rafmacalaba/datause-encoder-data huggingface dataset:
|With SGRY control<br>1.298_∗∗∗_<br>1.316_∗∗∗_<br>[0.270]<br>[0.280]<br>0.345_∗∗∗_<br>0.345_∗∗∗_<br>[0.089]<br>[0.112]<br>315,481<br>315,481<br>Yes<br>Yes<br>No<br>Yes<br>Yes<br>Yes<br>With SGRY control<br>-2.859_∗∗∗_<br>-2.700_∗∗∗_<br>[0.753]<br>[0.784]<br>-0.824<br>-0.603<br>[0.667]<br>[0.723]<br>315,481<br>315,481<br>Yes<br>Yes<br>No<br>Yes<br>Yes<br>Yes<br>RY from 2004 to 2007. Program expenditure<br>ssions include district and year-quarter fxed<br>July 2004 to June 2005 and from July 2007<br>dual spends in public works. The dependent<br>other controls are the same as in table2. All<br>t level. SGRY: Sampoorna Grameen Rozgar|
|---|
|**Panel A. Dependent Variable: Public Employment**<br>Without SGRY control<br>0.982_∗∗∗_<br>0.970_∗∗∗_<br>0.981_∗∗∗_<br>1.313_∗∗∗_<br>[0.267]<br>[0.266]<br>[0.271]<br>[0.271]<br>0.194_∗∗_<br>0.188_∗∗_<br>0.185_∗_<br>0.351_∗∗∗_<br>[0.077]<br>[0.077]<br>[0.100]<br>[0.090]<br>315,481<br>315,481<br>315,481<br>315,481<br>No<br>Yes<br>Yes<br>No<br>No<br>No<br>Yes<br>No<br>No<br>No<br>No<br>Yes<br>**Panel B. Dependent Variable: Private Employment**<br>Without SGRY control<br>-1.868_∗∗∗_<br>-1.970_∗∗∗_<br>-1.790_∗∗_<br>-2.780_∗∗∗_<br>[0.710]<br>[0.695]<br>[0.720]<br>[0.773]<br>-0.291<br>-0.400<br>-0.168<br>-0.726<br>[0.707]<br>[0.629]<br>[0.688]<br>[0.744]<br>315,481<br>315,481<br>315,481<br>315,481<br>No<br>Yes<br>Yes<br>No<br>No<br>No<br>Yes<br>No<br>No<br>No<br>No<br>Yes<br>ed on NSS 2004 – 2008 (Rounds 61 and 64) and program expenditure data for SG<br>ural Development, Government of India.<br>e order 10_−_2. Each column presents results from a separate regression. All regre<br>es are in logs. The sample is composed of all adults aged 18 to 60 interviewed from<br> variable in panel A is the percentage of days in the past seven days that an indivi<br>entage of days in the past seven days that an individual spends in private works. All<br>sampling weights. The standard errors in square brackets are clustered at the distric<br>ral Employment Guarantee Act.<br>_<_0_._01|
|_NREGAd,t × Dry_<br>_NREGAd,t × Rainy_<br>Observations<br>Worker Controls<br>District Controls FEs<br>SGRY Controls<br>_NREGAd,t × Dry_<br>_NREGAd,t × Rainy_<br>Observations<br>Worker Controls<br>District Controls FEs<br>SGRY Controls<br>_Source_: Author’s analysis bas<br>data is from the Ministry of R<br>_Notes_: All estimates are of th<br>effects. All dependent variabl<br>to June 2008. The dependent <br>variable in panel B is the perc<br>estimates are computed using<br>Yojana. NREGA: National Ru<br>_∗p <_0_._10_,∗∗p <_0_._05_,∗∗∗p _|
|**Table 4: The extent to**<br>**across countries and acr**<br>Country and year of<br>survey|**which firms t**<br>**oss firms wit**<br>**Percent of**<br>**time spen**<br>**of**|**ake actions to i**<br>**hin countries**<br>**management**<br>**t dealing with**<br>**ficials**|**nfluence p**|**olicy imple**<br>**Bri**|**mentation**<br>**bes**|**vary**|
|---|---|---|---|---|---|---|
||**Average**|**Standard**|**Percent**|**Amount**|**as apercen**|**t of sales**|
|||**Dev.**|**paying**|**Average**<br>**of those**<br>**paying**<br>**a bribe**|**Std dev**<br>**of those**<br>**paying**|**Std dev**<br>**of all**<br>**firms**|
|BurkinaFaso2006|11.01|15.30|97.7%|8.23|8.73|8.59|
|Guinea2006|3.74|6.84|92.4%|5.34|7.98|7.80|
|Cameroon2006|14.06|15.42|91.6%|3.88|8.87|8.44|
|Mauritania2006|6.10|12.70|90.4%|4.83|7.58|7.31|
|DRC2006|6.83|10.63|87.1%|5.13|6.67|6.45|
|Niger2005|13.20|19.39|86.2%|6.53|6.92|6.56|
|GuineaBissau2006|3.20|5.98|75.5%|4.66|5.38|4.98|
|Kenya2007|6.84|7.63|75.0%|3.82|4.46|4.20|
|Burundi2006|5.90|9.14|67.2%|8.89|7.01|7.07|
|Gambia2006|7.52|12.28|64.3%|6.62|8.49|7.15|
|Uganda2006|5.56|7.34|63.7%|6.20|6.80|6.08|
|Benin2004|8.29|14.39|57.6%|8.65|7.29|6.99|
|Tanzania2006|4.89|7.89|55.7%|5.83|6.86|5.87|
|Swaziland2006|4.66|7.08|55.3%|3.57|5.69|4.51|
|Angola2006|7.94|8.75|51.4%|6.21|7.43|6.04|
|Nigeria2007|6.63|9.43|46.9%|3.94|5.19|4.06|
|Botswana2006|5.42|9.10|45.7%|3.31|7.00|4.55|
|CapeVerde2006|14.06|18.55|33.3%|2.17|2.32|0.87|
|Ghana2007|3.69|6.27|33.3%|5.90|7.12|4.96|
|Lesotho2003|22.11|24.77|33.3%|1.79|3.06|1.93|
|Malawi2005|7.89|13.35|33.3%|6.73|8.43|5.78|
|Rwanda2006|4.99|9.43|29.1%|7.34|10.59|6.52|
|Mali2007|2.43|3.61|28.1%|4.07|5.06|3.24|
|Namibia2006|4.12|7.55|25.3%|4.96|7.63|3.65|
|Senegal2007|3.68|6.81|24.8%|6.30|8.19|4.90|
|Madagascar2005|22.19|23.00|24.2%|6.18|9.75|5.45|
|Zambia2007|5.56|8.32|20.1%|5.95|8.01|4.30|
|SouthAfrica2007|6.46|7.49|17.5%|4.97|9.07|4.23|
|Mauritius2005|11.28|14.94|17.1%|3.31|3.68|1.95|
|Mozambique2007|4.05|6.50|16.8%|8.71|9.07|4.93|
|Cross Countrymedian|6.28|9.12|49%|5.58|7.21|5.22|

