rafmacalaba/datause-encoder-data huggingface dataset:
|disbursements from the<br>ependent variable is the<br> 2 and 6 is the diference<br> disbursements from the<br>d Bank exceeding 2% of<br> change). _Country FE_ is<br>e diference in log-levels.<br>presented in parentheses<br>(6)<br>**ratio**<br>0.1380***<br>(0.0432)<br>-0.0239<br>(0.1495)<br>1,645<br>0.0759<br>YES<br>YES|
|---|
|h average annual <br>ns 1 and 4 the d<br>while in columns <br>_DP)_ is quarterly <br>s from the Worl<br>nnual percentage <br>ximated with th<br>ountry-level are <br>(5)<br>**non-haven**<br>-0.0283<br>(0.0294)<br>0.1555**<br>(0.0637)<br>1,652<br>0.0908<br>YES<br>YES|
|s 22 countries wit<br>uarterly. In colum<br>n-haven deposits; <br>les are: _Aid (%G_<br>terly disbursement<br>e quarter of the a<br> changes are appro<br>s clustered at the c<br>(4)<br>**haven**<br>0.1008***<br>(0.0322)<br>0.1068<br>(0.1416)<br>1,648<br>0.1013<br>YES<br>YES|
|sample comprise<br>he frequency is q<br>ge change in no<br>planatory variab<br>dicator for quar<br> (measured as on<br>. All percentage <br>. Standard error<br>(3)<br>**ratio**<br>4.9732***<br>(1.7153)<br>-0.0013<br>(0.1498)<br>1,645<br>0.0760<br>YES<br>YES|
|results: OLS The <br>s 1990-2010 and t<br>5 it is the percenta<br> deposits. The ex<br>_bursement_ is an in<br>ge change in GDP <br>f time fxed efects<br>nd 99th percentile<br>(2)<br>**non-haven**<br>-1.5112<br>(1.0354)<br>0.1475**<br>(0.0633)<br>1,652<br>0.0918<br>YES<br>YES|
|table shows our main <br>P. The sample period i<br>sits; in columns 2 and <br>n haven and non-haven<br>ual GDP. _High aid dis_<br> the quarterly percenta<br> _Time FE_ is a vector o<br> winsorized at the 1st a<br>)<br>(1)<br>**haven**<br>3.3913***<br>(1.1541)<br>0.1221<br>(0.1406)<br>1,648<br>0.1006<br>YES<br>YES|
|**Table 2: Main results.** The <br>World Bank exceeding 2% of GD<br>percentage change in haven depo<br>between the percentage change i<br>World Bank as a fraction of ann<br>annual GDP. _GDP (% growth)_ is <br>a vector of country fxed efects. <br>The deposit and aid variables are <br>(*** p_<_0.01, ** p_<_0.05, * p_<_0.1<br>Aid (%GDP)<br>High aid disbursement<br>(Aid >2% GDP)<br>GDP (%growth)<br>Observations<br>R-squared<br>country FE<br>time FE|

24
program, and households where no member registered for the work-ready pool. In addition to a household survey, a short community survey was also carried out with village town officers and other community leaders in the villages from which households were drawn. This paper uses the baseline survey, conducted between October 2007 and April 2008.

Our survey has near national coverage, covering Tongatapu, Vava’u and ‘Eua. Collectively these three islands contain 90 percent of the population and 92 percent of the RSE workers (Table 1). The design of the sample was complicated by the rolling recruitment of workers, and the fact that with no country-specific quotas under the RSE, it was not known ex ante how many Tongans would be selected for the scheme. We therefore based our choice of villages on lists obtained from the Tongan Labor Ministry, which contained the names of the RSE workers and the islands and villages they were from.<sup>5</sup> The town officer in each village then provided directions to households with RSE workers. In each of these villages we also used the town officers to identify households with RSE applicants who were part of the work-ready pool but who had not yet been selected, and we additionally surveyed households where all members were nonapplicants. In each village we aimed for approximately five households with a RSE worker, three households with a member of the work-ready pool who was not selected, and four households with a non-applicant.

