rafmacalaba/datause-encoder-data huggingface dataset:
it represents more than a doubling of mean employment growth in manufacturing industries (of 0.07 percent) between 2010 and 2014.

As discussed above, measured markups have two dimensions: pricing power and efficiency. Either of these could be linked to employment outcomes. However, since we control for efficiency levels through the inclusion of lagged productivity in the regression, our results can be interpreted as reflecting the impact of firms’ pricing power.

_Table_ 4 _: Effects of weaker competition on employment growth in South Africa manufacturing industries_

||Model 1|Model 2|
|---|---|---|
|Log of lagged markup|-1.014<sup>***</sup>|-1.124<sup>***</sup>|
||(0.043)|(0.044)|
|Low-intensity exporters|0.016|-0.006|
||(0.010)|(0.009)|
|Medium-intensity exporters|0.030<sup>*</sup>|0.001|
||(0.016)|(0.014)|
|High-intensity exporters|0.051<sup>**</sup>|0.005|
||(0.025)|(0.021)|
|Sales share|3.312<sup>***</sup>|-0.561|
||(0.991)|(0.492)|
|Age: 6-10 years|-0.017|-0.029<sup>***</sup>|
||(0.011)|(0.009)|
|Age: 11-20 years|-0.013|-0.032<sup>**</sup>|
||(0.015)|(0.013)|
|Age: 21-30 years|-0.000|-0.014|
||(0.020)|(0.017)|
|Age: 31-40 years|-0.000|-0.016|
||(0.029)|(0.029)|
|Age: 41 years or more|0.024|0.037|
||(0.042)|(0.047)|
|Real wage growth|-0.170<sup>***</sup>|-0.137<sup>***</sup>|
||(0.009)|(0.007)|
|Log of lagged TFP|0.874<sup>***</sup>||
||(0.029)||
|Lagged employment growth|-0.147<sup>***</sup>|-0.165<sup>***</sup>|

20
|Table R.UkK4: OLS E<br>||stimates,<br><br>|dapendent va<br>|riable in ti<br>|me<br>fhours|Filling&<br>ER<br>Cost|VAT|
|---|---|---|---|---|---|---|
|E-filing|O.152**|O.222|0.154**|O.170**|o.419|o.220°"*|
||(0.065)|(0.157)|(0.071)|(0.069)|(0.077)|(0.0841)|
|Turnaver|0.031*|0.041|0.035|0.025|0.000|0.039**|
||(0.017)|(0.026)|(0.022)|(0.019)|(0.020)|(0.019)|
|Tot. Ernployment||O.225***<br>(0.024)|O.244***<br>(0.050)|Q.217"** <br>(0.028)|O,223*** <br>(0.026)||O.214*** <br>(0.031)||O217***<br>(0.029)|
|VAT|O.312***<br>(0.079)|O.553***<br>(0.116)|0.050<br>(0.114)||-O.171"*<br>(0.094)||
|PT|“0.195**||-0.183"|“0.090|“0.086|“0.087|
||(0.053)||(0.098)|(0.104)|(0.144)|{0.117}|
|FIT|“0.196"""|-0,299**"|-0.102|“0.056||-0.355***|0.065|
||(0.065)|(0.054)|(0.091)|(0.086)|(0.083)|(0.108)|
|UT|“0.465"""|||“0.209|“0. 289%"|0.025|
||(0.100)|||(0.129)|(0.142)|(0.163)|
|SFP|0.165|O.229|0.040|0.003|0.035|0.098|
||(0.127)|(0.164)|(0.174)|(0.164)|(0.142)|(0.226)|
|Li|O.266**"|(.268***|@<.213**|O.208**|O.187**|| O.287*""|
||(0.065)|(0.100)|(0.087)|(0.084)|(0.085)|(0.103)|
|Region|YES|YES|YES|YES|YES|YES|
|Industry|YES|YES|YES|YES|YES|YES|
|Perception|YES|YES|YES|YES|YES|YES|
|Constant|5.ay72"""|S.510***|G.127"*"|6.200°"*)|4.756""**||4.980""*|
||(0.191)|(0.3013|{0.203}|(0.203)|<br>(0.216)|<br>{0.267}|
|Observations|1,623|580|1,044|1,085|1,623|1,080|
|R-squared|0.404|O.281|0.256|0.262|0.183|O.211|

