rafmacalaba/datause-encoder-data huggingface dataset:
- 34 -

- DEMEOCQ, M. and GUILLAUMONT, P., 1986, "Risk and Ratchet Effects of Export Earnings Instability: a Cross-Section Analysis," Etudes et Documents Provisoires, CERDI, Clermont-Fd.

- DEVARAJAN, S. and de MELO, J., 1987, "Evaluating Participation in African Monetary Unions: A Statistical Analysis of the CFA Zones," World Development, vol. 15, no. 4, April, p. 483-496.

- FAINI, R., ANNEZ, P. and TAYLOR, L., 1984, "Defense Spending, Economic Structure, and Growth: Evidence Among Countries and Over Time," Economic Development and Cultural Change, 32 (April 1984), 487-498.

- FEDER, G., 1983, "On Exports and Economic Growth," Journal of Development Economics, vol. 12, , p. 59-73.

- FEDER, G., 1986, "Growth in Semi-Industrial Countries: A Statistical Analysis," Chapter 9 in Chenery, H, Robinson, S, and Syrquin, M, (ed.), Industrialization and Growth, 263-282.

- FISCHER, S., 1987, "Economic Growth and Economic Policy," Symposium on GrowthOriented Adjustment Programs, World Bank and International Monetary Fund, Washington, 25-27 February.

- FRY, M.J. and LILIEN, D.M., 1986, "Monetary Policy Responses to Exogenous Shocks," American Economic Review (A.E.A. Papers and Proceedings), vol. 76, no. 7, May 1986, p. 79-83.

- GASTIL, R.D., 1979, Freedom in the World, Transaction Books, New Brunswick N.J.

- GENDBERG, H. and SWODOBA, K., 1987, "The Medium-term Relationship between Performance Indicators and Policy: A Cross-Section Approach," World Bank, E.P.D. Discussion Papers, Report no. EPD-01, March 1987, 96 pages.

- GONCALVES, M. and RICHTERING, J., 1986, "Export Performance and Output Growth in Developing Countries," Discussion paper no. 17, UNCTAD, Geneva.

- GUILLAUMONT, P. and GUILLAUMONT, S., 1986, "Strategies de developpement comparees: methode d'analyse et resultats generaux", in CERDI, 1986.

- GUILLAUMONT, P., GUILLAUMONT, S. and PLANE, P., 1987, "Participating in African Monetary Unions: An Alternative Evaluation", CERDI, March.

- GUPTA, K.L. and ISLAM, N.A., 1983, Foreign Capital, Savings and Growth. An International Cross-Section Study, Reidel Publishing Company.
Table A6: Correlation between migration and land tenure arrangements: Acreage, Extended Controls

||(1)<br>fxed rent|(2)<br>fxed (money)|(3)<br>fxed (crop)|(4)<br>sharecropping|(5)<br>relatives|(6)<br>contract length|(7)<br>written contract|
|---|---|---|---|---|---|---|---|
|migration|-0.279|-0.0768|-0.202|0.0685|0.118|0.194|-0.0675|
||(0.176)|(0.157)|(0.127)|(0.182)|(0.0945)|(0.166)|(0.125)|
|bank branches (number)|0.0239|0.0209|0.00304|-0.0254|0.00423|0.0129|0.0201|
||(0.0247)|(0.0205)|(0.0124)|(0.0233)|(0.0122)|(0.0153)|(0.0123)|
|road length (km)|-0.000366|-0.000518|0.000152|0.000279|-0.000118|0.00156***|-0.000629*|
||(0.000498)|(0.000484)|(0.000299)|(0.000514)|(0.000291)|(0.000480)|(0.000371)|
|2012 year|-4.445|-1.020|-3.425|-5.512*|7.714***|21.96***|5.725***|
||(3.150)|(2.858)|(2.113)|(3.174)|(1.925)|(3.034)|(2.093)|
|Observations|722|722|722|722|722|722|722|
|R-squared|0.090|0.181|0.059|0.095|0.053|0.141|0.057|

