rafmacalaba/datause-encoder-data huggingface dataset:
As with any panel, there was some attrition across rounds of the survey. However, the attrition was much less than we had anticipated, and the October 2007 survey includes 528 enterprises.<sup>4</sup> The project also included random grants of roughly $100 or $200, which were provided to just under 60 percent of the enterprises in the sample in either May 2005 or November 2005. These grants were intended to generate exogenous shocks to capital stock against which we can measure returns to capital. De Mel, McKenzie and Woodruff 2007a and 2007b explain the grants in more detail. For the purposes of this paper, we note only that receiving a grant does not appear to have any effect on the variables of interest. That is, even though we obtained information on entrepreneurial attitudes after the lottery draw, the responses of the grant recipients do not differ from the responses of the non-recipients.<sup>5</sup> In July 2007, we carried out two additional surveys in the same geographic area of Sri Lanka. Sampling at the household level in the same GNs from which the own account workers were selected, we identified 456 wage workers working at least 30 hours per week. To these wage workers we administered a survey gathering information on their current jobs, labor history, family background, and attitudes. We also gave them digitspan recall and Raven non-verbal reasoning tests, and played a lottery game to measure risk aversion. Most of the questions were taken from previous rounds of the SLMS.

The second survey we carried out in July 2007 was with 424 owners of enterprises hiring between 5 and 50 employees. Some of these were identified through the household screen used in the wage survey. The majority came from identifying and approaching visible businesses. The sample of business owners comes from the same GNs in which the own-account and wage worker samples were selected, and from nearby GNs as well. The latter we added because we did not identify a sufficient number of larger businesses in the original set of GNs. The survey of larger firm owners was identical to the survey of wage workers, with two exceptions. First, we added a short section gathering operating data on the enterprise. Second, questions on attitudes toward

> 4 Not every firm provided information on operating data in every round, but the response rates to the questions we use in the analysis here are nearly always close to 100%, conditional on being surveyed. 5 There is a difference in responses between the treated and untreated SLMS samples in only one of the questions asked after treatments were given. By one measure, those receiving treatment have higher financial literacy, though the difference is significant at only the .07 level.
<!-- Start of picture text --> Figure 1: Trucking unit cost and economic density in destination<br>©<br>7)<br>fe}<br>Oo<br>a<br>x<br>[S)<br>2<br>- oN<br>5 Ss,<br>®<br>a<br>£<br>© Oo<br>13)<br>c<br>2<br>La<br>a°<br>+<br>-4 -2 0 2 4<br>Difference in In(nightlight luminosity) between destinations<br><!-- End of picture text --> Figure 2: COVID19 daily deaths in India: 7days moving average <!-- Start of picture text --> 2)<br>oO<br>oO<br>t+<br>oO<br>8<br>a)<br>o<br>o<br>Q<br>o8<br>BR<br>—<br>=)<br>Zz<br>Oo<br>ro)<br>sd<br>°<br>01jan2020 01jul2020 01jan2021 01jul2021<br>Date<br><!-- End of picture text -->
# **V. Conclusion**

An analysis of the information practices of 185 economies shows that countries that fare well on a wide range of transparency and governance measures are also countries where firms do not have to contact an official to obtain basic regulatory information such as fee schedules. Instead, such information can be found either online or on billboards or through brochures that are readily available at the relevant agencies. While differences in income can explain in part why some countries make information easily accessible and others do not, the political system of each country also seems to play a role. Greater access to information is associated with more democracy and less corruption. Finally, the data also show that business regulation tends to be of greater quality in those countries where regulatory information can be easily accessed. The correlations presented here cannot answer the question whether greater access to information leads to better governance or whether better governance leads to greater access to information. They do, however, make the case that transparency and good governance tend to come together. The paper also shows that improving transparency does not always have to be resource-intensive and technology based. Sometimes printing a simple brochure can be enough.

