rafmacalaba/datause-encoder-data huggingface dataset:
|Observations|10,667|4,950|5,717|4,287|3,369|3,011|
|---|---|---|---|---|---|---|
|Adjusted R square|0.297|0.289|0.340|0.288|0.276|0.355|
|Entertainment|0.550***|0.409***|1.159***|0.461**|0.650***|0.040|
||(0.145)|(0.145)|(0.339)|(0.210)|(0.164)|(0.284)|
|Observations|10,641|4,940|5,701|4,270|3,363|3,008|
|Adjusted R square|0.283|0.285|0.305|0.272|0.281|0.315|
|Automobiles|0.388***|0.265**|1.434***|0.302**|0.516**|0.929***|
||(0.124)|(0.109)|(0.289)|(0.148)|(0.218)|(0.332)|
|Observations|10,534|4,885|5,649|4,218|3,325|2,991|
|Adjusted R square|0.234|0.243|0.230|0.232|0.238|0.238|
|Other vehicles|0.154|0.119|0.869**|0.074|0.366|-0.001|
||(0.141)|(0.129)|(0.359)|(0.129)|(0.372)|(0.586)|
|Observations|10,626|4,936|5,690|4,263|3,357|3,006|
|Adjusted R square|0.396|0.400|0.395|0.399|0.395|0.397|
|Durable goods|0.320***|0.184*|1.004**|0.317**|0.254|0.281|
||(0.120)|(0.105)|(0.396)|(0.135)|(0.193)|(0.916)|
|Observations|10,841|5,049|5,792|4,336|3,423|3,082|
|Adjusted R square|0.429|0.424|0.442|0.420|0.446|0.425|
|Medical|0.170**|0.130|0.442**|0.093|0.321**|0.471|
||(0.082)|(0.094)|(0.179)|(0.117)|(0.136)|(0.410)|
|Observations|10,733|5,004|5,729|4,279|3,395|3,059|
|Adjusted R square|0.377|0.366|0.396|0.388|0.382|0.358|
|Health and fitness|0.273**|0.143|0.544***|0.211|0.257|0.529|
||(0.110)|(0.102)|(0.185)|(0.142)|(0.153)|(0.325)|
|Observations|10,844|5,055|5,789|4,349|3,422|3,073|
|Adjusted R square|0.303|0.286|0.367|0.302|0.320|0.290|
|Education|-0.143|-0.116|0.093|-0.215**|0.079|-0.305|
||(0.114)|(0.126)|(0.268)|(0.085)|(0.203)|(0.373)|
|Observations|10,775|5,026|5,749|4,314|3,406|3,055|
|Adjusted R square|0.222|0.232|0.224|0.227|0.220|0.236|
|Home repair|-0.089|-0.126|0.055|-0.068|-0.172|-0.359|
||(0.080)|(0.085)|(0.310)|(0.089)|(0.143)|(0.629)|
|Observations|10,666|4,961|5,705|4,282|3,372|3,012|
|Adjusted R square|0.450|0.433|0.470|0.476|0.456|0.408|
|Gifts|-0.004|-0.005|0.390|-0.045|0.126|0.106|
||(0.101)|(0.096)|(0.273)|(0.132)|(0.127)|(0.577)|
|Observations|8,839|4,143|4,696|3,452|2,817|2,570|
|Adjusted R square|0.111|0.113|0.114|0.078|0.118|0.165|

Note: Extended model with additional variables of control, including, lagged log value of household expenditure per capita of the consumption category in question, household average years of schooling, household gender ratio, average age/10, age squared/100, dependency ration, and urban-rural dummies. Robust standard errors clustered at the county level are presented in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

·· 32
**Table 4b. Tariff Ceiling Bindings and Other Duties in Agricultural Products in selected LeastDeveloped Countries**

|Countries|Tariff Ceiling Bindings, %|Other Duties & Charges, %|
|---|---|---|
|Bahrain|35|2|
|Cameroon|80|230|
|Cote d'Ivoire|4, 64|200|
|Gabon|60|200|
|Ghana|40, 99|15|
|Kenya|62, 100|0|
|Madagascar|30|250|
|Mauritius|37, 82, 122|17|
|Nigeria|150|80|
|Senegal|30|150|
|Zimbabwe|25, 150|15|
|Antigua & Barbuda|100, 130||
|Belize|100, 110,||
|Brunei Darusalam|20, 50||
|Dominica|100||
|Guyana|100|40|
|Jamaica|100|80, 200|
|St. Lucia|100, 130||
|St. Vincent|100, 130||
|Trinidad & Tobago|100, 106, 110,126,156|15|
|Swaziland|0-597||

