rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 9**

# **Does the Effect of Migrant Opportunity Differ with Family Characteristics?**

||D<br>|ependent Varia<br>|ble: Enroll in<br>|High School<br>|=1<br>|
|---|---|---|---|---|---|
|Model|(1)|(2)|(3)|(4)|(5)|
|Migrant Share of Village Labor|-2.148**|-2.059**|-1.522*|-1.980**|-2.212**|
|Force|(0.907)|(0.869)|(0.764)|(0.912)|(0.874)|
|Father is Professional (1=yes)|0.137**<br>(0.049)|0.318**<br>(0.082)|0.094*<br>(0.052)|0.142**<br>(0.050)|0.141**<br>(0.050)|
|(Migrant Share)*(Father is<br>Professional)||-2.006**<br>(0.826)||||
|Father had Off-Farm Work (1=yes)|-0.035<br>(0.032)|-0.043<br>(0.032)|0.099<br>(0.066)|-0.037<br>(0.032)|-0.037<br>(0.032)|
|(Migrant Share)*(Father had Off-Farm<br>Work)|||-1.008**<br>(0.479)|||
|Father Enrolled in High School<br>(1=yes)|0.169**<br>(0.031)|0.175**<br>(0.031)|0.174**<br>(0.031)|0.272**<br>(0.052)|0.174**<br>(0.031)|
|(Migrant Share)*(Father Enrolled in<br>High School)||||-0.922**<br>(0.379)||
|Father completed High School (1=yes)|0.033<br>(0.040)|0.040<br>(0.041)|0.034<br>(0.040)|0.037<br>(0.041)|0.091<br>(0.071)|
|(Migrant Share)*(Father Completed<br>High School)|||||-0.461<br>(0.509)|
|Hansen J statistic|1.12|3.01|3.95|5.18|2.37|
|p-value, J statistic|0.773|0.808|0.684|0.521|0.883|
|F statistic, Number of Migrants|4.46|3.28|3.95|5.18|3.23|
|F statistic, interaction term||12.74|19.33|28.67|14.19|
|instruments=0<br>Number of Observations|3006|3006|3006|3006|3006|

Notes: In parentheses, we show robust standard errors clustered at the village-year level. All models control for factors related to village location with village fixed effects, and macroeconomic shocks using province-year fixed effects, as well as village and individual level controls listed in column 5 of Table 7. All models are estimated using an IV-GMM estimator that is efficient in the presence of heteroskedasticity and arbitrary within village-year cluster correlation (see Wooldridge 2002, page 193).

46
52 52 52 52 52 52 51 49 46 36 14 GO000000000000000000 <!-- Start of picture text --> e<br>® : :<br>Ww co) :<br>7 Om i<br>@<br>aJ<br>9jonCc§8<br>=<br>fo}<br>ie)<br>© : :<br>-10 5 0 5 10<br>Log(2)<br><!-- End of picture text -->
||EU27|United|**Appendix**|Rest of<br>hih<br>**Table 7**|Rest of<br>**. Share of**|**Output by**|**sector(%**|**of total**|**output, 20**|Rest of<br>**04)**|Rest of|Rest of|Middle<br>East and|Sub-|Rest of|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||with<br>EFTA|<br>States|Japan<br>|g<br>income<br>Annex 1|high<br>income|Brazil|China|India|Russia<br>|<br>East Asia|South<br>Asia|<br>ECA|<br>North<br>Africa|Saharan<br>Africa|<br>LAC|
|Agriculture|1.7|1.2|1.1|2.1|1.3|6.2|6.2|13.2|4.3|5.2|14.8|8.9|5.9|8.9|5.3|
|All energy|3.1|4.0|2.7|5.8|5.2|8.3|7.1|9.8|24.1|9.5|7.7|22.2|23.4|10.6|9.3|
|All manufacturing|35.3|23.9|31.8|25.9|42.2|33.9|49.8|35.4|23.8|47.5|31.5|24.4|24.3|29.3|41.3|
|_Energy intensive manufacturing_|11.1|7.2|10.0|9.2|13.0|11.5|15.7|10.9|10.7|10.7|4.7|8.9|7.4|9.8|12.6|
|_Other manufacturing_|24.3|16.8|21.8|16.7|29.1|22.3|34.1|24.5|13.1|36.8|26.8|15.5|16.9|19.5|28.7|
|Other industries|5.9|7.0|7.5|8.8|6.5|8.9|10.9|8.5|8.5|7.1|8.4|9.0|7.7|7.5|5.4|
|Service|53.9|63.9|56.9|57.3|44.9|42.8|26.0|33.1|39.3|30.7|37.6|35.5|38.7|43.6|38.7|
|Total|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|