13
Table 2. Heterogeneity by Quarter and Transfer Policy

|A. Heterogeneit<br>|y by quarter<br>||B. Heterogeneity by<br>|transfer comp<br>|onent|
|---|---|---|---|---|---|
|La|bor Income|GDP|La|bor Income|GDP|
|2008Q2Δ Transfer (paid)|0.27|0.90|2008Δ Tax rebates|0.36|0.64|
||(0.14)|(0.38)|(low-income)|(0.10)|(0.25)|
|2008Q3Δ Transfer|0.31|-0.19|2008Δ Tax refunds|0.07|-0.18|
|(withdrawn)|(0.12)|(0.33)|(middle-income)|(0.31)|(0.57)|
|2001Q3Δ Transfer (paid)|0.84<br>(0.34)||2001Δ Tax refunds|0.44<br>(0.24)||
|2001Q4Δ Transfer|0.03|||||
|(withdrawn)|(0.41)|||||
|P-value (equal coeff.)|0.41|0.10|P-value (equal coeff.)|0.59|0.24|
|State & quarter FEs|Yes|Yes|State & quarter FEs|Yes|Yes|
|Benchmark controls|Yes|Yes|Benchmark controls|Yes|Yes|
|Observations|1588|742|Observations|1588|742|

_Notes:_ Each column represents a regression of the growth rate of real labor income per capita or real GDP per capita on the change in normalized temporary transfers. The regressor is disaggregated by quarter of payment/withdrawal (Column A), or the transfer policy (Column B). All specifications have state and quarter fixed effects, with “benchmark” controls for population growth & US GDP Growth × State FE. Outliers >20% (annualized, absolute value) dropped. Robust std errors are in parentheses (clustered by state).

that payment and withdrawal multipliers are equal (in both labor income and GDP specications; p-values around 0.4 and 0.1), which justies the pooled ~~specication used in the main results in Table 1. For labor income, the 2008 multipliers are around 0.3 for both payment and withdrawal quarters and are remarkably similar to the pooled estimates in Table 1.~~<sup>~~29~~</sup> ~~For the 2001 trans-~~ fers (labor income) and 2008 transfers (GDP), the point estimates are larger when transfers are paid than when they are withdrawn, though these dierences are not statistically signicant. Specically, the 2001Q3 (labor income) 2008Q2 (GDP) payment-quarter multipliers are around 0.9, but multipliers <u>on withdrawal are close to zero. This means states receiving a larger trans-</u> ~~fer may continue to have higher relative income, even after that transfer was withdrawn, though the evidence is not strong enough to be denitive.~~<sup><u>30</u></sup> Column B of Table 2 breaks down pooled temporary transfers into three components described in Section 2.12008 refundable (low-income) rebates, 29 When pooling across 2001 and 2008 payment quarters, and across 2001 and 2008 withdrawal quarters, the estimates are similar to the 2008 multipliers due to their greater leverage (not reported).

> 30Dierence-in-dierence studies often include a graph demonstrating that the outcome variable exhibits a similar trend in high vs low treatment states pre-treatment, with a gap in the outcome variable opening up post-treatment. Appendix Figure 3.4 shows this pattern.

26
and Guinea were also important producers in the West Africa region, while Algeria, Tunisia and Morocco were important producers in the North Africa region.

**Table 1: World Iron Ore Production, 1950 -1985 (Mn. tons)**

|**Country/Region**|**1950**|**1960**|**1970**|**1980**|**1985**|
|---|---|---|---|---|---|
|**North America**|**102.9**|**108.3**|**139.6**|**97.0**|**87.8**|
|United States|99.6|88.8|91.3|70.7|48.8|
|Canada|3.3|19.5|48.3|26.3|39.0|
|**Latin America**|**5.8**|**43.4**|**79.1**|**154.1**|**156.5**|
|Brazil|2.0|9.3|30.0|114.7|120.0|
|Chile|3.0|6.0|11.3|8.6|5.8|
|Mexico|0.6|0.9|4.8|8.1|9.5|
|Peru|0.0|7.0|9.7|5.7|5.1|
|Venezuela|0.2|19.5|22.0|16.1|14.8|
|Other|0.0|0.7|1.3|0.9|1.3|
|**Pacific Basin**|**7.6**|**24.9**|**90.4**|**143.9**|**144.8**|
|Australia|2.4|4.4|51.1|95.5|95.3|
|India|3.0|10.7|31.4|40.7|42.5|
|Other|2.2|9.8|7.9|7.7|7.0|
|**Africa**|**7.0**|**14.3**|**49.0**|**60.0**|**55.8**|
|Liberia|0.0|3.0|23.7|18.2|16.1|
|Mauritania|0.0|0.0|9.1|8.6|9.2|
|South Africa|1.2|3.0|9.1|26.3|24.4|
|Other|5.8|8.3|7.1|6.9|6.1|
|**Western Europe **|**74.2**|**138.5**|**121.1**|**69.8**|**43.7**|
|EEC|58.5|111.6|82.2|32.7|16.4|
|Spain|2.1|5.6|7.1|9.9|6.7|
|**Sweden**|13.6|21.3|31.8|27.2|20.6|
|**Unallocated**|**3.0**|**14.2**|**27.3**|**23.2**|**15.5**|
|**Eastern Bloc**|**49.5**|**173.4**|**256.5**|**339.0**|**392.3**|
|**USSR**|44.0|106.0|195.5|250.0|248.0|
|**Eastern Europe **|3.5|9.3|10.0|6.0|6.3|
|**China**|2.0|55.0|43.0|75.0|71.0|
|**North Korea**|0.0|3.1|8.0|8.0|8.0|
|**World Total**|**250.0**|**517.0**|**763.0**|**887.0**|**896.4**|
|**Liberia’s share(%): **||||||
|Africa|0.0|21.0|48.4|30.3|28.9|
|World|0.0|0.6|3.1|2.1|1.8|

