rafmacalaba/datause-encoder-data huggingface dataset:
# **The Economics of Natural Disasters: Concepts and Methods**<sup>**1**</sup>

Stéphane Hallegatte (Météo-France, CIRED), Valentin Przyluski (CIRED)

> 1 A previous and shorter version of this text has been published in CESifo : Hallegatte S., Przyluski V., The economics of natural disaster, CESifo Forum 2 06/2010,
- 40 -

Table 16

**TURKEY Net Foreign Asset Position of Deposit Money Banks**

||(I<br>|n million<br>|of U.S. d<br>|ollars)<br>|||||
|---|---|---|---|---|---|---|---|---|
||1990|1991|1992|1993|1994|1995|1996|1997|
|Net Foreign Assets|1,177|1,797|**2,164**|**1,340**|**5,454**|**4,670**|**1,343**|**(888)**|
|Foreign Assets|4,973|5,486|8,549|10,719|8,724|9,976|9,452|10,553|
|Cash|305|365|384|432|559|666|920|927|
|Claims on banks abroad|3,818|4,822|7,624|9,529|7,007|8,079|6,651|6,742|
|Claims on other non-resident|850|298|541|758|1,159|1,232|1,881|2,884|
|Securities and bonds|5|31|249|328|484|648|1,144|1,813|
|Credits|753|204|205|325|529|379|461|645|
|Equity participation|34|37|58|88|118|145|194|337|
|Other|58|27|29|18|28|60|82|89|
|Foreign Liabilities|3,796|**3,688**|**6,385**|**9,379**|**3,271**|**5,307**|**8,110**|**11,442**|
|Interbankdeposits|504|82|116|354|328|1,272|1,556|2,398|
|Loans|2,837|2,962|5,465|8,100|1,869|2,100|4,254|5,907|
|Non-residents FCDs|247|412|571|731|953|1,806|2,028|2,931|
|Others|209|232|232|193|121|128|271|205|

Source: own calculation based on Central Bank data.

||Table 17<br>**Commercial**<br>**Banks' Exposure**<br>**to Nonresidents**<br>**(%**of total assets)<br> <br>|
|---|---|
||Claims on nonresidents<br>Liabilities to nonresidents|
|1981|0.1<br>0.1|
|1982|0.4<br>0.3|
|1983|0.9<br>0.8|
|1984|1.8<br>2.0|
|1985|0.2<br>0.2|
|1986|6.4<br>3.8|
|1987|6.6<br>3.9|
|1988|9.8<br>4.9|
|1989|8.0<br>4.5|
|1990|8.7<br>6.6|
|1991|9.5<br>6.4|
|1992|13.3<br>9.9|
|1993|14.9<br>13.0|
|1994|16.8<br>6.3|
|1995|14.6<br>7.8|
|1996|11.3<br>9.7|
|1997|11.2<br>12.1|

Source: for 1981-1989, Atiyas and Ersel (1994); for 1990-1997, own calculation based on Central Bank data.
Table 1: Type of Vessels Serving the Caribbean (2019)

|Type of Vessel|Number of v|essels<br>%<br>N|umber of Call|s<br>%<br>|DWT(million)|%|
|---|---|---|---|---|---|---|
|||Panel A. All vess|els in the Cari|bbean|||
|Tanker|1,198|38.2%|8,602|26.5%|274.24<sup>_∗_</sup>|35.7%|
|Bulk|678|21.6%|2,434|7.5%|102.01|13.3%|
|Container ships|541|17.3%|12,350|38.1%|278.80<sup>_∗_</sup>|36.3%|
|General Cargo|468|14.9%|6,666|20.6%|50.88<sup>_∗_</sup>|6.6%|
|Liquid vessels|236|7.5%|2,330|7.2%|61.12|8.0%|
|Barges|14|0.4%|51|0.2%|0.12<sup>_∗_</sup>|0.0%|
|Total|3,135|100.0%|32,433|100.0%|767.16|100.0%|
|||Panel B. All vessel|s servingPuer|to Rico|||
|Tanker|189|52.6%|321|19.7%|14.35|36.3%|
|Container ships|50|13.9%|840|51.5%|14.91|37.7%|
|Bulk|48|13.4%|103|6.3%|5.12|13.0%|
|General Cargo|42|11.7%|288|17.6%|2.31<sup>_∗_</sup>|5.8%|
|<br>Liquid vessels|25|7.0%|71|4.4%|2.83|7.2%|
|<br>Barges|5|1.4%|9|0.6%|0.03<sup>_∗_</sup>|0.1%|
|Total|359|100.0%|1,632|100.0%|39.55|100.0%|
|||Panel C. Jones Act|vessels - LLI d|atabase|||
|Container ships|5<sup>_†_</sup>|45.5%|221|92.5%|6.15|92.3%|
|Barges|4<sup>_†_</sup>|36.4%|4|1.7%|0.03<sup>_∗_</sup>|0.4%|
|General Cargo|1|9.1%|1|0.4%|0.00|0.0%|
|Bulk|1|9.1%|13|5.4%|0.48|7.3%|
|Total|11|100.0%|239|100.0%|6.66|100.0%|
|||Panel D. Jones Act v|essels - NBIC|database|||
|Barges|8<sup>_†_</sup>|57.1%|124|37.9%|1.52*|21.0%|
|Container ships|5<sup>_†_</sup>|35.7%|192|58.7%|5.31|73.3%|
|Bulk|1|7.1%|11|3.4%|0.41|5.7%|
|Total|14|100.0%|327|100.0%|7.24|100.0%|

