rafmacalaba/datause-encoder-data huggingface dataset:
alone, rather than with my family. But 24 years later, you will find me happily residing in Northern Virginia, attached once again to a new place, and where the thought of moving somewhere new once more is anathema to me.

I start with this personal anecdote because it illustrates a couple of points that I want to make about the role of migration in fostering wealth and well-being. The first is that there are enormous potential gains in income to be had from moving, both internally, and especially internationally. While this is true for countries like New Zealand, it is even more the case for people in developing countries where income levels and opportunities are much lower. The big puzzle is then why more people do not move, despite these potential gains. A lot of economic research has focused on the constraints that limit movement, including liquidity constraints, lack of information, moving costs, and policy barriers. These are certainly important, and I will discuss how research and policy have endeavored to understand and relax these. But this story illustrates two other factors that I think have been relatively understudied, which I call “Fears and Tears”. The band _Tears for Fears_ famously claimed everybody wants to rule the world. I think instead that fears of the unknown, and tears for the attachment felt to home, help explain why, instead, (almost) everybody ends up staying at home instead of going off to rule the world. I highlight how psychological factors like status quo bias and the inability to picture what one is giving up by not moving help shape these tears, and the potential implications of this idea for policies to increase migration.

# **2. What is the scale of mobility, and why should we care that more people are not moving?**

The United Nations estimates that there were 272 million international migrants in 2019.<sup>4</sup> While this might seem like a large number, it is only 3.5 percent of the global population. Emigration rates are lower still for the regions with the most poor people: emigrants are only 2.5 percent of the population of Sub-Saharan Africa and 2.2 percent of the population of South Asia (World Bank, 2016). Rates are much higher for small island states, with more than 40 percent of Jamaicans, Tongans, and Samoans living abroad. But even in these very small, isolated countries, many people do not move. Internal migration rates are much higher. Data on what constitutes an internal move is hard to compare exactly across countries, but the United Nations (2013) estimates

> 4 <u>https://www.un.org/development/desa/en/news/population/international-migrant-stock-2019.html</u>

3
# **Public Participation, Teacher Accountability, and School Outcomes:**

## **Findings from Baseline Surveys in Three Indian States**

Priyanka Pandey, Sangeeta Goyal and Venkatesh Sundararaman<sup>∗</sup>

> ∗ South Asia Human Development, World Bank. This work benefited from generous financial support from the EPDF trust fund and by Government of the Netherlands through the BNPP program.  We are also grateful to the Nike Foundation for generous financial support. We thank Nazmul Chaudhury, Amit Dar, Lant Pritchett and Michelle Riboud for very helpful comments. We are grateful to the Ministry of Human Resource Development and the respective Departments of Education in Karnataka, Madhya Pradesh and Uttar Pradesh for extending their cooperation.
**Figure 2: Historic and projected population dynamics in Brazil**

<!-- Start of picture text --> 250<br>200<br>150 Total<br>Urban<br>100 Rural<br>50<br>0<br>1950 1960 1970 1980 1990 2000 2010 2020 2030<br>Population (million)<br><!-- End of picture text --> Source: United Nations World Urbanization Prospects, 2003 Population growth is occurring across the Brazilian urban size distribution (Table 1, see also Lemos _et al_ . 2003). Of the 123 major urban agglomerations in Brazil, only three were above 2 million people in 1970 versus 10 in 2000. In the middle of the size distribution in 2000, there were 52 agglomerations with population between 250,000 and 2 million people compared to 25 in 1970. Since we are limiting analysis to cities that were agglomerations in 1991, we cannot track dynamics at the lower end of the distribution. This is because our set includes cities that were not yet agglomerations in 1970, while excluding cities of similar size in later years. However, among the 72 agglomerations that had at least 100,000 people in 1970 (Table 2), the average population more than doubled from 553,000 to 1,250,000 over the 30 year period.

