-1.7|-2.1|8.6|16.2|
|Mean|-3.0|-7.4|5.6|10.9|1.0|6.1|-3.3|-2.8|11.6|16.3|

Notes: The average growth rates are based on average DMSP values emitted by the core and periphery grid cells defined by the methodology described in section 3.2. See Figure 6 for a map showing the coreperiphery delineations across the country.

**Table 6: Absolute convergence in NTL across periods**

||**2000-2002**|**2002-2004**|**2004-200**|**6 2008-2009**|**2010-2011**|**2013-2017**|**2017-2019**|
|---|---|---|---|---|---|---|---|
||Recession|Rebound|Expansion|Recession|Rebound|Expansion|Recession|
|Ln NTL Base|||**Dep Var: Perc**|**ent Change i**|**n Period NTL**|||
|Year|2.00|-18.56*|-7.91|-0.44|-33.15***|-31.81***|-12.22***|
||[0.29]|[-1.99]|[-1.43]|[-0.05]|[-3.87]|[-3.18]|[-2.77]|
|Constant|-19.39|162.28**|67.74|0.31|291.20***|143.07***|56.29***|
||[-0.33]|[2.05]|[1.44]|[0.00]|[3.97]|[3.83]|[3.27]|
|Observations|44|44|44|44|44|44|44|
|R-squared|0.00|0.20|0.06|0.00|0.46|0.35|0.16|
|NTL Data|DMSP|DMSP|DMSP|DMSP|DMSP|VIIRS|VIIRS|

Notes: The average annual DMSP or VIIRS values are emitted by the core and periphery grid cells defined by the methodology described in section 3.2. See Figure 6 for a map showing the core-periphery delineations across the country. The change (dependent variable) is based on the value in the last year of a period and the value in the year before the period started (which is designated the base year). For example, for 2010-2011, the percentage change is between 2011 and 2009 and the base year is 2009.

25
Table A31: Placebo outcomes

# **Panel A: Short-term sample**

|_Dep. Var. (Y):_|_Climate ch_|_ange is threat_|_Vote for fu_|_el efciency_|_Think will _|_be working_|||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|||
|Drama|-0.011|-0.015|0.003|-0.001|0.008|0.013|||
||(0.035)|(0.035)|(0.046)|(0.046)|(0.034)|(0.034)|||
|Documentary|-0.034|-0.038|0.019|0.018|-0.001|0.018|||
||(0.045)|(0.044)|(0.056)|(0.055)|(0.043)|(0.042)|||
|Ybaseline|0.501<sup>_∗∗∗_</sup><br>(0.038)|0.467<sup>_∗∗∗_</sup><br>(0.041)|||0.577<sup>_∗∗∗_</sup><br>(0.040)|0.576<sup>_∗∗∗_</sup><br>(0.041)|||
|Controls||✓||✓||✓|||
|R-squared|0.271|0.280|−0.003|0.021|0.310|0.313|||
|P-value equal coef.|0.606|0.591|0.772|0.720|0.832|0.908|||
|Observations|606|606|606|606|606|606|||
|Mean Y (Control)<br>**nel B: Medium-t**|0.741<br>**erm samp**|0.741<br>**le**|0.505|0.505|0.709|0.709|||
|_Dep. Var. (Y):_|_Climate c_<br>(1)|_hange is threat_<br>(2)|_Vote for f_<br>(3)|_uel efciency_<br>(4)|_Think wil_<br>(5)|_l be working_<br>(6)|_Corruptio_<br>(7)|_n is an issue_<br>(8)|
|Drama|-0.000<br>(0.040)|-0.011<br>(0.039)|-0.007<br>(0.049)|-0.010<br>(0.050)|0.056<br>(0.046)|0.045<br>(0.047)|0.012<br>(0.031)|0.008<br>(0.033)|
|Documentary|0.021|0.029|0.065|0.072|0.005|0.005|0.001|0.007|
||(0.041)|(0.040)|(0.050)|(0.050)|(0.047)|(0.048)|(0.032)|(0.032)|
|Ybaseline|0.424<sup>_∗∗∗_</sup><br>(0.040)|0.380<sup>_∗∗∗_</sup><br>(0.044)|||0.001<br>(0.041)|-0.012<br>(0.043)|||
|Controls||✓||✓||✓||✓|
|R-squared|0.180|0.212|0.001|0.009|−0.002|0.022|−0.003|0.032|
|P-value equal coef.|0.597|0.328|0.147|0.108|0.267|0.393|0.727|0.987|
|Observations<br>|617|617|617|617|617|617|617|617|
|Mean Y (Control)|0.714|0.714|0.490|0.490|0.650|0.650|0.879|0.879|