Robust standard errors in parentheses, Sample weights are used in all regressions, “All” includes ail firms, which da not aulgerc® any af the tax related tasks. "UT=1" refers to unified tax payers ard otherwise, “UT=d". “WAT=1" refers to VAT payers and otherwise, “WAT=0", Tumover and total employment are in loge. Regianal and industry dummies are controlled but net reported and also, perception variables are excluded: Quantity of taxes paid, tax accounting procedure and periodicity of submitting farms, *** p<0.01, ** p<0.05, * pool
Policy Research Working Paper 10910

# **Abstract**

Humanitarian models of refugee assistance increasingly promote refugee self-reliance without offering a clear understanding of what constitutes self-reliance, or how to measure it, although measurement is essential to assess whether the promotion of self-reliance has been successful. This paper proposes an approach to measuring self-reliance rooted in global poverty measurement: a refugee is self-reliant if their self-earned income exceeds the locally relevant poverty line. In its empirical application, the paper uses estimates of self-reliance drawn from 11 consumption surveys, which present the universe of data that can be used for such purposes. Refugees in middle-income countries are found to be far more likely to be self-reliant than those in lower income countries, while refugees residing in urban and non-camp settings demonstrate higher levels of self-reliance than those in rural and camp environments, reflecting the presence of greater economic opportunities outside camps and in urban areas. The paper also finds evidence that aid and self-reliance are inversely correlated, with more aid spent in environments where refugee self-reliance is constrained. The paper argues that a different outcome should be envisaged, one in which governments that encourage refugees to be self-reliant receive more aid. Such an approach would be beneficial for refugees, who would gain financial autonomy, and for host country citizens, who often face high levels of poverty and at times are even poorer than refugees.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jhoogeveen@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
cells (7%). Projected temperatures in the central (highland) cell remain low by Ethiopian standards, but they are persistently 1° - 2° C. higher than historical figures.

Figure 5 displays results for the eastern grid point, and they are similar to those for the southern point in Figure 3. Figure 6 provides a more detailed representation for the western point, presenting the entire 9-cell matrix for the three periods. The use of a constant low-medium-high breakdown across all three periods enables us to see the pattern of climate transition clearly, along with shifts in seasonal patterns. Rainfall is in the left column for each pair. In the historical period (1960-2000), almost all of the observations are in low-temperature cells, while rainfall is more broadly distributed (low rainfall 12.5% probability; medium rainfall 55.0%; high rainfall 30%). The period 20012050 witnesses a marked stretching of the distribution, with observations in 8 of the 9 matrix cells. Temperatures begin extending into the medium and high regions, while rainfall remains varied and seasonal patterns exhibit significant differences across cells. By the period 2051-2100, all of the low-temperature cells have dropped out while rainfall remains highly varied. The highest-probability cell (56.0%) has high temperature and medium rainfall, while the second-probability cell (27.5%) has medium temperature and medium rainfall. The seasonal pattern of rainfall in the two high-probability cells has roughly the same shape, peaking in the fall. The seasonal rainfall pattern is clearly different in some of the lower-probability cells.

# **4.  Summary and Conclusions**

In this paper, we have developed an Ethiopian illustration for a downscaling methodology that summarizes information from historical climate data and future projections from 8 global climate models. This method can be applied to publicly12
based on ES for Sub-Saharan Africa (6 percent) but lower than what many studies have found for the informal private sector (e.g. Hallward-Driemeier 2013; Rijkers and Costa, 2012). The unconditional gender gap is also shown in the kernel density estimates of female-managed and male-managed firms in Figure 1.

