rafmacalaba/datause-encoder-data huggingface dataset:
Table D-1. City Size Growth Equation<sup>a,b</sup> (standard errors corrected for spatial dependence in parentheses)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Spatial GMM|Spatial OLS|Spatial GMM|Spatial OLS|
|Δ Ln(ruralpop. supplymarket|8.894***|3.216***|5.590***|3.064***|
|potential)|(2.078)|(0.703)|(1.790)|(0.639)|
|Δ Ln(rural income opportunities:|2.300|0.364|-0.700|0.198|
|marketpotential)|(1.834)|(0.389)|(0.738)|(0.271)|
|Δ Ln(marketpotential)|1.837|2.860***|3.956***|2.738***|
||(1.266)|(0.674)|(0.953)|(0.606)|
|Average schooling (t-1)|0.036|0.021|0.063***|0.021*|
||(0.027)|(0.013)|(0.016)|(0.012)|
|Δ Average schooling|0.115|0.067**|0.604***|0.097***|
||(0.117)|(0.031)|(0.116)|(0.026)|
|Δ Ln(inter-citytransport costs)|-0.121***|-0.092***|-0.132**|-0.088***|
||(0.044)|(0.027)|(0.051)|(0.025)|
|state capital dummy|0.080**|0.080***|0.220***|0.129***|
||(0.033)|(0.026)|(0.037)|(0.033)|
|Ln(population) (t-1)|||-0.057***|-0.018*|
||||(0.009)|(0.010)|
|Manu / service(t-1)|||0.190***|0.096***|
||||(0.033)|(0.018)|
|time dummies|Yes|Yes|Yes|Yes|
|Observations|246|246|246|246|
|Hansen J statistic<br>(overidentificationtest)|3.582||5.381||

*** significant at 1% level; ** significant at 5% level; * significant at 10% level.

- a. For (1), instruments are the IV list of Table 4, ln(distance to São Paulo), ln(transport costs to São Paulo, 1968), and ln(transport costs to state capital, 1968). For (3), we drop ln(industry capital per worker, 1970) from (1), and add ln(population, 1970), manu/service ratio (1970), manu/service ratio(1970)*ln(population, 1970), manu/service ratio(1970)*ln(income per capita, 1970), and manu/service ratio(1970)*ln(market potential, 1970).

- b. Coordinate variables are latitude and longitude. Cutoffs are 1.5 standard deviations of latitude and longitude (10.23, and 8.20), which correspond to about 900 miles.

47
# **Table 4: Estimates** **<u>of factor demand equations</u>**

||**Hired labor**<br>**(1)**|**Family labor**<br>**(2)**|**Total labor**<br>**(3)**|**Machine use**<br>**(4)**|**Fertilizer**<br>**(5)**|
|---|---|---|---|---|---|
|Output price|0.067***<br>(6.474)|-0.033***<br>(-5.439)|0.056***<br>(15.056)|0.102***<br>(12.648)|0.127***<br>(11.507)|
|**Factor prices**||||||
|Agricultural wage|-0.244***|0.133***|-0.116***|0.293***|0.564***|
||(-4.111)|(3.831)|(-4.032)|(6.307)|(8.911)|
|Nonagricultural wage|0.348***|-0.077**|-0.089***|0.310***|0.104|
||(5.792)|(-2.203)|(-3.069)|(6.595)|(1.627)|
|Price of bullocks|-0.142***|0.122***|0.035**|0.050*|0.086**|
||(-4.192)|(6.185)|(2.160)|(1.902)|(2.385)|
|Price of fertilizer|-0.004|0.092***|0.070***|0.048**|-0.388***|
||(-0.145)|(5.314)|(4.893)|(2.075)|(-12.299)|
|**Endowments**||||||
|Log of land|0.672***|0.321***|0.413***|0.201***|0.605***|
||(20.868)|(17.045)|(26.458)|(7.995)|(17.626)|
|# members14-64 years old|-0.479***|0.145***|0.027|-0.024|0.105**|
||(-10.588)|(5.502)|(1.232)|(-0.665)|(2.178)|
|Head is female|-0.163|0.188***|0.104*|-0.216**|0.001|
||(-1.470)|(2.895)|(1.932)|(-2.479)|(0.007)|
|Head’s age|1.156|0.582|0.701|-0.924|0.023|
||(0.744)|(0.642)|(0.932)|(-0.761)|(0.014)|
|Head’s age squared|-0.153|-0.050|-0.074|0.144|-0.016|
||(-0.756)|(-0.423)|(-0.752)|(0.911)|(-0.074)|
|Head’s education|0.013|-0.005|-0.011|0.101***|-0.037|
||(0.414)|(-0.262)|(-0.701)|(4.034)|(-1.079)|
|Constant|0.791|1.396|3.018**|0.212|2.791|
||(0.266)|(0.806)|(2.100)|(0.091)|(0.883)|
|No. of observations|10,204|10,204|10,204|10,204|10,204|
|R-squared|0.143|0.115|0.384|0.247|0.189|
|Number of farmers|4,254|4,254|4,254|4,254|4,254|