29
10 remittances were set to zero, the poverty headcount index in Lesotho would increase by 26 percent. In addition, a cessation of remittances would lead to a 52 percent increase in the poverty gap index. The poverty gap index is a more sensitive measure of poverty because it considers the depth of poverty, that is, the amount by which the average income of the poor falls short of the poverty line. In other words, a cessation of international remittances would lead to a larger increase in the depth of poverty as opposed to the level of poverty in Lesotho.

The third paper uses data from a 2003 survey of 1782 households in rural Mexico to examine the marginal impact of international remittances on welfare and poverty in that country (Taylor, Mora and Adams, 2005). As in Lesotho, international remittances account for a sizeable proportion of total per capita household income in rural Mexico: 15 percent. Most of these international remittances come from migrants working in the United States. To analyze the impact of these remittance flows on poverty, the paper develops a national poverty line, which includes the cost of basic food and nonfood items in rural Mexico.<sup>12</sup> According to this poverty line, about 58 percent of all rural Mexican households have per capita incomes below the poverty line. To demonstrate the impact of international remittances on poverty, the paper estimates the rural poverty effects of a 10 percent increase in international remittances. The paper finds that such an increase in international remittances would reduce the poverty headcount and the poverty gap indices by 0.77 and 0.53, respectively. In other words, in rural Mexico international remittances reduce both the level and depth of poverty.

