rafmacalaba/datause-encoder-data huggingface dataset:
treatment if the sector has little or no state owned enterprises. Finally, an increasingly competitive industrial structure brought on by the customs union will further expose inefficient state owned enterprises. The loss-making state owned enterprises will lose even more, making it increasingly costly to maintain inefficient SOEs. The drain on the state budget and the rest of the economy would likely become even more of a constraint on growth of the Turkish economy.

In summary, with participation with the EU in a customs union, Turkey now stands at an historical crossroads. One road it can take at this time is the successful road taken by Spain after it decided to accede to the EU (see box). Spain complemented its accession (and anticipated accession) to the EU with a significant reduction in the role of the state in the domestic economy.

On the other hand, despite rapid growth in the 1960s, Greece has made much less progress after its accession to the EU. Although both Greece and Spain adopted the external trade policies of the EU, Greece continued to support its state owned enterprises to a much greater degree. As a result its consolidated fiscal deficit and public sector borrowing requirements rose to a large share of GDP in the 1980s; this crowded out private investment (which plunged) and contributed to stagnant growth. Moreover, even the private sector is highly regulated. Bureaucratic control by state banks is one means of applying public influence in the private sector; and the Greek industrial structure is characterized by numnerous cases of featherbedding and subsidized credit to feeble producers. If, due to financial problems, a company is taken over by a bank, it subsequently obtains a soft budget constraint analogous the state enterprises in Eastern Europe of the 1980s. (See Katseli, 1990; and Bliss and Macedo, 1990) Between 1980 and 1992, Spain grew at over twice the rate of Greece. By 1992, among the four relatively poor members of the EU (Greece, Portugal, Spain and Ireland), Spain had by far the highest per capita income (at $14,000 it was about twice the level of Greece).<sup>5</sup> 5See The World Bank, World Development Report, 1994, table 1.

6
# **V-       Conclusion**

The analysis in this paper provides new empirical support to the dynamic relationship between corruption practices and youth unemployment. We find that when accounting for reverse causality, an increase in the rentseeking behavior among government officials when granting the public good (e.g., job opportunity in the public sector) increases the unemployment rate among young and educated job seekers. In the absence of efficient control and monitoring mechanisms, the proliferation of those practices forces a growing segment of the workforce either to join the crowd and pay the price to secure a job or to be left out of the official labor market and turn to the informal sector. We demonstrate that while the polity in the country does not seem to have a significant impact on the unemployment rate, other factors, such as the confidence of foreign investors, education and local labor market legislation, play a role in determining labor market equilibrium.

# _<u>References</u>_

Ades, A., and Si Tella, R., (1997) “ The New Economics of Corruption: a Survey and Some New Results”, Political Studies , Vol (45), pp. 496-515.

Aidt, T., (2009), “Corruption, institutions, and economic development”, Oxford Review of Economic Policy, Vol 25 (2), pp. 271-291.

Altavilla, C., and Caroleo, F., (2006) “Evaluating the Dynamic Effects of Active Labour Policies in Italy”, Labour 20 (2) pp. 349-382.

Arellano, M., and Bover., O., (1995), “Another look at the instrumental variable estimation of error-components models”, Journal of Econometrics, Vol(68).

Azfar, O., Lee, Y., and Swamy, A., (2001) “The causes and Consequences of corruption” The annas of the American Academy of Political and Social Science, Vol (573:42).

Bernal-Verdugo, L., Furceri D., and Guillaume, D., (2012) “Labor Market Flexibility and Unemployment: New Empirical Evidence of Static and Dynamic Effects”, the IMF Working Paper WP/12/64.

Page | 19
measure of liquidity defined as the ratio of cash to assets. Data to compute all these ratios come from Worldscope. Country-level averages, weighted by firm size, are used in our estimations. Finally, using data on interest coverage (the ratio of interest expenses to earnings) from Worldscope, we calculate the percentage of firms with interest coverage below 1. This variable captures the share of firms that are not able to meet their debt obligations with their earnings.

Among the proxies for macroeconomic vulnerability, we include a measure of fiscal imbalance and a number of measures of external imbalances. The fiscal account (budget surplus or deficit) to GDP is computed with data from the World Bank Development Indicators. As measures of external imbalances, we consider the current account balance to GDP, the financial account balance to GDP, and the share of reserves to GDP. All these variables come from the IMF Balance of Payment Statistics.

