rafmacalaba/datause-encoder-data huggingface dataset:
Overall, we find that parishes with a greater refugee presence, particularly after the recent influx, have better access to social services. Furthermore, we do not find evidence that greater levels of refugee presence provoke backlash in public opinion against migrants and migration policies. We report the results using the _Nearest + 20_ refugee presence measure, along with a 150km radius cutoff. As robustness checks, SI Section S4 shows the results using all presence measures and cutoffs. Figure 5 displays positive effects across all four development outcomes. For these plots, _higher values_ correspond to _greater access_ and _better infrastructure_ . Note that as per our DiD regression model described above, ‘Baseline presence’ (in the figures’ x-axis) refers to the multivariate association between a 1-SD increase in refugee presence and the outcome of interest. By contrast, ‘Presence _×_ 2006,’ ‘Presence _×_ 2011,’ ‘Presence _×_ 2016,’ and ‘Presence _×_ 2020’ refer to the _change_ in this multivariate relationship compared to the baseline year (usually 2001).

The top row of the figure displays the results for primary and secondary school access (the number of schools normalized by school-age children). It shows that in 2001, a 1-SD increase in refugee presence was associated with less access to _public primary schools_ (-0.04 schools per 1000 primary school-age kids). Parishes with a higher level of exposure to refugees begin witnessing greater access to public schooling starting in 2006 with an effect size of 0.1, and this positive effect increases in all subsequent years: 0.11 in 2011, 0.17 in 2016, and 0.28 in 2020. As expected, we observe positive effects starting in the 2000s, when Uganda’s refugee-hosting policies emphasized self-sufficiency for refugees and benefits for hosting communities. The largest effects are after 2014, when large numbers of South Sudanese refugees began to arrive (followed by an increase in humanitarian aid disbursements). For _private primary schools_ on the other hand, a 1-sd increase in refugee presence at baseline was associated with higher access (0.2 schools per 1000 primary school-age kids). Yet for subsequent years, there is no change based on greater refugee presence; there are no increases in the number of non-government-funded primary schools as a function of exposure. By 2020, the effect is negative at -0.11, which suggests a substitution effect. Since there are more investments in public primary schools, there is less need for non-government-funded primary schools in these refugee-hosting areas.

Turning to secondary schools, we find a similar pattern to primary schools. In the baseline year of 2001, areas with higher levels of refugee presence had, on average, better access to _private secondary schools_ (0.2 schools per 1000 secondary school-age kids) and somewhat worse access to _public secondary schools_ (-0.04 schools per 1000 secondary school-age kids). There was a small 20
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# _PART II_

_(Afternoon Session: 2:30 p.m.)_ _MR. BANH: I would like Mr. Steve Marris to continue hls analysis [that he started during lunch] of slightly gloomy shortrun to the buoyant medium/longer-run outlook for the world economy._ _Inflatlonary recession in the Unlted States..._ _MR. MARRIS: Let me try to look beyond the hard landing. This is something I am starting to try and think about. You always need to be counter-cyclical in our business. So my next book is probably going to be about the dangers of the dollar going up too soon and too muchl But taking up where I left off I believe that the beginnings of an inflationary recession in the United States are inevitable. What exactly is the timing I am not sure, and this recession, of course, is an unusual recession for the United States because it is ecternally generated. That means that there are three things which a;..e completely out of line with NBER cyclical indicator approach to recessions._ _The recession starts with interest rates going up rather than going down. It starts with inflation staying up or going up rather than coming down. On the other hand, it also has built into it a very strong boost to the economy coming from the lower dollar._

**_Do_** _Jones to The configuration that it points to is a recession, which "1000'_ **_plus_** _In the financial markets of the world starts looking rather horrible. something... It is generated, in fact, by financial phenomena. The Dow Jones goes down to "1,000" or so. Interest rates peak, go up. The long term Long-term rates rate, I maintain, will go up to 12 or 13 percent, and this incredible will rise.., euphoria in which this country [the United States] has been living for quite a while will blow up._