We follow common survey practice in defining a _household_ as a group of people sharing expenses and living together. Mean household size is 5.2 individuals, and 82 percent of households in our sample are nuclear households consisting of a head, spouse,

> 5 Due to the short time frame between recruitment and travel to New Zealand, we also interviewed 37 RSE workers at Auckland airport as they were arriving in New Zealand, and some households whose members had just left for New Zealand.

- 8 -
**Figure 1: Distribution of local-area remoteness and the geographical scale of dispersion**

|||60%<br>80%||||
|---|---|---|---|---|---|
||FSM|0%<br>20%<br>40%||||
||||0<br>250|500<br>750|1000 1250 1500 1750 2000 2250 2500 2750|
||Kiribati|0%<br>20%<br>40%<br>60%<br>80%||||
||||0<br>250|500<br>750|1000 1250 1500 1750 2000 2250 2500 2750|
||mon<br>d|s<br><br>40%<br>60%<br>80%||||
||olo<br>l|sa<br><br>20%||||
||S<br>I|0%||||
||||0<br>250|500<br>750|1000 1250 1500 1750 2000 2250 2500 2750|
|||80%||||
|||60%<br>||||
||MI|40%||||
|**are**|R|20%||||
|**sh**||0%||||
|**ion**|||0<br>250|500<br>750|1000 1250 1500 1750 2000 2250 2500 2750|
|**t**||80%||||
|**Popula**|anuatu|20%<br>40%<br>60%<br>||||
||V|0%||||
||||0<br>250|500<br>750|1000 1250 1500 1750 2000 2250 2500 2750|
|||80%||||
|||60%||||
||alu|40%||||
||uv|20%||||
||T|0%||||
||||0<br>250|500<br>750|1000 1250 1500 1750 2000 2250 2500 2750|
|||80%||||
||i|40%<br>60%<br>||||
||ij|2||||
||F|0%||||
|||0%||||
||||0<br>250|500<br>750|1000 1250 1500 1750 2000 2250 2500 2750|
|||80%||||
|||60%||||
||nga|40%||||
||To|20%||||
|||0%||||
||||0<br>250|500<br>750|1000 1250 1500 1750 2000 2250 2500 2750|

**Local-area remoteness (km)**

10
terms of the private-credit-to-GDP ratio, some countries from the CEE region had reached levels typical of some euro area countries.

**Chart 5. Private credit ratios in selected EU Chart 6. Financial and banking sector assets countries (as % of GDP; 2007 Q4)**

<!-- Start of picture text --> Romania<br>Poland<br>Slovak Republic<br>Czech Republic<br>Lithuania CEE countries<br>Hungary<br>Bulgaria<br>Slovenia<br>Latvia<br>Estonia<br>Finland<br>Belgium<br>Greece<br>Netherlands<br>Ireland<br>Cyprus<br>0% 50% 100% 150% 200% 250%<br><!-- End of picture text --> Source: IMF IFS, authors' calculations <!-- Start of picture text --> 180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>2005 2006 2007 2008 2009 2010<br>Financial sector assets (as% of GDP)<br>Banking sector assets (as % of GDP)<br>Share of banking sector in financial sector (%)<br><!-- End of picture text --> Source: CNB The Czech financial system is bank-based, similar to those in other European countries. While the depth of financial intermediation measured by the total assets of the financial sector in percent of GDP steadily increases, reaching 156 percent in 2010, the share of banks in the financial sector remains stable at around 75 percent (Chart 6). Compared to the euro area, where the financial sector assets account for some 570 percent of GDP, financial sector assets in the Czech Republic are still relatively low. The stock market is shallow in the Czech Republic, with capitalization of around 38 percent of GDP in 2010 (25 percent in 2003).

While the majority of loans to the private sector come from local banks, nonbank financial intermediaries (such as other companies engaged in lending or in leasing companies) also play some role (Chart 7). In 2004 their share in the loans to the nonfinancial private sector was almost 20 percent. However, increasing competition from banks and tax changes in past three years (which made leasing financing less favorable than earlier) have contributed to the decline of the relevance of these institutions. In 2010, their share in the loans to the nonfinancial private sector reached only 11 percent.

A remarkable increase in loans can be seen to the real estate industry, due mainly to the activities of real estate developers (Chart 8). Companies active in real estate markets now have an even higher share of loans than do manufacturing companies.