Finally, because the US crisis started in the real estate sector, we also consider variables related to this sector. In particular, we include the share of mortgage debt to GDP as of 2006, the cumulative growth rate in this share between 2003 and 2006, the average growth rate in the share of mortgage debt to GDP during this period, and the change in real estate prices between 2003 and 2006. These data come from multiple sources including Bank for International Settlements, EMF Hypostat, International Finance Corporation, Market Watch and World Bank.

# **4. Empirical results**

Following the methodology outlined in Section 3, we conduct different estimations to test for whether trade linkages, financial linkages or a wake-up call/demonstration effect can explain the transmission of the US crisis to other stock markets around the world.

# **Trade linkages**

Table 3 shows the estimations testing for whether trade linkages explain the comovement between the US and other countries’ stock markets. Surprisingly, we find that exports to the US and total exports to GDP have the reverse sign from the expected– countries with larger share of exports show less comovement with the US stock market. However, these results appear to be driven by changes in the exchange rate of the US dollar vis-à-vis other currencies in the midst of 10
# SUMMARY

The paper assesses the extent of inflation in Czechoslovakia during the period 1985-91 and identifies the main causes of inflation, based on a literature survey and some empirical studies.

The official prices in centrally planned economies (CPEs) were never perceived by central planners to be fully market-clearing, and the overall price level would, therefore, correspond to the level associated with the general equilibrium only by coincidence. What is missing in CPEs' official price indices including the consumer price index (CPI) is the "suppressed" inflation, which is manifested in queuing for products, forced substitution of demand and forced savings. Also missing is the "hidden" inflation, which is associated with dhe practices that disguise price increases behind "cosmetic" or other changes in product quality.

The paper argues that inflationary pressures in Czechoslovakia in the period 1985-89 originated mainly in the investment sector. Even though the investment sector was strictly controlled and the emergence of open inflation was extremely difficult, the scope for inflationary pressures was wide in Czechoslovakia. The pressures arose from a mixture of factors - high priority given to investments and social consumption, poor investment planning, and the accommodating government finance.

In Czechoslovakia, the official price indices show virtually no inflation between 1985 and 1989 when there were long waiting lists for such products as cars and state and cooperative flats. Trends in these price indices do not seem to depend on the methodology used for constructing them, according to the sensitivity tests conducted by the Federal Statistical Office of Czechoslovakia. Obviously, the official price indices of Czechoslovakia failed to capture the ful extent of economic disequilibrium during that period.

The paper finds, however, that the extent of understatement by the official price indices of inflationary pressures was not serious, especially compared to other CPEs. Estimates of hidden inflation for 1985-89 range from 0.5 percent to 2 percent per year in consumer markets and about 3 percent in the industrial sector. Those for suppressed inflation were less than 5 percent. The relatively small inflationary gap is indirectly confirmed by the fact that the sharp inflation associated with the recent price liberalization has subsided over a relatively short period.

The paper discusses the methods used for the estimates of hidden and suppressed inflation. It indicates that estimates of the hidden inflation were based on benchmark price comparisons between Czechoslovakia and market economies such as Austria, and those for the suppressed inflation, based on disequilibrium econometric models on asset holdings and "conjuncture tests." Finally, the paper discusses the 1990-91 price liberalization and the associated price rises, showing that both suppressed and hidden inflation have virtually disappeared now.
(2004) argue that countries with stronger democratic institutions may be better able to negotiate compromises among social groups to avoid unrest. However, the effectiveness of the state’s response may vary greatly even within democracies. In addition, authoritarian regimes may use the windfall revenues from positive shocks to strengthen the repression of potential challenges to their authority. In terms of the effect on conflict onset, this would make autocracies similar to effective democracies able to negotiate compromises among social groups.

This paper aims to overcome the limits of previous research in testing for the role of conditioning factors in explaining the relationship between trade shocks and conflict. It combines state of the art empirical methods, which improve on many existing studies, with a selection of theoretically relevant factors that may be expected to affect this relationship.

# **3. Data and variables**

We collect data from different sources. For the conflict data we mainly rely on the Uppsala Conflict Data Programme PRIO.<sup>10</sup> Although there are other sources for conflict data (e.g. Fearon and Laitin, 2003; Sambanis, 2004 and the Correlates of War, Sarkees and Wayman, 2010), PRIO has become the key data set for cross-country analyses of conflict determinants.