Note: Number of vessels, port calls, and dead-weight tonnage (DWT) of freight vessels arriving in Caribbean and Puerto Rican ports in 2019, by type of vessel and inferred JA status. Panels A.-C. are constructed from ship arrival data in the Caribbean purchased from LLI (2021). Panel D. summarizes cargo ship arrival data in PR sourced from NBIC (2024). Vessels’ JA status are identified as those that are U.S.-flagged and U.S.-owned. (Some vessels serving PR have been exempted from the JA’s U.S.-build requirement.) The NBIC data better capture barge traffic on U.S.-PR routes, while the LLI data allow comparisons with the broader Caribbean. _∗_ indicates an understatement of DWT in a category because data on one or more vessels lacked DWT information. _†_ indicates the presence of dual use JA vessels with “roll-on, roll-off” capacity for carrying vehicles.

13
- **FAO** , “La gestion de riesgos climaticos catastroficos para el sector agropecuario en Mexico: caso del componente para la atencion a desastres naturales para el sector agropecuario,” Technical Report 2014.

- **Fuchs, Alan and Hendrik Wolff** , “Concept and unintended consequences of weather index insurance: the case of Mexico,” _Working Paper_ , 2010.

- **and** , “Drought and Retribution: Evidence from a large scale Rainfall-Indexed Insurance

- Program in Mexico,” _American Journal of Agricultural Economics_ , 2011, _93_ (2), 505–511.

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- **Janzen, Sarah A. and Michael R. Carter** , “After the Drought: The Impact of Microinsurance on Consumption Smoothing and Asset Protection,” Working Paper 19702, National Bureau of Economic Research December 2013.

- **Karlan, Dean, Robert Osei, Isaac Osei-Akoto, and Christopher Udry** , “Agricultural Decisions after Relaxing Credit and Risk Constraints,” _Quarterly Journal of Economics_ , 2014, _129_ (2), 597–652.

- **Mobarak, Ahmed Mushfiq and Mark R. Rosenzweig** , “Informal Risk Sharing, Index Insurance, and Risk Taking in Developing Countries,” _American Economic Review_ , 2013, _103_ (3), 375–80.

- **OECD** , _Managing Risk in Agriculture_ , OECD Publishing, 2011.

- **Rosenzweig, Mark R. and Hans P. Binswanger** , “Wealth, Weather Risk and the Composition and Profitability of Agricultural Investments,” _The Economic Journal_ , 1993, _103_ (416), 56–78.

- **Rosenzweig, Mark R and Kenneth I. Wolpin** , “Credit market constraints, consumption smoothing, and the accumulation of durable production assets in low-income countries: Investments in bullocks in India,” _Journal of Political Economy_ , 1993, pp. 223–244.

16
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE SBEE is also affected by mispricing of services to the final consumer, though the difference between the average effective tariff and cost of production remains (table 12). Between 2005 and 2008 the average effective tariff charged by SBEE was between $0.17 and $0.18 per kilowatthour, near the average for Sub-Saharan Africa. The average operating costs, over the same period, ranged between $0.18 and $0.21 per kilowatt-hour (table 12). On average, SBEE has been able to recover only 72 percent of its operating costs, below the average cost-recovery ratio for low-income and middle-income countries (table 11).