Note: This table uses an OLS regression to present the correlation between the percentage of rented-in acreage under various terms and the number of high-skilled male out-migrants between the ages of 20-64 years (as a percentage of the total male out-migrants between the ages of 20-64 years). The population controls include total population, sex ratio, population density and share of high-skilled males. Remaining controls include the percentage of adverse rain events and the percentage of adverse temperature events. The agro-ecological controls include soils, length of the growing season (in days) and reference ETo (a measure of water stress). The dependent variable is as a percentage of acreage of rented in land. Robust standard errors. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.
# **Supporting Information**

## **S1 Hurricane Windstorm Model**

The local wind speeds experienced due to a hurricane are derived from a spatial hurricane windstorm model developed by the World Bank Group Latin America and the Caribbean Disaster Risk Management team (Pita et al., 2015). The model estimates maximum wind speeds at the height of 10 meters with a spatial resolution of 1 km<sup>2</sup> for observed hurricanes and tropical storms in Central America during our sample period. The structure of this wind field model is based on the well-known equation for cyclostrophic wind and sustained wind velocity by Holland (1980) and is calibrated for the Central America region. The model’s accuracy of the simulated wind speeds has been tested and validated with observed data. The maximum sustained wind speed experienced in a particular location due to a hurricane is given by: where _B_ is the Holland shape parameter, ∆ _p_ is the deficit of central and outer peripheral pressure (Pa), _ρ_ is the air density at the gradient height (kg/m<sup>3</sup> ), _RMW_ is the radius of maximum winds (m), _r_ is the radius of any location to the center of the storm (m), _VS_ is the hurricane translation speed measured (m/s), _f_ is the Coriolis parameter (1/sec), and _φ_ is the angle between the north and the storm heading.

The data source for the terrain roughness is the Global Land Cover Dataset 2000 and for the topography characteristics is the Shuttle Radar Topography Mission database. The method to estimate speed-up occurring in escarpments and ridges comes from the American Society of Civil Engineers in 1994 and to retrieve the wind gusts factors comes from Vickery and Skerlj (2005). For more detailed information about the hurricane windstorm model, see Pita et al. (2015).

## **S2 Implied monetary losses**

To convert the benefit of mangrove protection (mitigation of hurricane damage), measured in nightlights, into rough monetary values, we begin by calculating a conversion factor between nightlights and economic activity as measured by Gross Domestic Product (GDP). Using World Bank data on GDP in Parity Purchasing Power (PPP) in 2011 22
Table 5: Treatment Effects on Outpatients and Inactivity: Overall and Interacted with Indicators for Private and Unlicensed Health Facilities at Randomization

||Daily<br>Outpatients|Inactive|
|---|---|---|
||(1)|(2)|
|**Panel A:** _Overall Impact_|||
|Treatment|0.682<br>|0.027<br>|
||(1.629)<br>[0.676]|(0.021)<br>[0.199]|
|Observations<br>|1322|1348|
|R<sup>2</sup>|0.253|0.042|
|Control Mean|20.114|0.131|
|Impact: _{_%; SD_}_|_{_3%; 0.02_}_|_{_21%; 0.08_}_|
|**Panel B:** _Interaction with Private_|||
|Treatment|7.620**<br>|0.003<br>|
||(3.449)<br>[0.028]|(0.009)<br>[0.709]|
|Private HF at Randomization|-29.214***|0.170***|
||(3.082)<br>[0.000]|(0.025)<br>[0.000]|
|Private HF at Randomization x T|-9.321**|0.031|
||(4.239)|(0.030)|
||[0.029]|[0.299]|
|Observations|1322|1348|
|R<sup>2</sup>|0.419|0.090|
|Control Mean Public|41.424|0.000|
|Control Mean Private<br>|10.267<br>|0.189<br>|
|Impact Public: _{_%; SD_}_|_{_18%; 0.24_}_|_{_ .%; ._}_|
|Impact Private: _{_%; SD_}_|_{_-17%; -0.07_}_|_{_18%; 0.09_}_|
|Test T + Private x T = 0 (p-value)|0.365|0.238|
|**Panel C:** _Interaction with Unlicensed (Private a_|_nd active at randomization_|_only)_|
|Treatment|0.004|-0.015|
||(2.665)|(0.034)|
||[0.999]|[0.657]|
|Unlicensed HF at Randomization|-0.628<br>|0.092*<br>|
||(2.014)|(0.050)|
||[0.756]|[0.067]|
|Unlicensed HF at Randomization x T|-3.089|0.104*|
||(2.662)|(0.057)|
||[0.247]|[0.071]|
|Observations|919|944|
|R<sup>2</sup>|0.311|0.080|
|Control Mean Licensed|14.378|0.124|
|Control Mean Unlicensed|7.109|0.238|
|Impact Licensed: _{_%; SD_}_<br>|_{_0%; 0.00_}_<br>|_{_-12%; -0.05_}_<br>|
|Impact Unlicensed: _{_%; SD_}_|_{_-43%; -0.23_}_|_{_37%; 0.21_}_|
|Test T + Unlicensed x T = 0 (p-value)|0.002|0.046|

