rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendix 3: Endogeneity due to movements to coverage areas**

In this appendix we address one last concern with our identification strategy: That households move in response to the expansion of 3G/4G coverage. In our estimations we implicitly assumed that that coverage comes to households, but not vice-versa. That is, households are assumed to stay put, or at least only move due to factors that are assumed orthogonal to treatment roll-out. Movements are quite rare in our sample. Only 42 households in our final sample move between LGAs, and only 28 between states (which would be included in the former 42). We created an outcome variable based on households’ geographical coordinates in different waves. This captures moves between LGAs, states, but also moves within the same LGA. Between waves one and two, 4.65% of households moved, between waves two and three, 7.75% did.

The assumption of no endogeneity in movement can be directly tested by regressing this binary variable on the change in coverage. Results for this exercise are shown in table A 11. The model regresses the binary indicator, which can itself be thought of as a first difference, on first differenced right-hand side variables. As before, results are shown for the specification with a full set of control variables. Results are statistically insignificant, except for coverage for more than three years, which show statistically significant results at the ten-percent level, but with a negative point estimate. Given the set-up of our estimation, this would indicate fewer households moving into areas with high coverage after more than three years of receiving 3G/4G coverage. However, the sign of this effect is the opposite than the one that warrants our concerns- which is that higher coverage attracts betteroff households.

Table A11: Results for household move as the dependent variable

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Medium or strong 3/4G|-0.002<br>(0.009)||||
|Medium or strong 3/4G more than one<br>year||0.006<br>(0.009)|||
|Medium or strong 3/4G for more than<br>two years|||-0.005<br>(0.008)||
|Medium or strong 3/4G for more than<br>three years||||-0.014*<br>(0.008)|
|Observations|8,471|8,471|8,471|8,471|
|R-squared|0.004|0.004|0.004|0.004|
|Control variables|Yes|Yes|Yes|Yes|

_Notes_ : Standard errors reported in parentheses are clustered by LGAs. All these regressions include the additional set of controls (e.g., access to electricity, ownership of dwelling, household size, and a wealth index) although the coefficients for these variables are not reported to save space. *** p<0.01, ** p<0.05, * p<0.1.

44
**Table 8: Relationship between the employment rate of older persons (55-64) and hours worked by youth (15-24) and prime-aged persons (25-54) by education level**

||(1)<br>Youth<br>Primary|(2)<br>Youth<br>Secondary|(3)<br>Youth<br>Tertiary|(4)<br>Prime-aged<br>Primary|(5)<br>Prime-aged<br>Secondary|(6)<br>Prime-aged<br>Tertiary|
|---|---|---|---|---|---|---|
|Employment rate of old persons|-0.002|-0.002*|-0.000|-0.001|-0.002***|-0.001***|
|(55-64)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.000)|
|Natural log of hourly income of|-0.081***|0.040*|-0.023|-0.076***|0.004|-0.071***|
|youth (15-24)|(0.031)|(0.023)|(0.029)|(0.022)|(0.018)|(0.021)|
|Unemployment rate|0.008|0.013***|0.003|0.000|-0.000|0.000|
||(0.006)|(0.004)|(0.004)|(0.003)|(0.003)|(0.003)|
|Poverty rate|-0.005***|-0.003***|0.002*|-0.002**|0.000|0.000|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.000)|(0.001)|
|Share of employment in the|-0.005***|-0.002***|-0.002*|-0.006***|-0.002***|-0.002***|
|agriculture sector|(0.001)|(0.001)|(0.001)|(0.001)|(0.000)|(0.001)|
|Share of employment in the|-0.001|0.002*|0.002|-0.003***|0.000|0.001|
|industrial sector|(0.002)|(0.001)|(0.002)|(0.001)|(0.001)|(0.001)|
|Share of population with a|0.003***|0.002**|0.002**|0.002***|0.001|0.002***|
|primary education or less|(0.001)|(0.001)|(0.001)|(0.000)|(0.001)|(0.001)|
|Natural log of GRP per capita|0.069***|0.058***|0.068***|0.045***|0.039***|0.037***|
|(constant 2010 IDR)|(0.015)|(0.009)|(0.013)|(0.008)|(0.007)|(0.007)|
|Growth in GRP per capita|0.003*|0.001|-0.000|0.000|0.001|0.001|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Number of observations|272|272|272|272|272|272|
|R-squared|0.672|0.746|0.464|0.805|0.755|0.675|
|Year dummies|Yes|Yes|Yes|Yes|Yes|Yes|