||EU27<br>|United<br>|**Appendix**|Rest of<br>high<br>**Table 8.**|Rest of<br> <br>**Share of E**|**xports by**|**sector(%**|**of total**|**exports, 2**|Rest of<br>**005)**|Rest of<br>|Rest of|Middle<br>East and|Sub-<br>|Rest of|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||with<br>EFTA|<br>States|Japan<br>|<br>income<br>Annex 1|high<br>income|Brazil|China|India|Russia<br>E|<br>ast Asia|South<br>Asia|<br>ECA|<br>North<br>Africa|Saharan<br>Africa|<br>LAC|
|Agriculture|0.8|3.7|0.1|4.1|0.1|10.4|1.1|3.7|0.6|1.7|3.4|4.3|1.9|7.9|6.2|
|All energy|2.5|2.2|0.3|10.0|2.3|4.3|2.0|3.6|52.9|7.1|9.7|24.7|49.4|34.6|17.8|
|All manufacturing|69.4|69.5|90.3|66.6|77.3|68.5|89.9|69.7|36.0|79.6|70.5|53.0|32.6|37.7|61.4|
|_Energy intensive manufacturing_|20.6|19.0|17.5|21.6|16.9|20.6|12.0|19.3|25.4|14.0|4.6|25.7|11.4|17.4|15.0|
|_Other manufacturing_|48.8|50.5|72.8|45.1|60.5|47.8|78.0|50.4|10.5|65.6|65.9|27.2|21.2|20.3|46.4|
|Other industries|1.8|0.9|1.1|3.6|0.2|7.2|0.6|5.0|3.4|1.6|0.7|2.8|1.8|7.4|3.1|
|Service|25.5|23.8|8.3|15.6|20.0|9.6|6.4|18.0|7.2|10.1|15.8|15.2|14.3|12.5|11.6|
|Total|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|

44
19

# **V.   REFERENCES**

Amiti, Mary, Oleg Itskhoki and Jozef Konings (2014). “Importers, Exporters, and Exchange Rate Disconnect”, American Economic Review, 104(7): 1942–1978.

Antràs, Pol, Davin Chor, Thibault Fally, and Russell Hillberry (2012). “Measuring the Upstreamness of Production and Trade Flows”, American Economic Review Papers and Proceedings, 102 (3): 412-416.

Auboin, Marc and Michele Ruta (2013). “The Relationship Between Exchange Rates and International Trade: A Literature Review”, World Trade Review, 12(03), 577-605.

Bayoumi, Tamim, Mika Saito, and Jarkko Turunen (2013). “Measuring Competitiveness: Trade in Goods or in Tasks?”, IMF Working Paper 13/100, Washington: International Monetary Fund.

Bems, Rudolfs and Robert C. Johnson (2012). “Value-Added Exchange Rates”, NBER Working Paper No. 18498, Cambridge, MA: The National Bureau of Economic Research.

Bems, Rudolfs and Robert C. Johnson (2015). “Demand for Value Added and Value-Added Exchange Rates”, forthcoming IMF Working Paper.

Berman Nicolas, Philippe Martin and Thierry Mayer (2012). “How do different exporters react to exchange rate changes? Theory, empirics and aggregate implications”, Quarterly Journal of Economics 127(1):437-4493.

Bown, Chad P. and Meredith A. Crowley, (2013). “Import Protection, Business Cycles, and Exchange Rates: Evidence from the Great Recession”, Journal of International Economics v90, n1 (May 2013): 50-64.