We next turn to differences in the characteristics of female- and male-managed enterprises. Femalemanaged firms are less likely to have investment (37 vs 44 percent), slightly younger (16 vs 17 years) and much smaller in size (26 vs 45 employees) than male-managed firms. All differences are statistically significant at the 1 percent level. This is line with earlier studies that have found enterprises owned by women to be smaller and less capitalized than enterprises owned by men (Klapper and Parker 2010). Female managers also have about 2 years less experience on average than male managers, statistically significant at the 1 percent level. Female-managed firms tend to have a lower degree of outward orientation than malemanaged firms. About 9 percent of female-managed firms export 10 percent or more of their sales, and only 9 percent of female-managed firms have foreign ownership. In contrast, 12 percent of male-managed firms export, and 12 percent have foreign ownership. These differences are statistically significant at the 1 percent level.

Female-managed firms are more likely to face losses due to crime than male-managed firms (21 vs 19 percent), statistically significant at the 10 percent level. Female-managed firms are also less likely to pay for security than male-managed firms (52 vs 55 percent), statistically significant at the 5 percent level. In addition, female-managed firms are less likely to own a website than male-managed firms (40 vs 43 percent). The difference is statistically significant at the 5 percent level. Female-managed firms, however, report a lower degree of power outages than male-managed firms (12.6 vs 25.7 hours), statistically significant at the 1 percent level. Also, a significantly lower proportion of female-managed firms have power generators than male-managed firms (26 vs 34 percent). Thus female-managed firms are less likely to buffer the high levels of power outages faced by both male and female-managed firms. The difference is statistically significant at the 1 percent level.

8
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## **Table 8b: Lagged Industrial Value Added with Country FE’s**

Dependent variable: Industrial Value Added

||(Precip)|(Temp)|(P,T)|(W+1)|(T,W+1)|(W-1)|(T,W-1)|(T|,W-1,W+1)|(W-1,W+1)|
|---|---|---|---|---|---|---|---|---|---|---|
|Precip_1|-0.00<br>(0.00)||-0.00<br>(0.00)||||||||
|Temp_1||-0.00|<br>-0.01||-0.01||-0.06|||-0.04|
|||(0.04)|(0.04)||(0.05)||(0.05)|||(0.07)|
|WASP_minus1_|1|||||(0.11)|<br>-0.09<br>(0.11)|-0.08|(0.24)|<br>-0.35<br>-0.34<br>(0.24)|
|WASP_plus1_1||||-0.27<br>(0.13)*|-0.27<br>* (0.13)**|<br>|||<br>-0.55<br>(0.27)*|<br>-0.54<br>* (0.27)**|
|Observations|2760|2760|2760|1737|1737|1830|1830||1117|1117|
|Countries|129|129|129|127|127|126|126||105|105|
|R-squared|0.000|0.000|0.000|0.003|0.003|0.000|0.001||0.005|0.005|

Notes: Columns represent model specifications. Standard errors are clustered at the country level. Standard errors in parentheses: * significant at 10%; ** significant at 5%; *** significant at 1%. P = Precipitation; T = Temperature; W+1 = WASP(+1); W-1 = WASP(-1). The WASP_plus1_1, WASP_minus1_1 and Temp_1 variables are lagged by one year.

# **_Policy Implications_**

This analysis of climate effects on economic activity benefited from a more precise measure of precipitation variability than has been employed in previous analysis. The current literature is conflicted on the significance climate variables that affect GDP growth. The results of this analysis strongly support the hypothesis that anomalously low rainfall (drought) and anomalously heavy rainfall (possibly associated with flooding) are the key climate effects on GDP growth, overwhelming any temperature effect.

Projections of climate change impacts on GDP growth often utilize projected temperature changes from General Circulation Models (GCM) applied to an estimated temperature effect. The results of this analysis imply that the greater economic impacts from climate change will be due to changes in precipitation variability. Most significantly, an increase in drought area in a country of 1% was found to 22
makes an analysis of the labor market difficult. On some questions respondents could also provide multiple answers without ranking.