5
<!-- Start of picture text --> 8.0<br>14.0<br>7.0<br>12.0<br>6.0<br>10.0<br>5.0<br>E 8.0 €<br>2 2<br>6.0 3.0<br>4.0 2.0<br>2.0 1.0<br>0.0 0.0<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>=e GDP (left axis) Construction/GDP (right axis)<br><!-- End of picture text -->
||[0.780]|[0.917]|[0.923]|[0.883]|[0.875]|[0.296]|[0.280]|[0.279]|[0.354]|[0.456]|[0.545]|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Composite female|1.108***|1.032***|1.046***|0.687***|0.670***|1.398***|1.415***|1.428***|0.619***|0.566**|0.538*|
||[0.289]|[0.248]|[0.249]|[0.232]|[0.242]|[0.128]|[0.142]|[0.151]|[0.232]|[0.253]|[0.292]|
|D) By urban/rural for|women|||||||||||
|Policy|-0.183|-0.214|-0.299*|-0.388**|-0.492***|0.804***|0.712***|0.731***|0.258**|0.391**|0.449***|
||[0.173]|[0.188]|[0.182]|[0.193]|[0.191]|[0.129]|[0.138]|[0.118]|[0.107]|[0.168]|[0.165]|
|Policy * female|0.214|0.155|0.196|0.192|0.314|0.568***|0.633***|0.575***|0.061|0.112|-0.070|
||[0.205]|[0.194]|[0.177]|[0.234]|[0.224]|[0.203]|[0.187]|[0.186]|[0.265]|[0.309]|[0.370]|
|Composite female|0.031|-0.059|-0.102|-0.196|-0.178|1.372***|1.346***|1.307***|0.320|0.503**|0.379|
||[0.188]|[0.158]|[0.166]|[0.191]|[0.178]|[0.156]|[0.140]|[0.146]|[0.257]|[0.233]|[0.288]|

_Notes_ : The data include 10-year intervals on both sides of the cut-off, except for the following cases when the right hand side of the cut-off includes (a) 9 years, (b) 8 years, (c) 7 years, (d) 6 years, (e) 5 years, (f) 4 years. A separate logit regression is run for each grade level. The dependent variable is grade completion status. "Composite female" coefficient is the sum of the "policy" and "policy*female" coefficients. "Composite rural" coefficient is the sum of the "policy" and "policy*rural" coefficients. All specifications include linear time trends, which are allowed to be different before and after the policy and by gender in panels (A) and (B) and by rural/urban status in panels (C) and (D). Control variables also include dummies for 5 geographical regions, large city/small city, and gender. Standard errors are clustered at the level of year of birth. Statistical significance is *** at 1 percent level, ** at 5 percent level, * at 10 percent level.

_Source_ : Authors’ calculations based on 2003 and 2008 Turkish Demographic and Health Survey.

47
12 This of course implies that, for policymakers or researchers interested in any particular country, relying on the three stylized facts in Section 2 is not enough. Individual growth processes _can_ be made more or less “pro‐poor” and, depending on the objectives<sup>13</sup> of the policy maker, certain trade‐offs between higher average growth and higher growth for the poor may become relevant. These results should really come as no surprise, except perhaps to the most hardened representative‐agent macroeconomist. “Economic growth” is nothing but an average taken across the proportional output increases across firms (or sectors) and government agencies across the economy. Or, if one approaches it from the income account, it is an average taken across proportional income increases across all households (adjusting from public goods, retained earnings, and measurement error). It should be perfectly plain that policies that allocate resources and opportunities differently across these sectors, firms and households will likely affect both the distribution and the average of future incomes. In fact, perhaps more can be learned from examining how the underlying distribution of incomes changes over time, including in response to shocks and policy changes, than from repeated estimations of how one or more vertices in the triangle related by equation (3) affect the other ones. This microeconomic approach to growth and distributional dynamics is the subject of the next section.

# **4. The microeconomic approach**

The final level of work on the link between the dynamics of mean incomes (growth), poverty and inequality investigates this at the microeconomic level. These are studies that do not rely on summary measures of poverty or inequality as their primary data, but are based instead on full distributions of income or consumption expenditures, from representative household (or, in some cases, labor force) surveys. As noted in the introduction, this paper focuses on studies that investigate distributional dynamics under the anonymity axiom, and hence rely on repeated cross‐section, rather than panel, data.

