rafmacalaba/datause-encoder-data huggingface dataset:
Table 3: First-stage IV estimates

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Electricit|yAccess|Reliabl|e Electricity|
||Town|HH|Town|HH|
|Transmission Density|1.714***|2.772***|3.515***|3.218***|
||(0.58)|(0.45)|(0.44)|(0.44)|
|F Stat|8.708|37.651|63.836|54.428|
|Observations|33423|33338|33419|33338|

Notes: Robust standard errors in parentheses clustered at primary sampling unit (PSU) level. F-stat is the IV first-stage F test of instrument strength. All the regressions in the table control for individual/household, community, and country-level variables. Individual/household-level variables include the respondent’s gender, age, education, quality of housing, employment status, size of the household, and ownership of a TV or mobile phone. Community-level variables include the presence of piped-water systems, paved roads, schools, and hospitals. Country-level variables include total population, GDP PPP, share of the urban population, population density, value added of agriculture and service as a share of GDP, total natural-resources rents (% of GDP), IIAG score, and ethno-linguistic fragmentation score. _∗_ Significant at 10 percent level _∗∗_ Significant at 5 percent level _∗∗∗_ Significant at 1 percent level Table 4: Effect of Electricity access and reliability on tax compliance

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|||IV Reg|ression||
|Electricity Access in Town|0.346*<br>(0.18)||||
|Household Connected to Grid||0.216**<br>(0.09)|||
|Reliable Electricity in Town|||0.169**<br>(0.07)||
|Household has Reliable Electricity||||0.186**<br>(0.08)|
|F Stat|8.708|37.651|63.836|54.428|
|Observations|33423|33338|33419|33338|

Notes: Robust standard errors in parentheses are clustered at PSU level. F-stat is the IV first-stage F test of instrument strength. All the regressions in the table control for individual/household, community, and country level variables. Individual/household-level variables include the respondent’s gender, age, education, quality of housing, employment status, size of the household, and ownership of a TV or mobile phone. Community-level variables include the presence of piped-water systems, paved roads, schools, and hospitals. Country-level variables include total population, GDP PPP, share of the urban population, population density, value added of agriculture and service as a share of GDP, total natural-resources rents (% of GDP), IIAG score, and ethno-linguistic fragmentation score. _∗_ Significant at 10 percent level _∗∗_ Significant at 5 percent level

> _∗∗∗_ Significant at 1 percent level

26
<!-- Start of picture text --> Figure 3a<br>Required Contribution Rate with Pensions Indexed to Prices<br>(g=O%, q=1%, n=40, m=20)<br>16%<br>14%<br>j12%<br>10%<br>358% r=3%<br>6 % -..-. r=5%<br>2%<br>1 4 7 10 13 16 19 22 25 28 31 34 37 40<br>Year<br>30%<br>Figure 3h<br>,20% Required Contribution Rate with Pensions Indexed to Prices....... g=3<br>(Projected Benefit Basis: q=1%, n=40, m=20)<br>35%<br>30%<br>I25% r.3%, g=5%<br>120% - - - -<br>r=3%, g=3%<br>.115% -- --- --- --- -- r=5%. g=5%<br>10% r=5%, g=3%<br>5%<br>0% . 1 . 4 . 7 10 13 16 19 22 25 28 31 34 37 40 . . . . . . . .. . . . .<br>Year<br>Fgure23c<br>Required Contribution Rate with Pensions Indexed to Pnices<br>(Accumulated Benefit Basis: q=-1% n=40Q, m=-20)<br>45%<br>40%<br>35% ~~~~~~~~~~~~~~~~r--3%, g=-5%<br>Ix 30%<br>25% - - - - r--3% g=3%<br>r-5%, g-5%<br>U15%<br>10 r=5%. g=3%<br>5%<br>0%<br>1 4 7 10 13 16 19 22 25 28 31 34 37 40<br>Year<br><!-- End of picture text --> 21
employment, which is simply

> Our two statistics on worker flows, _accpert_ and _seppert_ give us information of

reallocations of people within and across establishments. As we mentioned in the introduction, however, it is also common to examine reallocations of jobs across establishments.10 Job flows statistics give us information about establishment-level changes in employment without taking into consideration the identities of the employees. For example, consider an establishment in which five employees have left since the last year and were replaced by five new employees. We would say that this establishment experienced worker flows in the form of five accessions and five separations. Since total employment has not changed, however, we would say that the establishment neither created nor destroyed jobs.