Direct cross-border credit to the private sector also plays a role in the Czech Republic, mainly in the case of nonfinancial corporations. Due to the large share of foreign ownership in the corporate sector resulting from past foreign direct investment, corporations often use intragroup financing directly from their parent companies abroad. An analysis of loans taken by nonfinancial corporations reveals that in 2010 slightly more than 50 percent of credit came from the domestic banks while the rest was from other nonbank financial intermediaries, from other (domestic) nonfinancial corporations, and from nonresidents, which include both parent nonfinancial companies abroad as well as direct cross-border loans from foreign banks (Chart 9).

5
In (7), the first two terms comprise the ordinary net present value of the infrastructure. The third term constitutes present discounted emissions costs due to infrastructure operation; and the last term is the present value of the retrofit cost when done at time _t_ * ; both the two last terms are discounted from the time of infrastructure investment.

A retrofit as noted removes<sup>_E_</sup> _t_<sup>from all periods beyond the retrofit time. (7) is concave</sup> with respect to _t_ *. Taking the derivative yields the first-order condition<sup>7</sup> The solution for _t*_ is given a general retrofit function _K(E)._ Bearing in mind that _t_ * ≥ 0 , we need to have

> If (10) did not hold, the “retrofit cost” _K_ would be so low that a “retrofit” would be chosen already at _t_ = 0 (the time of initial investment); and our model would not apply.

> 7 An alternative and more intuitive way to derive this condition in discrete time requires considering the discounted value of climate damages caused by one (infinitesimal) unit of installed energy capacity for one (small) discrete time unit at time _t_ * , found from (4). The current interest cost of this instalment is simply _rK_ '( _E_ ) . The optimal solution is found at the point where these two quantities are equal, which yields (9).

10
14 liabilities consist of currency held outside the banking system plus demand and interest-bearing liabilities of banks and nonbanks financial intermediaries. We use the King and Levine (1993 a,b) measure of financial depth, DEPTH, to evaluate whether stock market<sup>development is</sup> significantly correlated with growth even after controlling for financial depth.

## _D. Stock market data and summnary statistics_

We have data on stock market size and liquidity for 41 over the 1976-1993 period. The data are from the International Finance Corporation's (IFC) _Emerging Market_<sup>_Data Base_</sup> (EMDB) and the International Monetary Fund's (IMF) _International Financial_<sup>_Statistics._</sup> For the IAPM pricing error indicator, we only have data for 24 countries over the 1976-1993<sup>period.</sup> Specifically, we have data for the following countries, where a "*" indicates that these<sup>countries</sup> are also in the 24 country sample with pricing error data: Argentina(*), Australia(*),<sup>Austria,</sup> Belgium, Brazil (*), Canada, Chile(*), Colombia(*), Germany, Denmark, Spain, Finland,<sup>France,</sup> United Kingdom (*), Greece(*), Hong Kong, Indonesia(*), India(*), Israel, Italy,<sup>Jordan(*),</sup> Japan(*), Korea(*), Luxembourg, Mexico(*), Malaysia(*), Nigeria(*), The<sup>Netherlands, Norway,</sup> New Zealand, Pakistan(*), Philippines(*), Portugal(*), Singapore, Sweden, Thailand(*), Turkey(*), Taiwan(*), United States(*), Venezuela(*), and Zimbabwe(*).

# III. Stock Market Development and Long-Run Economic Growth

## _A. Cross-country growth regression framework_

**Structure:** This section empirically evaluates whether the index of stock<sup>market</sup> development, STOCK, is strongly linked to long-run economic growth.<sup>To conduct this analysis,</sup> we use pooled cross-country, time-series growth regressions. Each country<sup>has two observations,</sup> data permitting.<sup>5</sup> The first observation for each country uses data from the 1976-1985<sup>period.</sup> The second observation uses data from the 1986-1993 period. Thus, the dependent<sup>variable,</sup> GROWTH, is the real per capita growth rate averaged over the relevant period.