# **Panel B: Medium-term sample**

Notes: Heteroscedasticity-robust standard errors in parentheses. Controls are described in the notes to Table 2. * p < 0.10, ** p < 0.05, *** p < 0.01 Table A32: Index at baseline

||**_Loading factor_**|
|---|---|
|**Baseline Stance Index** _(Cronbach’s alpha = 0.56)_||
|Knows that father determines sex of child|0.176|
|Agreement (1-5) on “women should be virgin until marriage”|0.456|
|Agreement (1-5) on “a woman should be banned from entering the kitchen or household<br>shrine during her period”|0.442|
|Agreement (1-5) “parents should maintain stricter control over their daughters than their<br>sons”|0.497|
|Agreement (1-5) “girls should be allowed to wear whatever they want without being harassed”|0.251|
|Agreement (1-5) on “a husband is justifed in hitting or beating his wife if he suspects her of<br>being unfaithful”|0.458|
|Would tell anyone if fnds out that a friend beats or physically hurts his partner|0.214|

Notes: All variables were re-oriented so that the impact of treatments on each component of the index should be positive. The column on the right reports the loading factors used for the construction of the indexes with principal component.

43
prepared by the Peruvian Banking Superintendency, we defined small business loans as loans to businesses up to $500,000.

IV. **Profile of Small Business Lending in Latin America** Table 1 and 2 provide some summary statistics on the share and growth rate of small business lending by domestic and foreign banks in Argentina, Chile, Colombia, and Peru during 1997-2000. Also, within these tables we distinguish between large and small, foreign and domestic banks. We consider large banks to be those in the top 5 percentile of the distribution of banks assets.

According to Table 1, comparing all domestic banks with all foreign banks (i.e., without any regard for size), we find that foreign banks in all four countries in our sample devote a lower share of their total lending to small businesses. However, these shares are not significantly different in the case of Colombia. In fact, Colombia looks very different from the other countries not only because the shares of small business lending by domestic and foreign banks are not significantly different from each other, but also because the shares of lending to small businesses in Colombia are several orders of magnitude smaller than what we find for other countries. While for the other three countries the share of lending to small businesses ranges between 18 and 30 percent depending on the country and the origin of the bank, for Colombia this share does not exceed 2 percent. Again, this might be due to the fact that data on small business lending is available only for the 4,067 firms registered with the Superintendency of Businesses. Thus, while it seems acceptable to compare the share of small business loans for foreign and domestic banks in Colombia, it might not be fair to make comparisons between small business lending shares in Colombia and the remaining countries.

If we separate banks according to size and origin, we find that regardless of bank origin small banks lend more to small businesses than large banks. Further, in all four countries, small foreign banks devote a lower share of their total lending to small businesses than small domestic banks. The difference between small foreign and domestic banks is statistically significant in all countries except Colombia. In contrast, the picture for large banks is quite different. Although large foreign banks appear to lend less to 8
Table 5: Call engagement estimates applying the adaptively weighted m-estimator by Zhang et al. (2021) and the “winner’s curse” correction by Andrews et al. (2021).

|_Panel A: Binom_|_ial model estimates,_|_unweighted and with _|_adaptive weighting._|
|---|---|---|---|
||Unweighted<br>With school RE<br>(1)|Unweighted<br>Without school RE<br>(2)|Adaptively weighted<br>Without school RE<br>(3)|
|T1A|_−_2_._63<sup>_∗_</sup>|_−_2_._54<sup>_∗_</sup>|_−_2_._52<sup>_∗_</sup>|
||[_−_2_._80;_−_2_._45]|[_−_2_._79;_−_2_._3]|[_−_2_._78;_−_2_._27]|
|T1B|_−_2_._49<sup>_∗_</sup>|_−_2_._39<sup>_∗_</sup>|_−_2_._39<sup>_∗_</sup>|
||[_−_2_._62;_−_2_._36]|[_−_2_._54;_−_2_._24]|[_−_2_._55;_−_2_._24]|
|T1C|_−_2_._77<sup>_∗_</sup>|_−_2_._67<sup>_∗_</sup>|_−_2_._66<sup>_∗_</sup>|
||[_−_2_._92;_−_2_._62]|[_−_2_._86;_−_2_._49]|[_−_2_._85;_−_2_._46]|
|T2A|_−_2_._88<sup>_∗_</sup>|_−_2_._76<sup>_∗_</sup>|_−_2_._79<sup>_∗_</sup>|
||[_−_3_._09;_−_2_._67]|[_−_3_._09;_−_2_._43]|[_−_3_._20;_−_2_._39]|
|T2B|_−_2_._67<sup>_∗_</sup>|_−_2_._58<sup>_∗_</sup>|_−_2_._57<sup>_∗_</sup>|
||[_−_2_._84;_−_2_._49]|[_−_2_._82;_−_2_._34]|[_−_2_._81;_−_2_._33]|
|T2C|_−_3_._31<sup>_∗_</sup>|_−_3_._18<sup>_∗_</sup>|_−_3_._18<sup>_∗_</sup>|
||[_−_3_._55;_−_3_._06]|[_−_3_._59;_−_2_._77]|[_−_3_._59;_−_2_._77]|
|Num. students|2462|2462|2462|
|School RE|Yes|No|No|
|_Panel B: “Infere_|_nce on winners” co_<br>With school RE|_rrection on T1B._<br>Without school RE|Re-weighted|
||(1)|(2)|(3)|
|T1B|_−_2_._49<sup>_∗_</sup>|_−_2_._39<sup>_∗_</sup>|_−_2_._39<sup>_∗_</sup>|
||[_−_2_._66;_−_2_._32]|[_−_2_._59;_−_2_._19]|[_−_2_._60;_−_2_._18]|