The fourth paper uses data from a 2000 survey of 7276 households in urban and rural Guatemala to compare the observed level of welfare and poverty in that country with a counterfactual scenario without remittances (Adams, 2005a). To establish the counterfactual, the study uses predicted income functions to estimate per capita household incomes in two situations: excluding remittances (but including an imputation for the home earnings of international migrants had they stayed home); and including remittances.<sup>13</sup> The results show that in the including remittances situation, the mean level of international remittance-receiving households is about 40 percent higher than for households not receiving remittances. In other words, the receipt of international remittances greatly increases the level of per capita household incomes in Guatemala.
in Fig. 2. We conducted sensitivity analysis around the splitting percentage locations for training. We test 60%, 70%, and 90% in addition to the selected 80% value. Table 3 shows the implications for splitting assumptions for weight uncertainty.

Table 3 1Sensitivity of factor weights to splitting of testing and training data.

|**Factor**||**Road**|**distance**|||**Rai**|**nfall**|||**Land**|**cover**|||**Lith**|**ology**||**Sl**|**ope **||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Data spli**|**t**90/10|80/20|70/30|60/40|90/10|80/20|70/30|60/40|90/10|80/20|70/30|60/40|90/10|80/20|70/30 60/40|90/10|80/20|70/30|60/40|
|**Max**<br>**weight**|0.29|0.32|0.33|0.34|0.24|0.27|0.27|0.28|0.28|0.29|0.30|0.34|0.20|0.21|0.22<br>0.23|0.16|0.18|0.20|0.21|
|**Min**<br>**weight**|0.21|0.20|0.17|0.17|0.18|0.16|0.15|0.14|0.19|0.17|0.15|0.14|0.14|0.13|0.12<br>0.12|0.12|0.11|0.10|0.1|
|**Median**<br>**weight**|0.25|0.25|0.25|0.25|0.21|0.21|0.21|0.20|0.23|0.22|0.22|0.22|0.17|0.17|0.17<br>0.18|0.14|0.14|0.15|0.15|

# **3.4. Machine learning and factor weights**

In the fifth step of the proposed approach, we used machine learning to calculate the weights of the factors that have been identified in the first step in determining the occurrence of landslides. We used the Random Forest Classification Machine Learning Algorithm (Breiman, 2001) to develop classification models for predicting whether a location is a landslide or a non-landslide location. To this end, we used the data sampled in the fourth step to train a Random Forest Classification model in each iteration. The model target (i.e., dependent variable) is to predict a binary value (zero or one) representing whether a location is a landslide or a non-landslide location. The model features (i.e., independent variables) are the score values of the factors identified in the first step. A random forest model is an ensemble of decision trees; each tree is created based on a random selection from the training data and the available features (Svetnik et al., 2003). During model training, each tree in the forest is designed by selecting leaf nodes and splits to maximize prediction quality. The prediction of a random forest model is made by aggregating the predictions of individual trees in the forest based on the majority vote (i.e., the mean) (Breiman, 2001). In this study, we used the Gini criterion to measure the 15
**Table 7: Impacts on Financial Investment, Product Innovation and Social Media Quality**