> More precisely, define net employment growth in establishment _j_ and period _t_ as

> Now denote job creation in establishment _j_ and period  as _t_

> and denote job destruction in establishment _j_ and period _t_ as

We can now define the job-creation percentage and job-destruction percentage in period _t_ as respectively.

7
A large literature has helped document both theoretically and empirically that weak competition is arguably harmful for innovation and productivity growth (see Aghion and Hewitt, 1992 and Aghion et al, 2014). Results from cross-country regressions presented in Table 5 show that given the same level of relative income, mobile and banking concentrations are negatively correlated with technology adoption in the corresponding sector. In other words, a higher level of concentration is associated with lower penetration of the technology. Figure 8 provides a graphical illustration of the results in Table 5. The lines for bandwidth, internet users and ATMs are downward-sloping. For credit and debit card payments, the line slopes upward when bank concentration is very high. However, the confidence band becomes large. To summarize, this section shows that market concentration in GPT such as banking and telecom in MENA becomes higher than other comparators as income rises. This translates to lower adoption of the GPT technologies in MENA.

**Table 5: Concentration in mobile and banking operators and technology adoption**

||bandwidth per<br>internet user|internet users<br>(%)|Number of<br>ATMs per mil<br>capita|Payments by credit<br>and debit cards per<br>mil capita|
|---|---|---|---|---|
|relative income (t-1)|3.505***|0.616***|0.478***|0.490***|
||(0.690)|(0.0253)|(0.0722)|(0.0691)|
|mobile concentration|-0.0213|-0.00595***|||
||(0.0224)|(0.00121)|||
|mobile concentration^2|0.000000682<br>(0.00000167)|0.000000167*<br>(9.10e-08)|||
|bank concentration|||-1.198**|-2.410***|
||||(0.547)|(0.781)|
|bank concentration^2|||0.00649|0.0181***|
||||(0.00425)|(0.00657)|
|Constant|66.30|31.01***|73.31***|73.07***|
||(79.92)|(4.211)|(19.88)|(24.41)|
|Observations|2464|2658|178|182|
|year fixed effects|y|y|y|y|
|r2|0.108|0.621|0.385|0.344|

19
_“Our hardship is like time, it never stops.”_ – A Nepali woman in Makawanpur, Nepal, in a conversation with “Stories of Nepal”

# **1. Introduction**

In this paper, we explore some aspects of economic and social mobility in Nepal. The data required for this analysis come from a variety of national surveys conducted in the past two decades as well as from a nationally representative survey that was implemented in 2014 to specifically collect information on social and economic mobility as it is perceived in Nepal.

Nepal has undergone some dramatic political, social and economic changes in the past couple of decades. In a single generation, the country has gone from being a unitary, Hindu state ruled by an absolute monarch to a secular republic that has adopted a federal structure of governance. More than 14,000 Nepalis lost their lives between 1996 and 2006 in a violent Maoist conflict that took the country to the brink of a civil war that has fundamentally transformed the common Nepali psyche. This experience has not only altered the relationship between the state and its citizens but continues to have a profound influence on an evolving social contract.