Source: U.S. Bureau of Mines Based on the level of world exports for 1985, Liberia was the largest exporter of iron ore in Africa and ranked 7<sup>th</sup> among world exporters of iron ore with 16.2 million tons of exports or about 4.3 percent of total exports in 1985. This compared with its peak share of 7 percent of global exports in the 1970s. In the 1970s, the largest portion of Liberia’s exports (approximately 80 percent) were destined to the EEC with about 10 percent to Japan and about 8 percent to the United States. The structure of exports showed little change in the 1980s.

4
There are a number of agencies that are involved fully or partially, directly or indirectly in water resource related functions in the basin. The 1998 study prepared by JICA lists 35 organizations that have a role in water resources development and management in the basin. It also lists the tasks and duties of these organizations. The institutional framework for Brantas Basin management is presented in Figure 3, indicating the national, provincial and district level institutions that have primary or significant roles and responsibilities in the planning, development, operation, management or regulatory aspects of basin WRM. The roles of the primary institutions are presented in Box 1.

14
# **References**

Almeida, R. and P. Carneiro (2006). “Enforcement of Labor Regulation, Informal Labor, and Firm Performance,” _World Bank Policy Research Working Paper 3756_ , Washington, DC.

Almeida, R. and A. Fernandes (2006). “Openness and Technological Innovations in Developing Countries: Evidence from Firm-Level Surveys,” _World Bank Policy Research Working Paper_ 3985, Washington, DC _._ Andreoni, J., B. Erard and J. Feinstein (1998). “Tax compliance,” _Journal of Economic Literature_ , 36 (2), 818-860.

Ayyagari, M., A. Demirguc-Kunt, and V. Maksimovic (2007). “Firm Innovation in Emerging Markets”, _World Bank Policy Research Working Paper_ 4157, Washington, DC.

Azuma, Y. and H.I. Grossman (2002). “A Theory of the Informal Sector,” _NBER Working Paper No W8823._ Beck, T., A. Demirgüç-Kunt, L. Leaven, and V. Maksimovic (2006). “The Determinants of Financing Obstacles,” _Journal of International Money and Finance,_ vol. 25(6), 932-952.

Bencivenga, V.R., B.D. Smith and R.M. Starr (1995). “Transaction Costs, Technological Choice and Endogenous Growth,” _Journal of Economic Theory_ , 67, 52-177.

Claessens, S. and L. Leaven (2003). “Financial Development, Property Rights and Growth,” _Journal of Finance,_ vol. 58(6), 2401-2436.

Cunningham, W. and W. Maloney (2001). “Heterogeneity in Informal Self-Employment: An Application of Factor and Cluster Analysis," _Economic Development and Cultural Change,_ vol. 50, 131–156.

de la Torre, A., M. Martinez Peria, and S. Schmukler (2007). “Bank Financing to SMEs," Manuscript.

Dell’Ariccia, G. and E. Bonaccorsi (2004). “Bank Competition and Firm Creation,” _Journal of Money, Credit, and Banking_ , vol. 2, 36.

Dabla-Norris E., M. Gradstein, and G. Inchauste, (2007). “What Causes Firms to Hide Output? The Determinants of Informality,” _Journal of Development Economics,_ forthcoming.

De Paula, A. and J. Sheinkman (2006). “The Informal Sector,” mimeo, The University of Pennsylvania.

De Soto, H. (1989). _The Other Path: The Invisible Revolution in the Third World,_ Harper Collins.

Djankov, S., R. La Porta, F. Lopez de Silanes, and A. Shleifer (2002). “The Regulation of Entry,” _The Quarterly Journal of Economics_ , vol. 117(1), 1-37.