_Notes_ . Robust standard errors reported in parentheses and clustered at the market level. P- values are reported in brackets. *** (**) (*) denotes significance at 1% (5%) (10%) level. Regressions include randomization strata controls (by county and market size) and health facility level controls. HF = health facility.

23
14 higher.<sup>2 2</sup> Nevertheless, the differences between Java and the Outer Islands have been striking. They point to the critical role of the relatively labour-intensive industries for labour absorption on Java. The rapid rate of expansion of manufacturing employment - counterbalanced by the fall in agricultural employment - points to a major transformation in the labour market on Java.<sup>2 3</sup> Employment growth by major non-agricultural sector was relatively uniform in the Outer Island groups. Services (including construction) took a high share of new jobs, followed by trade and manufacturing. However, agricultural labour absorption varied substantially across province groups. It was higher in the resource and land abundant province groups but surprisingly low in Southern Sumatran provinces of Jambi, Bengkulu, South Sumatra and Lampung.

The reasons for slower rates of both aggregate and agricultural employment growth in the Southern Sumatra provinces - the regions with by far the most rapidly growing total and agricultural work force in the 1970s and 1980s - are not obvious. One possible explanation is that the rapid growth in manufacturing around Jakarta was beginning to influence labour markets in these southern Sumatran provinces. In the region of Lampung, there are appears to have been a reverse flow of migrants back to Java in search of jobs in manufacturing and services around Jakarta. This heralds an important reversal of the huge flows which took place in the other direction in the previous two decades. Rapid manufacturing growth on Java would seem to have begun to have a wider impact on labour markets Outside Java than in the previous two decades.

How did these patterns of employment growth impact on wages? The more rapid growth in manufacturing employment on Java is reflected in wage trends. Wages in all sectors except agriculture grew more rapidly on Java-Bali than in any of the other province groups (Table 8). They grew least rapidly in southern Sumatra and the other land abundant provinces.

The data point to problems experienced in the wake of large scale movement of labour into agriculture in the previous decade in southern Sumatra and land abundant provinces. The table also shows that the poorer densely/populated regions eastern

> 22 The employment elasticity implied for the manufacturing sector on Java from these figures was around 0.75, well above that recorded for the 1970s and early 1980s (around 0.5). 23 The rate of decline in agricultural employment may be overstated in the National Labour Force data for 1994. But the decline was so large that it suggests an important transformation was already underway.
**Over Time Variation.** Due to the annual change in the number of sources over time, making inferences regarding the marginal change in a country’s score over a short period of time is not advised (World Bank, 2011). Averaging the scores of countries over a few years to get a representative average for the time period is not problematic, due to the fact that sources from adjacent years are used to construct the indicator at any one time.

**Constituent Parts (data sources).** The two indicators are developed using a subcomponent of the data sources, as they are available and applicable by year. The 30 data sources can be divided into: (1) surveys of households and firms (nine sources), (2) commercial intelligence information generators (e.g. the Economist Intelligence Unit, four sources); (3) NGOs (9 sources, including Freedom House) and public sector organizations (e.g. the World Bank).