Source: Authors’ computation from 1982, 1999, and 2008 ARIS-REDS surveys.

Note: Dependent variable is the log of labor quantity (days) or monetary value of machinery hire and fertilizer. All regressions include time dummies and farmer fixed effects. Standard error is adjusted for clustering at the village level throughout, and asymptotic t-ratios are given in parentheses (*** p < 0.01, ** p < 0.05, * p < 0.1).

19
Women’s Labor Force Participation in the Kurdistan Region of Iraq: A Study of Social and Psychological Barriers Iman Sen<sup>1</sup> , Zeina Afif<sup>1</sup> , Varun Gauri<sup>1</sup> , and Gohdar Mohamed<sup>2</sup>

# **Keywords: Survey method, Social norms, Female labor force participation, Kurdistan Region of Iraq JEL Classification Codes: C83, D91, J29**

The paper is a product of the Mind, Behavior, and Development (eMBeD) unit. E-mails: zafif@worldbankgroup.org, <u>gohdar.mohamed@krso.gov.krd, iman.sen@gmail.com,</u> vgauri1@gmail.com. We are grateful to Sheekar and Rwanga, our local partners in KRI for data collection and program implementation. We thank Ghassan Alkhoja for his leadership and tremendous support on the project, and Tala Ismail, Benjamin Oien, Gonzalo Pons, and Sumiko Hayasaka for excellent research assistance. We gratefully acknowledge inputs by Gharam Dexter, Private Sector Specialist at the World Bank. The Multi-Donor Trust Fund for the Middle East and North Africa Region financed this study. The team worked under the guidance of Saroj Kumar Jha, World Bank Country Director; Hana Brixi, Practice Manager of the World Bank Group's Social Protection and Jobs Global Practice; Benu Bidani, Practice Manager of World Bank Group's Poverty and Equity Global Practice; and Oscar Calvo-Gonzalez, Practice Manager of World Bank Group's Poverty and Equity Global Practice and the Mind, Behavioral, and Development (eMBeD) team. The team thanks World Bank Iraq Country Representatives Yara Salem and Ramzi Neman for their valuable input and support. Valuable inputs were received from Rene Solano, Senior Social Protection Specialist, Matthew Wai-Poi, Senior Economist; and Jonna Lundvall, Senior Social Scientist at the World Bank.

This paper solely represents the authors and not (necessarily) the Government of Kurdistan or the World Bank Group. All errors remain our own. The paper 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/research.

