rafmacalaba/datause-encoder-data huggingface dataset:
Table 9: Understanding the Drivers of Changes in Log Welfare

||**Urban**|**Rural**|
|---|---|---|
|HH Size (Dist)|0.2|0.0|
|HH Size|0.0|0.8|
|Age Category (Dist)<br>|-0.2|-0.0|
|Age Category|1.3|-3.0|
|Hindu (Dist)|0.2|0.1|
|Hindu|0.1|-0.4|
|Low Caste|-0.0|0.8|
|HH Type (Dist)|1.9|0.2|
|HH Type<br>|1.0|-1.0|
|Occupation (Dist)|-0.2|-1.3|
|Occupation|0.7|-0.2|
|Sector (Dist)|-0.1|-0.2|
|Sector|0.4|-0.3|
|Expd on Services (Dist)|0.1|4.5|
|Expd on Services|-0.0|3.0|
|Expd on Recreation (Dist)|-0.7|1.3|
|Expd on Recreation|0.4|-0.3|
|Expd on Transportation (Dist)|-4.7|-3.1|
|Expd on Transportation|-2.3|-1.5|
|Rainfall|2.7|2.8|
|Time Trend|3.6|8.7|
|overall|||
|Diference|4.4|10.9|
|Mean Log Welfare (2011-12)*100|473.4|439.2|
|Mean Pred Log Welfare (2014-15)*100|477.7|450.1|

Sources: India National Sample Survey Office (NSSO) Surveys.

Table 10: Elasticity of Poverty to Growth by Model

||**Elasticity**|**Semi-Elasticity**|
|---|---|---|
|2004-05 to 2011-12|-0.6|-21.4|
|2009-10 to 2011-12|-3.0|-85.4|
|2011-12 to 2014-15 (Predicted)|.|.|
|Model 1|-2.1|-39.5|
|District dummies*Time Trend|-0.6|-12.8|
|Expd at the Extensive Margin|-0.9|-18.8|
|Constant Coefcient Model|-1.1|-22.4|
|Typical line-up method|-3.5|-60.6|

Sources: India National Sample Survey Office (NSSO) Surveys. Using 2009-10 and 2011-12 to predict in 2004-05.

26
which case reverse causality might be operating. This would imply that employment in urban sectors such as manufacturing would be negatively correlated with agricultural employment. However, this is not the case in the observed sample: employment changes in manufacturing are positively correlated with employment changes in agriculture (see Annex), which suggests that an exogenous force is pulling employment in both sectors at the same time.

**<u>Table 6: Poverty Changes and the Sectoral Employment Intensity of Growth</u>**

||(1)<br>Benchmark|(2)<br>Controls|(3)<br>Non Upper<br>MiddleIncome|(4)<br>Non<br>LAC<br>|(5)<br>Positive<br>Growth|(6)<br>Negative<br>Growth|
|---|---|---|---|---|---|---|
|Employment-intensive|||||||
|Growth in Agriculture|8.014***|8.900***|7.310***|6.467*|8.859***|0.224|
||(3.32)|(4.01)|(4.45)|(1.95)|(4.31)|(0.02)|
|Employment-intensive||||-|||
|Growth in Secondary Sector|-6.141***|-5.705***|-5.398***|159***|-5.999***|2.784|
||(-4.86)|(-4.48)|(-3.53)|(-4.41)|(-5.34)|(0.54)|
|Employment-intensive|||||||
|Growth in Tertiary Sector|-2.404|-3.111|0.450|-5.324*|-0.863|-18.76|
||(-1.28)|(-1.60)|(0.21)|(-1.84)|(-0.62)|(-1.58)|
|Change in Gini (%)||2.848***|1.661***|2.702***|2.179***|3.152|
|||(3.31)|(4.47)|(4.10)|(4.52)|(1.15)|
|Constant|0.0693|0.0966|-0.0217|0.120|0.0107|0.463*|
||(1.05)|(1.44)|(-0.31)|(1.16)|(0.19)|(2.13)|
|Observations|98|98|58|37|68|30|
|AdjustedR-squared|0.143|0.276|0.370|0.333|0.415|0.182|
|*p<.10,**p<.05,***p<.01|.T-statisticsin|parentheses.|||||