_But precisely because the whole thing is unrolling fairlv slowly and because the dollar has already been down far enough for long enough to be giving a strong boost, we are now getting anywhere between 1/2 to 1 percent of GNP per annum improvement in real terms in the external balance, and I think it's absolutely inevitable that_ it _will continue for another two to chree years._ _The recession may start as a sharp one and look racher nasty, but it will be short-lived. And then we get to the feedback onto the political scene._ **_U.S. budget_** _I am confidently predicting that the U.S. budget deficic_ **_deflcit will be_** _will be eliminated within the next -- I mean, action will be caken_ **_eliminAted..._** _which will lead to the elimination of the U.S. budgec deficic --_

**WP/8OLTHO/SF:Ig**
There is a large literature identifying various firm and country characteristics that impact firm productivity. These include the age of the firm (Jensen et al., 2001; Thompson, 2010); exporting activity (Melitz, 2003; Wagner, 2007); foreign ownership; gender of the top manager (Du Rietz & Henrekson, 2000; Islam et al., 2020); management quality and top manager experience (Bloom et al., 2012; Pfeifer, 2015; Syverson, 2011); financial constraints faced by firms (Beck et al., 2000; Fafchamps and <mark>Schündeln,</mark> 2013); physical infrastructure availability (Aterido et al., 2011, Dollar et al., 2005); worker skills and human capital (Syverson, 2011); quality of institutions and property rights (Aterido et al., 2011; Hall & Jones, 1999; Syverson, 2011); regulatory burden on the firms (Aghion et al., 2004; Djankov et al., 2002); corruption (Mauro, 1995; Fisman and Svensson, 2007); and other factors associated with overall economic development.

Our firm-level controls, all taken from the ES, include the following: proportion of firms’ annual sales exported directly ( _Exports_ ); dummy variable equal to 1 if foreign individuals, companies or organizations own 10 percent or more of the firm and 0 otherwise ( _Foreign Ownership_ ); the log of the age of the firm ( _Age of Firm_ ); dummy variable equal to 1 if the top manager of the firm is female and 0 otherwise ( _Female Top Manager_ ); the log of the number of years of firms’ top manager’s experience in the industry ( _Manager Experience_ ); dummy variable equal to 1 if the firm is financially constrained based on its recent loan applications (or lack of it) and 0 otherwise ( _Firm is Financially Constrained_ ); hours of power outages in a typical month over the last year ( _Power Outages_ ); regulatory burden proxied by the proportion of firms’ senior management’s time spent in dealing with business regulations ( _Time Tax_ ); dummy variable equal to 1 if the firm provides training to its employees and 0 otherwise ( _Firm Provides Training_ dummy variable equal to 1 if the firm reports the functioning of courts as an obstacle for its 19
**Table 5.** Major actions in Mali and Mozambique