> 1Mind, Behavior, and Development (eMBeD), World Bank. 2 Kurdistan Region Statistical Office
<!-- Start of picture text --> 16.0<br>1.4<br>14.0<br>1.3<br>12.0<br>1.2<br>10.0<br>1.1<br>1.0 8.0<br>0.9 6.0<br>ROA<br>ROE<br>Z-Score<br>ROA<br>ROE, Z-score<br>1995 1997 1999 2001 2003 2005 2007<br><!-- End of picture text -->

**Figure 13: Bank Return on Equity - Median Values By Income Group Over Time (1995-2007)**

<!-- Start of picture text --> 30.00<br>25.00<br>20.00<br>15.00<br>LOW INCOME<br>10.00<br>LOWER MIDDLE INCOME<br>UPPER MIDDLE INCOME<br>HIGH INCOME<br>5.00<br>ROE<br>1995 1997 1999 2001 2003 2005 2007<br><!-- End of picture text -->

**Figure 14: Bank Z-Score - Median Values By Income Group Over Time (1995-2007)**

31
# Table 5: **Regression Results** – Total Household Debt

This table reports the effect of debt relief on the total outstanding debt of surveyed households. Within a panel, each column reports results from a separate regression. The dependent variable is the change in total self-reported outstanding debt between the pre- and post-program periods in units of Rs ’000 and as a percentage of agricultural revenue in the crop season before the program. Regressions in panel A are estimated using the full sample. Panels B.1 and B.2 report restricted sample robustness checks. Panel B.1 reports estimates from a robustness sample consisting of housheolds with audited and matching land records. Panel B.2 reports estimates from a reduced bandwidth sample, which excludes observations in the top and bottom 25% of observations to either side of the program cutoff. All regressions exclude households not engaged in agricultural production. Interview effects include interviewer and month-of-interview dummies. Respondent controls include gender, age, years of education, household size, log of pre-program land owned, log of pre-program total debt. Robust standard errors, in brackets, are clustered by bank and district. + p _<_ 0.10 ** p _<_ 0.05 *** p _<_ 0.01.

||Model 1|Model 2|Model 3|Model 4|
|---|---|---|---|---|
|_Panel A: Full Sample_|(1)|(2)|(3)|(4)|
|100% Relief|-26.44+|-24.98+|-24.69+|-25.94**|
||[14.81]|[12.25]|[13.39]|[12.45]|
|Observations|2,246|2,246|2,191|2,246|
|R-squared|0.04|0.04|0.05|0.04|
|_Panel B: Robustness Samples_<br>Audited accounts|||||
|100% Relief|-22.39|-25.03+|-25.02+|-25.57|
||[18.70]|[14.84]|[14.94]|[15.06]|
|Observations|1,460|1,460|1,419|1,460|
|R-squared|0.05|0.06|0.07|0.06|
|Reduced bandwidth sample|||||
|100% Relief|-30.28+|-27.16+|-26.16|-28.77+|
||[16.33]|[15.60]|[16.70]|[15.75]|
|Observations|1,645|1,645|1,600|1,645|
|R-squared|0.04|0.06|0.07|0.06|
|Interviewer effects|Yes|Yes|Yes|Yes|
|Bank*district effects|No|No|Yes|Yes|
|Local linear control function|Yes|Yes|Yes|No|
|Quadratic control function|No|No|No|Yes|

36
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|VARIABLES|||Heig|ht-for-age z-s|cores|||
||(0.083)|(0.087)|(0.086)|(0.085)|(0.086)|(0.087)|(0.087)|
|Observations|2,471|2,471|2,471|2,483|2,483|2,483|2,483|
|R-squared|0.043|0.053|0.054|0.064|0.059|0.065|0.062|

Source: Authors’ calculations. Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Per capita kcal is the per capita caloric availability, 1-SSR is the share of calories derived from non-starchy staple food, 1-SSEXR is the expenditure share of non-starchy staple food in the total food expenditure, FCS is the food consumption score and DDS is the household level dietary diversity score, PC consumption is the per capita consumption, and Food share is the share of food expenditure on total expenditure.