The natural starting point for these studies – although the term had not yet been coined when a number of them were written – is the growth incidence curve (GIC). Defined (by Ravallion and Chen, 2003) as the quantile‐specific rate of economic growth between two points in time as a function of each percentile _p_ in [0, 1], it can be written as follows, in continuous or discrete time: The discrete‐time version in equation (7’) makes it clear that the GIC is simply the proportional difference between the quantile function at each percentile of the income distribution.

If we define economic growth as the proportional change in the mean of the income distribution, then growth can be expressed as a function of the growth incidence curve. By changing the integration � � variable, note that: ����� ������ ��������� . Then it follows that: 13 His or her “social welfare function”, to use a slightly older language.
# 3.2 **Titling and land values**

Even if one finds a positive impact of land tenure security on investment, this will, without strong assumptions on the benefits from such investments, not provide us with an estimate of the net impact of such an intervention on land values and thus households' net asset position. This figure is of interest to obtain an idea of the impact of such a program on household welfare and thus the maximum cost for such a program from a social point of view as well as possible cost recovery measures. To get such an estimate will require an examination of the extent to which, over and above any potential impact on investment and the value of land attached assets, possession of title will also increase land values.

To provide the background for analysis, note that the value V of any asset such as land equals the discounted net present value of cash flows generated by this asset.

where r, is the return received in period t and a 1 is the discount factor with i being the opportunity cost _1Ii_ of capital. The notion that all factors that can possibly affect the expected stream of returns to land will be capitalized in land values has long provided the underpinning for estimation of hedonic land price regressions of the form V, = f(Z) where Z is a vector of exogenously given quality characteristics (e.g. Rosen 1974). Availability of self-reported land values for each of the plots under consideration makes it easy to implement this methodology. However, while this methodology is appropriate in cases where quality characteristics are exogenous, problems may arise if land values are also affected by unobservable household characteristics. Consider the discount rate i which may be household specific, depending on whether or not the household has access to capital at the market interest rate. If, as is commonly assumed, poor households have higher discount rates and are less likely to be titled, the coefficients from a simple hedonic regression may be biased. Formally, let i denote households and p plots within the household. In this case, estimation of will produce biased estimates of the coefficient vector/I because of correlation between the unobservable household-specific error term 4t, and Z. Indeed, this appears to have been a problem in many crosssectional studies of this issue. To eliminate ai and thus obtain an equation that will yield unbiased estimates via ordinary least squares, we make use of the availability of information on multiple plots per household to estimate a fixed effect household-level regression. Let _V_ be the mean value of all plots for any given household and Z the mean of the associated characteristics. Then, as shown, among others by 14
liberalization of imports of less-competitive services from the EU could lead to sub-optimal development of these sectors in the EPA-countries when the investors from the EU are not the most efficient available providers of the services concerned. Liberalization of services imports from all sources tends to increase competition among service providers and is likely to lead to greater efficiency gains and more rapid growth.

Source: World Bank (2008).

In this context, the EPA can help to reinforce positive elements in the domestic reform agenda by anchoring pro-competitive policies in the agreement itself. It is subsequently more difficult for domestic lobby groups to reverse policy reforms in order to preserve or enhance their economic rents, as changes would require consent by the EU. This role of enhancing policy credibility seems particularly important for activities that depend on large-scale, long-term investors coming to the country.

Nevertheless, the conclusion of an EPA even with favorable rules of origin (see Box A.1), appropriate services provisions, and accompanying fiscal reforms is no panacea. Due to high MFN tariffs, the persistence of import bans and other non-tariff barriers, and a highly concentrated export base, even a doubling of non-fuel exports to the EU and to regional partners ─ although welcome ─ would hardly generate the economic growth rates that Nigerian policy makers are aspiring to. An additional stimulus for growth and development is called for, and it seems advisable for policy makers to not lose focus on the question of how to maintain and strengthen the country’s competitiveness in the global market. In fact, preferential integration and active participation in global markets do not present exclusive or opposing choices. Many successful countries have indeed build their strategy around a paradigm of “open regionalism”, which implies negotiating reciprocal preferences with partner countries while actively integrating into international markets at the same time. Strengthening Nigeria’s non-fuel export performance is a major challenge and requires attention to the incentives that actual and potential exporters face, the costs of trade and transport logistics to get products efficiently to overseas markets, and the effectiveness of trade support institutions that help private sector firms to discover and exploit international market opportunities.

