rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 8310

# **Abstract**

Although there is wide recognition of the negative consequences of policy volatility for countries’ long-term economic growth, there is limited empirical work on this subject. One of the reasons is the difficulty of measuring policy volatility over long periods of time, especially in developing countries. This paper contributes to this literature by constructing a proxy for policy volatility that exploits the information content of the priorities conveyed in presidential speeches.

The study creates a policy volatility measure using a Latent Dirichlet Allocation algorithm on a novel data set of 953 presidential speeches in 10 Latin American countries and Spain. The paper shows that the proxy for policy volatility is negatively correlated with long-term growth over 1940–2010. The results are robust to a large set of changes in the construction of the proxy for policy volatility.

This paper is a product of the Poverty and Equity Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at ocalvogonzalez@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
SMEs do not differ from male-headed non-SMEs in productivity performance, but different from the world average, female-headed small firms in Africa also do not suffer from significant productivity disadvantage.

# **Interactions with country-level cultural and institutional environments**

Countries differ tremendously in their cultural and institutional environments, which could facilitate or hamper female business leaders. In Table 13 we thus interact FemHead with three types of variables: (i) the indicators on gender equality; (ii) the equalizing institutional environment as indicated by the rule of law; and (iii) the cultural environment as indicated by individualistic orientation of the country. The other control variables are the same as before. Since we control for the country-level fixed effects, we do not directly control for the above country-level indicators.

The female leadership effect on productivity performance does not hinge on indicators on gender equality, as shown by the consistent insignificance of the interaction terms of FemHead with the three indicators of gender equality. Thus, gender discrimination does not seem to hinder female business leaders in firm performance once they are in the leadership position. The effects of gender discrimination on business leaders are likely in selecting whether women become business leaders.

The female leadership effect on productivity level is significantly stronger in countries with a better rule of law. Increasing the rule of law from below to above the median (i.e., Good Rule of Law from zero to one) is associated with a reduction in the labor productivity effect of female leadership from -0.217 to -0.109, or a reduction by half, a large effect. This finding is consistent with the personal network hypothesis that the rule of law mitigates the effect of women leaders’ lack of personal networks and therefore reduces their disadvantage.

The female leadership disadvantage in productivity level and growth is significantly higher in countries with stronger individualistic orientation. Increasing the individualism score of a country from below to above the median is associated with an increase of LP disadvantage from 4 to 26 log points, and the LP Growth disadvantage from non-existent to 3.2 percentage points. Thus, only in highly individualistic societies do we observe female business leadership disadvantage. This finding is consistent with the 21
2 of table 1 illustrates that by the end of the project in 2015, some 63 percent of cultivated land and 22 percent of degraded land had been covered by SWC measures and 8 percent of the project area with tree plantings. The cumulative area ultimately affected by the measures (see appendix table 1) suggests that, after a slow start in 2009, when there was activity on only about 3 percent of the project area (2.5% of cultivated land, 0.4% of degraded land, and 0.1% of newly planted area), activity picked up, to achieve the totals shown in table 1.

Data on the precise location and timing of project interventions are unfortunately not available. Instead, for each type of intervention we have annual data on area affected at the micro-watershed level, which we use in the regressions as a proxy for treatment intensity, as detailed below.<sup>7</sup> Using a DID strategy, variations over time in micro-watershed coverage, together with the geographic discontinuity created by microwatershed boundaries, can provide a basis for identifying project impacts on vegetation cover and soil moisture. The impact of such activities can then be evaluated beyond the effects of the project’s planning and institution-building components or other initiatives (possibly building on institutional capacity created by TBIWRD) to establish SWC structures. The estimated effects of SLM investment are thus likely to be conservative.<sup>8</sup> Rainfall data used in this study are based on the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), which integrate 0.05° (approximately 5 km) resolution satellite imagery with ground-based weather station data to create gridded rainfall time series with 5-day temporal resolution (Funk et al. 2014). Daily rainfall was also measured at the five hydro-stations mentioned earlier; figure 3 shows the rainfall patterns in these areas during the project period. The basis for aggregating rainfall for regression purposes is Ethiopia’s three seasons (Ethiopia National Meteorology Agency 2017).<sup>9</sup> As table 1, panel 3, illustrates, the study areas receive some 1,500 mm of rain annually: 77 percent in the rainy season, 15 percent in the pre-rainy season, and 8 percent in the dry season. Control and treatment areas are very similar in terms of mean elevation (2,464–2,522 m) and slope (11.28° and 11.76°), and, as shown in table 2, columns 7 and 8, seasonal rainfall patterns.

> 7<sup>While specific interventions may be reported in different units (e.g., the length of terraces constructed is in km rather than ha) for reporting</sup> purposes, local development agents convert these into the area affected.