58
# **TABLE 2. Literature review of long-run income elasticities of demand for commodities**

|**Authors and Publication Year**|**Data/sample**|**Methodology**|**Results**|
|---|---|---|---|
|Stuermer (2017)|12 advanced economies and 3<br>EMDEs, annual data, 1840- 2010|Auto- regressive distributive lag|Income elasticity of demand is estimated to be 1.5 for<br>aluminum, 0.9 for copper, 0.7 for zinc, 0.6 for tin, and<br>0.4 for lead.|
|Burke and Csereklyei (2016)|132 countries, annual data,<br>1960- 2010.|Ordinary least squares (OLS)<br>with panel data, in levels and<br>growth rates.|Aggregate income elasticity of energy demand is<br>estimated to be 0.7. Income elasticity is found to rise<br>with higher incomes, in contrast to other studies. This<br>results from the inclusion of low income countries,<br>which typically have a much lower income elasticity of<br>demand for energy as they rely on non- commercial<br>fuels (biomass). Controlling for this results in constant<br>elasticities across income groups.|
|Csereklyei and Stern (2015)|93 countries, annual data, 1971-<br>2010.|OLS in growth rates.|Average income elasticity of energy demand is<br>estimated to be between 0.6 to 0.8. As income rises,<br>the rate of growth of energy use per capita declines.|
|Huntington, Barrios, and Arora (2017)|Review of 38 papers providing<br>258 estimates of price and<br>income elasticities of energy<br>demand.|Review of existing studies.|Income elasticity of oil demand is found to be 0.5 on<br>average, and 0.9 for natural gas.|
|Fouquet (2014)|UK energy use, annual data,<br>1700- 2000.|Vector error correction model|Long run income elasticity for energy demand for<br>transport peaks at 3 before declining to around 0.3 as<br>income rises.|
|Joyeux and Ripple (2011)|30 OECD and 26 non- OECD<br>countries, annual data, 1973-<br>2007|Error correction model with<br>pooled mean group estimators.|For OECD countries, income elasticity estimated to<br>be 1.1, for non- OECD countries, income elasticity of<br>energy demand estimated to be 0.9.|
|Jakob, Haller and Marschinski (2011)|30 EMDEs and 21 advanced<br>economies, annual data, 1971-<br>2005.|Difference- in- differences<br>estimator on panel data.|Find income elasticity of primary energy demand of<br>0.63 for EMDEs and 0.18 for advanced economies<br>(although statistically insignificant).|
|Vanin et al. (2014)|Review of 10 global economic<br>models for agricultural<br>commodities|Review of different modeling<br>approaches|Find median income elasticities for rice and wheat<br>close to 0.1. First and third quartile range of estimates<br>range from 0 to 0.2.|

18
postal accounts. Data on post access points are collected by the UPU. The IMF’s Financial Access Survey provides data on financial access such as branch penetration. Data on GDP per capita come from the World Bank’s World Development Indicators database. Appendix 3 provides detailed descriptions of all indicators and their sources.

# **3. Empirical Methodology**

To examine the determinants of individuals’ choice of the type of account owned, we specify a multivariate estimation model. In particular, we fit a multinomial logit regression model to the response variable account ownership since it has four distinct categories: 0 if an individual does not have an account, 1 if the account is at the post office only, 2 if the individual owns an account both the post office and a financial institution, and 3 if the account is at a financial institution only. We choose no account (0) as our baseline category.

where yij is account ownership type of an individual _i_ residing in country _j_ . The variables xij and zj are the vectors of country and individual-level characteristics, respectively. Their corresponding vectors of parameters are given by β and γ. εij denotes the error term.

Among the individual-level characteristics we include in xij are the following socioeconomic variables that may be associated with account ownership. We include dummies for gender, income quintiles based on the income of respondents in a country, whether a respondent lives in a rural area, the respondent’s marital status, educational attainment, and employment status. We also include age and age squared in years and the logarithm of household 9
# **VI. Conclusions and Policy Implications**

Manufacturing processes are being increasingly automated throughout the world. In recent years, automation has accelerated across many developing economies, from initially low levels of use, which has led to convergence in robot diffusion across countries. Despite this marked trend, there is limited empirical evidence on the effects of these technologies beyond developed economies.

In developing economies much of the attention has been around the possible adverse effects of robots, such as reshoring of production back to advanced economies, unemployment due to laborsaving and the future of manufacturing-led growth models (De Backer and DeStefano, 2021). This paper considers one of the potential positive implications: quality upgrading. Automation can reduce production errors by allowing repetitive tasks to be performed to a consistent level of accuracy. Many of these robots operate with limited human interaction, and so one potential upside of any labor-saving technological change is reducing human error in production.

