rafmacalaba/datause-encoder-data huggingface dataset:
Table 9: Mincer regressions for waged workers: wage premium in return migrants

||(1)|(2)<br>Bangladesh|(3)<br>|(4)|(5)<br>Nepal|(6)|(7)|(8)<br>Pakistan|(9)|
|---|---|---|---|---|---|---|---|---|---|
||All se|ctors|Construction<br>sector only|All se|ctors|Construction<br>sector only|All s|ectors|Construction<br>sector only|
|Dependent variable|ln(Hourly<br>wage)|ln(Hourly<br>wage)|ln(Hourly<br>wage)|ln(Hourly<br>wage)|ln(Hourly<br>wage)|ln(Hourly<br>wage)|ln(Hourly<br>wage)|ln(Hourly<br>wage)|ln(Hourly<br>wage)|
|Return migrant|0.064**|0.052*|0.276***|0.019|0.065**|0.092**|0.163***|0.154***|0.263***|
||(0.028)|(0.027)|(0.083)|(0.032)|(0.031)|(0.036)|(0.032)|(0.030)|(0.044)|
|_Age category (age 20-29 _|_as the refere_|_nce group)_||||||||
|Age: 30-39||0.058***|0.039*||0.133***|0.049*||0.107***|0.044***|
|||(0.010)|(0.022)||(0.019)|(0.027)||(0.008)|(0.016)|
|Age: 40-49||0.095***|0.072**||0.182***|0.068**||0.226***|0.085***|
|||(0.011)|(0.030)||(0.021)|(0.030)||(0.010)|(0.018)|
|Age: 50-59||0.119***|0.070**||0.234***|0.079*||0.358***|0.145***|
|||(0.013)|(0.035)||(0.026)|(0.042)||(0.012)|(0.023)|
|_Education level (illiterate_|_as the refe_|_rence group)_||||||||
|Education: Primary||0.092***|0.092***||0.156***|0.081***||0.082***|0.065***|
|||(0.008)|(0.019)||(0.022)|(0.026)||(0.008)|(0.014)|
|Education: Lower sec.||0.163***|0.085***||0.189***|0.099***||0.139***|0.157***|
|||(0.010)|(0.025)||(0.024)|(0.034)||(0.009)|(0.017)|
|Education: Higher sec.||0.475***|0.227***||0.331***|0.196***||0.369***|0.159***|
|||(0.014)|(0.051)||(0.021)|(0.032)||(0.008)|(0.018)|
|Education: Tertiary||0.980***|1.039***||0.878***|0.753***||0.994***|0.555***|
|||(0.018)|(0.161)||(0.024)|(0.096)||(0.010)|(0.065)|
|Rural||-0.183***|-0.118***||0.012|0.018||-0.089***|-0.146***|
|||(0.016)|(0.036)||(0.015)|(0.021)||(0.006)|(0.012)|
|Married||0.066***|-0.008||0.113***|0.045||0.098***|0.066***|
|||(0.013)|(0.040)||(0.024)|(0.036)||(0.009)|(0.017)|
|Head of household||0.037***|0.080**||0.018|0.021||0.085***|0.018|
|||(0.013)|(0.037)||(0.018)|(0.025)||(0.010)|(0.019)|
|Household size||0.015***|0.020**||-0.017***|-0.025***||0.007***|0.003|
|||(0.004)|(0.010)||(0.005)|(0.007)||(0.002)|(0.004)|
|Number of dependents||0.004|-0.003||0.013*|0.026**||-0.000|0.005|
|||(0.005)|(0.014)||(0.008)|(0.012)||(0.002)|(0.005)|
|Constant|3.672***|3.445***|3.544***|4.309***|3.863***|4.134***|4.360***|3.840***|4.163***|
||(0.004)|(0.025)|(0.065)|(0.008)|(0.034)|(0.049)|(0.003)|(0.012)|(0.025)|
|Observations|26890|26890|2545|5314|5314|1867|44135|44135|9366|