|Area|Mali|Mozainbique|
|---|---|---|
|*<br>Macroeconomic stability|The macroeconomic indicators<br>improved dramatically, as real<br>GDP growth reached<br>approximately 7 per cent in 1997,<br>up from 0.6 per cent in 1990.<br>Average annual inflation, as<br>measured by the consumer price<br>index for Bamako, was reduced<br>from 12.4 per cent in 1995, to 4<br>per cent in 1998. Both the<br>external account deficit and fiscal<br>deficit were reduced, and a<br>prudent credit policy was<br>pursued.|The economic growth rate<br>jumped from 4.0 per cent in 1990<br>to 13.3 per cent in 1997.<br>Inflation was reduced from 70 per<br>cent in 1994 to single digits by<br>1997.|
|*<br>Trade liberalization|The trade openness ratio<br>increased from 49 per cent in<br>1990 to 60 per cent in 1997, with<br>a reduction in tariffs and the<br>elimination of several non-tariff<br>barriers.|The trade openness ratio<br>increased from 53 per cent in<br>1990 to 63 per cent in 1997. In<br>1996, the Government<br>rationalized and lowered the tariff<br>structure, averaging around 14<br>per cent|
|*<br>Privatization|After a slow start, privatization<br>receipts reached $22 million in<br>1997, including the sale of<br>several enterprises in the financial<br>and manufacturing sectors.|Mozambique's privatization<br>programme is one of the most<br>active in Africa as well: more<br>than 900 state enterprises have<br>been privatized, including the<br>entire banking sector and a<br>number of state manufacturing<br>firns. The privatization receipts<br>reached $37 millions in 1997.|
|*<br>Focus on one/ few major<br>projects|Investment projects in the mining<br>sector (gold) were realized by<br>Rand Gold and Ashanti,<br>facilitated by the reform of the<br>Mining Code in 1991.|The development of the new $1.3<br>billion MOZAL aluminum<br>smelter facility.|
|*<br>Political stability|<br>In March 1991, a series of clashes<br>between the people and the army<br>culminated in the arrest of the<br>President. In January 1992, the<br>Alliance pour la democratie au<br>Mali (ADEMA), leading a<br>coalition of opposition parties,<br>established electoral dominance,<br>while its candidate was elected<br>President. He was recently<br>reelected in May 1997 for another<br>five-year term.|The General Peace Agreement in<br>1992 between FRELIMO and<br>RENAMO and the general<br>elections that followed in 1994<br>were important steps towards<br>national reconciliation and<br>stability. FRELIMO won the first<br>national election. The opposition,<br>RENAMO, retains almost 45 per<br>cent of the seats in parliament.|
|*<br>Implementation of new laws<br>and accession to international<br>agreements related to FDI|.<br>Mining Code (1991)<br>*<br>Investment Code (1995)<br>*<br>Multilateral Investment|.<br>Industrial Free Zone (1994)<br>.<br>Multilateral Investment<br>Guarantee Agency (1994)|

16
Number of observations 250 1166 1166 1166 1166 1164 1166 Note: (1) Symbol + indicates correlation statistically significant at 0.1 level; * indicates correlation statistically significant at 0.05 level; ** indicates correlation statistically significant at 0.01 level; (2) CAP support are the sum of payments for the period 2008-2013; (3) Unemployment rate data is at NUTS2 level; (4) EU15 are Belgium, Denmark, Greece, Finland, France, Germany, Ireland, Italy, Luxumbourg, Malta, Netherlands, Portugal, Spain, Sweden and the UK Table 7. Cross-tabulation of per hectare Pillar 1 support (2008-2013) and farm economic size (2010)

|Pillar 1 support per||Farm size cl|assification(1|= smallest)|||
|---|---|---|---|---|---|---|
|hectare classification (1|||||||
|= lowest)|1|2|3|4|5|Total|
|1|0|7|103|48|75|233|
||0.00%|3.00%|44.21%|20.60%|32.19%|100.00%|
|2|36|31|70|47|49|233|
||15.45%|13.30%|30.04%|20.17%|21.03%|100.00%|
|3|50|54|31|55|43|233|
||21.46%|23.18%|13.30%|23.61%|18.45%|100.00%|
|4|56|58|19|44|56|233|
||24.03%|24.89%|8.15%|18.88%|24.03%|100.00%|
|5|72|87|20|39|15|233|
||30.90%|37.34%|8.58%|16.74%|6.44%|100.00%|
|Total|214|237|243|233|238|1165|

Table 8. Cross-tabulation of per hectare Pillar 2 support (2008-2013) and farm economic size (2010) Farm size classification (1 = smallest)

|Pillar 2 support per<br>hectare classification<br>|||||||
|---|---|---|---|---|---|---|
|(1 = lowest)|1|2|3|4|5|Total|
|1|0|3|108|59|63|233|
||0.00%|1.29%|46.35%|25.32%|27.04%|100.00%|
|2|3|13|73|61|83|233|
||1.29%|5.58%|31.33%|26.18%|35.62%|100.00%|
|3|23|61|24|62|63|233|
||9.87%|26.18%|10.30%|26.61%|27.04%|100.00%|
|4|74|78|21|38|22|233|
||31.76%|33.48%|9.01%|16.31%|9.44%|100.00%|
|5|114|82|17|13|7|233|
||48.93%|35.19%|7.30%|5.58%|3.00%|100.00%|