We examine how robot diffusion affects export quality – distinguishing upgrading effects between developed and developing economies, and between initially high- and poor-quality products. To do so we rely on cross-country and cross-industry data which cover 59 countries and 16 manufacturing sectors over a span of 16 years. Our instrumental variable, robot diffusion in (preexisting) foreign export customers, strongly predicts robot adoption in the home country-industry.

We find that robots lead to export quality upgrading, however the gains are not spread evenly. The majority of the quality upgrading is achieved by developing countries, more so than developed economies. Looking within exporters, we find that robots lead to the upgrading particularly of initially low-quality products – and this is true in both developed and developing countries. Since developing countries tend to export lower quality goods, we find stronger effects overall for developing economies. These results suggest that developing countries may have greater potential for quality catch-up through automating their production.

There are several important implications for policy. First, technology always creates winners and losers. While automation poses risks, it also presents new opportunities for developing countries. Joining Global Value Chains and trading with advanced economies can offer developing economies the potential to specialize in what they are currently best at, benefit from knowledge spillovers and create productive employment. However, stringent quality standards can be a major barrier to developing countries participating and thus leveraging these benefits. Automation may 36
|**Ta**<br>Depen|**ble 6: Pooled P**<br>**Small**<br>dent variable: L<br>|**roduction Fun**<br>**manufacturi**<br>ogof annual v<br>|**ctions –OLS (**<br>**ng firms**<br>alue-added in<br>|**1 of 2)**<br>1000 Birr<br>|||
|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
||**coef/sd**|**coef/sd**|**coef/sd**|**coef/sd**|**coef/sd**|**coef/sd**|
|**_Factors_**|||||||
|lnK|0.183***|0.226***|0.217***|0.215***|0.215***|0.223***|
||(0.058)|(0.044)|(0.047)|(0.043)|(0.049)|(0.048)|
|LnL|0.528**|0.890***|0.785***|0.795***|0.743***|0.748***|
||(0.212)|(0.149)|(0.157)|(0.149)|(0.162)|(0.159)|
|**_Activities_**|||||||
|Food and beverages|-0.549|-0.494*|-0.634*|-0.591**|-0.604|-0.496|
||(0.348)|(0.276)|(0.357)|(0.281)|(0.373)|(0.371)|
|Garments and textiles|<br>-0.520*|<br>-0.455*|<br>-0.540**|<br>-0.509**|<br>-0.493*|<br>-0.408|
||(0.270)|(0.248)|(0.270)|(0.249)|(0.285)|(0.288)|
|Leather|1.302*|1.338*|1.417*|1.448*|1.424*|1.638**|
||(0.745)|(0.749)|(0.766)|(0.764)|(0.785)|(0.791)|
|Wood, furniture & metal|<br>-1.672***|<br>-1.635***|<br>-1.710***|<br>-1.670***|<br>-1.709***|<br>-1.633***|
||(0.523)|(0.519)|(0.507)|(0.502)|(0.511)|(0.492)|
|**_Management_**|||||||
|<br>Female management|-0.414*|-0.416*|-0.496**|-0.493**|-0.535**|-0.437*|
||(0.227)|(0.221)|(0.217)|(0.215)|(0.222)|(0.225)|
|Manager’s schooling|-0.084|-0.072|-0.105|-0.101|-0.103|-0.086|
||(0.066)|(0.065)|(0.066)|(0.065)|(0.066)|(0.068)|
|Manager’s schooling<sup>2</sup>|<br>0.007|<br>0.006|<br>0.008*|<br>0.008|<br>0.007|<br>0.006|
||(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|
|**_Rural town_**|||||||
|<br>Rural Area|-0.475||||||
||(0.334)||||||
|Rural Area*ln L|<br>0.414<br>(0277)||||||
|Rural Area*lnK|.<br>0.043<br>||||||
||(0.079)||||||
|**_Location Dummies_**|||||||
|Addis|||0.306||0.197|0.292|
||||(0.267)||(0.266)|(0.272)|
|Other city of over 200,000 people|||0.313||0.106|-0.120|
||||(0.310)||(0.314)|(0.332)|
|Rural town|||0.275||0.495|0.461|
||||(0.421)||(0.463)|(0.496)|
|Other rural area|||<br>-0.257|-0.524**|<br>0.010|<br>0.047|
||||(0.428)|(0.205)|(0.477)|(0.502)|
|**_Utilities usage_**|||||||
|<br>Electricity usage|||||0.691**|0.675**|
||||||<br>(0.279)|(0.287)|
|Power outages|||||<br>-0.349*|<br>-0.420*|
||||||(0.208)|(0.218)|
|Owns a landline|||||<br>0.403|<br>0.442|
||||||(0.320)|(0.322)|
|Owns a cell phone|||||-0.064|-0.056|
||||||(0.262)|(0.262)|