Source: Author’s calculations using Sakernas data Notes: Robust standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1.

24
<!-- Start of picture text --> Figure 7: Sub-national revenue<br>Developing<br>as % of consolidated government revenue<br>Transition<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>19801981 1982 19831984 198519861987 1988198919901991 199219931994 199519961997 19981999<br><!-- End of picture text --> Source: IMF Government Finance Statistics, various years and World Bank Fiscal Indicators <u>(http://www1.worldbank.org/publicsector/decentralization/fiscalindicators.htm)</u> The above discussion implicitly assumes that assignment of taxes entails control over the tax base, tax rates and collection authority. This need not be the case and higher level governments may instead, in the interest of harmonization and minimizing collection and compliance costs, allow lower level governments to either levy a supplementary rate on own base (tax base sharing) or agree to share the proceeds from specific taxes in a pre-determined way (shared taxes). Under such arrangements, tax base determination usually rests with the higher-level government with lower levels of government levying supplementary rates on the same base. Tax collection is by one level of government, generally the central government in market economies and the local government in transition economies with proceeds shared downward or upward depending upon revenue yields. Only a handful of developing and transition economies have adopted tax base sharing. A sub-national surcharge on the personal income tax is permitted in Brazil and Croatia. Russia allows a surcharge on the corporate income tax. Provincial governments in Pakistan allow local governments to have a supplementary rate on property transfer taxes. While the practice of tax base sharing is uncommon, sharing the proceeds of various taxes on a tax by tax basis is frequently practiced in DTEs. In transition economies, in 1999, 49.3% of sub-national government revenues 21
4 poverty assessments are based – are seldom representative at the level of any but the largest metropolitan areas in the developing world. They are certainly not representative for smaller towns and cities, and information is not usually disaggregated along these lines.<sup>5</sup> In this paper, we draw upon the considerable additional insights generated by small area poverty estimation (based on the combination of welfare estimates from household surveys with “sample” sizes from National Censuses) to investigate the relationship between poverty and city size in eight developing countries, namely Albania, Brazil, Kazakhstan, Kenya, Mexico, Morocco, Thailand and Sri Lanka. We find substantial variation in the incidence and depth of consumption poverty across city sizes in seven of the eight countries. For all seven countries where the data permits some kind of disaggregation of the incidence of public service access, there is also considerable variation across city sizes. In all cases, poverty is lowest and service availability is greatest in the largest cities – precisely those where governments, the middle-classes, opinion-makers and airports are disproportionately located. This leads us to ask whether, alongside Lipton’s original urban bias, there might also exist a “metropolitan bias” in the allocation of resources (including policy attention) to larger cities, at the expense of smaller towns, where most of the urban poor are located.

There are a number of caveats which require that our results be treated with care. First, although our samples within countries are representative, our _sample of countries_ is not. Although these eight countries are located in all six regions into which the World Bank routinely divides the developing world, they are not random draws.<sup>6</sup> They are countries where there was an early interest in (and the data required for) constructing a poverty map. Second, we use national, rather than international, poverty lines. This has the advantage that poverty is measured in the terms which each particular country’s residents feel is appropriate. But it has the disadvantage that poverty does not mean the same living standard across the eight countries. Third, we do not systematically adjust for cost-of-living differences across different cities. These differences may be expected to be

> 5 One exception was a poverty profile of Brazil by Ferreira, Lanjouw and Neri (2003).