Source: WTO Country Schedule of URA Tariff Commitments 25
||**Source**|
|---|---|
|Voice and Accountability||
|Quality of the Bureaucracy|Kaufinann, Kraay and Zoido-Lobaton (1999a).<br>International Country Risk Guide (ICRG), average|
|Civil Liberties|1984-1998.|
|Property rights and rule-based<br>governance<br>Government Effectiveness|Freedom House, average 1972-1998.<br>Country Policy and Institution Assessment (CPIA), the<br>World Bank 1998.|
|Graft<br>Law and Order Tradition|Kaufinann, Kraay and Zoido-Lobaton (1999a).<br>Kaufmnann, Kraay and Zoido-Lobaton (1999a).<br>International Country Risk Guide (ICRG), average|
|Political Instability and|1984-1998.|
|Violence<br>Political rights|Kaufinann, Kraay and Zoido-Lobaton (1999a).|
|Regulatory Burden|Freedom House, average 1972-1998.|
|Rule of Law|Kaufmann, Kraay and Zoido-Lobaton (1999a.<br>Kaufmann, Kraay and Zoido-Lobaton (1999a).|

The quality of political institutions will clearly also be an important factor for growth. As the recent literature on corruption (e.g. La Porta et al, 1997) has shown, arguments that corruption "greases the wheels" of growth simply do not stand up to empirical scrutiny (Tanzi and Davoodi, 1997). A central hypothesis emerging from our social cohesion framework is that strong social cohesion makes it easier to improve the quality of institutions..

# **4. New Evidence on Social Cohesion, Institutions and Economic Performance**

The central story of economic growth over the last 50 years has been the contrast between the years 1950-74 and 1975-2000. The former was a time of general prosperity, in which all strategies yielded positive outcomes; rich and poor countries, open and closed economies, temperate and tropical countries-everyone did well. The twenty-year period between 1974 and 1994, however, was disastrous for virtually everyone except the 16
# **1. Weakened Lending Practices – Betting on the Collateral Rather than the Borrower**

**Ten years of ballooning property prices led to excessive optimism by investors and lenders.** In the U.S., depending on the index employed, national average house prices rose between 53 and 86 percent between the mid-1990s and 2006.<sup>2</sup> At the same time, mortgage originations rose by five times, peaking at $3.9 trillion in 2003 (Chart 1). Markets such as Los Angeles and New York have strongly outperformed the national average and many other cities. National indexes for real rents and house prices largely moved together until 2000, when they diverged, and real house prices moved to a level 70 percent higher than that of real rents. Later, as the property balloon deflated in 2006 and 2007, rising subprime defaults spurred a reevaluation of credit spreads and credit market conditions that reflected broader and more fundamental issues.<sup>3</sup> Apparently, individuals viewed real estate as a foolproof investment opportunity until they decided that it was not, at which point prices began to decline (André, et al., 2006, Shiller, 2007).

> 2 Shiller uses the Case-Shiller house price index, a repeat-sales index that samples properties from twenty metropolitan areas in the country, excluding several states. André, et  al. use the OFHEO house price index, which excludes higher priced properties, and samples from a much larger number of metropolitan areas. (Leventis, 2007).

> 3 The economic stability in Europe and the U.S since the mid-1980s (falling GDP volatility, low inflation) has been termed the “great moderation.” It has been blamed in part for lowered credit risk spreads and persistent search for yield on the part of investors. (Bernanke 2004, Stock and Watson 2003).

2
econometric research affects their interpretation and validity. The second is instrumental variables estimation. We note that there are many bad or unconvincing instruments used in migration research, and that Bartik or shift-share instruments have been thought of as more plausible, but have also been the subject of several recent econometric advances. We outline in detail issues to consider in using shift-share instruments for migration. Finally, we also consider the possibilities for using regression-discontinuity methods for migration research, discussing how language tests, points systems, and discontinuities in time can and cannot be used.

An underlying theme in much of the recent econometric research is that treatment effects are likely to be heterogeneous, and this heterogeneity has implications both for the validity and interpretation of many estimates. We conclude by noting this means we should aim to move away from talking about “the” impact of migration, and that likely areas for future work will involve approaches that work to better identify who is most likely to be affected by migration policies, use machine-learning methods to better estimate treatment effect heterogeneity, and consider external validity issues more fully.