27
Next, as an alternative to correct for differences in observed characteristics between mining and non-mining districts, we use a matching procedure based on time invariant and 1993 characteristics (which precede the mining boom). Specifically, we match producing districts with various subsamples of non-producing districts of similar characteristics using a propensity score built upon a probit regression (Table 5). The matching variables are, at the district level, the percentage of households without electricity in 1993; the illiteracy rate in 1993; the log of population in 1993; the percentage of urban population in 1993; the log of the area in square kilometers; and department dummies. We then estimate the Average effect of Treatment on the Treated (ATT) using an Epanechnikov Kernel with a bandwidth of 0.2. We obtain standard errors through bootstrapping, using 100 repetitions.

We then extend the analysis along three important dimensions. First, we go beyond average effects by exploring whether varying magnitudes of mining activity affect socioeconomic indicators differently. We do so by substituting Ipd[PD] in Equation (1) with the log of (one plus) the cumulated value of mineral production in each district between 2002 and 2006. Similarly, we substitute Ipd[NPDPP] with the log of (one plus) the value of mineral production in other districts of the corresponding province between 2002 and 2006.

Second, we refine the spatial analysis by considering the _geographic_ proximity between districts, instead of _administrative_ proximity (determined by whether districts belong to the same province). Specifically, we use mapping software to identify direct neighbors of mining districts, as well as their second and higher order neighbors. We then run the following regression specification: where, Ipd[First Neighbor] is a dummy variable that takes the value of one if a non-producing district shares a border with a producing district. Under this specification, the omitted districts (the control group) are the second and higher order neighbors (i.e., non-producing districts that are not first neighbors). This approach can be useful in two aspects. One, by focusing attention on districts that share borders and are thus more likely to be similar, it helps addressing further potential omitted variable biases. Two, it allows exploring how much geographic proximity, as opposed to administrative proximity, matters for reaping the benefits of a mine. Specifically, while under specification (1) all non-producing districts in producing provinces are treated as equals, specification (2) distinguishes between first and higher order neighbors, treating them differently even when province fixed effects are introduced.

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# **Figure 7. Growth Accounting exercise for Mongolia since transtion**

<!-- Start of picture text --> 25.00%<br>20.00%<br>15.00%<br>10.00%<br>5.00%<br>0.00%<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>-5.00%<br>-10.00%<br>-15.00%<br>Output growth TFP growth Factor growth Linear (TFP growth)<br><!-- End of picture text --> Source: Authors’ estimates based on the following assumptions: Cobb-Douglas production function with CRTS (γ=1) and capital share α=0.5.

**Table 9. Sensitivity analysis of TFP growth in 2004**

|TFP gr|owth estimates in 2004 (<br>(Cobb-Douglas)<br> <br>|%)<br>|
|---|---|---|
||α=0.3<br>α=0.4|α=0.5|
|Γ=1 (CRTS)|5.8<br>5.2|4.5|
|Γ=1.2 (IRTS)|5.0<br>4.2|3.4|
|γ=0.8 (DRTS)|6.7<br>6.2|5.7|
|TFP gr|owth estimates in 2004 (<br>(CRTS CES)|%)|
||σ=0.8<br>σ=1|σ=1.2|
|α=0.5|7.1<br>4.5|2.6|

_Source: Authors’ estimates Note: Growth rates are in log form._ Although the aggregate indicators suggest that Mongolia used capital efficiently, not all sectors enjoyed high returns to capital. Over the past two years, a number of sectors seemed to lag behind the better performing ones in the past 2 years, and in two sectors— manufacturing and transport—the returns to capital were not only low, they were negative (Table 10). Mongolia’s structure of production has evolved in a way that has increased its dependence on ores and metals (Table 11).<sup>33</sup> The share of primary commodities in total merchandise exports grew from 67 percent in 1992 to 87 percent in 2005, while the share of manufactured exports dropped from 33 percent to 13 percent (Table 12). At the same time the non-metal manufacturing base has narrowed considerably, and consists mainly of textiles and apparel.

> 33 The mining share in total value added underestimates the contribution of the sector as it does not reflect the output of the artisinal mining sector.