||Investment<br>Index|Innovation<br>Index|<br>Website<br>Effectiveness|Facebook<br> Effectiveness|Twitter<br> Effectiveness|Instagram<br>Effectiveness|Global<br>Quality<br> Effectiveness|Global<br>Quantity<br>Effectiveness|Unconditiona<br>Effecti<br>Overall Soc|l Conditional<br>veness<br>ial Media|
|---|---|---|---|---|---|---|---|---|---|---|
|Assigned to Insourcing|0.030|0.083**|0.843**|0.954***|0.273|0.392|0.695**|0.596**|0.616**|0.349|
||(0.027)|(0.032)|(0.360)|(0.338)|(0.207)|(0.283)|(0.296)|(0.282)|(0.240)|(0.278)|
|Assigned to Outsourcing|-0.035|0.068**|1.211***|1.118***|0.259|0.676**|0.807***|0.875***|0.817***|0.725***|
||(0.025)|(0.032)|(0.370)|(0.343)|(0.205)|(0.282)|(0.304)|(0.295)|(0.249)|(0.276)|
|Assigned to Training|-0.029|-0.003|-0.035|0.242|-0.000|0.300|0.134|0.070|0.126|-0.107|
||(0.026)|(0.031)|(0.322)|(0.336)|(0.202)|(0.276)|(0.287)|(0.273)|(0.230)|(0.279)|
|Assigned to Consulting|-0.024|0.049|0.400|0.576*|0.105|0.088|0.544*|0.315|0.298|-0.085|
||(0.026)|(0.031)|(0.347)|(0.341)|(0.198)|(0.284)|(0.297)|(0.278)|(0.230)|(0.268)|
|Mean of Control Group|0.306|0.351|2.089|3.722|1.460|2.547|3.337|3.237|1.670|2.827|
|Sample Size|678|678|753|753|753|753|753|753|753|508|
|P-value: all treatments zero|0.057|0.011|0.002|0.004|0.494|0.136|0.023|0.013|0.004|0.004|
|P-value: all treatments equal|0.036|0.020|0.004|0.038|0.480|0.233|0.095|0.029|0.018|0.002|

Notes: Regressions control for stratification batch fixed effects. Robust standard errors in parentheses. *, **, *** denote significance at the 10, 5, and 1 percent levels. **Investment index** is the average of dummies for taking new loan financing, new equity financing, and making a big investment (100,000 Naira or more); **Innovation index** is the proportion of 17 innovative activities being done by firms; **Website, Facebook, Twitter,** and **Instagram Effectiveness** are averages of 12 ratings on each of these forms of social media; **Global Quality** and **Global Quantity** are single question ratings; and **Overall Social Media Effectiveness** is an average of these 50 ratings of functional and aesthetic quality. The Unconditional estimates code firms with no social media as 0, conditional estimates are for firms with some social media or website presence The investment index and innovation index were measured in round 2 only; Social media quality measured in October 2019.

42
most important contributor to household vulnerability, is due to unobserved factors having greater association with aggregate risk rather than idiosyncratic risk.

**Table 4.** <u>Correlations among components of vulnerability</u>

||**Poverty**|**Aggregate Risk**|**Idiosyncratic risk**|**Unexplained risk**|
|---|---|---|---|---|
|Poverty|1.0000|0.9816***|0.2444***|0.2431***|
|AggregateRisk|0.9816***|1.0000|0.2427***|0.2359***|
|Idiosyncraticrisk|0.2444***|0.2427***|1.0000|0.0398|
|Unexplainedrisk|0.2431***|0.2359***|0.0398|1.0000|

Source: Author’s calculations based on Serbia SLS 2002 and 2003.

# **_Geographic Heterogeneity in Rural Vulnerability_**

Table 5 presents vulnerability and its components by region. Southeast Serbia is the most vulnerable, followed by east Serbia. Over 43 percent of rural residents in Southeast Serbia are vulnerable compared only about 11 percent in Vojvodina. Thus vulnerability in Southeast Serbia is almost fourfold compared to Vojvodina. Another interesting observation from Table 5 is that the contribution of poverty to vulnerability increases with the level of vulnerability. For example, poverty contributes only 44 percent of vulnerability in Vojvodina, while it accounts for over 84 percent of the vulnerability in Southeast Serbia. While further research is needed to explain more fully the regional disparities in vulnerability, an important policy implication of the current finding is that appropriately targeted interventions on the basis of the degree of poverty would have greater impact for reducing vulnerability.