Despite these changes, there is an enduring perception of “structural inequalities,” some of which are deeply encoded in the legacy of the country’s social structure (e.g., the caste system) and some are driven by what has been widely acknowledged to be a spatially imbalanced development experience. A discussion of mobility becomes pertinent because in many ways mobility is the fundamental characteristic of any development process that can neutralize these deep inequalities. From an intergenerational perspective, a society in which children can aspire to achieve and indeed can achieve levels of education, jobs, and living standards that are materially different from the levels enjoyed by their parents is a society that will automatically begin to redress some of these inequalities. Mobility is not just important from the point of view of equity, it also has efficiency benefits. If talent is distributed more equally in society than the opportunities to exercise these talents, then a mobile society will be better able to mobilize and utilize these talents by allowing people from all segments of society to contribute to the growth process. A mobile society in which individuals from all walks of life, including those with historical disadvantages, can succeed or fail based on nothing else but their effort and hard work, will also have higher levels of motivation and effort and this will have implications for growth.

The rest of the paper proceeds as follows. Section 2 presents the results of our analysis on intra-generational mobility. Section 3 presents the results on inter-generational mobility. Section 4 presents and discusses our findings on perceptions of mobility. The last section concludes.

# **2. Mobility across Generations**

Economists and sociologists have primarily conceptualized intergenerational mobility along the lines of “origin independence”. A mobile society is one in which the children of lawyers or doctors and those of 2
51 -

-

Since the values of MPRS are generally higher for the richer states, it would appear imperative to follow policies for reducing consumption inequality even in the high growing states.

Both growth and inequality elasticities are considerably higher for the ultra poor than for the poor implying that increasing inequality will hurt the ultra poor more than the poor. Also, growth benefits the ultra poor proportionately more than the poor. From the point of view of the ultra poor, it appears even more imperative to combine a high growth profile with an emphasis on policies aimed at reducing inequality in real consumption per capita. To this extent, the basic anti-poverty policy response in the form of public employment and asset (resource) augmenting programme for the ultra poor and the poor, appears to be sound. However, their effectiveness needs to be eva'Luated. This and other poverty alleviation strategies of the government are assessed in the following sections.
Policy ReseaRch WoRking PaPeR 4675

# **Abstract**

This paper analyzes characteristics of nonfarm enterprises, their employment growth patterns, and constraints in doing business in rural Tanzania. Using unique survey data, the authors describe a low-return sector struggling to compete in a difficult business environment. However, about one-third of rural enterprises are growing fast. Most enterprises engage in agricultural trade. Due to a rapidly growing agricultural sector in recent years, limiting demand-side constraints, rural enterprise constraints in Tanzania mainly operate from the supply side. This suggests that, in particular, access to finance, road infrastructure, and rural cell phone communication is correlated with employment growth. A major finding is that subjective and objective measurements of business constraints are broadly comparable. The authors discuss a number of factors that would help to unleash the full potential of private sector-led growth in rural areas. The findings show that marginal improvements in the rural investment climate matter for growth.

This paper—a product of the Agriculture and Rural Development Unit, Africa Region—is part of a larger effort to understand factors determining the investment climate and private sector initiative in rural areas. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at tidiane.kinda@ecogestion.uclermont1.fr and jloening@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
# **5.4 Heterogeneities by local levels of corruption**

To determine which communities in our sample had the largest issues with corruption and mismanagement, we conducted a survey of all local officials in the areas that would be part of the study before the start of the experiment. As described above, we asked officials to name the most corrupt or mismanaged sub-county in their district.<sup>21</sup> We then count the number of times a sub-county is listed and create an indicator of whether a given sub-county is in the top most cited sub-counties. If a sub-country is mentioned more than once we consider it to have high reported corruption. This is the case in 33% of the sample sub-counties.