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TABLE 5: Impact of changes in public expenditure on changes in a child’s nutritional level by child/household characteristics

|_Dependent Variable_:|||Z-score (he|ight for age)|||
|---|---|---|---|---|---|---|
||Ru|ral IV|Statistically<br>Significant<br>Differential Impact|Urb|an IV|Statistically<br>Significant<br>Differential Impact|
||Lowest birth<br>weightquartile|Higher birth<br>weightquartiles||Lowest birth<br>weightquartile|Higher birth<br>weightquartiles||
|Total public spending|-0.038|-0.103||0.061|0.296||
||(0.041)|(0.043)*|No|(0.140)|(0.152)*|Yes|
|N|156|507||257|939||
||Malnourished<br>in 1<sup>st</sup>round|Not malnourished<br>in 1<sup>st</sup>round||Malnourished<br>in 1<sup>st</sup>round|Not malnourished<br>in 1<sup>st</sup>round||
|Total public spending|-0.131|-0.032||0.216|0.283||
||(0.045)**|(0.043)|No|(0.277)|(0.124)**|No|
|N|196|492||129|1086||
||Indigenous|Not indigenous||Indigenous|Not indigenous||
|Total public spending|-0.030|-0.103||-0.169|0.346||
||(0.086)|(0.034)***|No|(0.106)|(0.164)**|Yes|
|N|375|313||208|1006||
||Poor|Notpoor||Poor|Notpoor||
|Total public spending|-0.079|-0.054||0.019|0.239||
||(0.036)**|(0.040)|No|(0.292)|(0.114)**|No|
|N|371|317||322|892||

_Source:_ Young Lives Study Round 1 and 2, ENAHO 2002, 2006, and Ministry of Economic and Finance Budgetary Expenditure for 2000–6.

_Note_ : Robust standard errors clustered by region-year in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%. Includes regional fixed effects, individual fixed effects, and time fixed effects and all previous controls in the original regression in Table 3. Spending measured in 100 million soles. The instrument for all IV estimations is the amount of unanticipated mining revenues. The instrument is proved to be strong for every sub-sample.

32
globalization index (Dreher et al. 2000).<sup>5</sup> Political integration variable shows up as significant and positive while the social integration variable is insignificant.

Next we examine the impact of deregulation and financial liberalization on codependence. We use the database created by Abiad Detragiatche and Tressel (2010) that quantifies financial reforms over a thirty year time period. Results under models 6 and 7 show that reforms that have led to international capital liberalization and stock market liberalization have increased co-dependence. Reforms that have led to stronger bank supervision, however, have decreased co-dependence (model 8). We also examine the impact of bank concentration as measured by assets of 3 largest banks as a share of assets of all commercial banks. As mentioned earlier, there has been a substantial increase in concentration in both developing and developed countries. As the recent crisis has demonstrated, large complex financial institutions can cause systemic disruptions affecting all other financial institutions. We find concentration to increase co-dependence. This is in contrast to Beck, Demirgüç-Kunt, and Levine (2006) who show that countries with a more concentrated banking system are less likely to suffer a systemic banking crisis. Finally, we examine the impact of moral hazard on co-dependence. If there is an implicit guarantee provided by the State to cover losses stemming from a systemic crisis, banks will have incentives to take on correlated risks (Acharya 2005). Guaranteed banks will not have incentives to diversify their operations, since the guarantee takes effect only if other banks fail as well. We use the deposit insurance coverage ratio (Beck and Demirgüç-Kunt 2004) as a proxy for moral hazard. We find a positive and significant relationship between moral hazard and codependence. Overall, our results suggest that countries which are more integrated, and which have liberalized financial systems and weak banking supervision also have higher co-dependence in their banking sector.

# **4. Conclusion**

This paper examines time-series and cross-country variations in default risk co-dependence in the global banking sector. We compute weekly changes in default probabilities based on the Merton (1974) model for over 1,800 banks in over 70 countries. We show that systematic default risk has a significant global component in the banking sector accounting for 20% of the

> 5 KOF means “Konjunkturforschungsstelle” - Swiss Economic Institute.

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