**Table** **<u>5.</u>** Rural vulnerability, <u>poverty and risk across regions in Serbia</u>

||**Vulnerability**|**Poverty**|**Poverty**<br>**contribution to**<br>**vulnerability**<br>**(%)**|**Risk**|**Risk**<br>**contribution to**<br>**vulnerability**<br>**(%)**|
|---|---|---|---|---|---|
|Vojvodina|0.113|0.0498|44.1|0.0632|55.9|
|West Serbia|0.1973|0.1362|69.0|0.061|30.9|
|Central Serbia|0.2118|0.1563|73.8|0.0554|26.2|
|East Serbia|0.261|0.1859|71.2|0.0751|28.8|
|Southeast<br>Serbia|0.4316|0.3639|84.3|0.0677|15.7|
|**Total**|**0.2282**|**0.1648**|**72.2**|**0.0634**|**27.8**|

Source: Author’s calculations based on Serbia SLS 2002 and 2003.

Decomposition of risk into its aggregate, idiosyncratic and unexplained components suggests that aggregate shocks are the largest contributors to risk and uncertainty in rural Serbia (Table 6). The drought of 2003 and its sharply negative impact on agricultural production was the main 11
Thus, spatial correlation between cities declines linearly and is zero beyond 1.5 standard deviations of latitude and longitude.

Appendix Tables A to D report the two-step spatial GMM and spatial OLS results which correspond to each specification of Tables 2 to 5-2. In general we find that the GMM results are robust and the spatial GMM results are very similar to the clustered ones.

# **<u>Decomposition of City Growth Residuals</u>**

We now use the residuals from the GMM estimations in Table 5-2 (1), and examine if they have any systematic association with time invariant local characteristics. Our main interest is in examining if local management or governance, and inter industry linkages are associated with city growth. In principle, autonomous local government would actively work to provide local public goods for its constituents, and develop policies to stimulate growth and manage externalities. For our analysis, we have two measures of local government efforts: (1) existence of laws to collect IPTU tax (property tax), (2) percentage of population under land zone laws.

In terms of inter industry linkages; we expect a clustered or densely populated region to provide a rich environment for competition and collaboration among firms and workers in the region, which lead to economic growth. As Saxenian (1994) observed, regional development is more distinct in a region consisting of many small size firms than that of a few large firms.<sup>14</sup> A city with a rich set of forward and backward linkage

> 14 Saxenian (1994) examined different regional economic performances between Silicon Valley in California and Route 128 in Massachusetts. Dense social networks and open labor market in Silicon Valley have facilitated informal communication and collaborative practices, and produced a regional network-

21
9 Table 2: CROSS-INDUSTRY RANK CORRELATIONS -- CHANGES IN PROTECTION WITH CHANGES IN INDUSTRIAL CHARACTERISTICS

|||Pe|rcenti|les (n)|||
|---|---|---|---|---|---|---|
|PROTEC correlated with:|<br>10th|25th|50th|75th|90th|99th|
|Gross Output<br>(RQn)|-.327<br>(1.50)|-.193<br>(.86)|-.062<br>(.26)|.157<br>(.69)|.133<br>(.59)|<br>.108<br>(.47)|
|Value Added<br>(RVAn)|-.481<br>(2.39)|-.338<br>(1.56)|-.251<br>(1.13)|-.168<br>(0.74)|.003<br>(0.01)|<br>.054<br>(.23)|
|Employment<br>(RLn)|.251<br>(1.13)|-.055<br>(.25)|.260<br>(1.17)|.520<br>(2.67)|.397<br>(1.89)|<br>.251<br>(1.13)|
|Efficiency ..abor<br>(REn)|.075<br>(.33)|-.018<br>(.08)|.348<br>(1.61)|.395<br>(1.87)|.222<br>(.99)|<br>.181<br>(.80)|
|Value Added<br>per Efficiency<br>LaboL (RVAEn)|-.577<br>(3.08)|-.434<br>(2.10)|-.410<br>(1.96)|-.304<br>(1.39)|-.414<br>(1.98)|-.071<br>(.31)|
|Value Added<br>per unit<br>Capital (RVAKn)|-.392<br>(1.85)|-.246<br>(1.11)|-.199<br>(.88)|-.323<br>(1.48)|-.236<br>(1.06)|-.221<br>(.98)|
|Gross Output per<br>unit Efficiency<br>Labor (RQEn)|-.451<br>(2.20)|-.418<br>(2.01)|-.462<br>(2.27)|-.319<br>(1.47)|-.516<br>(2.63)|-.022<br>(.09)|
|Gross Output per<br>unit Capital (RQKn)|-.218<br>(.96)|-.143<br>(.62)|-.158<br>(.70)|-.414<br>(1.98)|-.419<br>(2.01)|-.358<br>(1.67)|