While all sources define a civil war as an internal conflict with at least 1,000 battle deaths, there are significant differences in the data on fatalities and the criteria used to code the onset of wars, what counts as a war, and how to treat breaks in violence. These differences lead to dramatically different civil war variables.<sup>11</sup> We are reluctant to test the relationship between trade shocks and conflict using all sources of conflict data, because the differences among them would likely affect the analysis, and could lead to very different results.

Instead, we follow the same practice as B-B, who base their analysis on the less frequent PRIO and COW measures of conflict. B-B (p.13) assert that these databases are theoretically most relevant to measuring the impact of commodity price changes on conflict, “as they capture the ebb and flow of incentives for war as incomes rise or fall”. We use the PRIO data set as our main source of conflict data, and only use the COW data for robustness, for three main reasons. First, the PRIO data set has effectively become the standard reference for cross-country studies on the determinants of conflict. The majority of the recent studies we have reviewed use only this data set (e.g. Nunn and Qian, 2013; Bruckner and Ciccone, 2010; Hull and Imai, 2013; Lin and Michaels, 2011), while the rest use it along with other data sources (e.g., B-B, Besley and Persson, 2008). Second, we have performed independent checks on both data sets and found PRIO to be generally

> 10 These datasets are available at http://www.prio.no/Data/Armed-Conflict/UCDP-PRIO/.

> 11 For example B-B report that in the PRIO dataset major civil wars are coded in 7% of the country-years, compared to 20% of the country-years in the Fearon and Laitin data.

8
Table 2. Recursive correlations between a country's per capita GDP Growth and average LAC GDP growth (blue type) and household consumption (black type). Sample for 1992 is 1971-1992, for 2003 is 1971-2003.

||1992|1993|1994|1995|1996|1997|1998|1999|2000|
|---|---|---|---|---|---|---|---|---|---|
|Argentina|0.53|0.54|0.55|0.56|0.56|0.58|0.58|0,59|0.58|
||0.62|0.62|0.63|0.63|0.63|0.64|0.63|0.64|0.63|
|Belize|0.48|0.48|0.41|0.44|0.41|0.37|0.37|0.34|0.35|
||-0.06<br>|-0.04<br>|-0.11<br>|-0.11<br>|-0.11<br>|-0.10<br>|-0.11<br>|-0.13<br>|-0.10<br>|
|Bolivia|0.44|0.44|0.46|0.41|0.42|0.44|0.42|0.44|0.44|
||0.31|0.32|0.33|0.32|0.32|0,34|0.31|0,30|0.30|
|Brazil|0.78|0.79|0.79|0.77|0.76|0.74|0.74|0,74|0.74|
||0.66|0.68|0.69|0.60|0.60|0,59|0.59|0.60|0,59|
|Chile|0.26|0.27|0.27|0.22|0.23|0.24|0.24|0.26|0.26|
||0.51|0.51|0.52|0.50|0.50|0.49|0.48|0.48|0.48|
|Colombia|0.56|0.53|0.55|0.51|0.48|0.46|0.44|0.45|0.45|
||0.39|0.40|0.30|0.29|0.28|0.26|0.27|0.32|0,32|
|Costa Rica|0.57|0.57|0.57|0.56|0.54|0,54|0.52|0.46|0.45|
||0.26|0.29|0.31|0.31|0.30|0.31|0.30|0.31|0.29|
|Dominican Republic|0.48|0.48|0.48|0.45|0.46|0.48|0.47|0.41|0.42|
||0.09|0.01|0.04|0.07|0.07|0.09|0.07|0.07|0.10|
|Ecuador|0.58|0.56|0.56|0.56|0.56|0.56|0.56|0.57|0.57|
||0.43|0.42|0.43|0.43|0.43|0.44|0.42|0.44|0.45|
|Guatemala|0.72|0.72|0.72|0.68|0.67|0.67|0.66|0,65|0.64|
||0.61|0.62|0.64|0.61|0.61|0.61|0.59|0.58|0,58|
|Guyana|0.51|0,50|0.52|0.47|0.47|0.49|0.49|0.47|0.46|
||0.11|0.15|0.20|0.20|0.20|0.22|0.22|0.20|0,20|
|Honduras|0.42|0.43|0.35|0.34|0.34|0.35|0.35|0.38|0.39|
||0.33|0.33|0.28|0.29|0.29|0.28|0.28|0.30|0,32|
|Haiti|0.39|0.37|0.28|0.31|0.31|0.33|0.32|0.30|0.30|
||0.32|0.36|0.07|0.08|0.08|0.10|0.09|0.08|0,08|
|Jamaica|-0.25|-0.25|-0.24|0.25|-0.25|-0.26|-0.26|-0.26|-0.26|
||-0.40|-0.23|-0.23|-0.23|-0.23|-0.23|-0.23|-0.23|-0.22|
|Mexico|0.55|0.54|0.54|0.55|0.56|0,58|0.57|0.55|0.56|
||0.54|0.51|0.52|0.45|0.45|0.47|0.45|0.43|0.44|
|Nicaragua|-0.05|-0.04|-0.03|-0.06|-0.04|-0.02|-0.02|-0.05|-0.05|
||0.12|0.11|0.16|0.15|0.15|0.16|0.16|0.14|0.15|
|Panama|0,23|0.23|0.23|0.24|0.23|0.26|0.25|0.24|0,23|
||0,33|0.33|0.27|0.26|0.27|0,28|0.26|0.27|0.24|
|Peru|0.63|0.63|0.64|0.57|0.57|0.58|0.58|0.57|0.57|
||0.23|0.25|0.31|0.27|0.27|0.28|0.29|0.29|0,30|
|Paraguay|0.62|0.62|0.59|0.58|0.56|0.53|0.52|0,53|0.50|
||0.56|0.55|0.57|0.57|0.57|0.56|0.56|0.58|0.57|
|El Salvador|0.13|0.14|0.16|0.12|0.12|0.13|0.13|0.12|0.12|
||0.00|0.03|0.06|0.05|0.05|0.05|0.05|0.05|0.05|
|Trinidad|0.46|0.45|0.46|0.42|0.43|0.43|0.41|0.38|0.39|
||0.38|0,33|0.22|0.22|0.21|0.19|0.17|0.07|0,09|
|Uruguay|0.65|0.65|0.66|0.67|0.67|0.68|0.67|0,68|0,66|
||0.33<br>|0.35<br>|0.37<br>|0.38<br>|0.37<br>|0.38<br>|0,37<br>|0,38<br>|0,36<br>|
|Venezuela|0.34|0.34|0.30|0.26|0.26|0.29|0.29|0.33|0.34|
||0.12|0.09|0.02|0.03|0.03|0.02|0.03|0.05|0,06|
Table 1. First Stage Results for Conflict Intensity, Nepal DHS, 1996-2006