In summary, we find that aggregate employment and productivity-intensive growth are clearly associated with poverty, but the evidence suggests that their effects are equivalent. A similar result holds for the sectoral pattern of growth. What seems to matter most is the employment and productivity sectoral profile of growth. We find that employmentintensive growth in agriculture is correlated with rising poverty while employmentintensive growth in the secondary is correlated with reductions in poverty. On the other hand, productivity-intensive growth in agriculture is correlated with poverty reductions while productivity-intensive growth in the secondary and tertiary sectors has an ambiguous relation with poverty. The effects of the agricultural profile of growth are difficult to disentangle. The evidence also shows that there is some heterogeneity among countries, with upper middle income countries showing a different behavior from the rest. The correlation between growth profiles and poverty reduction also shows asymmetries between positive and negative growth spells, although our sample of negative growth spells is rather small.

22
# <u>Table A11. Reconciling mean and median regression results – second specification</u>

||**Paper re**<br>|**gression**<br>|**High Cook**<br>|**’s distance**<br>|**High standar**<br>|**dized residuals**<br>|
|---|---|---|---|---|---|---|
||_Median_<br>Monthly earnings|_Mean_<br>Monthly earnings|_Median_<br>Monthly earnings|_Mean_<br>Monthly earnings|_Median_<br>Monthly earnings|_Mean_<br>Monthly earnings|
||(TC)|(TC)|(TC)|(TC)|(TC)|(TC)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Years of schooling (a1)|0.063|0.69|-0.11|-0.42|-0.21|-0.29|
||(0.84)|(1.06)|(0.80)|(0.82)|(0.76)|(0.84)|
|Out Village (a4)|32.9**|8.35|33.1**|23.4|31.0**|12.3|
||(13.2)|(22.8)|(13.9)|(14.4)|(13.2)|(15.3)|
|Cognitive skills (a2)|0.26|2.79|-0.48|4.15|-0.048|4.27|
||(3.59)|(5.00)|(3.36)|(3.25)|(3.39)|(3.44)|
|SEM skills (a3)|14.3***|13.0***|14.6***|12.8***|15.0***|12.2***|
||(2.71)|(3.14)|(2.51)|(2.24)|(2.31)|(2.11)|
|Interaction YrsSchooling and Out Village (b1)|4.78***|9.36***|4.53***|6.16***|4.75***|7.35***|
||(1.44)|(2.51)|(1.47)|(1.72)|(1.39)|(1.80)|
|Interaction Cog and Out Village (b2)|13.6**|-4.71|**15.6*****|**5.51**|14.3***|-1.00|
||(6.35)|(11.5)|(5.17)|(6.37)|(4.62)|(6.87)|
|Interaction SEM and Out Village (b3)|-7.37|-10.8|-5.56|-9.53*|-5.99|-5.91|
||(5.17)|(7.68)|(4.71)|(5.11)|(4.52)|(5.15)|
|Constant|36.3|46.1***|25.7|29.4|37.0|43.5***|
||(46.7)|(10.0)|(16.9)|(18.1)|(50.0)|(7.42)|
|Observations|1978|1978|1895|1895|1902|1902|
|R-squared|0.10|0.13|0.12|0.21|0.12|0.21|

(continued) 91
CÔTE D’IVOIRE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE A much more detailed study of irrigation potential in Côte d’Ivoire was conducted by the Ministry of Agriculture in 2005 and used to develop the country’s national irrigation plan. The plan provides for the rehabilitation of around 3000 hectares of irrigated areas and the creation of 139,000 hectares of new irrigated areas. The total investment cost amounts to $1,657 million with an average investment cost per hectare of $11,600. Interestingly, the extent of irrigated area falls within the range that is identified in the AICD simulations, even if the assumption for the unit cost of irrigation development is several times larger. Nevertheless, the study notes that the benefits of the future irrigation investments depend on the development of the feeder road network.