> 6 The World Bank divides the developing world into sub-Saharan Africa (AFR), East Asia and the Pacific (EAP), Eastern Europe and Central Asia (ECA), Latin America and the Caribbean (LAC), the Middle-East and North Africa (MENA); and South Asia (SAR).
_15_ credit activity predicted from the calculated subsidy levels. These calculations provide the first step in the transmission of directed credit into growth. The two most comprehensive studies, Gale (1991)'3 and Bosworth, Carron, and Rhyne (1987),"4 both arrive at their estimates of the effectiveness of directed credit in this manner. While these estimates are extremely useful, before applying their results to evaluating directed credit programs, several points need to be considered. First, each of these studies bases the estimates on previously determined demand elasticities. These previously determined elasticities are based on small changes in the cost of the activity. The removal or addition of government programs in all but the mortgage sector involves a fairly large interest rate change. With such a large structural change, ;he elasticities of demand may not remain constant. This is particularly important since these elasticities are generally taken from a single study at a single point in time and thus represent reasonable, but not necessarily robust, estimates.'<sup>5</sup> Given that the government programs usually involve a number of restrictions on type of recipients and use of funds, it is also likely that the credit demand elasticity related to a government program is less than that for an unrestricted change in cost. This is a particularly important issue for certain programs, such as education, where the elasticity used covers a change in the cost of education, not a change in the cost of education credit. Since not all education expenditure is financed through the credit market, the demand elasticity with respect to a particular type of cost would be less than for unrestricted cost changes.

Second, in many cases government intervention has included setting up financial institutions, the government-sponsored enterprises, to deal with ma,ket failure. Market failure, in " William G. Gale, "Economic Effects of Federal Credit Programs," American Economic Review 81 (1), March 1991, pp. 133-52.

t4Barry P. Bosworth, Andrew S. Carron, and Elisabeth H. Rhyne, The Economics of Federal Credit Programs (Washington, D.C.: Brookings Institution, 1987).

> 1 Gale reports that the results were not affected much by using different demand elasticities.
# _Education and Skills_

Both theory and empirical evidence suggest that investment in human capital is central to promoting innovation and growth (Aghion and Howitt 1998). The best estimates of the rate of return to education in developing countries (from micro-econometric studies that take endogeneity and measurement errors issues seriously) line up with those for developed countries -- each additional year of schooling is associated with a 6-10% increase in earnings. Duflo (2001), for example, uses a large primary school building project in Indonesia to establish that the economic returns to extra schooling provided via this program lie in this range. This evidence appears robust across both methods and locations (Card 1999). After accounting for measurement error, the effects of changes in educational attainment on income growth in cross-country data are at least as great as microeconometric estimates (Krueger and Lindhal 2001). These results suggest that access to education is an important source of source of 1<sup>st</sup> advantage in a location.

This still leaves open the central question of how such an expansion in education is best achieved. New work in the area is addressing this, looking at the market conditions under which it is provided and the incentives faced by education providers. One strand of research focuses on policy design. Banerjee and others (2003) have used randomized experiments to look at whether NGO implemented remedial education and computer assisted learning programs in India affected learning. Randomized intervention has also been used in western Kenya to evaluate whether improving the quality of education by increasing the supply of textbooks and improving child health affect attendance and attainment (Glewwe, Kremer and Moulin 2000; Kremer and Miguel 2001). Another strand focuses on whether there needs to be changes in the organization of policy delivery. Political representation, decentralization and involvement of non-government organizations and the private sector are 16
on taxpayers in various tax brackets and interpolation using the Pareto distribution. But Piketty (2003:1007) notes that he could check the reliability of such methods because tax microdata was available for later periods, in addition to the tax tables.