||**State-Caused**<br>|**Casualties**<br>||**Maoist-Cau**<br>|**sed Casualt**<br>|**ies**<br>|**Total Casual**<br>|**ties**<br>||
|---|---|---|---|---|---|---|---|---|---|
||_(1)_|_(2)_|_(3)_|_(1)_|_(2)_|_(3)_|_(1)_|_(2)_|_(3)_|
|Intercept|2.505|-0.039|0.474<sup>***</sup>|1.039|0.083|0.286<sup>***</sup>|3.544|0.045|0.760<sup>***</sup>|
||(1.553)|(0.263)|(0.124)|(0.753)|(0.117)|(0.057)|(2.267)|(0.375)|(0.179)|
|Forested|2.247<sup>***</sup>|1.700<sup>**</sup>|0.646<sup>**</sup>|0.873<sup>**</sup>|0.653<sup>**</sup>|0.217|3.120<sup>**</sup>|2.353<sup>**</sup>|0.863<sup>**</sup>|
||(0.658)|(0.637)|(0.277)|(0.319)|(0.284)|(0.127)|(0.961)|(0.909)|(0.400)|
|Roads|-0.197|||0.212|||0.015|||
||(1.648)|||(0.799)|||(2.406)|||
|Elevation|-0.126|||-0.047|||-0.173|||
||(0.108)|||(0.052)|||(0.157)|||
|Rivers|0.747|||20.021|||20.768|||
||(159.832)|||(77.518)|||(233.380)|||
|Temperature|-0.102|||-0.035|||-0.137|||
||(0.070)|||(0.034)|||(0.102)|||
|Rain|-0.341|||-0.315|||-0.656|||
||(0.627)|||(0.304)|||(0.915)|||
|R<sup>2</sup>|0.699|0.354|0.295|0.609|0.288|0.184|0.678|0.340|0.264|
|F|11.67<sup>***</sup>|7.14<sup>**</sup>|5.44<sup>**</sup>|7.48<sup>**</sup>|5.27<sup>**</sup>|2.94|10.55<sup>**</sup>|6.70<sup>**</sup>|4.67<sup>**</sup>|