Chen, Natalie and Luciana Juvenal (2013). “Quality, Trade, and Exchange Rate Pass-Through”, CEPR Discussion Paper 9744.

Constantinescu, C, A Mattoo and M Ruta (2015). “The Global Trade Slowdown: Cyclical or Structural?,” IMF Working Paper No. 15/6, Washington, DC.

Di Nino, Virginia, Barry Eichengreen and Massimo Sbracia (2011). “Real Exchange Rates, Trade and Growth: Italy 1861-2011”, Bank of Italy Economic History Working Papers, No. 10.

Duval, Romain, Kevin Cheng, Kum Hwa Oh, Richa Saraf, and Dulani Seneviratne (2014). “Trade Integration and Business Cycle Synchronization: A Reappraisal with Focus on Asia,” IMF Working Paper, 14/52.

Eichengreen, Barry (2007). “The Real Exchange Rate and Economic Growth”, Commission on Growth and Development Working Paper 4, World Bank.

Eichengreen, Barry and Gupta, Poonam (2013). “The Real Exchange Rate and Export Growth: Are Services Different?”, Policy Research Working Paper Series 6629, The World Bank.
<!-- Start of picture text --> 3.00<br>2.46<br>2.50<br>2.23<br>1.95<br>2.00<br>1.66<br>1.46<br>1.50<br>1.15<br>1.04<br>0.97 0.97<br>1.00 0.84<br>0.73<br>0.50<br>0.00<br>-0.50 -0.33<br>2025 2030 2035 2040<br>Net Zero No lab. mar. frictions Crowding out<br>% Change from BaU<br><!-- End of picture text -->

**Figure 5:  percentage point differences in GDP relative to the baseline**

The negative impact on growth is primarily due to higher electricity prices. The increase in electricity price comes from (1) the decarbonization of the power system, which has no cost over the short-run, but becomes more costly when the share of renewable energy becomes very high, which requires expensive dispatchable non-carbon generation (here, gas with CCS) to maintain the system stability; (2) the large increase in electricity demand after 2030 (figure 6), due to electrification of buildings and transportation (with heat in building dominating). In the RNZP scenario, power demand would increase by 50% over the reference case in 2040, leading to a 20% increase in electricity prices. One magnifying factor in the price increase is the slow expansion of renewable energy, caused by labor market rigidities (e.g., skills mismatch, etc.). When these rigidities are removed, electricity prices fall even though demand is 76% higher than in the baseline scenario.

This finding can be generalized to all constraints on renewable energy expansion (e.g., ill-designed market regulations, financing issues, land market issues, etc.). For example, mobility of capital or others factor of production or structural factors such as ability of firms to substitute an intermediate input or factor of production as well as substitutability of different commodities with each other would be critical to determine the final impact. If capital is not fully mobile as assumed in our scenarios, that might prevent firms or consumers from changing their demand for specific 15
**Table 3**