In Table 13 we present the results of dividing the sample by this indicator and testing for the impacts on the number of cattle owned by individuals. The impact of the treatments is concentrated entirely in communities in the sub-counties noted by local officials as most corrupt or mismanaged. The social accountability training treatment indicator is marginally not statistically significant, while the interaction between social accountability training and project information scorecard is significant at the less than 1% level and very large. Households in areas that are reported more corrupt or mismanaged that received both treatments have, on average, an additional 0.77 to 1.19 animals. This is an increase of between 35% and 59% over the control group. This result suggests there are substantial distributional effects from the interventions, and so there could be large benefits from targeting such a program to areas that have the biggest issues with corruption or mismanagement. We also test for differential mechanism effects (not shown), which we presented in Tables 10 to 12, and find no difference in these outcomes between areas of high or low reported corruption. We conclude that people made similar levels of complaints in these areas, but they were most effective in the areas reported as more corrupt.

While we do not observe a large difference in control means across the high and low reported corruption groups in Table 13, we cannot rule out that this measure could also be correlated with other community characteristics, including performance of local government and overall poverty levels. We compare the results of the reporting of officials and the scores in the scorecards and find a significant relationship between the two. Communities in sub-counties that are most more likely to be corrupt have a lower absolute score of 2.39 points, out of an average

> 21 A district is composed of approximately five sub-counties.

26
Bottom 40, the Top 60 and the overall population between 2011 and 2018.<sup>14</sup> The trend mirrors the evolution of the total income, with strong annual real growth in the first half of the decade for the Bottom 40 (8.2%) and the Top 60 (4.6%). This strong growth came to a halt between 2016 and 2018, a period that marked the exchange rate volatility crisis and the rapid increase in inflation. Real incomes posted negative growth rates, on average -0.8% for the Bottom 40 and -2% for the rest of the population. The labor income results show that the drop in overall income growth for the bottom of the distribution was driven by stagnant employment related to real income levels after 2016.

Figure 8: Real incomes of 1st and 10th decile (2011-2018, 2011 =100) <!-- Start of picture text --> 150 18<br>16<br>140<br>14<br>130 12<br>10<br>120<br>8<br>110 6<br>4<br>100<br>2<br>90 0<br>2011 2012 2013 2014 2015 2016 2017 2018<br>1st Decile 10th Decile CPI<br>Real Income<br>Annual CPI Change (%)<br><!-- End of picture text --> _Source_ : World Bank calculations using data from TUIK Figure 9: Real labor income of the Bottom 40 and Top 60 (2011-2018, 2011 =100) <!-- Start of picture text --> 160<br>148 147 146<br>150<br>140 134<br>130<br>120<br>117<br>120<br>125 125<br>107 121 120<br>110<br>113<br>100 107<br>104<br>90<br>2011 2012 2013 2014 2015 2016 2017 2018<br>Bottom 40 Top 60 Overall<br><!-- End of picture text --> <!-- Start of picture text --> Source : World Bank calculations using data from TUIK<br><!-- End of picture text -->

> 14 The decision to use the Bottom 40 and Top 60 as opposed to the 1st and 10th deciles has to do with a relatively large number of workers from the bottom decile with zero earnings. Since the population of interest for this part (full-time workers) is not the full population, the Bottom 40 and Top 60 do not match exactly the groups defined using total per capita consumption or total per capita income.

20
than those from different ethnicities, and argues that this is because co-ethnicity with an incumbent president reduces informational asymmetries between the state and taxpayers. In line with these previous findings, we hypothesize that national affinity operates the incentive to hide information about income from the state revenue au- by reducing thorities. Therefore, self-reported rates of national affinity are positively correlated with self-reported attitudes toward tax compliance.

We summarize the underlying relationship between public goods provision and tax compliance attitudes, on the one hand, and the role of mechanisms like national identity, on the other hand, in Figure 1, and examine this relationship using survey data from 36 African countries. The provision of public goods reinforces both affinities to the nation (national identity) and the fiscal pact that underlies the exchange relationship of paying taxes and in return enjoying public goods and services.