- Since questions were asked only of employers or managers, and not of employees, this provides a picture of the functioning of the firm.  Its lack of connection to a household survey or a survey of employees prevents it from being a comprehensive analysis of the labor market.

- Translation of the questionnaire into Bahasa Indonesian and the re-translation of the responses into English posed challenges.

# **_(i)  Characteristics of Urban Enterprises_**

19. (a) <u>Employment in Urban Enterprises: Urban enterprises, even formal ones, are very</u> small establishments. Table 1 below shows that almost three-fourths of formal enterprises have less than 10 workers, almost one-fifth employ 10-20 persons, and a little over 8 percent have more than 20 workers. Thus, even most formal enterprises would conform to the international definition of microenterprises and some would be considered small enterprises. Only a very small proportion is large or even medium enterprises.

**Table 1:  Distribution of Number of Workers in Formal Enterprises**

|**Number of Workers**|**Percent of Formal Enterprises**|
|---|---|
|Less than 10|72.4|
|10 to 20|19.1|
|21-50|6.7|
|51-100|1.4|
|More than 100|0.4|
|**Total**|100|

Source: Urban Enterprises Survey (UES), 2004.

20. Informal enterprises seem almost entirely to be household ventures, operating with the labor of the owner or family members. Table 2 shows that over 53 percent of informal enterprises have only one person, about 47 percent employing between 2-10 and less than 0.5 percent having over 10 workers. The majority of the workers in household enterprises are family workers and large numbers are either unpaid or paid in kind. Therefore, informal enterprises are not a major source of employment for workers other than their owners.

**Table 2: Distribution of Number of Workers in Urban Informal Enterprises**

|**Number of Workers**|**Percent of Informal Enterprises**|
|---|---|
|1|52.7|
|2-10|46.9|
|10-20|0.2|
|21-50|0.2|
|**Total **|100.00|

Source: UES, 2004.

21. Since there is little information on gender differences in employment in Timor-Leste, the results of the survey are important. Ownership by sex showed that owners of formal enterprises were predominantly men, the gender differences among wage workers in these enterprises tell a familiar story. The gender gap by skill level appears to be the most pronounced among manual, and less so among service workers. Either manual work in formal enterprises is considered a “male” job, or women’s’ skill level in manual work is limited and hence they are unlikely to be in 11
# **Footnotes**

1 Blimpo (2014) evaluated the effect of financial incentives on secondary school children in Benin and found large gains on test scores. This policy, however, did not target access directly and provided no additional resources upfront.

2 Schools serve as test centers. In almost all cases, students take the exams at the school they attend.

3 The major sub-national units in The Gambia are 6 regions. Region 1 includes the capital Banjul, with regions 2–6 at increasing remove heading east along the Gambia River bisecting the country. Below these subnational units, there are 43 districts as of 2013.

> 4 For purposes of this paper, public schools refer to both government and grant-aided schools, the latter of which are publicly funded but administered privately. Both types of public schools are eligible for the scholarship program, while private schools are not.

> 5 We follow the Gambian convention in referring to the 2000–2001 academic year as 2001, to 2001–2002 as 2002, and so on.

> 6 The average value changed over time because of changes in the exchange rate (the average value of a US dollar per Gambian Dalasi was approximately 13, 15, 20, 27 between 2000 and 2003) and also changes in the composition of students covered (middle and high school students) over time as the program got scaled.

7 Although it is possible that girls were previously paying less than 100% of the nominal fee, leading to an increase in school resources due to the policy, denial of school services for non-payment was a common practice.

8 We omit exam data from 2004 because student gender is missing for that year.

9 The map in figure S1 of the supplemental appendix shows secondary schools in 2011, the most recent year for which location data are available. Region 1 schools and Upper Basic (middle) schools are excluded from the estimation sample but shown on the map to illustrate the locations of secondary schools throughout the system. Not all schools in the sample appear on the map due to missing location data.