_Note:_ Robust standard errors in parentheses. Sample is restricted to male _waged workers_ aged 20-59. Data source: Bangladesh HIES 2016-17; Nepal LFS 2017-18; Pakistan LFS 2014-15 and 2017-18. * p _>_ 0.1, ** p _>_ 0.05, *** p _>_ 0.01.
Table 5.2: Probability of being Indigent in Argentina <u>Household Heads in Dispersed Rural Areas, 2003</u>

||dF/dx|P>|z||x-bar|
|---|---|---|---|
|_Skill characteristics_||||
|Age|-0.002|0.000|48.87|
|Education|-0.022|0.000|5.84|
|_Gender_||||
|Male|-0.058|0.000|0.87|
|_Family characteristics_||||
|Nonworking children <15<br>years of age/total number of<br>members in the household|0.386|0.000|0.25|
|_Landholdings*_||||
|0.1–100 ha.|0.057|0.000|0.633|
|100.1–250 ha.|-0.014|0.003|0.089|
|>250 ha.|-0.025|0.000|0.059|
|_Pseudo R2: 0.1173_<br>_Observed P:_ _0.189_<br>_Predicted P: 0.160_||||

_Note_ : Excluded categories: No land _Source_ : Own calculation based on World Bank survey 2003.

Figure 5.1: Average number of children (all ages) and children under 15 years of age in rural households <u>by age group of household head in Argentina, 2003</u> <!-- Start of picture text --> 4.0<br>All children <15 yrs old<br>3.5<br>All children of the head of the household<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>18 - 23 25-30 31-35 36-40 41-45 46-50 51-55 56-60 61-65 66-70 71-75 76-80 81-85 86-88<br>Head of household age cohort<br>Source: Own calculation based on RHS 2003.<br>Average number of children<br><!-- End of picture text --> 26
|IN Swachh Bharat Mission Support Operation|Raghava Neti|
|---|---|
|IN Swachh Bharat Mission Support Operation|Raghava Neti|
|IN Swachh Bharat Mission Support Operation|Raghava Neti|
|IN Swachh Bharat Mission Support Operation|Raghava Neti|
|Access to Sustainable EnergyProject|FengLiu|
|Access to Sustainable EnergyProject|FengLiu|
|Access to Sustainable EnergyProject|FengLiu|
|Access to Sustainable EnergyProject|FengLiu|
|Access to Sustainable EnergyProject|FengLiu|
|Access to Sustainable EnergyProject|FengLiu|
|Access to Sustainable EnergyProject|FengLiu|
|Access to Sustainable EnergyProject|FengLiu|
|Access to Sustainable EnergyProject|FengLiu|
|SustainingReforms for Inclusive Growth|Alexis Sienaert|
|TZ-Zanzibar ImprovingStudent Prospects Project|Tanya June Savrimootoo|
|TZ-Zanzibar ImprovingStudent Prospects Project|Tanya June Savrimootoo|
|TZ-Zanzibar ImprovingStudent Prospects Project|Tanya June Savrimootoo|
|TZ-Zanzibar ImprovingStudent Prospects Project|Tanya June Savrimootoo|
|TZ-Zanzibar ImprovingStudent Prospects Project|Tanya June Savrimootoo|
|TZ-Zanzibar ImprovingStudent Prospects Project|Tanya June Savrimootoo|
|TZ-Zanzibar ImprovingStudent Prospects Project|Tanya June Savrimootoo|
|TZ-Zanzibar ImprovingStudent Prospects Project|Tanya June Savrimootoo|
|TZ-Zanzibar ImprovingStudent Prospects Project|Tanya June Savrimootoo|
|Climate Change Adaptation Project|Gaurav D. Joshi|
|Climate Change Adaptation Project|Gaurav D. Joshi|
|Climate Change Adaptation Project|Gaurav D. Joshi|
|Climate Change Adaptation Project|Gaurav D. Joshi|
|MX: ExpandingRural Finance|Patricia Caraballo|
|MX: ExpandingRural Finance|Patricia Caraballo|
|MX: ExpandingRural Finance|Patricia Caraballo|
|MX: ExpandingRural Finance|Patricia Caraballo|
|MX: ExpandingRural Finance|Patricia Caraballo|
|MX: ExpandingRural Finance|Patricia Caraballo|
|MX: ExpandingRural Finance|Patricia Caraballo|
|MX: ExpandingRural Finance|Patricia Caraballo|
|MX: ExpandingRural Finance|Patricia Caraballo|
|National Agricultural and Rural Inclusive Growth Project|James Muli Musinga|
|National Agricultural and Rural Inclusive Growth Project|James Muli Musinga|
|National Agricultural and Rural Inclusive Growth Project|James Muli Musinga|
|National Agricultural and Rural Inclusive Growth Project|James Muli Musinga|
|National Agricultural and Rural Inclusive Growth Project|James Muli Musinga|
|National Agricultural and Rural Inclusive Growth Project|James Muli Musinga|
|National Agricultural and Rural Inclusive Growth Project|James Muli Musinga|
|National Agricultural and Rural Inclusive Growth Project|James Muli Musinga|
|Transparencyand Efficiencyin Tax Administration|Alberto Leyton|
|Transparencyand Efficiencyin Tax Administration|Alberto Leyton|
|Transparencyand Efficiencyin Tax Administration|Alberto Leyton|
# **_Psychological and Conceptual Barriers_**