5 The maximum potential number of observations is 82. In fact, we have 79 observations because<sup>we are missing</sup> data on Turkey, Pakistan, and New Zealand for the 1976-1985 period.
the poor use them: in Punjab, in villages with private schools, the share of private schooling in total enrollment for the _poorest_ one-third is 15 percent and among the richest one-third 29 percent. Remarkably, for the country as a whole, _the fraction of private enrollment for the poor in villages where there are private schools (13 percent) is greater than the share of private enrollment (11 percent) for the rich in villages where there are none._ Finally, the existence of private schools is strongly associated with greater female education. In both Punjab and NWFP, there is a large and significant difference of close to 20 percentage points in overall female enrollment in villages with private schools compared to those without. Comparisons of the gender-ratio in private and public schools show that a dramatic impact of the penetration of private schools is that there is a smaller gender gap in private school enrollment across the country—the share of female enrollment in private schools is consistently 3-5 percentage points higher than in government schools in educational data from all available sources.<sup>16</sup> The LEAPS data provide further evidence on the association between enrollment and the presence of private schools by looking “inside” villages and contrasting enrollment patterns in settlements with and without private schools.<sup>17</sup> Figure 3 shows male and female enrollment in settlements with and without private schools for the 112 villages in the survey. There is a 21 percentage point increase in male enrollment and a 29 percentage point increase for female enrollment (which is lower in every category) in settlements with private schools compared to those without and a 29 percentage point increase for female enrollment.

Table 2 looks at the association of school-presence and enrollment in a multivariate regression context. The first two columns presents differences between households located in settlements with and without private schools; the first column is a cross-section regression while the second introduces village-level fixed effects. As expected, enrollment increases sharply with age but the increases taper off as the child becomes older (the negative coefficient on the square term in age). Also as expected, the income and education of the household significantly increase enrollment levels for children.

> 16 The difference using the PEIP census and EMIS numbers is 37 percent vs. 42 percent in 1999-00 and 40 percent vs. 44 percent in PIHS 2000 data.

> 17 Using geographical markers, we divided every village into “settlements”; for the sample of 112 villages, there are 215 settlements.

11
|**Table F4: Gende**|**r Disaggregated Im**<br>(1)|**pact of COVID-19 Scho**<br>(2)|**ol Closures on Care**<br>(3)|**if Working from**<br>(4)|**Home**<br>(5)|(6)|
|---|---|---|---|---|---|---|
||Who looks|after the children or de<br>|pendent(s) most if y<br>|ou are quarantin<br>|ed and/or working f<br>|rom home...|
||Self|They take care of<br>themselves|Non-household<br>member care|Spouse or<br>partner care|Household<br>member care|Other Care|
|Female Respondent|0.281***|0.015|0.008|-0.277***|0.002|-0.002|
||[0.02]|[0.01]|[0.01]|[0.02]|[0.01]|[0.01]|
|School closure due to COVID-19|0.002|-0.012|-0.003|0.009|0.010|0.001|
||[0.01]|[0.01]|[0.00]|[0.02]|[0.01]|[0.01]|
|School closure due to COVID-19*Female|0.003|0.004|0.002|-0.019|0.005|0.000|
||[0.02]|[0.01]|[0.01]|[0.02]|[0.02]|[0.01]|
|Academic Break|-0.009|-0.006|0.002|0.016|0.007|-0.006|
||[0.01]|[0.01]|[0.01]|[0.02]|[0.01]|[0.01]|
|Academic Break*Female|0.001|0.007|-0.006|-0.026|0.013|0.006|
||[0.02]|[0.01]|[0.01]|[0.02]|[0.02]|[0.01]|
|Change in movement workplaces|-0.001**|-0.001***|-0.000|0.002***|-0.001|-0.000|
||[0.00]|[0.00]|[0.00]|[0.00]|[0.00]|[0.00]|
|Change in movement retail & recreation|0.001***|-0.000|-0.000|-0.001***|0.001**|0.000|
||[0.00]|[0.00]|[0.00]|[0.00]|[0.00]|[0.00]|
|GDP per capita (Ln)|0.010**|0.006*|-0.001|0.011**|-0.029***|-0.001|
||[0.00]|[0.00]|[0.00]|[0.01]|[0.00]|[0.00]|
|Constant|0.062|-0.026|0.037|0.410***|0.413***|0.039*|
||[0.05]|[0.04]|[0.03]|[0.06]|[0.05]|[0.02]|
|Observations|34327|34327|34327|34327|34327|31370|
|Adjusted R-squared|0.14|0.01|0.01|0.12|0.04|0.00|
|<br>SC+SC*FEMALE(Coeff)|0.01|-0.01|-0.00|-0.01|0.01|0.00|
|<br>SC+SC*FEMALE(SE)|0.02|0.01|0.01|0.02|0.01|0.00|
|SC+SC*FEMALE(P-value)|0.77|0.37|0.91|0.52|0.27|0.88|
|Mean Male<br>Notes:* significant at 10% level, ** significant at 5% le|0.14<br>vel, *** significant at|0.07<br>1% level.|0.03|0.47|0.18|0.02|