# **2 What is the problem experimental methods help to solve?**

Many research and policy questions surrounding migration are causal questions.<sup>2</sup> In the immigration literature this typically involves estimating the impact of immigration on wages or employment rates of natives. A large part of the development literature in migration attempts to answer questions like what is the impact of migration on the incomes, health, and life opportunities of migrants; and what is the impact of having a household member migrate or of receiving remittances on outcomes of household members remaining in the sending community, such as the education and health of children, their levels of entrepreneurship and labor supply, and on community poverty and inequality levels. A common approach to answering such questions is to use data on migrants and non-migrants, and attempt to control for differences between them in a linear regression framework. For example, researchers may attempt to estimate an equation of the form: Where _Migranti_ is a dummy variable which takes the value 1 if the individual is a migrant, and zero if he or she is not, and **X** _i_ observed characteristics of the individual which are presumed not to have changed with migration (e.g. age, sex and education level,

> 2This section draws heavily on our chapter in the first edition of this handbook (McKenzie and Yang, 2012).

3
**Figure 5: Projected General Government Fiscal Deficits**

<!-- Start of picture text --> 12.0<br>10.0<br>8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>2002/03 2003/04 2004/05 2005/06 2006/07<br>Base Case (CL=0)<br>Reform Case (CL=0)<br>percent of GDP<br><!-- End of picture text --> In the base case, deficits rise to 11.4 percent of GDP by 2006/07. Under reforms, deficits fall steadily to just over 7 percent of GDP. While there is not much difference in interest payments between the two scenarios initially, (because of the high level of initial debt, the absence of primary fiscal surpluses even in the reform scenario and similar levels of nominal interest rates), a substantial difference emerges by 2006/07. Moreover, the reform scenario permits 0.9 percentage points of GDP more of capital and developmental expenditure by the 2006/07 as a result of refocusing subsidies towards rural infrastructure and agricultural R&E; and subsidies are reduced to a minimum food social safety net of 0.5 percent of GDP under reforms compared to 2.3 percentage points in the base case.

_Given the upbeat mood about growth for 2003/04, we also ran a base case with a growth rate of 6.5 percent for 2003/04, with the other rates unchanged, i.e., there is a rebound from the low growth of 2002/03 but the trend rate does not rise_ . This has only a marginal impact: the debt/GDP ratio is 1 percentage point lower by 2006/07 and the fiscal deficit drops to 11.3 percent of GDP compared to 11.4 percent otherwise. This underlines that one year of good growth is not going to alleviate the fiscal situation and that a sustained effort is unavoidable.<sup>62</sup> What happens to revenue deficits? Under reforms, the increase in revenues (+2.4 percentage points of GDP), elimination of petroleum subsidy plus the portion of SEB losses already incorporated in state budgets (0.9 percentage points) and refocusing part of food and fertilizer subsidies towards capital spending (0.9 percentage points) act to reduce the general government revenue deficit by about 4.2 percentage points of GDP by 2006/07. Interest payments rise slightly, from 6.6 percent of GDP in the base year to about 7 percent of GDP by 2006/07. Thus, the net reduction in the revenue deficit will be close to 4 percentage points of GDP, not enough to eliminate it but reducing it to about 2 percent of GDP compared to current levels of over 6 percent. Without reform, the debt burden and ratio of interest payments to revenue will become unmanageable and revenue deficits will rise further. This is likely to fuel inflationary expectations and raise interest rates. This will in turn dampen investment and lead to anemic growth.

> 62 We are grateful to Suresh Tendulkar for making this point.

23
Table 4. Comparison of Actual Credit Supply and **Planned** Credit Supply

|Year|Growth Rate|in Volume (100 mill|ion yuan)|
|---|---|---|---|
||Planned Target (a)|Actua Volume (b)|(a)/(b)|
|1983|354|378|1.07|
|1984|423|988|2.34|
|1985|715|1486|2.09|
|1986|950|1685|1.77|
|1987|1225|1442|1.17|
|1988|...|1518|...|
|1989|...|1851||
|1990|1700|2757|1.60|
|1991|2100|2878|1.37|
|1992|2800|3864|1.38|

Source: Fan Gang et al (1993).

Table **S.** Comparison of Actual Currency **hlued** and **Planned** Target

|year|Growth Rate|in Volume (100 mill|ion yuan)|
|---|---|---|---|
||Planned Target (a)|Actual Volume (b)|(a)/(b)|
|1983|60|90.7|1.51|
|1984|80|262.3|3.28|
|1985|150|195.7|1.30|
|1986|200|230.5|_1.15_|
|1987|230|236.1|1.03|
|1988|200|679.5|3.40|
|1989|400|210.0|0.53|
|1990|400|300.0|0.75|
|1991|500|535.0|1.07|
|1992|600|1158.0|1.93|

Source: Fan Gang et al (1993).