**Summary Statistics of Maize Prices in Tanzania (June 2002 –July 2014)**

||**--------**<br>**Tsh/kg**|**Average Price --**<br>**, Real, Sep 2010**|**------**<br>**= 100**|**----------------**<br>**Standard de**|**---- Volatility -----**<br>**viation of logarit**|**---------------**<br>**hmic change**|
|---|---|---|---|---|---|---|
||_Full_|_No ban_|_Ban_|_Full_|_No ban_|_Ban_|
|**U.S. Gulf**|248|272|219|6.9|7.1|6.6|
|**South Africa**|282|297|264|8.1|8.1|8.1|
|**Nairobi**|400|394|409|9.6|9.9|9.3|
|**Tanzania, average**|332|333|332|12.9|12.0|13.6|
|**Central**|||||||
|_Dodoma_|356|351|362|10.1|8.8|11.1|
|_Singida_|333|330|336|11.2|10.2|12.0|
|_Tabora_|343|351|334|13.2|13.6|12.5|
|**Coastal**|||||||
|_Dar es Salaam_|357|362|349|10.2|9.4|10.7|
|_Lindi_|356|361|350|20.0|19.1|21.0|
|_Morogoro_|349|342|357|13.3|13.6|12.6|
|_Mtwara_|353|351|355|21.9|20.5|23.5|
|**Lake**|||||||
|_Bukoba_|359|361|357|11.3|12.0|10.2|
|_Musoma_|378|384|370|11.4|11.3|11.2|
|_Mwanza_|385|390|380|10.3|10.0|10.3|
|_Shinyanga_|353|353|352|10.2|8.4|11.5|
|**Northern**|||||||
|_Arusha_|334|324|346|9.6|8.7|10.0|
|_Moshi_|348|337|362|10.3|9.0|11.1|
|_Tanga_|333|330|336|14.0|12.3|15.4|
|**Southern Highlands**|||||||
|_Iringa_|279|277|282|13.2|12.8|13.7|
|_Mbeya_|281|292|267|10.2|9.6|10.9|
|_Songea_|242|237|247|17.3|13.3|21.2|
|_Sumbawanga _|247|259|232|14.1|13.1|15.2|

**Notes:** Both Tanzanian and external prices are expressed in real Tanzanian shillings (September 2010 = 100, deflated by the total CPI). Volatility is defined as standard deviation of logarithmic change, multiplied by 100. Out of the 144 monthly observations, 64 correspond to Ban and 80 to No ban regimes.

26
**Table 4: Loans per treatment**

|Dependent Variable: Loans tak|en (% of endow|ment) - Loans|Treatments||
|---|---|---|---|---|
||I|II|III|IV|
|Treatment: Observability|-0.094**<br>(0.04)|-0.090**<br>(0.04)|-0.090**<br>(0.04)|-0.091**<br>(0.04)|
|Treatment: (Effort-based) inequality|-0.157***|-0.153***|-0.157***|-0.145***|
||(0.04)|(0.04)|(0.04)|(0.04)|
|Interaction: Observability X Inequality|0.175***|0.165***|0.172***|0.157***|
||(0.05)|(0.05)|(0.05)|(0.05)|
|Consumption (self reported)||0.004|0.003|0.003|
|16 = High||(0.00)|(0.00)|(0.00)|
|Financial IQ||-0.028**|-0.021|-0.022*|
|5 = High||(0.01)|(0.01)|(0.01)|
|Competitiveness||0.035|0.032|0.033|
|5 = Highly competitive||(0.02)|(0.02)|(0.03)|
|State of personal finances||0.000|0.001|0.006|
|5 = Very good||(0.01)|(0.01)|(0.01)|
|Cognitive Reflection Test|||-0.023**|-0.023**|
|3 = High|||(0.01)|(0.01)|
|Clarity of instructions|||0.012|0.008|
|5 = Always clear|||(0.01)|(0.01)|
|Gender||||-0.027|
|1 = Female||||(0.03)|
|Age (in years)||||0.018***<br>(0.00)|
|Constant|0.208***|0.196*|0.156|-0.222|
||(0.04)|(0.11)|(0.12)|(0.16)|
|R-squared|0.044|0.073|0.085|0.152|
|P|0.001|0.000|0.000|0.000|
|Observations|322|322|322|322|

Notes: OLS regressions. The dependent variable is the proportion of total endowment that was spent. * 10%, ** 5%, *** 1% significance level. Clustered standard errors (by group) in parentheses.