Notes: _∗_ Value of zero lies outside of the 95% confidence interval. (1) Frequentist estimate, unweighted and with school random effects as in the original model specification (Table 1, Column 2). (2) Frequentist estimate without school random effects. (3) Frequentist estimate without random effects, applying adaptive weights as in Zhang et al. (2021). Panel A: full estimates for all treatment groups. Panel B: Median estimate and adjusted confidence intervals for T1B, applying corrections for inference on the best arm as in Andrews et al. (2021). Note that this correction is only theoretically valid in column (3) where the underlying estimator is asymptotically normal.

35
**Figure 7:** Deviations from Predicted Electricity Consumption in Dhaka **Deviation of electricity consumption from model prediction** Percent

<!-- Start of picture text --> 2020 2021<br>10<br>5 1 st Lockdown<br>0<br>-5<br>-10<br>-15<br>Deviations below the line<br>-20<br>are statistically significant<br>-25 at the 5 percent level<br>-30<br>-35 2 nd 3 rd & 4 th<br>-40 Ripple Lockdown Lockdown<br>-45<br>January February March April May June July August September October November December January February March April May June July August September October<br><!-- End of picture text --> However, unlike during the first lockdown, electricity consumption was not below the model prediction during the second one. Importantly, and in contrast to the first lockdown, the readymade garments factories remained open. In July 2021, during the third and fourth lockdowns, electricity consumption declined by 20 percent, with the deviation being significant at the 1 percent level. While this was a large decline, it was much lower than during the first lockdown. The wave was contained by mid-August, after which electricity consumption gradually recovered.

For the other divisions, the picture is mixed. In Chattogram, the deviations of actual electricity consumption from the model predictions follow a very similar pattern as in Dhaka, although the decline in April 2020 was somewhat smaller. The resemblance is expected since the two divisions have similar structural characteristics. However, in the other divisions, the decline was much weaker, suggesting that electricity consumption there is measured with huge errors or economic activity in these divisions was impacted less.<sup>10</sup> The declines in electricity consumption in Dhaka and Chattogram are very much in line with the decline in mobility. The Google mobility data are based on aggregated smartphone GPS data. Thus, mobility trends are a decent measure for Dhaka compared with the peripheral regions, which have low smartphone penetration. Figure 8 shows that workplace mobility in Bangladesh dropped substantially during the first lockdown and much less during the later ones. Despite the containment measures, workplace mobility was only marginally below the

> 10 For the other divisions, the deviations from the model-predicted average growth of electricity consumption are not statistically significant even at the 10 percent level. The results by division are provided in table A1 in the appendix.

15
_Figure 9. Incidence, progressivity and marginal contributions of taxes_ _a. Concentration shares for taxes, by decile_ _b. Progressivity of taxes (Kakwani Index)_ <!-- Start of picture text --> 1<br>0.9 All taxes 0.22<br>0.8<br>0.7<br>0.6 All indirect<br>0.12<br>0.5 taxes<br>0.4 Petr. Duty 0.20<br>0.3<br>0.2 Excise 0.06<br>0.1<br>0 VAT 0.14<br>PIT 0.42<br>0.0 0.2 0.4 0.6<br>1 2 3 4 5 6 7 8 9 10<br>Kakwani Index<br>c. Redistributive and Poverty Reduction Effects of taxes<br>3<br>2.16<br>2 1.51<br>0.76<br>1 0.44<br>0.15<br>0<br>-0.01<br>-0.32<br>-1<br>-1.02 -0.91<br>-2 -1.85 -1.86<br>-3<br>Redistributive Effect<br>Poverty Reduction Effect<br>PIT VAT Excise Petr. Duty All indirect taxes All taxes<br><!-- End of picture text --> _Source: Authors’ calculation based on UNHS 2016/17._ _Note for c: A positive redistributive effect means inequality declines, while a negative redistributive effect means inequality increases (in Gini points). A positive poverty reducing effects means poverty is declining while a negative poverty reducing effect means poverty is increasing (in percentage points)._ purchases. Excise taxes on tobacco may have dynamic effects (over the medium- to long-term) including reduced long-term health expenditure, higher labor productivity and longer life expectancy which are not captured here, and which _may_ change the medium- to long-term estimates of the impact of this tax on _future_ rates of poverty. See Fuchs et al. (2019).

29