# **(iii) Control of Corruption Cluster/Governance Dimension**

The aim of this measure, like the CPI, is to capture the extent to which public policy makers abuse their public office for private gain. The aim of the index is thus (World Bank, 2010): _“...designed to capture...extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as "capture" of the state by elites and private interests.”_ Despite a formal definition that may appear to be more skewed towards political corruption, the CC, like the CPI, is primarily composed of sources, some of which are the same as the CPI, concerned with measuring both political and bureaucratic corruption. As Table 2 shows, three of the five representative sources used to construct the CC are concerned with general corruption (e.g. ‘pervasiveness of corruption’; see Table 2); one source is focused on bureaucratic corruption (‘an assessment of the intrusiveness of the country’s bureaucracy’; see Table 2) and one source is concerned with political corruption (‘is corruption in government widespread?’; see Table 2). In short, the CC is very similar to the CPI, although, unlike the CPI, one of its 14
# **1 Introduction**

During the last two decades, many developing countries have signed Regional Trade Agreements (RTAs) in the attempt to better integrate their economies into regional and global markets and improve the export performance of their firms – see Freund and Ornelas (2010); Limão (2016). In particular, out of the 190 RTAs signed during the period 2000-2020, 175 involved at least one developing country as a member. Over the same period, the content of RTAs has widely changed (Hofmann et al., 2017; Mattoo et al., 2020). The average RTA signed by a developing country at the end of the period covered roughly 30 percent more policy areas than at the beginning of the period, with issues like technical and sanitary standards, investment, and intellectual property rights protection being added to the more traditional areas covered by RTAs (such as tariff liberalization and the reduction of other border barriers). We refer to these more complex trade agreements as “deep RTAs” or simply “Deep Trade Agreements” (DTAs hereafter).

The effects of regional trade agreements on trade have been largely studied, but there remain some important gaps in the literature. In part motivated by the changing depth of RTAs, a number of studies have empirically investigated the trade effects of different types of trade agreements. A key finding of this literature is that the heterogeneity of trade agreements matters (Baier et al., 2018, 2019), and that the depth of RTAs can help explain different trade outcomes (Orefice and Rocha, 2014; Mattoo et al., 2017). At the same time, theoretical models have emphasized a different type of heterogeneity, namely that the reduction of trade costs affects different types of firms differently. Larger and more productive firms can more easily take advantage of the change in trade costs than smaller and less productive firms (Melitz, 2003; Melitz and Redding, 2015). As deep trade agreements can reduce trade costs among members by eliminating tariffs and by reducing other frictions due to regulatory differences and/or policy uncertainty, a natural extension of this literature is that DTAs should affect firms differently based on their characteristics, promoting the export performance of larger firms and leading to a selection effect.

This paper empirically investigates the effect of RTAs’ depth on the export performance of heterogeneous firms. The analysis combines firm-level export data for 31 developing countries from the World Bank Exporter Dynamics Database (Fernandes et al., 2016) and information on the detailed content of more than 300 RTAs from the World Bank Deep Trade Agreements database (Hofmann et al., 2017). The richness of the data allows to precisely take into account the heterogeneous consequences of deep RTAs on the exports of different firms. We use a decomposition à la Berman et al. (2012) to study the extent to which the effects of deep RTAs 1
rants in Kinshasa [The World Bank Reproducible Research Repository]. https://doi.org/ 10.60572/g9ks-a898

- Coad, L., Fa, J. E., Abernethy, K., Van Vliet, N., Santamaria, C., Wilkie, D., El Bizri, H. R., Ingram, D. J., Cawthorn, D.-M., & Nasi, R. (2019). _Towards a sustainable, participatory and inclusive wild meat sector_ . CIFOR.

- Crowne, D. P., & Marlowe, D. (1960). A new scale of social desirability independent of psychopathology. _Journal of Consulting Psychology_ , _24_ (4), 349.

- Dhar, D., Jain, T., & Jayachandran, S. (2022). Reshaping adolescents’ gender attitudes: Evidence from a school-based experiment in India. _American Economic Review_ , _112_ (3), 899–927.

- Edderai, D., & Dame, M. (2006). A census of the commercial bushmeat market in Yaound ́e, Cameroon. _Oryx_ , _40_ (4), 472–475.