Table 4 displays the results of the OLS regressions. We find similar results as with the expenditure regressions. When endowments are equal, observability significantly reduces the amount of loans taken ( _p<0.05_ in model I, _p<0.05_ in model IV) in line with what we observe in the figures. When subjects earn their endowments, the amount of loans is also reduced, similar to what we observed earlier, a finding we attribute to the house-money effect ( _p<0.01_ in models I and IV). Importantly, however, when purchasing is observable, and when this purchasing can signal ability, the amount of loans taken significantly increases ( _p<0.01_ in models I and IV), as subjects front-load their consumption. This pattern is robust to a series of controls. Importantly, however, we don’t observe a significant relationship with self-reported consumption ( _p=0.425_ in model IV), but those with higher financial IQ taken lower levels of 17
al. (2024) provided more current evidence on AI use, which includes generative AI, based on the Business Trends and Outlook Survey (BTOS). They found that the AI use rate rose from 3.7% in September 2023 to 5.4% in February 2024, with an expected rate of 6.6% by early fall 2024. AI users often utilize AI to substitute for work tasks and equipment or software, though few firms report reductions in employment due to AI use yet. IBM Global AI Adoption Index 2023 is among the few studies assessing firms’ AI adoption around the world. Globally, 42% of enterprises with more than 1000 employees reported having actively deployed AI in their business. India (59%), the United Arab Emirates (58%), Singapore (53%), and China (50%) are leading the way in AI usage. Companies in the financial services industry, industrial equipment, and telecommunications are most likely to be using AI.

While most firms are still trying to figure out how AI can add value for their businesses, individuals and employees have been embracing the technology much faster. Pew Research Center has conducted two rounds of surveys among US adults to shed light on ChatGPT usage by individuals. By Feburary 2024, 23% of US adults report ever using ChatGPT, up from 18% in July 2023. Users are concentrated in those aged 18-29 and highly educated. Most users use ChatGPT for tasks at work, learning, and entertainment (McClain 2024). The 2024 Work Trend Index Annual Report by Microsoft and LinkedIn highlights that 75% of knowledge workers in 31 surveyed economies use AI at work in 2024. Four out of five AI users bring their own AI tools to work, and this is even more common at small and medium-sized companies. Individuals in administrative and support services, real estate and retail industries report the highest generative AI adoption. Reported generative AI usage is the highest in India, Thailand, Indonesia and China (above 90%).

The contribution of this paper is threefold. First, to the best of our knowledge, this is the first paper that uses website traffic data to offer real-time and granular insights into the usage of various generative AI tools across countries. While there is a growing body of research on AI adoption in advanced countries (Acemoglu 2024; Babina, Fedyk, He, and Hodson 2024; McElheran, Li, Brynjolfsson, et al. 2024; Goldfarb, Taska, and Teodoridis 2023), evidence in developing countries remains scarce. Many existing studies only offer a snapshot of AI adoption instead of dynamic trends. There is also a lack of cross-country comparisons of AI adoption. This paper addresses this gap by innovatively leveraging website traffic data obtained from Semrush. This type of data is particularly valuable in a rapidly evolving research domain like AI. By harnessing granular traffic 6
Figure 2 Accra Cumulative distribution functions Figure 3 Other Cities Cumulative distribution functions <!-- Start of picture text --> 100 _ _ __ _ _ __ _ _ _ 100___<br>80<br>60 7<br>40 4<br>20 20<br>O- O<br>150 300 450 600 150 300 450 600<br>. 1987 - 1992 1987 - .. 1992<br><!-- End of picture text --> Figure 4 Rural Areas Cumulative distribution functions <!-- Start of picture text --> 80 AP,<br>60<br>40<br>150 300 450 600<br>__ 1987- ..... 1992<br><!-- End of picture text --> In conclusion, the above analysis confirms the results from past studies that social welfare has improved in Ghana from 1988 to 1992; in terms of expenditure per capita and inequality, mostly at the lower end of the income distribution. However, further analysis shows that the improvement happened only in Other Cities and in Rural Areas. In Accra there is a severe decrease in social welfare, in terms of expenditure level and income inequality. In Other Cities and Rural Areas, the expenditure level has improved. In addition inequality has improved for Other Cities through out the whole distribution, but for Rural areas, it mostly occurred at the lower end of distribution.