The burden of SBEE inefficiencies, or hidden costs, has increased over time (figure 16). Total hidden costs increased from 39 percent of SBEE’s revenue in 2006 to 51 percent in 2009. In absolute terms, this represents more than a two-fold increase in losses, from $32 million in 2006 to $69 million in 2009 (table 13). Hidden costs are mainly driven by underpricing of services and to a lesser extent by distributional losses, particularly in recent years. In 2008 underpricing increased dramatically because of the escalation of productions costs, which were not offset by adjustments in tariffs.

Large inefficiencies at CEB and SBEE drained off as much as $72 million dollars in 2008, creating financial instability at the utilities. Those inefficiencies were almost

**Figure 15. Power production costs by CEB and SBBE in regional context**

<!-- Start of picture text --> 45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Operating Costs Capital Costs<br>US cents per KwH<br>Mali Niger Benin (SBEE) Togo (CEB) Cape Verde Burkina Faso Ghana Senegal Cote d'Ivoire Nigeria<br><!-- End of picture text --> _Source_ : World Bank 2009a; AICD estimates.

**Figure 16. Hidden costs of power generation**

Percentage of revenues <!-- Start of picture text --> 140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>Collection inefficiencies Underpricing Losses<br>Percentage of revenues<br><!-- End of picture text --> Source: Derived from Briceño-Garmendia, Smits, and Foster 2009; World Bank 2009a; SBEE annual report.

1.5 percent of the GDP in 2008, with CEB’s inefficiencies accounting for 1 percent of GDP.

Looking ahead, simulations suggest that, with further development of regional trade, SBEE’s longrun marginal cost would drop to $0.18 per KWh, which could represent a significant improvement over historical levels of $0.26 per KWh (figure 17a) and will be more closely aligned with current tariff levels. The benefit would come mainly from greater availability of more cost-efficient traded power, which would reduce reliance on emergency thermal generation. Nonetheless, costs of power will remain high in absolute terms. For CEB the long-run marginal cost and historical costs would be at the same level (figure 17b); hence there do not seem to be benefits from further trade.

30
Financial Regulation and Government Revenue

# **Appendix A: Compliance**

Table A1: Regulation and Compliance: Monthly Variation (Million Real Birr)

|Variable||NB|E Bills (Real B|irr)||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Lending _×_ Policy|0.258***|0.273***|0.256***|0.255***|0.254***|
||(0.00879)|(0.00992)|(0.00951)|(0.00959)|(0.00976)|
|Policy|42.91***|2.290|-51.93**|-96.10***|166.9***|
||(15.06)|(18.25)|(25.90)|(29.10)|(61.66)|
|Bank FE||Yes|Yes|Yes|Yes|
|Year FE|||Yes|Yes||
|Month FE||||Yes||
|Non-Param. Month FE|||||Yes|
|Observations|538|538|538|538|538|
|Number of banks|14|14|14|14|14|
|Adjusted _R_<sup>2</sup>|0.890|0.950|0.959|0.960|0.959|
|Mean Dep. Var.|501.9|501.9|501.9|501.9|501.9|
|SD Dep. Var.|616.6|616.6|616.6|616.6|616.6|

_Note:_ This table reports OLS estimates. The unit of observation is bank level. Robust standard errors are in parentheses. NBE Bills is the amount of bills issued by the NBE in million birr, and it is a continuous variable. Its mean and standard deviation are reported in the final two rows. The quantity of NBE Bills is regressed on “Lending _×_ Policy”, which is the interaction between Private Lending (in million birr) and a continuous variable, and the Policy dummy, taking unit value from April 2011 onward. We also report the Policy dummy per se. The null hypothesis is that this coefficient is 0.27, as prescribed by the NBE directive, and we cannot reject this. In column (1) we exploit all variation, and in column (2) only within-bank by introducing bank fixed effects. In columns (3) and (4) we also add year and month FE, respectively. In column (5) we remove parametric month and year FE and replace these with non-parametric effects for each month of each year (non-param. month FE). The adjusted _R_<sup>2</sup> of these regressions is also reported. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

22
28 Next, we turn to the coefficients of correlation between different measures of fiscal decentralization and subordination (Table 18).