Table <u>continues on the next page.</u>

-39-
Table A3. Attrition

||Women|Level|Househol|d Level|
|---|---|---|---|---|
|Panel A: Treatment|(1)|(2)|(3)|(4)|
|Treatment (T)|-0.004|-0.003|0.01|0.011|
||(0.010)|(0.011)|(0.009)|(0.010)|
|Baseline Contols?|N|Y|N|Y|
|R-squared|0.02|0.02|0.00|0.00|
|Panel B: T1vs T2|||||
|Importance of voting (T1)|-0.022|-0.022|-0.004|-0.004|
||(0.012)|(0.012)|(0.011)|(0.011)|
|Importance of voting and secret balloting (T2)|0.020|0.021|0.029*|0.030**|
||(0.013)|(0.017)|(0.014)|(0.015)|
|Baseline Contols?|N|Y|N|Y|
|R-squared|0.03|0.03|0.01|0.01|
|Mean dependent variable|0.035|0.035|0.026|0.026|
|Observations|2,734|2,734|1,019|1,019|

Note: Regressions (1) and (2) are at the women level and (3) to (4) are at the household level. In the latter the controls take the average value of the women within a household. The dependant variable takes the value 1 if woman attrited. All regressions include village fixed effects and the following control variables collected during pre-election visit are included when noted: Woman has NIC or CNIC, voted in last elections, woman is a member of MRDO and the number of females in the household. Standard errors are reported in parenthesis below the coefficients and are clustered at the geographical cluster level. The symbols *, **, *** represent significance at the 10, 5 and 1 % respectively.
views the family network and the party network as two distinct types of social capital, the positive effects of both types tend to operate in lagging regions and in rural areas. They differ in one crucial aspect, however, in that the positive effect of the family network tends to benefit poor households, while the positive effect of the more organized and modern party network tends to benefit non-poor households.

The rest of the paper is organized as follows. Section 2 describes our data set in more detail and the variables that we construct from it, in particular those that we use to measure social capital. Section 3 provides summary statistics describing rural-urban consumption patterns and how usage of formal and informal finance varies with household characteristics. Section 4 lays out our approach to estimation and hypotheses for the explanatory variables used in our regressions. In section 5 we discuss our empirical results, while section 6 offers conclusions.

# **2. Data and Variables**

# **a. Data Set**

We rely on data from the Chinese Household Finance Survey (CHFS) of 2013, which was collected by Chinese Household Finance Research Center of Southwestern University of Finance and Economics. The data are representative of Chinese households due to their stratified random sampling design in three stages: first, random sampling of counties; then communities within a county; then random sampling from a community.<sup>9</sup> When choosing counties, all of the counties in China were first ranked according to GDP and classified into groups. Within each group a random sample of counties was then selected. In the end, a total of 262 counties, roughly 10% of all Chinese counties, were selected to be part of the sample. Within each county, four communities were chosen. Then within a community, 25-50 households were chosen for urban areas, and 20 households for rural areas. The final CHFS sample consists of 28,100 households and 98,000 individuals in 1,029 communities, 262 counties, and 29 provinces.

> 9 A community is either a village in rural areas or a neighborhood committee (Ju wei hui in Chinese) in urban areas.

7