16
# **B Migrants Referred by RAMV Migrants and by Local Organizations**

Table B.1. Baseline Characteristics for Migrants

|||Non-RAMV|Non-RAMV|Me<br>(Bonf|an Differe<br>erroni P-v|nce<br>alue)|
|---|---|---|---|---|---|---|
||RAMV|Referrals|Organizations|(1)-(2)|(1)-(3)|(2)-(3)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Knew of job opportunity before migrating [=1]|0.343<br>(0.475)|0.359<br>(0.480)|0.334<br>(0.472)|1.00|1.00|1.00|
|Migrated for health reasons [=1]|0.109|0.093|0.099|1.00|1.00|1.00|
||(0.312)|(0.291)|(0.299)||||
|Friends or family in Colombia [=1]|0.702|0.755|0.747|0.075|0.165|1.00|
||(0.457)|(0.431)|(0.435)||||
|Ever worked in Venezuela [=1]|0.983|0.966|0.978|0.090|1.00|0.120|
||(0.129)|(0.181)|(0.146)||||
|Employed at private frm in Venezuela [=1]|0.610|0.598|0.573|1.00|0.915|1.00|
||(0.488)|(0.491)|(0.495)||||
|Employed with Government in Venezuela [=1]|0.150|0.137|0.169|1.00|1.00|0.795|
||(0.357)|(0.344)|(0.375)||||
|Self-employed or employee in Venezuela [=1]|0.188|0.169|0.173|1.00|1.00|1.00|
||(0.391)|(0.375)|(0.379)||||
|Lived in Venezuela with parents or siblings [=1]|0.421|0.483|0.449|0.030|1.00|1.00|
||(0.494)|(0.500)|(0.498)||||
|Lived in Venezuela with partner or spouse [=1]|0.586|0.515|0.537|0.015|0.165|1.00|
||(0.493)|(0.500)|(0.499)||||
|Had a dwelling in Venezuela [=1]|0.865|0.845|0.881|1.00|1.00|0.870|
||(0.341)|(0.362)|(0.324)||||
|Had electricity in Venezuela [=1]|0.994|0.992|0.996|1.00|1.00|0.225|
||(0.080)|(0.088)|(0.062)||||
|Had running water in Venezuela [=1]|0.876|0.857|0.852|1.00|1.00|1.00|
||(0.330)|(0.350)|(0.356)||||
|Had sewage in Venezuela [=1]|0.937|0.925|0.932|1.00|1.00|1.00|
||(0.242)|(0.264)|(0.251)||||
|Gap between last job and migration (months)|1.053|0.775|0.709|1.00|0.540|1.00|
||(4.397)|(3.301)|(3.340)||||
|Time in Colombia (months)|27.506|20.799|19.471|0.000|0.000|0.015|
||(10.949)|(9.441)|(8.322)||||
|Observations|1,709|888|1,051|2,597|2,760|2,108|

_Notes:_ The table reports data on migrants’ demographic variables while in Venezuela or in Colombia before the program rollout. It takes into account all the migrants surveyed, which corresponds to 3,900 refugees in 2,232 households. It also presents the mean difference between pairs of three groups of migrants: (i) those in the RAMV, (ii) those not in the RAMV but referred by RAMV migrants (Non-RAMV Referrals), and (iii) those not in the RAMV who were referred by local organizations (Non-RAMV Organizations). Columns (iv)–(vi) report the Bonferroni p-values corrected for multiple hypothesis testing.

52
- Hervitz, M.H. (1985), “Selectivity, Adaptation, or Disruption? A Comparison of Alternative Hypotheses on the E¤ects of Migration on Fertility: The Case of Brazil.”International Migration Review 19(2): 292-317.

- Hiday, V.A. (1978), “Migration, Urbanization, and Fertility in the Philippines.” International Migration Review 12(3): 370-385.

- Javorcik, B., C. Özden, M. Spatareanu and C. Neagu (2006) “Migrant Networks and Foreign Direct Investment.”World Bank Working Paper no. 4046. Washington DC.

- Katav-Herz, Shirit (2003), "A Model of Parental Demand for Child Labor with High Fertility Norms", Review of Economics of the Household, 1(3): 219-233.

- Kugler, M. and H. Rapoport (2006), “Skilled Emigration, Business Networks and Foreign Direct Investment.”Economic Letters.

- Kulu, H (2003), “Post-war Immigration to Estonia: A Comparative Perspective,” in R. Ohliger, K. Schönwälder and T. Triada...lopoulus (eds.) “European Encounters, 1945-2000: Migrants, Migration and European Societies since 1945.”Aldershot: Ashgate, pp. 38-52.