Notes: DHS=Demographic and Health Survey. Weighted to national level with weights provided by the Nepal DHS in each year. Standard errors in parentheses. The notation<sup>***</sup> is p<0.01,<sup>**</sup> is p<0.05,<sup>*</sup> is p<0.10. Model (1) includes each regressor measured as of 1994 as linear variables; Model (2) includes only forested in 1994 as a linear variable; and Model (3) includes only forested in 1994 as a binary variable. F-statistics reported in the table are the partial F-statistic values for the “Forested” variable. All regressions have 15 observations at the sub-region level. Source: Authors‟ calculations based on MHP/NE/MI (2007), MH/NE/ORC (2002), and Pradhan _et al._ (1997).

32
_29_ <!-- Start of picture text --> Benefit Obligations to Be Paid from the Pension Fund<br>under the Civil Service Retirernent Ran<br>(% of concurrent GDP)<br>6<br>5<br>0 Unf unded<br>4<br>Funded 4.01<br>3<br>2<br>1 0.93<br>0<br>THT Pension<br><!-- End of picture text --> Note: These liabilities are 22.5 percent of total estimated civil service benefit obligations. Sources: Staff calculations based on Taspen's data.

**_Budgetary Implications_** The under-funding of the civil service plan is expected to have a major impact on the budget and the domestic financial markets. The budgetary expenses arising from the government's contractual responsibility is projected to rise significantly. Furthermore, substantial new outlays would be needed to cover THT and pension obligations that are now being paid by Taspen. The severity of this impact is due in part to the demographic shifts, which would increase the number of retirees relative to the work force. Since the civil service represents a relatively mature work force (see Chapter 1), the rise in the dependency ratio occurs sooner than the rest of the population.

Based on current practices under the plan, and the standard macroeconomic assumptions as given in the accompanying charts, the benefit payments under Taspen's THT program are projected to rise significantly and to exceed cash inflows (consisting of contributions and investment income) by the year 2006. Thereafter the THT assets would be drawn down to meet the required payments. By the year 2015, the THT assets would be completely depleted. To pay for the THT benefits thereafter, the government would need to make additional cash outlays, which are not required today. (See the charts below.) The size of this extra annual outlay would rise to about the same as the employees' contributions to the THT program or 3.25 percent of wages.
Table C2: Individual Big 5 personality: Job search spells, no. of applications, tenure, entry salary