46
Given our estimated distributions, we can check whether the US is a tougher market. To do this, we compare the probability of being active in the US market versus the EU market by integrating over the relevant demand and productivity shocks. In our estimates, the probability of trying the EU (US) market is 17% (13%), while the probability of surviving, conditional on having tried the market, is 60% (23%) for the EU (US). These numbers are consistent with the US being a tougher market and having a larger extent of failed entry.<sup>39</sup> Table 5. Elasticities of substitution

||EU|US|
|---|---|---|
|�|1.34|1.45|
|Std. Error|0.033|0.027|

Table 5 gives the demand elasticities in each market. These are more than unity and close to the estimates obtained in Demidova, Kee and Krishna (2012). They are a little higher in the US and are similar in magnitude to those found in other structural models like Foster, Haltiwanger and Syverson (2012).<sup>40</sup> The estimates of various ...xed costs are given in Table 6. The costs of entering the industry are about $77,000, which are about a third of the cost of entering the EU market and similar in magnitude to the cost of entering the US market. Fixed costs of production as well as the documentation costs are low. Note that these numbers, when added up, give a ...gure close to the sunk cost estimates in Das, Roberts and Tybout (2007) for knit wear (of about .5 million), which is a part of the non-woven apparel industry. Our estimates are, unfortunately, not directly comparable to theirs for two reasons. First, our numbers are for a particular subset of wovens, while theirs are for knit wear. Second, our numbers should be interpreted as annualized values since our model is static.

> 39In previous work, see Demidova, Kee and Krishna (2012), we showed that when the EU o¤ered EBA preferences to Bangladesh, ROOs were less restrictive in non-wovens than in wovens. As expected, we saw the number of ...rms in wovens rising by 33% and in non-wovens by 80% from 1999 to 2003. This con...rms the importance of entry as a channel of adjustment.

> 40The estimated elasticity in Foster, Haltiwanger and Syverson (2012), who focused on homogenous goods producers in the US, is 1.8. Given that Mens/Boys Cotton Trousers can be viewed as more di¤erentiated goods, our estimates of elasticities being less than theirs are not very surprising.

26
51 THE HOUSING MARKET OF KUMASI In contrast, there has been a significant decline in the percentage of single person households. These have decreased from a dominant 27 percent in 1970 to only 13 percent in 1986. This represents a potentially important change in household organization which needs some explanation. Asante traditional marriage is non-coresidential. The husband and wife do not become a joint corporation, united in residence and economies. Obligations to their respective (and separate, as marriage is exogamous) lineages do not diminish on marriage. It is traditional for a man to live separately from his wife, in a different house, where she joins him for conjugal relations and to which she may send some of his meals. This arrangement is perceived to give the woman more economic freedom than coresidential marriage and enables polygyny without internal domestic problems. While this practice is now less than universal, it is still prevalent and, according to a survey by Diko (1981) in Bantama (close to Kumasi city center), desired by many married men and women. This survival of a traditional norm is consistent with other features of Asante life where tradition is very tenacious, especially during the economic hardships of the last decade (see Tipple, 1983; 1984a; and 1987b for further discussion of the survival of traditional values). It has been argued elsewhere (Tipple, 1984a and 1984b) that the increasingly constrained housing supply in Kumasi has, over the last two decades, reduced the ability of men to move out of the marital room and in to a room by themselves.91 This is reflected in the dramatic reduction in single person households not only as a proportion, but also in real terms. The 1986 data only reinforce the previous observations.

It should be noted in passing that those married men who live unwillingly in the same rooms as their wives and children represent a potentially large reserve of latent households. Thus, should the housing stock be increased signiificantly in the future, there is a strong likelihood that at least some of the new rooms will be filled by newly liberated, single person households. The difference this could make has been discussed in Tipple (1984a and 1984b).

Households have the ability to decamp some members at night to ease the problem of crowding. In a city where traditional ties remain strong, it is quite common for male children to go and sleep in the room of a mature brother or an uncle, or female children with a mature sister or an aunt. The difference this makes to the size of households is seen by comparing the total size with the co- resident size, as can be seen from Annex Table 3.8.

There may have been a very slight reduction in household size since 1980, although the difference is insignificant.

_ai_ Another reason might be the poor economic conditions 80-86 that might have lead to reduced migration (thus, a reduction in the number of young men).
one of the two indices. Moreover, Egypt has higher debt levels and much lower weight in both indices than countries such as Colombia, Hungary and Malaysia<sup>13</sup> .