Absolute values of t ratios are in parentheses This reduction is most striking among plants in the third largest quartile, and is stongest when employment is measured in terms of number of workers. Second, however, reductions in protection are associated with higher value added and output among the smallest plants. This effect is stongest in the lowest decile, and is nonexistent among plants above the median. So there
Peltzman, S. 1976. Toward a More General Theory of Regulation, J.L.Econ. 19(2) _,_ 211–240.

Rijkers, B., Baghdadi, L., Raballand, G. 2015. Political Connections and Tariff Evasion: Evidence from Tunisia. World Bank Econ. Rev.

Rijkers, B., Arouri, H., Freund, C., Nucifora, A. 2014. Which Firms Create the Most Jobs in Developing Countries? Evidence from Tunisia. Lab. Econ. _,_ 31, 84-102.

Shleifer, A., Vishny, R.W. 1993. Corruption. Q. J. Econ. 108(3): 599–617.

Shleifer, A., Vishny, R.W. 1994. Politicians and Firms Q. J. Econ. 109(4), 995–1025.

Stigler, G.J. 1971. The Theory of Economic Regulation. Bell J. Econ. Manag. Science. 2(1), 3–21.

OECD, 2012. OECD Investment Policy Reviews: Tunisia 2012.

25
Table 2 compares the patterns of regional convergence in the first and second halves of the sample period and shows that the convergence coefficient is negative and significant in the two sub-periods as well as all the sectors. The sizes of the estimates are also systematically larger in the 2002-10 period compared with the estimates in 2010-18. In the 2002-10 period, the estimates are -0.038 (all sectors), -0.079 (agriculture), vs -0.059 (extractives), -0.041 (manufacturing), and -0.035 (services). The corresponding estimates in 2010-18 are -0.019, -0.020, -0.021, -0.030, and -0.017. The convergence rate is especially low for services.

Table 1: Growth of Aggregate and Sectoral Labor Productivity 2002-18 (Labor Productivity Computed at the Industry Level)

||All Sectors|Agriculture|Extractives|Manufacturing|Services|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Initial labor productivity (log)|-0.023<sup>_∗∗∗_</sup>|-0.031<sup>_∗∗∗_</sup>|-0.030<sup>_∗∗∗_</sup>|-0.028<sup>_∗∗∗_</sup>|-0.020<sup>_∗∗∗_</sup>|
||(0.0011)|(0.0076)|(0.0053)|(0.0020)|(0.0012)|
|Constant|0.153<sup>_∗∗∗_</sup>|0.201<sup>_∗∗∗_</sup>|0.210<sup>_∗∗∗_</sup>|0.186<sup>_∗∗∗_</sup>|0.137<sup>_∗∗∗_</sup>|
||(0.0069)|(0.0447)|(0.0353)|(0.0124)|(0.0073)|
|Industry FEs|No|No|No|No|No|
|State FEs|No|No|No|No|No|
|Obs.|2,266|81|112|625|1,448|
|R<sup>2</sup>|0.294|0.228|0.291|0.440|0.255|

*** Source: World Bank staff using RAIS. Note: Robust standard errors in parentheses. _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

# **4.2 Conditional convergence**

A multiplicity of factors influences the pace of regional convergence in productivity. Some are geographic (e.g., resource endowment, sea access), institutional (e.g., regulatory capacity, market distortions), and policy. To this end, the regional convergence regressions are now estimated after controlling for industry fixed effects (the levels and productivity growth rates inherently differ due to differences in production and market structure) and state fixed effects (to capture the effect of mostly geographic factors on regional development). In addition, these regression account for states’ initial level of development (approximated by poverty rates) and determinants of development (e.g., population size and age composition, labor market features, and infrastructure).

Table 3 presents tests for the presence of regional conditional convergence. Column (1) shows that including the industry and state fixed effects accelerates productivity growth and promotes re12