- Fa, J. E., Wright, J. H., Funk, S. M., M ́arquez, A. L., Olivero, J., Farf ́an, M. A., Guio, F., Mayet,<sup> ́</sup> L., Malekani, D., Louzolo, C. H., et al. (2019). Mapping the availability of bushmeat for consumption in Central African cities. _Environmental Research Letters_ , _14_ (9), 094002.

- Fjeldsoe, B. S., Marshall, A. L., & Miller, Y. D. (2009). Behavior change interventions delivered by mobile telephone short-message service. _American Journal of Preventive Medicine_ , _36_ (2), 165–173.

- Foerster, S., Wilkie, D. S., Morelli, G. A., Demmer, J., Starkey, M., Telfer, P., Steil, M., & Lewbel, A. (2012). Correlates of bushmeat hunting among remote rural households in Gabon, Central Africa. _Conservation Biology_ , _26_ (2), 335–344.

- Food and Agriculture Organization, Centre for International Forestry Research, French Agricultural Research Centre for International Development, & Wildlife Conservation Society. (2019). Sustainable Wildlife Management Programme [Accessed: April 4, 2024]. http: //www.fao.org/3/ca5100en/ca5100en.pdf

34
71 on April 1, 2015 and is now levied on 97 RON gasoline. However, the price of 97 RON gasoline was not increased in April. In December 2015, the deputy finance minister reported that moving to the managed float system was expected to save RM 11 billion (US$2.8 billion) in 2015 ( _Bernama Daily_ 2015).

**Table 16: Evolution of fuel retail prices in Malaysia**

|Date|97 RON<br>RM/liter|95 RON<br>RM/liter|Diesel<br>RM/liter|LPG<br>RM/kg|97 RON<br>$/liter|95 RON<br>$/liter|Diesel<br>$/liter|LPG<br>$/kg|
|---|---|---|---|---|---|---|---|---|
|Sep 9, 2014|2.75|2.10|2.00|1.90|0.86|0.66|0.63|0.60|
|Oct 2, 2014|2.75|2.30|2.20|1.90|0.85|0.71|0.68|0.58|
|Nov 19, 2014|2.55|2.30|2.20|1.90|0.76|0.68|0.65|0.57|
|Dec 1, 2014|2.46|2.26|2.23|1.90|0.72|0.66|0.65|0.55|
|Jan 1, 2015|2.11|1.91|1.93|1.90|0.60|0.55|0.55|0.54|
|Feb 1, 2015|2.00|1.70|1.70|1.90|0.55|0.47|0.47|0.52|
|Mar 1, 2015|2.25|1.95|1.95|1.90|0.62|0.54|0.54|0.53|
|Apr 1, 2015|2.25|1.95|1.95|1.90|0.61|0.53|0.53|0.51|
|May 1, 2015|2.25|1.95|1.95|1.90|0.63|0.55|0.55|0.53|
|Jun 1, 2015|2.35|2.05|2.05|1.90|0.64|0.56|0.56|0.52|
|Jul 1, 2015|2.55|2.15|2.05|1.90|0.68|0.57|0.55|0.51|
|Aug 1, 2015|2.45|2.05|1.95|1.90|0.64|0.54|0.51|0.50|
|Sep 1, 2015|2.35|1.95|1.80|1.90|0.54|0.45|0.42|0.44|
|Oct 1, 2015|2.45|2.05|1.95|1.90|0.58|0.48|0.45|0.45|
|Nov 1, 2015|2.45|2.05|1.90|1.90|0.57|0.48|0.44|0.44|
|Dec 1, 2015|2.45|1.95|1.90|1.90|0.57|0.46|0.44|0.44|

_Source:_ Government announcements.

The Government of Malaysia has openly and repeatedly acknowledged abuses at every level of the supply chain in the form of diversion of low‐price fuels for illegal uses. In particular, out‐smuggling of fuels has been exacerbated by additional price subsidies provided to fishing vessels, which are ideally suited for smuggling. In August 2012, the government announced that a representative of the Malaysian Fisheries Development Authority would in the future verify each catch landed, and those with insufficient catch would no longer be permitted to buy lower‐priced fuel ( _Borneo Post_ 2012).