**Reforms in key market development building blocks are necessary to enhance investability and increase the participation of MENA countries in emerging market indices.** While the exact criteria for the GBI-EM are not released, investability scores for 34 countries with minimum public bond market size of US$ 3 billion are computed for eligibility to GEMX. The criteria takes into account capital controls, taxes, secondary market liquidity, size of the investor base, quality of regulations and market infrastructure.14 The four MENA countries in this sample, Egypt, Morocco, Tunisia and Lebanon received low scores ranking respectively 21<sup>st</sup> , 29<sup>th</sup> , 30<sup>th</sup> and 33<sup>rd</sup> . Tunisia and Lebanon did not reach minimum scores for inclusion in the GEMX.

**MENA markets also fall significantly behind those of most other regions in terms of investments from EM dedicated bond funds (Figure 9).** Taking a sample of 33 EMs from all regions and weighting flows with GDP,15 we observe that bond fund allocations to MENA are much lower than those to LAC, ECA and SSA, the latter represented by Nigeria and South Africa. The low participation of non-resident investors in the region is a key characteristic of MENA’s investor base and is discussed in more detail in section 3.5 below.

13 As of November 2009 domestic debt levels in (USD billions) were: (i) for MENA-5 – Egypt ($116), Lebanon ($40), Morocco ($33), Jordan ($10) and Tunisia ($9). (ii) Costa Rica ($6), Kenya ($6), Peru ($11), Colombia ($49), Hungary ($36), Malaysia ($57).

14 For more details see Crisil (2009) “Gemloc Investability Indicators - Phase 2 extension.” 15 We include 33 EMs with minimum bond market capitalization of US$ 3 billion. The countries are by region, East Asia Pacific (EAP): China , Indonesia, Malaysia, Philippines, Thailand, Vietnam; Europe and Central Asia (ECA): Croatia, Hungary, Kazakhstan, Poland, Romania, Russia, Turkey, Ukraine; Latin America and Caribbean (LAC): Argentina, Brazil, Chile, Colombia, Costa Rica, Mexico, Peru, Uruguay, Venezuela; Middle East and North Africa (MNA): Egypt, Lebanon, Morocco, Tunisia; South Asia (SAR): India, Pakistan, Sri Lanka; Sub-Saharan Africa (SSA): Kenya, Nigeria, South Africa.

13
sectors and the disturbance term _ωt_ +1 because this includes unexpected shocks to wages, we follow Artuc, Chaudhuri and McLaren (2010) and use past wage dierences as an instrument. Given the assumptions of the labor choice model, past wage dierences should be uncorrelated with shocks at _t_ + 1 . The key identication assumptions for this IV to work are that i) the idiosyncratic shocks _ε_ are iid shocks; ii) the state of the economy evolves as a Markov process. The Markov assumption is straightforward.<sup>19</sup>

# Assessment and Goodness of Fit

We end this section with an assessment of the estimation. First of all, note that the observed moments and simulated moments (at the estimated parameters) are well-matched by the SMM. This can be seen at the bottom of Table 2.

Figure A1 below establishes that the SMM objective function is well-behaved around the solution and that a minimum is achieved. To show this, we plot the SMM objective function as a function of one of the adjustment cost parameters while keeping the other four parameters constant at their solution value. In each panel, the dashed vertical line denotes the solution to the minimization problem.

Figure A2 provides more intuition about how the SMM identies these dierent parameters. Each panel shows the response of the dierence between the observed and the simulated moments (squared) to a dierent adjustment cost parameter. In panel A, we show that moment 2 (the correlation between investment and protability) identies the xed cost parameter _γ_ 1 . The quadratic cost parameter ( _γ_<sup>2</sup> ) and the irreversibility parameter ( _γ_<sup>3</sup> ) are both identied from the opposing forces of moment 1, the serial correlation in investment, and moment 3, the negative spike moment (panels B and C). The labor mobility cost across sectors ( _Ce_ ) is identied from the serial correlation between the change in wages and the change in employment (moment 4). The mobility cost in and out of non-employment ( _Cu_ ) is identied from the serial correlation in non-employment (moment 5) and the correlation between the change in non-employment and in the average wage (moment 6). These are shown in panels D and E of Figure A2.<sup>20</sup>

> 19Note that in this Euler equation the expectation is taken over realizations of all the state variables so that it is consistent with any structure in the economy provided it evolves as a Markov process. In particular, workers expectations are perfectly consistent with our formulation of rm behavior.

> 20Note that our model does not match the spikes as well as it does the other moments. This is because the spikes correspond to estimating a percentile of rms located in the tail of the rm distribution, which is much harder to replicate in the simulation of the model than a measure of central tendency such as a mean (or a correlation). As Figure A2 shows, the moment does however play a crucial role in identifying the trade-os between inaction in investment or smoothing investment faced by rms.

46