||Log(s<br>sp|earch<br>ell)|Log(<br>applica|no.<br>tions)|Log(te<br>yea|nure in<br>rs)|Log(<br>sal|entry<br>ary)|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Social networks|-0.242<sup>_∗∗_</sup>|-0.223<sup>_∗∗∗_</sup>|-0.232<sup>_∗∗_</sup>|-0.168|-0.161|-0.184<sup>_∗_</sup>|-0.069|-0.085<sup>_∗∗_</sup>|
||(0.098)|(0.083)|(0.101)|(0.118)|(0.098)|(0.106)|(0.042)|(0.040)|
|Schooing in years|0.066<sup>_∗∗∗_</sup>|0.021|0.041<sup>_∗∗∗_</sup>|0.022<sup>_∗_</sup>|-0.007|-0.007|0.056<sup>_∗∗∗_</sup>|0.033<sup>_∗∗∗_</sup>|
||(0.011)|(0.014)|(0.012)|(0.012)|(0.008)|(0.011)|(0.006)|(0.008)|
|Age at hire|-0.012<sup>_∗_</sup>|-0.009|0.014<sup>_∗∗_</sup>|0.014<sup>_∗∗_</sup>|-0.010|-0.007|0.023<sup>_∗∗∗_</sup>|0.013<sup>_∗∗∗_</sup>|
||(0.007)|(0.008)|(0.006)|(0.006)|(0.007)|(0.007)|(0.004)|(0.003)|
|Father’s pri.school|0.029|0.135<sup>_∗_</sup>|0.040|0.077|-0.017|-0.064|0.057<sup>_∗_</sup>|0.022|
||(0.082)|(0.077)|(0.069)|(0.073)|(0.060)|(0.059)|(0.033)|(0.027)|
|Mother’s pri.school|0.030|0.053|0.030|0.132<sup>_∗_</sup>|0.052|0.055|0.046|0.021|
||(0.067)|(0.058)|(0.058)|(0.071)|(0.054)|(0.069)|(0.035)|(0.040)|
|Math z-score|0.017|-0.008|0.062|0.031|-0.022|0.030|0.075<sup>_∗_</sup>|0.096<sup>_∗_</sup>|
||(0.034)|(0.041)|(0.041)|(0.042)|(0.043)|(0.050)|(0.042)|(0.049)|
|Language z-score|-0.091<sup>_∗∗_</sup>|-0.049|0.014|-0.020|0.072|0.029|-0.096<sup>_∗∗∗_</sup>|-0.082<sup>_∗∗_</sup>|
||(0.037)|(0.046)|(0.051)|(0.057)|(0.052)|(0.054)|(0.035)|(0.040)|
|_Big 5 personality traits_|||||||||
|Conscientiousness|-0.074<sup>_∗∗_</sup>|-0.020|-0.024|-0.072<sup>_∗∗_</sup>|0.020|0.058|-0.029|-0.044<sup>_∗_</sup>|
||(0.036)|(0.035)|(0.028)|(0.029)|(0.023)|(0.038)|(0.018)|(0.026)|
|Emotional stability|-0.020|-0.037|-0.055<sup>_∗_</sup>|-0.057<sup>_∗_</sup>|0.042<sup>_∗_</sup>|0.082<sup>_∗∗_</sup>|0.026<sup>_∗∗_</sup>|0.034<sup>_∗∗_</sup>|
||(0.031)|(0.031)|(0.030)|(0.033)|(0.023)|(0.032)|(0.013)|(0.015)|
|Agreeableness|0.026|0.098<sup>_∗_</sup>|-0.028|-0.024|-0.029|-0.035|0.007|-0.021|
||(0.036)|(0.055)|(0.036)|(0.036)|(0.020)|(0.032)|(0.015)|(0.013)|
|Extraversion|0.021|0.006|0.060<sup>_∗_</sup>|-0.008|0.045|0.061<sup>_∗_</sup>|0.031|0.009|
||(0.032)|(0.038)|(0.033)|(0.022)|(0.033)|(0.037)|(0.021)|(0.018)|
|Openness|0.063<sup>_∗∗_</sup>|0.039|-0.027|0.023|0.000|-0.006|0.005|-0.057<sup>_∗∗∗_</sup>|
||(0.026)|(0.036)|(0.042)|(0.051)|(0.035)|(0.046)|(0.020)|(0.017)|
|Firm-occ fxed efect||X||X||X||X|
|R-squared|0.225|0.137|0.176|0.085|0.032|0.040|0.398|0.252|
|N|3315|3315|3315|3315|3315|3315|3315|3315|

_Note_ . The dependent variable log( _tenure_ ) is constructed by treating 0 year of tenure as 0.5 year, 1 year as 1.5 year, and so forth. The other controls included are entry year fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels:<sup>_∗∗∗_</sup> = 1%,<sup>_∗∗_</sup> = 5%,<sup>_∗_</sup> = 10%.

53
better be prepared to justify the smaller amount transferred.

In line with our empirical findings, the model predicts reporting behavior where the migrant is truthful about his income only at its lower levels that prohibits the migrant from fulfilling the remittance expectations held at home. When income is high, the migrant does not need to reveal his income truthfully as long as his remittance meets the prescribed threshold, and he would rather not report or underreport his income out of fear of disadvantageous contractual revisions in the future. The threshold for remittance is reflective of the degree of informational asymmetry and so is the reported earnings ratio because it directly depends on the threshold. As our model predicts that expected remittance increases in the threshold, we can also predict that remittance rises, all else equal, in the reported earnings ratio, which we observe in data. The predicted remittance behavior is confirmed by a piecewise remittance regression that has a much steeper slope at lower levels of income. We also check the robustness of the model by testing its comparative statics predictions.

The following section briefly describes migration background relevant to our study. Section II presents our remittance model with asymmetric information and its theoretical predictions. Section III discusses the dataset, Sections IV and V provide empirical analysis of the observed reporting and remittance behavior, respectively. The last section concludes the study.

# I. MIGRATION OVERVIEW

According to Qatar’s 2010 census, approximately 90 percent of its 1.7 million population of age fifteen or older were foreign born, rendering it the nation with the highest share of immigrants in the world (see the World Bank’s _Migration and Remittances Factbook_ 2011). The majority of immigrants to Qatar originate from developing countries, with a significant 5