Improving export performance will require movement of resources from less productive to more productive exporting firms as the latter expand the range of markets into which they sell as well 24
industry.<sup>6</sup> In this case we observe once again that in 2008 Mexico City and the State of Mexico were among the states with the highest average levels of firm-level productivity but in 2013 they do not appear in this group anymore while Quer ́etaro and Guanajuato are emerging as highly productive states. On the other hand, Jalisco and Nuevo Le ́on were the states with the highest-level of TFP in 2013 according to Census data.

> 6The calculation of this index is based on Aw et al. (2000) as will be explained in more detail in Section 5.

13
as it requires estimation of a large number of parameters, a problem accentuated in our case by the large number of countries. For our baseline model (3), we therefore prefer a simpler formulation using fixed effects only in the exporter ( _θj_ ), importer ( _δi_ ) and product-year  _k_  _t_  dimensions. The last term has the advantage of controlling for unobserved effects time-varying effects specific to each category of electronics that are likely to influence trade, such as technological innovation. The importer and exporter fixed effects control for country-specific unobserved factors.

Plugging (2) into (1) and replacing the “multilateral resistance” terms with appropriate fixed effects, we obtain:

# **_Data_**

Our panel covers three categories of electronic products described in Table 3 imported by the EU-15 countries from 131 countries exporting countries<sup>20</sup> over the period 1990-2006. Export and tariff data were compiled through WITS from COMTRADE and TRAINS, respectively under the SITC revision-2 nomenclature. Core gravity variables, such as bilateral distances, colonial and common language dummies, were obtained from the CEPII web-site<sup>21</sup> . Data on standards was explained in section 3.

Exports to the EU in electronics have increased over time. In average, information technology products represent 50 percent of electronic exports to the EU meanwhile consumer electronics and electronic components represent 32 percent and 18 percent, respectively (figure 4). Table 6 shows EU import composition by exporter regions. East Asian & Pacific countries are clearly the major source of EU imports, with exports accounting for more than 80 percent in each category.

# Figure 4 here

As shown in figure 5, EU tariffs for electronic products decreased continuously as the electronics sector experienced a period of sustained liberalization. This pattern coincides with the implementation of the Information Technology Agreement (ITA), a sectoral tariff-cutting multilateral agreement negotiated by an important number of WTO member countries in 1996 for several IT products.<sup>22</sup>

> 20 The sample size for 131 exporting countries, 15 importing countries, 3 electronic categories, and 17 years is 100,215.

> 21 http://www.cepii.fr/anglaisgraph/bdd/distances.htm

> 22 ITA set tariffs for all products listed in its Declaration at a zero-level after an implementation period. As to the three categories we consider, almost half (47%) of their number of tariff lines were affected by ITA, accounting for 25 % of total EU imports volume in these categories

13
# **Table 6: Global M&A activity in banking and policy restrictiveness**

||(1)<br>|(2)|(3)|
|---|---|---|---|
|Log(GDP) 2007|0.6611***|0.6786***|0.6544***|
|Log(GDP per capita) 2007|0.6133***|0.3982***|0.5967***|
|Population Density (people/sqkm)|0.0007<br>|0.0005|0.0008|
|STRI (25, 50]|-0.4418*|||
|STRI (50, 75]|-2.5633***|||
|STRI (75, 100]|-20.9608***|||
|Restr on Branches only||0.5189||
|Operational Restr||-0.8227***||
|Major Restr Establishment||-0.8571***||
|No acquisitions (0%)|||-3.5001***|
|Minority ownership|||-0.7339|
|Limit number of licenses|||-0.1728|
|Restriction repatriation earnings|||-1.0985**|
|No recourse/appeal|||-0.0068|
|No branch entry|||0.0595|
|Restr on cross-border trade|||-0.1471|
|Constant|-1.5984<br>|0.5918|-1.3882|
|Observations|93<br>|93|93|
|Pseudo R-sq|0.7950<br>|0.8071|0.8016|
|LogL|-1.158e+05|-1.090e+05|-1.121e+05|

Dependent variable is total value of M&A inflow to the Banking sector in million USD. Poisson estimation with robust standard errors.