Considered in detail under the previous section on existing explanations and in the annotated bibliography.

|**T**<br>|**able 1 Responding to Climate Change: Psychological and Conceptual Barr**<br>|**iers**<br>|
|---|---|---|
|Cognitive<br>dissonance|Individuals may block out or distance themselves from certain information<br>in order to maintain coherent meaning systems.|Summarized in<br>Cohen, Stanley<br>2001|
|Desire to protect<br>individual<br>identity|People are motivated to avoid threats to identity. Such threats may be<br>mitigated by selective perception and cognition, and/or redefining the<br>situation to make it reflect a more favorable view of the self|Victor Gecas and<br>Peter Burke 1995|
|Mental Models,<br>conceptually<br>flawed|Incorrect mental models lead to widespread confusion in the general public,<br>including confusion between ozone hole and global climate change, between<br>weather and climate, and regarding causes of climate change|Bostrom, Ann, M.<br>Granger Morgan,<br>Baruch Fischoff<br>and Daniel Read<br>1994|
||“widespread misunderstanding” of how climate change works caused<br>respondents to systematically underestimate the need for immediate action .<br>. . Low public support may arise from misconceptions of climate dynamics.|Sterman and<br>Sweeney 2007|
|Role of affect,<br>efficacy and<br>negative<br>|Affect:<br> Affect, the positive or negative evaluation of an object, idea, or<br>mental image, has been shown to powerfully influence individual processing<br>of information and decision-making. Climate change evokes negative affect.|Leiserowitz,<br>Anthony 2006,<br>2007|
|emotions as<br>response<br>barriers|Efficacy:<br> “People stop paying attention to global climate change when they<br>realize that there is no easy solution for it. Many people judge as serious<br>only those problems for which they think action can be taken.”|Krosnick, Jon,<br>Allyson Holbrook,<br>Laura Lowe and<br>Penny Visser<br>2006|
||Efficacy:<br> Personal efficacy regarding climate change is strongest predictor<br>of concern. Increased levels of information about global warming have a<br>negative effect on concern and sense of personal responsibility.|Kellstedt, Paul,<br>Sammy Zahran<br>and Arnold<br>Vedlitz 2008|
||Individuals may block out or distance themselves from certain information<br>in order to maintain a sense of self-efficacy|Gecas and Burke<br>1995|
||Negative emotions:<br> Individuals may block out or distance themselves from<br>certain information in order to maintain desirable emotional states|Rosenberg Morris<br>1991, Stoll-<br>Kleeman et al.<br>2001, Meijndes,<br>Midden and<br>Wilke. 2001.|

34
# **Is India’s Manufacturing Sector Moving Away from Cities?**

Ejaz Ghani, Arti Grover Goswami, and William R. Kerr

**Keywords:** Urbanization, structural transformation, transition, development, manufacturing, agglomeration, industrialization, growth, India.