Piketty’s work stimulated the creation of the World Top Incomes Database (WTID) in 2011, which created top income inequality statistics for 30 countries using similar data to that used in Piketty’s original work (Alvaredo et al., 2016). WTID was later subsumed into the World Inequality Database, which extended WTID to create Distributional National Accounts (DINA). The DINA project aims to provide estimates of the distributions of income and wealth that are consistent with national accounts and harmonized over time and countries (Alvaredo et al. 2016). To do so requires household survey data, national accounts data and ideally either tax microdata or aggregated data. And since comparisons of these data sources often show that household surveys do under-capture incomes and wealth, some of the research on measuring the top tail of the income distribution and the challenges of doing so that we discuss in the next section has come from researchers associated with the DINA project directly.

# The Extent of Income Inequality

A reasonable definition of the top tail of the income distribution is the top 10 percent. Using the World Inequality Database (WID) we briefly describe the share of the top 10% of the income distribution, using the most recent estimate for each region or country. Across all countries for which there is data, and excluding extrapolated or interpolated statistics, the share of the top 10% is 47%. It is only 37% in Europe but is 48% in the US, 56% in Sub-Saharan Africa, 58% in Latin America, 43% in China and 57% in India. These estimates are from a variety of sources and are of varying quality, but the bottom line is that the top of the distribution receive a very large share of total income. The next section discusses the challenges of accurately capturing the top tail using survey and administrative data.

# Challenges to Accurately Capturing the Top Tail in Household Surveys

In this section we document the challenges faced by researchers wanting to use household surveys to measure the top tail of the income distribution. Following this, we focus on challenges when using other types of data, mostly administrative data such as tax or social security records or house prices. Administrative data have been used precisely because of the challenges encountered when using household surveys. However, these types of administrative data are very rare in low-income countries and are still unusual in middle income countries. Thus, household surveys are still very important, which is why we spend some time documenting the challenges associated with using them. In doing so we follow recent papers by Lustig (2020) and Ravallion (2022), although we complement these papers in a variety of ways.

4
government. In other words, the exclusion restriction assumes that the productivity and product scope of any one domestic owned ...rms do not a¤ect the number of FDI ...rms in the industry, which is determined only by the government. On the other hand, the world price of cotton fabrics clearly should not a¤ect the productivity of domestic garment ...rms other than through its negative impact on imported fabrics variety.

Columns (3) and (4) of Table 10 present the second stage results. The IV estimates are both positive and statistically signi...cant. These estimates are also larger than the least squares estimates, suggesting that the IV estimates are better in handling measurement errors in N<sup>D</sup> and N<sup>I</sup> : Due to the space constraint, the ...rst stage results are not reported but is available upon request. Instead, I report the ...rst stage F statistics in Table 10. In both cases, the ...rst stage regressions have F statistics that are greater than 10 and the coe¢ cients have the expected signs. These results con...rm that an increase in the number of FDI ...rms raises the number of local input variety and the total input variety, which leads to higher productivity for domestic ...rms.

To study the e¤ect of FDI on product variety of the ...nal goods sector, consider that for each ...rm i; Yi represents a composite output of di¤erent ...nal good varieties, One can think of Yi as the production possibility frontier (PPF) of ...rm i (e.g. GAP, Old Navy), and each ...rm i produces many varieties of the ...nal good (e.g. T-shirts, sweaters). The concavity of Yi is ensured by �< 0; which is the constant elasticity of substitution in production between the di¤erent varieties of yvi; vi = 1; :::; Vi: Combining (17) with (21) shows that an expansion of the variety of intermediate inputs works much like a positive productivity shock which causes a outward shift in ...rm i<sup>0</sup> s PPF and, at given prices of each ...nal good variety, may lead to an expansion in the output variety as some previously not pro...table varieties may now become pro...table. Figure 4 demonstrates this for a two variety case. Under the ...xed price level, in the original equilibrium, ...rm i only produces variety 1; but as the PPF shifts out due to an increase in input variety, ...rm i also produces variety 2 in the new equilibrium.