In column 3, we include policy variables separately. The list differs slightly from that in Table 5 because of the addition of variables that are specific to the banking sector (restrictions on cross-border trade and branch banking), and because of the exclusion of one variable ̳discriminatory licensing criteria‘ for want of sufficient variation within this single sector. The results in column 3 are consistent with those shown in columns 4-6 of Table 5, except that not all variables which are significant across all sectors in Table 5 also exert a significant effect within the banking sector.<sup>39</sup>

> 39  In addition, to the extent that observable variables are _alternative_ proxies for measuring overall restrictiveness, their _joint_ significance is the relevant criterion (Lubotsky and Wittenberg, 2006).

42
|**Appendix 5: Impacts on Primary Outcomes Conditio**|**nal on Surviv**|**al**|||||
|---|---|---|---|---|---|---|
|Daily|Weekly|Main Product|Weekly|Main Product|Photo|**Aggregate**|
|Sales|Sales|Sales|Profits|Profits|Inventories|**Index**|
|**Panel A: Impact of Assignment to Training, Allowing**|**for Spillover**|**s**|||||
|Assigned to Training*1 Year<br>165**|294|126|69|46|604|0.051*|
|(77)|(287)|(329)|(75)|(102)|(392)|(0.029)|
|Assigned to Training* 3 Years<br>145|967**|380|182*|124|487|0.080**|
|(94)|(382)|(294)|(98)|(107)|(1017)|(0.035)|
|Spillover Group * 1 Year<br>30|‐557*|168|‐79|‐23|357|‐0.012|
|(78)|(290)|(368)|(70)|(105)|(404)|(0.028)|
|Spillover Group * 3 Years<br>‐24|‐46|169|‐61|‐17|607|‐0.001|
|(95)|(354)|(303)|(86)|(108)|(982)|(0.033)|
|Mean of Pure Control Group<br>1386|6818|3993|1702|1348|10694|0.107|
|Sample Size<br>11339|11305|10460|11277|10381|4775|11319|
|P‐value: Training 1 year=3 years<br>0.804|0.027|0.509|0.177|0.567|0.902|0.324|
|P‐value: Spillover 1 year=3 years<br>0.574|0.138|0.998|0.840|0.964|0.797|0.733|
|P‐value: Training effect zero both years<br>0.097|0.034|0.435|0.180|0.494|0.307|0.068|
|<br>**Panel B: Impact of Mentoring Compared to Training**|**Alone, Allow**|**ing for Spillover**|**s**||||
|Assigned to Mentoring<br>171|1289**|628|295**|132|2081|0.107**|
|(132)|(529)|(395)|(132)|(142)|(1560)|(0.045)|
|Assigned to Training Alone<br>165|792*|360|233*|183|‐658|0.087*|
|(118)|(456)|(383)|(119)|(140)|(1093)|(0.044)|
|Spillover Group to Mentoring<br>‐124|‐28|355|‐68|1|2020|‐0.015|
|(120)|(467)|(367)|(102)|(133)|(1414)|(0.039)|
|Spillover Group to Training Alone<br>139|26|243|62|66|‐460|0.046|
|(118)|(438)|(441)|(110)|(150)|(1220)|(0.042)|
|Sample Size<br>4862|4843|4772|4828|4767|2193|4845|
|P‐value: Mentoring= TrainingAlone<br>0.967<br>Notes: Robust standard errors in parentheses, cluste<br>*, **, and *** indicate significance at the 10, 5, and 1<br>All regressions control for randomization strata fixed<br>survey round fixed effects. Panel B uses only survey<br>between round 3 and round 4. Aggregate index is th<br>description of the different outcome variables.|0.354<br>red at the m<br>percent lev<br>effects , for<br>rounds 4 an<br>e average of|0.559<br>arket level<br>els respectively.<br>the baseline va<br>d 5, since mento<br>standardized z‐|0.674<br>lue of the<br>ring interv<br>scores of t|0.761<br>outcome and fo<br>ention was carr<br>he other variabl|0.058<br>r<br>ied out<br>es. See data|0.693<br>appendix for|

50