10 The strategy resembles that of Lucas and Mbiti (2012a, 2012b), who study the effect of free primary schooling in Kenya using variation in pretreatment local enrollment rates.

> 11 An alternative possibility mentioned by a referee is that the policy led to greater scrutiny of “ghost” students, reducing reported enrollment. The direction of misreporting in response to the policy is therefore unclear.

> 12 Increased enrollment in private schools in later periods partially explains the fall in male enrollment. We explore private school enrollment later in the paper.

25
|||(1998)|
|---|---|---|
|**Log of GDP**|Log of GDP (constant 2000 US$).<br>|World<br>Development<br>Indicators|
|**Log of GDP per capita**|Log of GDP per capita (constant 2000 US$).|World<br>Development<br>Indicators|
|**GDP growth**|GDP growth (annual %).|World<br>Development<br>Indicators|
|**Inflation**|Inflation, consumer prices (annual %).|World<br>Development<br>Indicators|
|**Passport cost to GDP per**<br>**capita**|Passport cost normalized by the country’s GDP per<br>capita expressed in US dollars, inflation adjusted:<br>(passport cost/ GDP current dollars per<br>capita)*100/((1+inflation90/100)(1+inflation91/100)...<br>.((1+inflation2004/100))|Passport cost:<br>McKenzie (2005)*;<br>GDP: World<br>Development<br>Indicators|
|**Female labor market**<br>**participation rate**|Female labor market participation rate (%) –<br>percentage of female population aged 15-64 who are in<br>labor market.|World<br>Development<br>Indicators|
|**Population density**|Population density (people per sq km).|World<br>Development<br>Indicators|
|**Urbanization**|Urban population (%) – percentage of population living<br>in the urban sector.|World<br>Development<br>Indicators|

17
dependent variables, for example a discovery of natural resources would increase both the country’s electricity consumption and value added generating bias in the coefficients if not captured in the regressions. The reverse causality refers to a two-way, causal relationship between the value added and electricity consumption. Countries with higher GDP consume more electricity because of larger demand, and countries with sufficient levels of electricity have larger GDPs, as firms do not face electricity constraints.

This paper uses the two-step difference and system panel GMM approaches to improve the OLS and fixed effects estimators by addressing the endogeneity and reverse causality issues. First, the two-step difference and system GMM exclude country fixed effects that might generate bias from the regressions. The two-step difference GMM applies the first difference to the regression variables and levels for the instruments, while the system GMM augments the latter with level regressions but uses the first difference for the instruments. Second, both of these approaches use lags of the endogenous explanatory variables as instruments. The exogeneity of these internal instruments can be validated by two tests: the Hansen test of overidentifying restriction and the second and third autocorrelation tests. If the instruments are valid, then the two-step system GMM emerges as a preferred estimator, as it is unbiased compared to the OLS and fixed effect coefficients and more efficient than the two-step difference GMM estimators. This is the case for all three sectors when the system GMM is selected as a preferred estimation approach.

The estimation results show that electricity consumption has a significant and positive impact on the value added in manufacturing in both all countries and non-high-income countries. A one-percent increase in electricity consumption generates a 0.022-percentage point increase in the sectoral manufacturing value added in non-high-income countries. Also, the regressions yield positive and significant results in all three sectors for all countries in our sample. The coefficients are lower than the coefficient in the manufacturing sector for non-high-income countries, though. Once high-income countries are excluded from the sample, the coefficients in the agriculture and services sectors become insignificant, indicating that an increase in electricity consumption has no impact on the value added in the agriculture and services sectors. These results may be because the manufacturing sector is more homogenous in terms of its production technologies as it produces tradable goods and faces global competition, which lead to technology transfer and absorption. In addition, agriculture and services in developing countries are very different than in advanced economies. Traditional agriculture in developing countries is primarily subsistence farmers manually laboring to perform tasks, while agriculture in developing countries relies on 19