**Table 18: Coefficients of Correlation between Measures of Fiscal Decentralization and Subordination in Russian Regions**

|**Covariates / Year**|**1994**|**1995**|**1996**|
|---|---|---|---|
|Ratio between local governments<br>total expenditures<br> <br>||||
|before transfers and the consolidated<br>regional budget<br>expenditures<br>-Ratio between local governments<br>total<br>revenues and the consolidated<br>regional budget revenues|0.89<br>089<br>(83)|0.80<br>080<br>_(85)_|0.76<br>0.76<br>(83)|
|Ratio between local governments<br>total expenditures||||
|before transfers and the consolidated<br>regional budget<br>expenditures<br>-Ratio between regional transfers to local<br>governments<br>and local governments<br>total expenditures|-0.33<br>(82)|-0.10*<br>(85)|-0.09*<br>(85)|
|Ratio between federal transfers to regional governments<br>and total regional expenditures<br>- Ratio between local<br>|-0.48<br>|-0.33<br>|0.004*<br>|
|governments<br>total revenues and the consolidated<br>regional budget revenues|(81)|(82)|(80)|
|Ratio between federal transfers to regional governments<br>and total regional expenditures<br>- Ratio between regional<br> <br>|_0.50_|0.60|0.53|
|transfers to local governments<br>and local governments<br>total expenditures|(80)|(83)|(82)|

Numbers in parenthesis represent the number of regions used in the correlation analysis. Unless otherwise indicated, all correlation coefficients are significant at the 99% level of confidence. * Statistically insignificant at the 95% level of confidence.

As seen from the first row of Table 18, there is a strong positive correlation between the share of local governments in consolidated regional final expenditures and the ratio of local governments total revenues to the consolidated regional budget revenues. However, the statistical link between the two measures of fiscal decentralization weakened over time, dropping from 0.89 in 1994 to 0.76 in 1996. One possible explanation of the above trend might derive from quickly expanding deficits of some but not all regional budgets: local governments' share in expenditures in such cases does not correspond to their share in revenues.

The second raw of Table 18 suggests that there is no link between the incidence of regional transfers and degree of fiscal decentralization. High dependence of local governments on regional transfers indicates neither high nor low decentralization level.

Another interesting finding of the correlation analysis is that in 1994-95 the size of federal grants to regions relative to the consolidated regional expenditures was negatively correlated with the share of local governments in consolidated regional expenditures (third raw of Table 18). In 1996, this correlation coefficient became statistically insignificant. At the same time, however, the sizes of the federal and regional transfers relative to the total regional and local governments total expenditures, respectively, remained strongly positively correlated (see the last row of Table 18).
**<u>Table 6. Inventory equation: Arellano Bond dynamic panel regression</u>**