To formally show that, I consider a symmetric equilibrium, where within each ...rm i; the price for each variety of Y is the same ensuring that the quantity produced for each variety is also the 30
more positive, the chances are that this paper would not have been written. But in the face of mounting evidence that capital market liberalization and access to external finance have more likely increased vulnerability than spurred growth, some hard issues arise. MACs should in general aim to lower public indebtedness. Their debt tolerance would go up with better policies, institutions and governance. But becoming debt tolerant is a time-consuming process. Can IFIs help to speed it up? For example, for countries that are developing a track record of fiscal credibility, can the IFIs help speed up the attainment of permanently lower debt-to-GDP levels by buying out private debt to lengthen maturity and lower borrowing costs? Given the magnitude of outstanding public external debt for countries like Turkey and Brazil ($53 billion and $60 billion respectively, at end-2003) and already high Bank-IMF exposure levels ($21 billion and $28 billion respectively, see Table 8), such a course does not seem feasible given the current resource base of the IFIs.

**Table 8.  Selected Countries with Large Exposure to the IMF and World Bank, 2003**

||**Outstanding Cr**<br>bln|**edit and Loans**<br>US$|Shar<br>**Total Exte**<br>|e in<br>**rnal Debt**<br>%|Sha<br>**Public Se**<br>|re in<br>**ctor Debt**<br>%|Sha<br>**G**<br>|re in<br>**DP**<br>%|
|---|---|---|---|---|---|---|---|---|
||**WB**|**IMF**|**WB**|**IMF**|**WB**|**IMF**|**WB**|**IMF**|
|Argentina|7.92|10.69|5.61|11.24|4.17|8.35|6.09|12.20|
|Brazil|8.66|19.06|4.15|13.46|2.98|9.66|1.75|5.67|
|Indonesia|10.27|6.92|7.64|7.60|6.85|6.81|4.95|4.92|
|Russia|6.43|3.25|3.63|2.73|4.24|3.18|1.42|1.06|
|Turkey|5.27|16.06|3.71|16.64|3.15|14.15|2.21|9.92|
|Uruguay|0.75|1.63|5.95|19.17|5.49|17.67|6.68|21.50|

_Source:_ WB Annual Report, 2003; IMF Financial Statement, 2004; author's calculations In the absence of a fundamental shift in the thinking about market-based development finance for MACs, therefore, MACs have little option but to adjust to their market-determined external financing constraint and reduce public indebtedness through fiscal restraint and institutional reform. This general conclusion follows from the evidence pointing to low thresholds at which public debt risks becoming fiscally unsustainable and the even lower thresholds at which debt begins to harm economic growth.

38
TABLE 5-8 Sensitivity Results for Fiscal Variables Regressions on Investment Share

||Beta|Stan.Err.|T-Stat|N|Re|Other Incl. Vars.**|
|---|---|---|---|---|---|---|
|Sovernment|Consumption|Share<br>(GOV)|1960-1989||||
|High|.309|-114|2.71|101|.09|LAAM,MIX,STDI|
|Low|.083|.110|.76|78|.36|X, BMP ,GDC|
|Government<br>|Consumption<br>|Share<br>(GOVX<br>|)<br>1974-198<br>|9<br>|||
|<br>High|<br>-.004|<br><br>.175|.03|75|.09|AFRICA, LAAM, CIVL24|
|Low|~.616|.203|3.03|59|18|BMP,GDC,CIVL|
|Total Gover|nment Expend|iture Share|(TEX)<br>1974|-1989|||
|High|-119|-056|2.14|86|.08|CIVL,SOC,MIX|
|Low|039|-052|.74|85|-20|X, AFRICA, LAAM|
|CentralGov|ernmentSurp|lus/Deficit|asShare|(DEF)|1974-19|89|
|<br>High|<br>.050|<br>-165|<br>31|74|.08|AFRICA,MIX,<br>BMP|
|Low|-.067|-191|.35|65|.04|STDI,BMP,GDC|

** no additional variables included in the Investment regressions

Underlined variables show additions which make coefficient insignificant or change signs 24 T£ one does not allow the extreme bounds procedure to choose the continent dummies, the coefficient on GOVX becomes insignificant with the inclusion of STDI.

40