**JEL Classification:** J61, L10, L60, O10, O14, O17, R11, R12, R13, R14, R23

_Author institutions and contact details:_ <u>Ghani: World Bank, Eghani@worldbank.org; Grover Goswami: World Bank, agrover1@worldbank.org; Kerr: Harvard University, Bank of</u> Finland, and NBER, wkerr@hbs.edu.

_Acknowledgments_ : We are grateful for helpful suggestions/comments from Mehtab Azam, Martha Chen, Edward Glaeser, Himanshu, Ravi Kanbur, Vinish Kathuria, Amitabh Kundu, Peter Lanjouw, Katie McWilliams, Om Prakash Mathur, Pratap Bhanu Mehta, P.C. Mohanan, Rakesh Mohan, Enrico Moretti, Partha Mukhopadhyay, Stephen O’Connell, Inder Sud, Arvind Virmani, and Hyoung Gun Wang. We are particularly indebted to Henry Jewell for excellent data work and maps. We thank the World Bank's South Asia Labor Flagship team for providing the primary datasets used in this paper. Funding for this project was provided by the World Bank and Multi-Donor Trade Trust Fund. The views expressed here are those of the authors and not of any institution they may be associated with.
Policy Research Working Paper 8626

# **Abstract**

Despite the well-known positive effects of tobacco taxes on health outcomes, policy makers avoid relying on such taxes because of their possible regressive impact. Using an extended cost-benefit analysis to estimate the distributional effect of cigarettes in the Russian Federation, this paper finds that the long-run impact may in fact be progressive. The methodology applied incorporates the negative price effect caused by an increase in tobacco taxes, combined with a presumed future reduction in medical expenditures and a rise in working years caused by a reduction in the rate of smoking among the population. The analysis includes estimates of the distributional impacts of price rises on cigarettes under various scenarios, based on information taken from the Russia Longitudinal Monitoring Survey—Higher School of Economics for 2010–16. One contribution is the quantification of impacts by allowing price elasticities to vary across consumption deciles. Overall, cigarette taxes exert a positive long-term effect on household incomes, although the magnitude depends on the structure of the conditional price elasticity. If the population is more responsive to tobacco price changes, then it would experience greater gains from the health and extended work-life benefits.

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/research. The authors may be contacted at afuchs@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
wisdom suggests that lower oil prices should affect the Gulf States more severely than the overall emerging markets. The finding that Gulf States’ index is less affected than the overall emerging market index suggests that other channels, above and beyond the lower oil revenue channel, might be at play.

Table 5.3 MSCI Gulf States Index

|VARIABLES|∆log (Index)|∆log (Index)|
|---|---|---|
|∆_Log( Oil Price)_|0.0714<br>(0.0773)|0.0554<br>(0.0617)|
|Lags|No|Yes|
|Constant|-0.000235<br>(0.000444)|-8.04e-05<br>(0.000401)|
|Observations|699|696|
|R-square|0.018|0.121|
|Robust sta<br>*** p<|ndard errors in paren<br>0.01, ** p<0.05, * p<|theses<br>0.1|

To investigate the possibility further, we examine the impacts of lower oil prices on individual important oil-exporting emerging markets. Key emerging oil exporters - Brazil, Colombia, Mexico and the Russian Federation - show tremendous sensitivity to the declines in oil prices. The coefficients are much larger than the overall MSCI Emerging Market Index, and the Dow Jones U.S. Market Index. In particular, a 10% decline in the WTI oil price reduces the stock index of Brazil by 3.01%, Colombia by 4.13%, Mexico by 2.21% and Russia by 4.57%. The declines here suggest heavy capital flight of investors from these markets when they see oil prices drop. Since these countries are large and likely systemically important, the magnitude of the impacts is concerning. The indices of many smaller Gulf States, however, are not significantly affected by lower oil prices. This finding is consistent with the results in table 5.4 and reinforces the possibility that other channels beyond the oil revenue channel could affect emerging markets.