|Sector|All||Agricul<br>Mining|ture &<br>|Manufa|cturing|Power/<br>Constru|Water/<br>ction|Retail &<br>Wholesa|<br>le|Transp|ort|Other s|ervices|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|
|βQt-1|0.239|(0.062) ***|0.214|(0.113) *|0.100|(0.137)|0.204|(0.105) *|0.056|(0.173)|0.319|(0.169) *|0.349|(0.079) ***|
|βQt-2|0.056|(0.027) **|0.029|(0.038)|-0.066|(0.041) *|-0.008|(0.048)|0.0026|(0.0453)|0.005|(0.052)|0.117|(0.041) ***|
|βQt-3|0.081|(0.037) **|||||||||||||
|βCITY|-0.056|(0.051)|0.059|(0.085)|-0.191|(0.146)|-0.249|(0.115) **|0.412|(0.355)|-0.003|(0.099)|-0.076|(0.104)|
|βBP|-0.012|(0.056)|0.040|(0.107)|0.144|(0.099)|0.101|(0.159)|-0.201|(0.166)|-0.116|(0.158)|-0.043|(0.066)|
|βMAI|-0.865|(0.436) **|-1.310|(0.776) *|-0.727|(0.468)|-1.096|(0.556) **|0.177|(0.322)|-1.008|(1.040)|-0.664|(0.694)|
|βI|-0.0004|(0.0004)|-0.051|(0.016) ***|-0.022|(0.108)|-0.033|(0.013) **|-0.0014|(0.0002) ***|-0.018|(0.002) ***|0.000|(0.000)|
|βY|0.065|(0.027) **|0.027|(0.049)|0.068|(0.042) *|-0.003|(0.046)|0.176|(0.084) **|0.168|(0.073) **|-0.021|(0.058)|
|βL|0.331|(0.044) ***|0.296|(0.096) ***|0.355|(0.087) ***|<br>0.432|(0.131) ***|<br>0.527|(0.122) ***|<br>0.104|(0.107)|0.419|(0.079) ***|
|βdiverse|-0.022|(0.044)|0.047|(0.051)|0.091|(0.203)|0.195|(0.318)|0.222|(0.232)|-0.304|(0.301)|-0.080|(0.079)|
|t=2014|||0.031|(0.034)|0.026|(0.032)|-0.039|(0.022) *|-0.007|(0.041)|0.055|(0.037)|0.004|(0.022)|
|t=2015|-0.008|(0.020)|0.018|(0.040)|0.071|(0.053)|-0.014|(0.039)|-0.051|(0.069)|0.016|(0.047)|-0.016|(0.031)|
|t=2016|0.119|(0.046) ***|0.171|(0.086) **|0.162|(0.097) *|0.060|(0.108)|0.060|(0.095)|0.097|(0.094)|0.193|(0.061) ***|
|t=2017|0.112|(0.047) **|0.178|(0.083) **|0.208|(0.099) **|0.060|(0.113)|0.127|(0.098)|0.025|(0.097)|0.140|(0.063) **|
|t=2018|0.115|(0.046) **|0.239|(0.083) ***|0.247|(0.095) ***|<br>0.074|(0.121)|0.061|(0.126)|-0.056|(0.104)|0.104|(0.069)|
|constant|3.739|(0.833)***|6.251|(1.376)***|7.104|(1.706)***|5.157|(1.216)***|7.166|(1.526)***|3.874|(1.744)**|3.194|(0.840)***|
|Obs.|17475||4014||2592||3019||1914||2296||7886||
|Group|7614||1553||1344||1335||1104||986||3451||
|Wald chi|133.02||49.7||45.1||31.93||1756.26||307.4||91.91||
|Arellano-|Bond test:||||||||||||||
|AR(1)|-6.19|***|-3.41|***|-2.4|***|-4.35|***|-1.16||-2.77|***|-5.82|***|
|AR(2)|-1.63||0.98||-1.86|*|-0.53||1.09||-1.63||-2.48|**|
|AR(3)|-1.06||-0.55||1.34||0.32||0.82||0.98||1.01||
|Hansen|||||||||||||||
|test|38.02||33.35||37.99||25.68||35.57||30.72||34.53||

Note: The dependent variable is the firm's total value of inventory in logarithm. The robust standard errors are shown in parentheses. *, ** and *** indicate the 10%, 5% and 1% statistical significance, respectively.
Our preferred model specification in column 5 shows that mothers exposed to higher doses of the INPRES program completed an additional 1.1 years of education, improved primary completion rate by 13.3 percentage points, and increased literacy rates by 13.2 percentage points. Our estimates are much larger than the estimates in Akresh et al. (2018); Duflo (2001); Mazumder et al. (2019). However, larger estimates are expected given that our study focuses on mothers living in rural Indonesia in adulthood. Given negative selection into rural areas in developing countries (Young, 2013), mothers in our study are those who are particularly disadvantaged. These disadvantages are clear from the low levels of education among mothers born in high INPRES districts. In our sample, mothers born in the high intensity districts have an average of 5.8 and 6.8 years of education in the low and high exposure birth cohorts (respectively). In contrast, the educational attainment of high INPRES districts in Akresh et al. (2018); Duflo (2001); Mazumder et al. (2019) is over 8 years across birth cohorts.

Our identification strategy assumes that there are no omitted time-varying and district-specific variables that are correlated with school construction. A key threat to identification is if improvements in educational attainment among mothers in high intense INPRES districts simply reflect mean reversion. This is plausible since program intensity was directly determined by enrollment rates in 1972. Another key threat to our identification assumption is if other governmental interventions occurred during the same time and used similar allocation as the school construction program. For example, the second largest government program during this period was for water and sanitation (Duflo, 2001). Thus, our diff-in-diff estimates may be upwardly biased if it confounds the effect of school construction with mean reversion that would have occurred in the absence of the program or with other government interventions.