# _3. c) Changes in inequalitv and social welfare by localitv and socio economic groups_

As demonstrated above, there are significant differences in the social welfare changes among different localities. To see if these changes are universal within a locality 7
<u>Table 3. Correlates of caregiver under- and over-estimation using the whole sample, Madagascar, 2016, N=3,361</u>

||<br>Model (1)||<br>Model (2)||
|---|---|---|---|---|
||Under-estimation<br>OR(95% CI)|Over-estimation<br>OR(95% CI)|Under-estimation<br>OR(95% CI)|Over-estimation<br>OR(95% CI)|
|Child Characteristics|||||
|Age (months)|0.95** (0.92 - 0.98)|1.01 (1.00 - 1.03)|0.95** (0.92 - 0.98)|1.01 (0.99 - 1.03)|
|Gender|0.87 (0.67 - 1.11)|1.00 (0.88 - 1.13)|0.87 (0.67 - 1.12)|0.98 (0.86 - 1.11)|
|Birth Order|0.99 (0.86 - 1.14)|1.01 (0.94 - 1.09)|0.99 (0.86 - 1.14)|1.01 (0.94 - 1.09)|
|HAZ|1.09 (0.96 - 1.25)|0.82** (0.75 - 0.88)|--|--|
|WAZ|--|--|1.13 (0.98 - 1.31)|0.85** (0.79 - 0.92)|
|Caregiver Characteristics|||||
|Age (years)|0.99 (0.97 - 1.02)|1.00 (0.99 - 1.02)|0.99 (0.97 - 1.02)|1.00 (0.99 - 1.01)|
|Education|||||
|No school [ref]|1.00|1.00|1.00|1.00|
|Primary or less|1.04 (0.73 - 1.48)|0.80* (0.65 - 0.98)|1.03 (0.72 - 1.47)|0.80* (0.65 - 0.99)|
|<br>Secondary or Higher|1.15 (0.72 - 1.84)|0.64** (0.49 - 0.84)|1.13 (0.70 - 1.82)|0.65** (0.49 - 0.85)|
|Depression Score|1.12 (0.95 - 1.31)|1.05 (0.96 - 1.15)|1.11 (0.95 - 1.31)|1.05 (0.96 - 1.15)|
|Belief of influence on child intelligence|||||
|None [ref]|1.00|1.00|1.00|1.00|
|Some|0.72 (0.49 - 1.06)|0.81 (0.63 - 1.05)|0.72 (0.49 - 1.06)|0.81 (0.63 - 1.06)|
|A lot|0.71 (0.49 - 1.03)|0.72** (0.58 - 0.91)|0.70 (0.49 - 1.02)|0.73** (0.58 - 0.91)|
|Household Characteristics|||||
|Household Size|1.01 (0.95 - 1.07)|1.00 (0.97 - 1.03)|1.01 (0.95 - 1.07)|1.00 (0.97 - 1.03)|
|Family Care Indicator Score|0.96 (0.77 - 1.20)|1.04 (0.88 - 1.23)|0.97 (0.78 - 1.20)|1.03 (0.88 - 1.22)|
|Wealth Quintiles|||||
|<br>Q1 (lowest) [ref]|1.00|1.00|1.00|1.00|
|<br>Q2|0.88 (0.60 - 1.31)|1.16 (0.88 - 1.52)|0.87 (0.59 - 1.29)|1.18 (0.90 - 1.54)|
|<br>Q3|0.80 (0.53 - 1.21)|1.17 (0.87 - 1.58)|0.80 (0.53 - 1.20)|1.18 (0.87 - 1.59)|
|<br>Q4|0.72 (0.47 - 1.11)|0.86 (0.65 - 1.15)|0.71 (0.46 - 1.10)|0.87 (0.66 - 1.16)|
|Q5(highest)|0.53*(0.31 - 0.88)|0.73(0.53 - 1.02)|0.51*(0.31 - 0.86)|0.74(0.53 - 1.03)|

Multinomial logistic regression with matched caregiver-perceived child intelligence and ASQ-I ranking as the reference outcome category. All estimations adjusted for treatment arm and region, and corrected for clustering at the village level. Model (1) included height-for-age z-score while Model (2) included weight-for-age z-score.

** p<0.01, * p<0.05

17
system of labor relations administrative appeals, Gallagher (1995) concludes that it is "a system at war with itself." He finds that in the three years between 1991 and 1994, 56 percent of the dismissal decisions reached at the regional level (P4Ds) were modified at the national level (P4P), and 15 percent of those appealed to the Ministry of Manpower were reversed at that higher level.