25
-29-

Table **6: Comparing resource availability and education outcomes in countries at** $400 **and $200** in **per capita** GNP, **1993**

|a/<br>Indicator|Richer country's<br>gb<br>advantage|Percentage<br>contribution to richer<br>country's advantage|
|---|---|---|
|Budget allocations|||
|Overall govermnent budget as % of GNP|1.00|0.0|
|Public spending on education as % of government budget|1.13|33.0|
|Primnary education as % of public spending on education|0.95|-13.2|
|Primary education spending as % of GNP|1.07|19.8|
|School-aged children's share of population|1.06|17.3|
|Teacher pay relative to per capita GNP|1.26|62.9|
|_Public spending per primary school-aged child_|1.43|100.0|
|Enolmenit roe|I 21.|.<br>_52.7_|
|NPupil-teachf**r**atio|1'<br>!<br>.:.||
|_Publicsphid_<br>.4<br>_e3_||1-0.0|

Source: authors' calculation based on data in previous table.

a! The variables that are not italicized are defined in the previous table. The first two italicized variables refer to the net impact of the preceding three variables; while the last italicized variable refers to the net impact of the preceding two variables.

b/ Following equation (3), the richer country's advantage for the indicators in rows 1,2,3 and 8 are computed as the ratio between its data and that of the poorer country; its advantage in the indicators in rows 5,6, and 9 are computed as the inverse of the ratio between its data and that of the poorer country. For rows 4 and 7, the data are the products of the data in the preceding 3 rows; and for row 10, the figure is the product of the data in the preceding two rows.

Taking all the factors into account, the resources available per school-aged child in country B is 1.43 times (=1.0 x 1.13 x 0.95 xl.06 x 1.26) as high as in country A. The relative contribution of the various sources to country B's resource advantage appears in the last column. It shows that nearly two thirds of its advantage stem from the fact that teacher salaries in country B fell relative to the per capita GNP. Had teachers salaries maintained the same relation to the per capita GNP as in country A, the availability of resources for primary education in country B would have been only 1.14 times (=1.0 x 1.13 x _0.95_ x 1.06 x 1.0) as high as that in country A.

Turning now to the resource deployment side of the equation, we note from the data in the shaded part of the table that country B uses its extra resources to expand primary school coverage by a factor of 1.21 relative to coverage in country A, and to increase spending
Table A.4: Productivity Decompositions and Policy Changes: Alternative Measure of Capital

||Aggregate|Average|Reallocation|
|---|---|---|---|
||(1)|(2)|(3)|
|Final Goods Tariff|-.049|-.039|-.010|
||(.025)<sup>_∗_</sup>|(.030)|(.015)|
|Input Tariff|-.498|-.494|-.004|
||(.104)<sup>_∗∗∗_</sup>|(.115)<sup>_∗∗∗_</sup>|(.061)|
|FDI Reform|.015|.040|-.026|
||(.013)|(.013)<sup>_∗∗∗_</sup>|(.010)<sup>_∗∗∗_</sup>|
|Delicensed|-.006|.005|-.011|
||(.016)|(.016)|(.011)|
|Obs.|17074|17074|17074|
|_R_<sup>2</sup>|.093|.086|.014|

Each observation is a state-industry. Dependent variable names are given at the top of each column. “Aggregate” indicates market-share-weighted mean productivity, “Average” indicates unweighted mean productivity, and “Reallocation” indicates the covariance between market share and productivity. All specifications are fixed-effects analyses at the state-industry level, and include year dummies. Each observation is weighted by the total number of firms in the state-industry across all years, and standard errors are clustered at the state-industry level. An alternative method of deflating capital, based on the perpetual inventory method of Harrison (1994), as modified by Sivadasan (2009), is used.