# Information and communication technologies

## **Achievements**

Like many African countries, Côte d’Ivoire has gone through an ICT revolution during the last decade. By 2005, the country had established a partially competitive mobile market with two operators of roughly equal size and mobile phone penetration had reached9 percent, which was fairly typical for the peer group (table 15). Between 2006 and 2009, the government awarded four more mobile licenses. As a result, competition intensified, and mobile penetration had reached 51 percent by 2008. Nevertheless, the price of a representative basket of mobile services remains comparatively high by both regional and global standards at around $15 per month.

**Table 15 Benchmarking ICT indicators**

||Unit|Low-<br>incomefragile<br>countries|**Côte d’Ivo**|**ire**|Middle-income countries|
|---|---|---|---|---|---|
|||Mid 2000s|**2005**|**2008**|Mid 2000s|
|GSM coverage|% population|62.6|**55.0**|**54.0**|95.1|
|International bandwidth|Mbps/person|0.9|**3.0**|**40.0**|25.4|
|Internet|subscribers/100 people|0.1|**0.1**|**3.0**|1.5|
|Landline|subscribers/100 people|9.0|**10.2**||34.8|
|Mobile phone|subscribers/100 people|8.0|**9.0**|**51.0**|30.6|
||**Côte d’Ivoi**|**re**|Countries with a<br>submarine ca|ccess to<br>bles|Other developing regions|
||**2005**|**2008**|Mid 2000|s|Mid 2000s|
|Price of monthly mobile basket|**14.0**|**15.0**|10.0||9.9|
|Price of monthly fixed-line basket|**20.0**|**25.0**|11.8||n.a.|
|Price of 20-hour Internet package|**66.3**|**47.0**|47.28||11.0|
|Price of a 3-minute call to the United<br>States|**2.0**|**0.9**|1.44||2.0|
|Price of inter-Africa calls, average|**0.6**|n.a.|0.57||n.a.|

Source: Minges and others 2009, derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data. — = data not available. n.a. = not applicable. Côte d’Ivoire is connected to the SAT3 submarine cable, which provides good connectivity to the internet. International bandwidth has improved markedly in recent years. Yet the national 27
# **2 USING AND APPLYING SAVINGS TO EXPLAIN CHANGES IN CONSUMPTION**

In standard national accounting, gross national savings is calculated as the residual of income and consumption. Gross savings is the amount of annual gross income that is not consumed, and thus can be used for investment finally which adds to national wealth. Positive savings indicate an increment to overall national wealth. Standardly, national accounting only measures the increments to produced capital (or capital stock consisting of machinery, equipment, physical structures including infrastructure, and urban land area), yet social capital (human capital, quality of institutions, and the governance of goods and people) and environmental capital (land, forests and sub-soil resources) are not considered. Alternative savings measures have been proposed in order to also factor in investments in those capital classes, a key concept being that of genuine savings. Genuine savings can be derived from gross national savings, which is standardly reported in national accounting statistics, and four types of adjustments can be distinguished as suggested in World Bank (2006) as follows (see also figure 1):

- (1) The depreciation of fixed capital representing the consumption of capital is deducted from gross savings leading to _net savings_ .

- (2) Current education expenditures representing investments in human capital (standardly, in national accounting these expenditures are considered a consumption item)<sup>5</sup> are added in order to obtain _net savings_ plus education expenditure.

- (3) The depletion of natural resources is factored in reflecting the decrease in the natural asset base due to the extraction and harvesting of resources leading to _genuine savings_ excluding air and climate change damages.

- (4) In a final step, social costs due to air pollution and climate change may be subtracted leading to an estimate of _genuine savings_ including air and climate change damages.

> 5 Although there is some discussion in the literature, which holds on the contrary that educational expenditure is closer to consumption than investment.