[1] Female is a dummy variable where 1 = female-led business and 0 if male-led business. School Closure due to COVID-19 is a dummy variable where 1 = schools closed in country at time of survey wave.

[2] OLS pooled regressions include Region of the world and Time Fixed Effects and full set of controls as indicated at the bottom of the table.

[3] Outcomes for individuals who are responsible for care within the household.

72
The term _Tij,t_<sup>_k,s_isthematrixofpossiblytime-varyingbilateraltradecostvariablesand</sup><sup>_α_</sup> _t_<sup>_k,s_</sup> _φk,s_ (1 _−σk_ ) is the associated vector of coefficients: � _t_<sup>_ks_</sup> _ij,t_ � _≡_ exp _αt_<sup>_k,sT_</sup> _ij,t_<sup>_k,s_</sup> ; where the empirical � � parameter _φ_<sup>_k,s_</sup> measures the elasticity of ‘true’ trade costs with respect to the ‘observed’ ones, and is allowed to vary by type of good and buyer. The _m_ and _e_ terms denote importer-year and exporter-year fixed effects, each specific to a buyer and type of good. To avoid collinearity and consistently with the structural gravity model in eq (2) (see Anderson and Yotov, 2010b), we normalize _exp_ ( _m_<sup>_k,s_</sup> _USA,t_<sup>) = 1</sup><sup>_⇒P k,s_</sup> _USA,t_<sup>= 1inallourestimations.16</sup> In specifying the trade cost function, we follow two approaches. First, we include time-invariant determinants of trade barriers that have been extensively used in the literature – in addition to time-variant and policy-driven variables including measures that capture changes in trade barriers specific to the public procurement market. In this case, the trade cost function is specified as follows: where the _SMCTY_ indicator equals one if _i_ = _j_ , i.e. if the the trade flow is internal. The coefficient _β_ 1 thus identifies the (partial) border effect, i.e. how much trade within national borders is different from trade with other countries, controlling for other bilateral determinants of trade and for multilateral resistance terms. We control for a standard set of other time-invariant determinants of bilateral trade. The variable _DIST_ is the the log of the population-weighted bilateral distance (Mayer and Zignago, 2011). _CONTIG_ is a dummy equal to one if the two countries in the pair share a border, _COLONY_ equals one if the two countries share colonial history, _LANG_ equals one if the two countries share an official language, and _LEGAL_ is a dummy for common legal origin. These variables are sourced from CEPII (Mayer and Zignago, 2011).

The other determinants of trade costs are variables that measure participation in trade agreements, with a focus on trade policies related to government procurement. The _TANOPROC_ indicator equals one if the two countries in the pair are part of a TA at time _t_

> 1 _−σk_

> 16Given our structural interpretation of the model, the normalization implies that � _Pj,t_<sup>_k,s_</sup> � = _Ej,t_<sup>_k,s/E_</sup> _USA,t_<sup>_k,s_exp</sup> � _m_<sup>_k,s_</sup> _j,t_ �. It follows that _PUSA,t_ = 1.

12
# _C. Robustness checks_

The literature makes a distinction between military and non-military foreign interventions (e.g., interventions through diplomacy and trade sanctions), but most studies do not distinguish between different types of external military interventions. This paper focuses on non-neutral and non-humanitarian foreign military interventions (Int_nh), but in this section we turn our attention to another type of foreign military intervention – an intervention that is neutral, i.e. designed not to favor one warring group over another, and that is done for humanitarian reasons. Our hypothesis is that neutral and humanitarian military interventions (NH) are not significantly associated with conflict. We test this hypothesis by including in our model (8) both types of interventions: Int_nh and NH.