To assess mean reversion, we estimate a model that includes an interaction between high exposure birth cohort and enrollment rates in 1971. Similarly, we check the sensitivity of our treatment effect estimates to other major government programs during the same period by estimating a model that includes an interaction between high exposure birth cohort and the intensity of the water and sanitation program. The inclusion of these interaction increases our treatment effect from 1.064 19
has emerged from previous studies that includes (industry-specific) values of _σ_ between 3 and 11 (see Hummels, 2001; Baier and Bergstrand, 2001; Head and Ries, 2001; Klenow and Rodríguez-Clare, 1997). Broda and Weinstein (2006), whose study includes the most comprehensive estimation of substitution elasticities among Armington varieties, make further efforts to link their results to the ‘Rauch classification’ and find that for “commodities” the estimated elasticity is about three times as large as the one for “differentiated” goods.

To check whether the majority of traded goods indeed conforms to the presumption of substitutable products, Table (5) lists the most traded product categories, in terms of HS 2-digit headings, which gives a sense of the broad economic sectors involved. It is striking to find that two broad product categories clearly dominate the top five ranks of exports to the EC over the period 1996–2001: on the one hand, machinery and electrical equipment, or vehicles, are exported at substantial volumes (HS headings 84, 85, 87) as well as textiles, apparel and footwear products (HS headings 61–64) on the other hand. Furthermore, toys (HS 95) and leather products (HS 42) figure prominently. The top six ranks featuring these sectors account for slightly more than half of total export volume.

# **{ Table (5) about here }**

With respect to the degree of substitutability, the list of most traded products accords well with the detailed study by Hummels (2001) on the value of _σ_ at the SITC 2-digit level: while most of his estimates of _σ_ range between 3 and 8, the ones for machinery, electrical equipment or apparel are at the upper bound, occasionally even attaining doubledigit numbers. It is interesting to note that already Sapir (1981) in his early analysis finds that the trade creating effects of the European GSP system mainly arose from laborintensive products with substantial preferences margins.

The sectoral analysis therefore reinforces the presumption that North–South trade is still dominated by rather homogenous products for which elasticities of substitution could 16
**Table 1: Relationship between the employment rate of older persons (55-64) and labor market outcomes of youth (15-24)**

||(1)<br>Youth<br>Employment|(2)<br>Youth<br>Unemployment|(3)<br>Youth<br>Hours of work(log)|(4)<br>Youth<br>Hourlywage(log)|(5)<br>Youth<br>Monthlyincome(log)|
|---|---|---|---|---|---|
|Employment rate of old persons|0.453***|-0.529***|-0.001|-0.003|-0.005*|
|(55-64)|(0.070)|(0.071)|(0.001)|(0.003)|(0.003)|
|Natural log of hourly income of|0.661|3.520**|0.028|||
|youth (15-24)|(1.865)|(1.658)|(0.023)|||
|Unemployment rate|||0.016***|0.033***|0.045***|
||||(0.004)|(0.010)|(0.011)|
|Poverty rate|-0.170***|0.158***|-0.003***|0.004|0.005|
||(0.062)|(0.053)|(0.001)|(0.003)|(0.003)|
|Share of employment in the|0.025|-0.029|-0.002**|-0.002|-0.002|
|agriculture sector|(0.049)|(0.043)|(0.001)|(0.002)|(0.002)|
|Share of employment in the|0.070|0.088|0.002*|0.001|0.005*|
|industrial sector|(0.081)|(0.070)|(0.001)|(0.003)|(0.003)|
|Share of youth (15-24)|0.101|-0.288*|-0.011***|-0.001|-0.006|
||(0.162)|(0.147)|(0.002)|(0.006)|(0.006)|
|Share of population with a primary|0.279***|-0.149***|0.001|0.001|0.003*|
|education or less|(0.045)|(0.042)|(0.001)|(0.002)|(0.002)|
|Natural log of GRP per capita|4.155***|-3.138***|0.044***|0.238***|0.286***|
|(constant 2010 IDR)|(0.698)|(0.760)|(0.009)|(0.027)|(0.029)|
|Growth in GRP per capita|-0.163**|0.054|0.002**|-0.004|-0.001|
||(0.070)|(0.048)|(0.001)|(0.003)|(0.004)|
|Number of observations|272|272|272|272|272|
|R-squared|0.454|0.526|0.797|0.675|0.713|
|Year dummies|Yes|Yes|Yes|Yes|Yes|

Source: Author’s calculations using Sakernas data Notes: Robust standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1.

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