While smart cards have been used in an attempt to control sales of subsidized fuels to public transport operators and fisheries, the government has admitted their ineffectiveness. In June 2014, for example, the Public Accounts Committee (PAC) pointed out in a press conference that the fleet card for diesel (intended for public transport operators) could be used by anyone to buy an unlimited supply of diesel ( _Bernama Daily_ 2014).

In May 2014, the government launched “Ops Titik,” designed to stem illegal diversion of subsidized diesel. In announcing Ops Titik, the Domestic Trade, Cooperatives and Consumerism Minister said that the operation would be conducted jointly with the police, Customs Department, the Malaysian Maritime Enforcement Agency, the Malaysian Anti‐Corruption Commission, and local councils. The government reports that between May 15 and December 22, 2014, Ops Titik identified 516 cases of abuse and seized subsidized diesel, recovering RM 24,364,814 (US$7.5 million) (http://www.kpdnkk.gov.my/kpdnkkv3/
evidence from the analysis shows that improvements in these areas will help firms to reach to their full potentials.

Access to finance is perceived as the single most severe obstacle by firms of all sizes (see Figure 2). Especially the medium size firms are the most constrained group of firms. The stringency of labor regulations which could be a quite distortive for growth, rank relatively low among firms’ concerns. It is cited as the most severe obstacle by only 4 percent of medium size firms. Several other indicators in the survey confirm that SMEs in Turkey are dependent on bank finance.

Turkish firms are more dependent than their peers in other countries on bank finance to fund their investments in fixed assets<sup>16</sup> . This is especially true for medium size firms, for which bank finance accounts for 47 percent of total funding. However when compared to micro and large firms, applications of SMEs for bank credit are faced with onerous collateral requirements and high rejection rates. Small and medium size firms are required 100 and 91 percent of loan value as collateral in respective order (see Figure 3). Notwithstanding the higher collateral requirements, the amount of rejected loan applications is also substantially higher for SMEs (17 percent) compared to more creditworthy large firms (12 percent).

In Turkey, there are several organizations that address the financial bottlenecks to the expansion of SMEs. Organizations such as the Small and Medium Scale Enterprises Development Organization (KOSGEB), the Credit Guarantee Fund (KGF), and the union of Chambers and Commodity Exchanges of Turkey (TOBB) provide financial and/or non‐financial assistance for development of SMEs. However many SMEs are either not aware of alternative financial sources that are available to them or they have difficulties in accessing these sources. Hence expanding the reach of support schemes of these organizations would lead to significant improvements in the growth prospects of SMEs in Turkey.

> 16 See Chapter 2 of Investment Climate Assessment Report of Turkey (2010)

10
- 18 -

specification results, we can not reject the significant effect of capital flows on private investment, although the effect is not as clear as it is for consumption.

**Table 12** Results for Real Fixed Capital Formation (FCF)

|Dependent Variable|Private FCF||Public FCF||
|---|---|---|---|---|
||Without M2|With M2|Without M|2<br>With M2|
|Constant|3180|80134|969|-1551|
||(2.72**)|(0.03)|(2.43**)|(3.88**)|
|Real GDP|0.08|0.08|0.04|0.09|
||(4.60**)|(5.13**)|(2.5**)|(4.12)|
|Real Interest Rate|810|-152|-6089|-4616|
||(0.78)<br>0.14)||(-3.40**)|(-2.74**)|
|Real M2||-3.08E-09||-1.9E-09|
|||(-1.65)||(-2.85**)|
|Capital Account|-0.01|-0.04|-0.03|0.007|
||(-0.19)|(-0.82)|(-0.58)||
|AR(1)|0.91|0.99|(0.14)||
||(12.4**)||-0.40|-0.52|
||(I16.81**||(-2.25**)<br>3.08**)|(|
|Ri<br>0.37|0.88||0.89|0.22|
|Durbin-Watson Statistic|1.90||2.15|1.59|
|1.51|||||
|F-Statistic<br>3.89|61.8||51.3|2.44|
|p-value of F-Statistic<br>0.01|0.00||0.00|0.07|

Note: T-Statistics in parentheses. * :significant at 10% level, **:significant at 5% level