4
**Table 1: Validation of corruption risk indicators using Single bidding as the main corruption risk proxy in an binary logistic regression framework- North Macedonia 2011-2022**

||||**Single b**|**idding is**|**the main**|**depende**|**nt variabl**|**e**|
|---|---|---|---|---|---|---|---|---|
|**Independent**<br>**variables**|**Corruption risk**<br>**level**|**(1)**|<br>**(2)**|<br>**(3)**|<br>**(4)**|<br>**(5)**|<br>**(6)**|<br>**(7)**|
|Procedure<br>type|Medium Risk<br>High Risk|0.309***<br>(0.012)<br>2.271***<br>(0.031)||||||0.130***<br>(0.017)<br>2.039***<br>(0.034)|
|Advertisement<br>period|<br>High Risk||0.460***<br>(0.011)|||||<br>0.165***<br>(0.015)|
|Decision<br>period|Medium Risk<br>High Risk|||0.427***<br>(0.012)<br>0.371***<br>(0.011)||||0.185***<br>(0.013)<br>0.337***<br>(0.012)|
|Tax Haven|Foreign Supplier<br>Supplier registered<br>in a tax haven||||0.828***<br>(0.038)<br>1.627***<br>(0.315)|||0.564***<br>(0.042)<br>0.953**<br>(0.344)|
|Supplier<br>Dependence||||||0.490***<br>(0.023)||<br>0.509***<br>(0.024)|
|<br>Benford’s Law|High Risk||||||0.073****<br>(0.010)|<br> <br>0.080***<br>(0.011)|
|N<br>pseudo R<sup>2</sup><br>All validity regr<br>identified by C<br>The stars repr|essions controls for c<br>PV divisions, and con<br>esent the significance|265,920<br>0.067<br>ontract v<br>tracting y<br>levels **|265,920<br>0.050<br>alues, bu<br>ear. Rob<br>* p<0.01,|265,920<br>0.050<br>yer types a<br>ust standa<br>** p<0.05,|267,920<br>0.047<br>nd locatio<br>rd errors s<br>* p<0.1|267,920<br>0.047<br>ns, contra<br>hown in b|<br>265,920<br>0.046<br>ct’s suppl<br>rackets be|<br>265,920<br>0.073<br>y type and markets<br>low the coefficients.|

Table 2 presents the exact definitions used to create the corruption risk indicators. All indicators are formulated to fall between 0 and 1. For some indicators, we also identify a medium risk category which takes the value of 0.5 (example procedure type risk). Each awarded contract receives a score for each indicator depending on the availability of the variable required to calculate the indicator. All risk indicators are simply averaged to calculate the composite risk score (CRI) for each contract (missing indicator values are not considered in the average score, hence the weight of individual risk factors might vary based on how many other factors are non-missing).

14
- Encouragement of technical developments within the sector

- Consideration of an increased role for professionals to assist with supervisory tasks

- Collaboration with home supervisors of foreign insurers operating in the jurisdiction

- Initiating a program to enhance market discipline through (if necessary) revised public disclosures of financial information by firms, revised disclosures to customers, and encouragement of industry publications.

Initially, the most significant challenges will rest with the development of a standard solvency system that should get early attention. Over time, as technical skills increase within the sector and as market discipline grows, then it will be possible to progressively move to a greater reliance on the alternative pillars of the supervisory structure.

In an environment that is usually marked by a rapid pace of change, the challenges are even greater.

Overall, countries should start now in their efforts to make legislation sufficiently flexible so that the new accounting standards can be easily adopted as they are implemented, gather data for analysis - particularly for parameterization of standard formulae for the solvency margin requirement - and develop technical capacities within the supervisory authority.

At the same time, although recognizing that it will take longer, countries should encourage marketplace development of technical capacity and strengthen market discipline. Even in industrial countries, this can represent a challenge.

Supervisory oversight needs to encourage as well as compensate for weaknesses in the other pillars of the structure - technical capacity within the sector may be limited and market discipline is less likely to be fully developed. Strengthening of supervisory oversight is needed to compensate until other capacities can be grown in the sector.