There are two types of dismissal cases that go before the Ministry of Manpower; mass dismissals (often in the context of plant closings) and individual dismissals (where the issue at stake is severance payment. In any given year individual termination cases can represent two times the volume of mass termination cases (although the number of workers affected in the second case can be 20 to 30 times larger). During the two fiscal years of 1992/93 and 1993/94, the number of individual termination cases for cause amounted to 98 percent of that caseload, indicating that an enormous amount of the P4Ps time is spent in adjudicating matters of individual discipline.

<!-- Start of picture text --> Figure 4<br>Dismissals: Increasing the work-load of the Ministry of<br>Manpower<br>35000<br>30000 .<br>25000<br>20000__ _ Number of workers _ _ _ _ _ _<br>15000<br>5000 :<br>1990/91 1991/92 1992/93 1993/94 1994/95<br><!-- End of picture text --> Source: Ministry of Manpower The main reason for the large number of cases that go through mediation is that none of the parties looses by going that route. For each of them, the payoff of going forward with the case is the possibility of turning the case in their favor. If a committee judges a dismissal to be "justified," the worker gets no severance, else, he/she gets the legally required severance. A system can be conceived so that the two parties stand to loose from going to mediation, encouraging them to settle privately. This can be accomplished by moving in the following direction: (1) Recognition of "economic reason" as a motive for dismissal.

(2) Settlements thorough official channels must involve predetermined compensation.

21
# _CALCULATING GROWTH RATES_

For the purpose of this paper, growth rates are based on the measure proposed in Haltiwanger et al. (2010).<sup>30</sup> The growth rate is calculated as follows: Where _g_ is growth and _yt_ is output at time _t_ and _yt-1_ is output in the previous period. A result in the value of 1, using this measure, implies a growth rate of 200 (Refer to Table A4for correspondences between the Haltiwanger growth measure and the regular measure). This measure has similarities to log differences while also accommodating entry and exit in the growth rate. In addition to the benefit of accommodating entry and exit of firms, this measure is used instead of the more standard measures of growth so there is no need to be concerned about the differences in the size of the base from which growth is calculated; a relatively smaller base yields higher growth rates, all things being equal, meaning that even small changes in output, when starting out from a small base, look like big changes in growth.

This measure of growth rate however, requires particular attention to the start and end of a panel of output figures. For a firm/sector entering the panel at time t output does not exist in t-1, similarly a firm/sector leaving the panel output does not exist at the time of exit. Adjustment for this was made by calculating growth rates from year 2 in the panel and not calculating growth rates for the year of exit. In addition, any change from or to an output level of zero implies a growth rate of either +2/-2 (signifying infinite growth rates). This is particularly tricky when the zero value appears in the middle of the panel. Adjustment for these is made by removing them from the analysis of growth rates.

**Table A4: Comparison with the Haltiwanger growth rate**

|**STANDARD**|**HALTIWANGERGROWTHRATE**|
|---|---|
|**GROWTHRATE**||
|5%|0.05|
|10%|0.10|
|20%|0.18|
|50%|0.40|
|100%|0.67|
|200%|1.00|
|500%|1.43|
|1000%|1.67|
|2000%|1.82|
|5000%|1.92|

In addition, this way of calculating growth yields symmetrical results whether growth is positive or negative. For example, growth in output from 100 to 200 units represents 100 percent increase. However, when growth is negative, from 200 to 100 units, this represents a 50 percent decrease. Using the growth calculation proposed by Haltiwanger et al., using the same example, the growth rates would be 2/3 and -2/3, respectively. This symmetry in positive and negative growth is important for the analysis.

> 30 Haltiwanger, John C., Jarmin, Ron S. and Miranda, Javier, “Who creates jobs?: Small vs. large vs. young (August 1, 2010). United States Census Bureau, Center for Economic Studies, Paper No. CES-WP-10-17. Available at SSRN: <u>http://ssrn.com/abstract=1666157</u> or http://dx.doi.org/10.2139/ssrn.1666157

26