The preceding discussions demonstrate that the existence of a functioning fiscal pact between taxpayers and governments serves the purpose of lowering the effective cost of tax collection. High levels of public tax morale – motivated both by a sense of civic duty or national identity and the implicit exchange relationship through the provision of public goods and services – obviate the need for governments to expend resources in the close monitoring of economic activities (both at the individual and firm levels) and in punishing violators of the tax code. It is also clear that successful tax mobilization is dependent on both state capacity (ability to target potential tax sources, deter tax evasion, and provide public goods) and legitimacy (the ability to elicit quasi-voluntary tax compliance by the public). The previous observation links taxation to state development (Bates and Lien, 1985; Tilly, 1990; Olson, 1993), while the latter emphasizes the importance of the implicit social contract that underpins the fiscal pact between states and citizens (Levi, 1988; Andreoni et al., 1998).

Much of the literature on the development of state-level fiscal capacity tends to focus on the longer term. The stylized account is that the need for revenue to pay for interstate wars forced early modern states to establish the organizational capacity required to effectively enforce taxation (Tilly, 1990; Besley and Persson, 2008). Such organizational architecture included both the bureaucracy required to administer a tax regime and the coercive capacity to deter tax avoidance. A related argument is that states typically build or restructure their extractive capacities during crises that call for a re-negotiation of the social contract. For example, Scheve and Stasavage (2010) find that most advanced democracies were able to impose relatively higher tax rates on top earners during wars, and that this was politically feasible precisely because it was viewed as a bargain with low-income earners who served in the wars.

6
Figure 4: Sample Distribution of the Welfare Results of Armenian-CIS FTA—30,000 simulations.

<!-- Start of picture text --> 4.5<br> 4<br> 3.5<br> 3<br> 2.5<br> 2<br> 1.5<br> 1<br> 0.5<br> 0<br>-0.05 0.05 0.15 0.25 0.35<br>Percentage of consumption<br><!-- End of picture text --> 68
fundamental explanation than shifting interests. This is that a great deal of the literature has used Foreign Direct Investment (FDI) data, enthusiasm for which has been dimmed by the increasing recognition of a point first made long ago: “FDI does not correspond directly to any measure of real investment [...] It is more accurately thought of as a measure of financial flows...”<sup>4</sup> Measured FDI includes, importantly, flows that for tax reasons (routing intra-group payments so as to take advantage of reduced withholding tax rates and other tax or treaty provisions), and perhaps for other reasons too, pass through some ‘conduit’ jurisdiction while generating real investment somewhere else. This means that the same underlying investment can be recorded multiple times as it passes through conduits, giving rise to a form of double counting. And such conduit flows cannot be unravelled from the FDI data, which typically report the location of affiliates’ immediate parents, not that of the ultimate parent company. The importance of these points jumps out from the data, in the existence of ‘investment hubs’ with inward investment clearly far in excess of levels that are plausible for domestic investment, given the size of the domestic economy.<sup>5</sup> Such investments, with no real links to the local economy, may account for around 40 percent of global FDI.<sup>6</sup> Relying on FDI data to infer tax effects on cross-border real investment is thus, at best, highly problematic.

There are of course analyses that do not use aggregate FDI data, with much attention coming to be focused on firm-level data (as for example in Liu (2020) and Millot et al. (2020)). While this has evident importance and appeal, results from aggregate FDI-based studies continue to shape views on tax-responsiveness; and untangling what FDI data do or do not say about tax effects has its own importance from the wider macroeconomic perspective of understanding and managing cross-border capital flows. Moreover—even leaving aside the problems with FDI data—much of the literature focuses only on the impact on inward FDI of host country taxation, and so simply does not speak to the key issue of cross-country

> 4Slemrod (1990), p.83.

> 5See for instance International Monetary Fund (2014), Blanchard and Acalin (2021), Damgaard, Elkjaer and Johannesen (2019) and Coppola et al. (2021).

> 6Aykut, Sanghi and Kosmidou (2017), Damgaard, Elkjaer and Johannesen (2019).

3