41
# **1. Introduction**

A new survey of 1,948 retail stores in India compiled by the World Bank’s Enterprise Surveys shows that 19% of the stores use computers for their business. Computer usage is higher among stores that find labor regulations to be more burdensome. Specifically, 16% of the stores who report labor regulations as “no obstacle” for their business use computers. Corresponding figures for stores reporting labor regulations as “minor”, “moderate”, “major or very severe” obstacles are 24%, 30% and 34%, respectively. We test if computer usage in India’s retail sector is driven by the severity of labor regulations.

Existing empirical work has paid little attention to the technology-labor regulations nexus.<sup>1</sup> Few studies which do exist treat the choice of technology as exogenous to labor regulations. For example, Card et al (1999) take computer usage in the Canada, France and the US as an exogenous technological shock. They find that the effect of this shock on wages and employment depends on labor regulations in the three countries. In recent theoretical contributions, Alesina and Zeira (2006) and Blanchard and Philippon (2006) have argued that the choice of technology should be treated as endogenous to labor regulations. Our findings confirm this argument.

# **2.  Data and Main Variables**

We use store level data collected by the World Bank in 2006.<sup>2</sup> The data are a pure cross section of 1,948 stores spread over 16 states and 41 cities of India. Our dependent variable, _Computers_ , is a dummy which equals 1 if a store uses a computer for running its

> 1 See, for example, Botero, Djankov, La Porta, Silanes and Shleifer (2004).

> 2 The survey and methodology for data collection are available at <u>www.enterprisesurveys.org.</u>

1
Then, by observing **X** we cannot distinguish between these two models. This should be familiar from econometric textbook discussions on identification (e.g. Greene, 2004). Devising “simple” structures in which as many factor loading as possible are zeros, facilitates identification and interpretations of the factors. We will explore simple structures in detail. We now formally discuss the two popular methods of factor analysis that we will use: the Principal Components (PC) method and the pure factor analysis model which we estimate by maximum likelihood (ML).

# **Fixed versus Random Factors**

A distinction is made between models that presume the factor matrix **F** in (1) to be fixed, and models that presume **F** to be random. The random factors model is appropriate when we want to extend our inferences to different samples (say, of individuals), while the nonrandom factors model is appropriate when the specific observations (here countries), and not just the model structure, are of interest. The KAM data pertain to specific countries, and are exhaustive across countries, which makes a compelling case for the use of fixed-factor models. However, if inferences from the factor analysis were to be applied to countries not in the sample, or to the same countries but in a future period, then it is advisable to use random-factor models. The likelihood function for (identified) models with random **F** is well defined (see e.g. Anderson, 1984 p 552). Estimation of models with non-random **F** proceeds based on least squares criteria (for which, unlike the random factors case, no distributional assumptions need be made unless statistical testing is to be done).

6
Molina-Millán, Teresa, Tania Barham, Karen Macours, John A. Maluccio, and Marco Stampini. 2019. "Long-Term Impacts of Conditional Cash Transfers: Review of the Evidence." _The World Bank Research Observer_ 34 (1):119-159. doi: 10.1093/wbro/lky005.

- Molina-Millán, Teresa, Karen Macours, John A. Maluccio, and Luis Tejerina. 2020. "Experimental long-term effects of early-childhood and school-age exposure to a conditional cash transfer program." _Journal of Development Economics_ 143.

- Morris, S. S., R. Flores, P. Olinto, and J. M. Medina. 2004. "Monetary incentives in primary health care and effects on use and coverage of preventive health care interventions in rural Honduras: cluster randomised trial." _Lancet_ 364 (9450):2030-7.

- Özler, Berk. 2020. "How should we design cash transfer programs?". <u>https://blogs.worldbank.org/en/developmenttalk/how-should-we-designcash-transfer-programs.</u>

- Pastore, Francesco. 2018. "Why is youth unemployment so high and different across countries?" _IZA World of Labor_ :420.