- Lee, B.S. and L.G. Pol (1993), “The In‡uence of Rural-Urban Migration on Migrants Fertility in Korea, Mexico and Cameroon.”Population Research and Policy Review 12(1): 3-26.

- Lee, B.S. and S.C. Farber (1985), “The In‡uence of Rapid Rural-Urban Migration on Korean National Fertility Levels.”Journal of Development Economics 17:47-71.

- Lindstrom, D.P. and S. Giorguli Saucedo (2002), “The Short- and Long-Term E¤ects of U.S. Migration Experience on Mexican Women’s Fertility.” Social Forces 80(4): 1341-1368.

- Lindstrom, D.P. and Munoz-Franco (2005), On contraceptives

- Lopez, R. an M. Schi¤ (1998), Migration and the Skill Composition of the Labor Force: the Impact of Trade Liberalization in LDCs, Canadian Journal of Economics, 31(2), 318-336.

- Martine, G. (1975), “Migrant Fertility Adjustment and Urban Growth in Latin America.”International Migration Review 9(2): 179-191.

- Mayda, A.M. (2007), International Migration: a Panel data Analysis of the Determinants of Bilateral Flows, Georgetown University Working paper.

36
**Table 7: Organized Log(Employment) and Log(Real Output): Sub- sample Main States**

||(1)<br>(2)|(3)|(4)|
|---|---|---|---|
||Employment|Real O|utput|
||_Other-sector Emply_|_oment Spillov_|_ers_|
|Horizontal Employment Spillovers from||||
|Unorganized Sector|0.1552***<br>0.0469***|||
||(0.0184)<br>(0.0163)|||
|Forward Employment Spillovers from||||
|Unorganized Sector|0.0652<br>2.0531**<br><br>|||
||(0.2070)<br>(0.8152)|||
|Backward Employment Spillovers from||||
|<br>Unorganized Sector|0.4445***<br>1.0713**<br><br>|||
||(0.1298)<br>(0.4797)|||
|Average TFP of Horizontal-linked|_Other-sector Produ_|_ctivity Spillov_|_ers_|
|Unorganized Plants|-0.0056<br>-0.0038|-0.0011|-0.0217|
||(0.0430)<br>(0.0406)|(0.1145)|(0.1093)|
|Average TFP of Forward-linked Unorganized||||
|<br>Plants|-0.0038<br>-0.7937**|-0.1157|9.8805***|
||(0.1614)<br>(0.3863)|(0.4855)|(2.2549)|
|Average TFP of Backward-linked||||
|Unorganized Plants|0.1743+<br>-0.5479++|0.9763***|0.8713|
||(0.1093)<br>(0.4255)|(0.3217)|(0.7202)|
||_Own-sector Emplo_|_yment Spillov_|_ers_|
|Forward Employment Spillovers from||||
|Organized Sector|1.6194***<br>-1.8836**|||
||(0.3384)<br>(0.9540)|||
|Backward Employment Spillovers from||||
|Organized Sector|1.4876***<br>1.7075***<br><br>|||
||(0.1775)<br>(0.5061)|||
||_Other-sector Out_|_put Spillover_|_s_|
|Horizontal Output Spillovers from||||
|Unorganized Sector||0.1513***|0.0417**|
|||(0.0214)|(0.0195)|
|Forward Output Spillovers from Unorganized||||
|Sector||0.4670+|-0.0588|
|||(0.3038)|(0.7613)|
|Backward Output Spillovers from||||
|Unorganized Sector||-0.1473|-0.3540|
|||(0.2620)|(0.8018)|
||_Own-sector Out_|_put Spillovers_||
|Forward Output Spillovers from Organized||||
|Sector||3.8611***|1.1547|
|||(0.9374)|(1.9170)|
|Backward Output Spillovers from Organized||||
|<br>Sector||1.7203***|10.7908***|
|||(0.5568)|(1.4388)|
|Constant|-34.4485***<br>-24.2966**|-|-|
||(5.2770)<br>(11.4007)|(23.5197)|(40.5045)|
|Observations|4352<br>4352|4352|4352|
|R-squared|0.1743<br>0.4173|0.1290|0.3606|

_Note: All specifications contain panel (state-industry) fixed effects as well as year fixed effects. Columns 2 and 4 include state-year and industry-year fixed effects. Standard errors are clustered by the state-industry level. Significance at the 20% level is indicated by ++, at the 15% level by +, at the 10% level by *, at the 5% level by ** and at the 1% level by ***._ 36
Table .