56
The analysis differentiates large and small farmers since the theory predicts scale advantage among large farmers when they face wage growth. Small farmers are predicted to increase their labor supply to non-agricultural work, and/or rent out their land to large farmers. In contrast, large farmers are likely to increase their operational size by purchasing or renting in land from smaller farmers, and to invest in machines to substitute for labor.

In the farm profit equation, the main issue is whether land and machines show complementarities, and if so, under what conditions. As in land and machine investments, large and small farmers are distinguished in order to identify their behavioral differences.

In all the above estimations, first differences are taken to wipe out unobserved fixed error components, which could lead to bias in the cross-sectional estimation. The key explanatory variable in the first-differenced form is village-level real wage growth separately for agriculture and non-agriculture work (see details on the construction of the wage variables in Section 4.3). Labor can be imperfectly substitutable between agricultural and non-agricultural work due to differences in the required tasks, so I use the village-level wages for the two sectors in the land and machine investment equations.

# **3. Data**

# **3.1 Household Survey**

The data come from two rounds of household survey conducted in rural areas of Indonesia. The primary source of our data is the village and household level surveys that we conducted in 2007 and 2010 for 98 villages in seven provinces (Lampung, Central Java, East Java, West Nusa Tenggara, South Sulawesi, North Sulawesi, and South Kalimantan) under the Japan Bank for International Cooperation (JBIC) Study of Effects of Infrastructure on the Millennium Development Goals in Indonesia (IMDG). Figure 1 shows the locations of surveyed villages. In 2010, we revisited all of the 98 sample villages to 6
# **5. Testing the hypotheses using data on SP responses to the pandemic**

In Section 3.2, we saw that the SPECs have not changed over time in a way that is suggestive of a shift in development priorities in poor and middle-income countries such that more of their national income is devoted to SP. Yes, there has been an increase in the SP share of GDP over time among initially poor countries, but this has been driven by the fact that they are no longer quite so poor. However (as noted above), there may well be a degree of stickiness over time in the SPECs. (Line ministries are known to be resistant to cuts in their budgets.) Possibly public spending decisions simply adjust very slowly to the new priorities.

The COVID shock induced SP responses in varying degrees across the world, as documented by <u>Gentilini et al. (2021). This shock-induced change in policies may better reveal</u> the true change in the underlying policy priorities (even prior to the pandemic), in that the pandemic gave policy makers a new opportunity to implement the policies they truly want—a break from the past.

Studying the pandemic responses might also provide a new perspective on what countrylevel factors influence the success of pro-SP policy reforms. It may well be that the country characteristics relevant to SP spending in normal times differ from those that matter most in responding to a large shock, or in policy reforms. For example, ICT infrastructure is likely to matter to the scope for scaling up existing programs while general government effectiveness may matter more to the capabilities for introducing new programs, which can be administratively demanding.

We find that poorer countries devoted a smaller share of their GDP to social protection in response to the pandemic. The bold line in Figure 6 plots the relationship.<sup>32</sup> (We explain the dashed line below.) The pattern is similar to Figure 1, although the mean shares of GDP are lower when we focus on the pandemic response alone. Similarly to Figure 1, we still see a marked tendency for the share of GDP devoted to the SP response to rise with GDP per capita. The factor of three or more in the ratio of mean SP share for high-income countries over that for low-income countries is also evident in Figure 6.<sup>33</sup>

> 32 - This is an updated version of a similar graph found in Demirgüç <u>Kunt et al. (2022)</u> though the latter paper used a linear functional form in representing the line of best fit. 33 Granted, there are some notable differences; for example, the U.S. is well below the regression line in Figure 1, but well above it in Figure 6. However, taken overall, we do not find that the pandemic-induced policy shock generated a cross-country SPEC that looks very different to what we found prior to the pandemic.

23