- Schultz, Paul T. 2004. "School subsidies for the poor: evaluating the Mexican Progresa poverty program." _Journal of Development Economics_ 74 (1):199-250. doi: https://doi.org/10.1016/j.jdeveco.2003.12.009.

- Shah, Manisha, Sarah Baird, Jennifer Seager, Benjamin Avuwadah, Joan Hamory, Shwetlena Sabarwal, and Amita Vyas. 2024. "Improving Mental Health of Adolescent Girls in Low- and Middle-Income Countries." _Journal of Human Resources_ 59 (S):S317. doi: 10.3368/jhr.1222-12707R2.

- Shah, Manisha, Jennifer Seager, Joao Montalvao, and Markus Goldstein. 2023. "Sex, Power, and Adolescence: Intimate Partner Violence and Sexual Behaviors." _National Bureau of Economic Research Working Paper Series_ No. 31624. doi: 10.3386/w31624.

- Shah, Manisha, and Bryce Millett Steinberg. 2021. "Workfare and Human Capital Investment." _Evidence from India_ 56 (2):380-405. doi: 10.3368/jhr.56.2.1117-9201R2.

- Shonchoy, Abu S., Fujii  Tomoki, and Raihan  Selim. 2018. "Barriers to Labor Migration for the Rural Poor: Experimental Evidence from a Vocational Training Program in Bangladesh." _Available at SSRN 3395229_ .

- Temin, M., and C. J. Heck. 2020. "Close to Home: Evidence on the Impact of Community-Based Girl Groups." _Glob Health Sci Pract_ 8 (2):300-324. doi: 10.9745/ghsp-d-20-00015.

- Waidler, Jennifer , Ulrike  Gilbert, Aroldia  Mulokozi, and Tia  Palermo. 2022. "A “Plus” Model for Safe Transitions to Adulthood: Impacts of an Integrated Intervention Layered onto A National Social Protection Program on Sexual

36
|**Table 10: Determinants of generation II’s **|**reproductiv**|**e choices**|||||||
|---|---|---|---|---|---|---|---|---|
||**Numb**<br>**childre**|**er of**<br>**n born**|**Shar**<br>**daughte**|**e of**<br>**rs born**|**Surviva**<br>**daug**|**l rate of**<br>**hters**|**Surviva**<br>**so**|**l rate of**<br>**ns**|
|Female spouse’s father died after 1994|0.182**|0.113*|0.013|0.001|-0.055***|-0.060***|-0.011|-0.007|
|or alive|(0.035)|(0.029)|(0.030)|(0.035)|(0.005)|(0.002)|(0.017)|(0.012)|
|Education gap female-male||0.061||0.006||0.001*||-0.001|
|||(0.042)||(0.006)||(0.000)||(0.002)|
|Share of female spouse’s gifts at<br>||-1.166||0.013||0.041||-0.041|
|marriage|||||||||
|||(0.669)||(0.198)||(0.119)||(0.135)|
|Reform*Female spouse’s father died|-0.239**|-0.170**|-0.128**|-0.113*|0.104***|0.102***|0.004|0.002|
|after 1994 or alive|(0.035)|(0.029)|(0.030)|(0.035)|(0.005)|(0.002)|(0.017)|(0.012)|
|Reform*Education gap female-male||-0.055||0.002||-0.003**||-0.003|
|||(0.042)||(0.006)||(0.000)||(0.002)|
|Reform*Share of female spouse’s||1.105||0.021||-0.198||0.074|
|gifts at marriage||(0.669)||(0.198)||(0.119)||(0.135)|
|Observations|717|717|717|717|555|555|643|643|
|R-squared|0.581|0.593|0.467|0.472|0.390|0.396|0.431|0.434|

_Note:_ All regressions include village fixed effects as well gender and state specific year of birth fixed effects. Figures in brackets are robust standard errors clustered by state. * significant at 10%; ** significant at 5%; *** significant at 1%.

25