|All Women: Sample|Characteristics Means|by Training Status|
|---|---|---|
|CHARACTERISTICS|With<br>Training|Without<br>Training|
|No. of observations|1,060|2,766|
|Lima|666|1,286|
|Other Urban Areas (OUAs)|394|1,480|
|Mean years of age|29.1*<br>|33.8<br>|
||(9.0)|(14.7)|
|Age groups<br> <br>|||
|15-<br>19 years old|0.09*|0.23|
|20-<br>29 years old<br>|0.51*<br>|0.22<br>|
|30- 39 years old<br>|0.27<br>|0.19<br>|
|40- 49 years old|0.08*|0.17|
|50- 59 years old<br> <br>|0.03*|0.12|
|60-<br>65 years old|0.01|0.06|
|Schoolinz Attained<br>|||
|Years of schooling|9.8*|7.0|
|<br>|(2.9)|(4.0)|
|Primary<br>(0-5 yrs)<br>|0.12*<br>|0.42<br>|
|Some secondary(6-9 yrs)<br> <br>|0.15*|0.23|
|Secondary<br>(10 yrs)<br>|0.47*|0.21|
|Some higher (11-13 yrs)<br> <br>|0.13*<br>|0.05|
|Higher<br>(14+yrs)|0.12*|0.07|
|School last attended<br>|||
|was public|0.78|0.77|
|Free meals last<br>|||
|primary school|0.40|0.31|
|Background|||
|Father's years of|||
|schooling|6.7|5.5|
|Mother's years of|||
|schooling|4.8|3.6|
|Father's occupation farmer|<br>0.20*|0.36|
|Married or as if|0.45|0.51|
|Migrant|0.60|0.55|

- 13 -
- 14 -

in Mozambique are MZN3,000 to 7,000, depending on industry. Labor quality may have to be taken into account, when local labor costs are considered. Primary school enrolment rates in 2007 are used to control for this.

|**Table 2. Summary statistics of variables used for firm locational model estimation**|
|---|

|Variable|Obs|Mean|Std. Dev.|Min|<br>Max|
|---|---|---|---|---|---|
|No. of existing firms in 2008|43808|275.5|577.1|3.0|<br>3423.0|
|Road density (km per 100 km<sup>2</sup>)|43808|11.9|14.4|0.0|<br>72.7|
|Transport cost to a port (US$ per ton)|43808|25.3|16.5|0.1|<br>74.9|
|District population in 2007|43808|163391.5|115413.8|9506.4|682691.4|
|Population density (per km<sup>2</sup>)|43808|343.7|780.6|0.8|<br>3203.6|
|Local monthly wage (Metical)<sup>1</sup>|43808|2705.9|1656.8|150.0|<br>7883.0|
|Primaryschool enrolment(%, gross)|43808|117.1|24.3|56.4|<br>239.7|

1 Average monthly wage at the district level, based on the 2008 Agriculture Sample Survey. In case no data are available in a given district, the closest district data is used as a proxy.

# **IV. MAIN ESTIMATION RESULTS**

26. To estimate the translog cost function, both SUR and SFA are performed. The results are found broadly consistent with economic theory ( **Table 3** ). The implied price elasticities of production factor demand calculated based on Equation (6) range from -0.5 to -1.0 ( **Table 4** ). The elasticity of labor demand is estimated at about -0.6, the largest in absolute terms, implying that a small change in wages would result in a large change in employment. This is relatively large compared with the literature (e.g., Lichter et al. 2014) and may reflect the fact that the level of industrialization remains low in Mozambique and the demand for low-skilled labor is normally more elastic than the demand for high-skilled labor. The elasticity of electricity demand is estimated at -0.3, which is consistent with the literature, though it is statistically insignificant.<sup>6</sup>

> 6 The price elasticity of manufacturing energy demand is estimated at -0.28 to -0.49 in absolute terms in the United States (Anderson, 1981). As per Pindyck and Rotemberg (1983), the elasticity of U.S. manufacturing is - 0.36 in the short run, -0.58 in the medium run, and -0.99 in the long run. Energy demand elasticities also differ across industries (Denny _et al_ ., 1981). The short-run elasticity varies from -0.61 in the paper industry to nearly zero in the tobacco, metal fabricating, and machinery electrical industries. The long-run energy elasticities are also different between -0.01 to -0.73.