**Table 6 Logit Regressions for the Incidence of Civil Wars (PRIOCW)** **<u>(1965-1999)</u>**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|LGDPPC|-0.272|-0.351|-0.355|-0.296|-0.376|-0.380|
|LPOP|0.397***|.0.418***|0.419***|0.453***|0.477***|0.477***|
|PRIMEXP|-1.707|-1.423|-1.380|-2.678|-2.476|-2.428|
|MOUNTAINS|-0.001|-0.002|-0.002|0.004|0.003|0.003|
|NONCONT|0.430|0.355|0.364|0.546|0.372|0.369|
|DEMOCRACY|0.094|0.159|0.152|0.568|0.687|0.683|
|ETHFRAC|-0.054|-0.329|-0.345|0.366|0.123|0.125|
|ETHPOL|**2.450****|**2.672*****|**2.909*****|**2.120****|**2.243****|**2.378****|
|RELFRAC|-2.095|-1.877|-2.055|-2.821|-2.736|-2.854*|
|RELPOL|1.851|1.208|1.290|2.410|1.733*|1.797|
|Int_nh|**1.473*****|0.349|0.770|**1.240*****|-0.222|0.011|
|<br>Int_nh ˟ RELPOL||1.918*|1.943*||**2.428****|**2.433****|
|Int_nh ˟ ETHPOL|||-0.755|||-0.416|
|NH|1.011**|0.741|1.176|0.944*|0.078|0.389|
|NH ˟ RELPOL||0.510|0.510||1.404|1.366|
|NH ˟ ETHPOL|||-0.792|||-0.519|
|MENA||||2.117**|**2.220****|**2.200****|
|ASIA||||1.033|1.271|1.260|
|AFRICA||||0.855|0.875|0.854|
|LAAM||||0.348|0.378|0.356|
|Intercept|-8.030***|-7.497***|-7.618***|-9.758***|-9.246**|-9.275**|
|<br>N|859|859|859|859|859|859|
|McFadden’s R<sup>2</sup>|0.184|0.191|0.192|0.219|0.231|0.231|
|McFadden’s Adjusted R<sup>2</sup>|0.148|0.150|0.145|0.172|0.178|0.173|
20 58.

58. Both wind and solar electric pumps convert low density power flows into pumped water, Conversely, a diesel pumpset is designed to convert a dense form of potential energy into a relatively large mechanical torque on demand. The smallest diesel engines deliver about 2.5 kW of power at rated load, while many small-scale pumping applications could be satisfied with less than 500 Watts of power, four hours daily. Consequently, in many small village water supply and irrigation applications, diesel pumpsets supply only a fraction of the water they are rated to deliver over their lifetimes. Because of the technical lower limit to diesel pumpset size, photovoltaic and windpumping technologies carve out an economically competitive niche in small scale mechanized water supply.

59. Figure 8 shows representative village water supply costs at 40m head provid5d by diesel, photovoltaic, and wind energy sources." Because fuel accounts for only 5-20% of total water costs, the diesel cost curves are largely insensitive to fuel price changes. In comparison to PV pumpsets and windpumps, the hypotheticai 67% drop in diesel fuel price from _45e_ to 15¢/liter makes diesel pumping economically competitive with PV and wind at 12% and 21% smaller village populations.

<!-- Start of picture text --> Irrigation Water Costs<br>34 m3/ha/day Head = 8m<br>Power source costs only ($/m3)<br>0.5 _ f Diesel 0 l5c/1<br>- Diesel e 45c/1<br>0.4 W i Fnd 0 3m/sec<br>W l ind 0 4m/sec<br>X x- pV O 4kWh/m2<br>0.3 \ PV @ 5kWh/m2<br>02 :<br>0.1<br>0 0.5 t 1.5 2<br>Irrigated Area (hectares)<br><!-- End of picture text --> Figure 9 Irrigation Water Supply Cost: PV, Wind & Diesel 22/ Water cost curves generated by the World Bank Handpump Model, Urban Water Supply Department. Water costs include annualized costs of each pumpset, the well, and storage. Assumptions for village water supply and irrigation models are presented in the Annex.