> 8<sup>One wayto correct for such bias could be to select control areas from outside the Tana-Beles sub-basins and use matching techniques, but there</sup> would be no way to account for differences in watershed demographic profiles, cultural characteristics, and landscapes..

> 9 Each season covers four months: October 1 to January 31 is the dry season ( _Bega_ ), February 1 to May 31 the pre-rainy season ( _Belg_ ), and June 1 to September 30 the rainy season ( _Kiremt_ ).

9
# **Table 4: Socio-demographic and other selected participant characteristics at baseline, by actual treatment status**

||||Peer adher|ence sup<br>|port only|Peer adhere<br>|nce and<br>|nutritional|||
|---|---|---|---|---|---|---|---|---|---|---|
||Control|[1]||[2]||su|pport [3]||Total||
||mean|n|mean|n|p-value[a]|mean|n|p-value[b]|mean|n|
|Age (years)|37.4|63|37.8|153|0.385|37.8|183|0.392|37.7|399|
|Female (%)|73.0|63|77.3|150|0.251|76.5|179|0.288|76.2|392|
|Education:|||||||||||
|None|4.8|3|0.0|-|**0.010**|4.3|8|0.425|2.7|11|
|Primary|20.9|13|33.7|51||24.5|45||27.5|109|
|Some secondary|59.6|37|47.6|72||48.6|89||50.0|198|
|Grade 12/post-secondary|14.5|9|18.5|28||22.4|41||19.7|78|
|_Total_|_100.0_|_62_|_100.0_|_151_||_100.0_|_167_||_100.0_|_396_|
|Marital status:|||||||||||
|Single|61.9|39|72.3|110|0.240|71.2|129|0.259|70.2|278|
|Not cohabiting with partner|9.5|6|9.2|14||9.9|18||9.6|38|
|Cohabiting with partner|28.5|18|18.4|28||18.7|34||20.2|80|
|_Total_|_100.0_|_63_|_100.0_|_152_||_100.0_|_181_||_100.0_|_396_|
|Median pre-ART initiation CD4 [IQR]|117.0|59|120.5|144|0.362|136.0|168|**0.059**|127.0|371|
||[63-172]||[72-179]|||[80-186]|||[72-183]||
|Mean monthlyincome [ZAR]|439|63|416|150|0.401|447|179|0.460|434|392|

_Source_ : Authors’ own calculations.

26
designed correctly – improve student learning. Massive amounts of government resources are funneled into training programs. Of 171 World Bank projects with education components between 2000 and 2012, nearly two-thirds included professional development to support teachers. Despite the significant resources spent on in-service teacher training programs, rigorous evidence on the effectiveness of such programs remains limited. Overall, evidence for the small share of programs that have been evaluated is mixed, and it is often reported that most current teacher education programs are outdated and over-theoretical. At the same time, many evaluations fail to provide sufficient details on the actual content or delivery mechanisms of the trainings to inform the design of successful programs.

This paper focuses on in-service training, motivated partly by the fact that it faces fewer institutional constraints to change in many countries than do mechanisms that improve teacher practice at pre-service, although the latter are also imperative to teaching quality. In other words, it is often easier for a government to implement an innovative in-service teacher training program than to reform pre-service education, because the latter relies on more institutions outside the Ministry of Education. Furthermore, in-service teacher training policies tend to receive support from teachers’ unions, and provide an opportunity to improve the quality of both existing teachers (the stock) as well as future teachers (the flow).

Nonetheless, there is a sizeable evidence gap when it comes to whether resources spent on in-service teacher training are improving learning, partly owing to a lack of instruments designed to measure teacher professional development. Instruments exist to capture the design of teacher policy – SABER Teachers (World Bank, 2013) – on one end of the policy-practice spectrum, and how teachers behave in the classroom – Stallings (Stallings, 1977), the Classroom Assessment Scoring System or CLASS (La Paro & Pianta, 2003), and others – at the other end of the spectrum. However, to date, there exists no instrument to capture the step between teacher policy design and 3
# **1. Introduction**

Over the last three decades, international trade in services has outpaced the growth in merchandise trade. As a share of world GDP, services trade increase from 7.7% of GDP in 1990 to 12.9% in 2017. Over the same period, merchandise trade in goods also expanded, albeit more slowly, from 30.9% to 44.1%, so that the ratio of services to merchandise trade grew from 25.0% to 29.3%.<sup>2</sup> Today, there is rising evidence that the emergence of a digital economy that facilitates a shift from trading in “atoms and molecules” to trading in “bits and bytes”, paraphrasing Quah (1996), has given further impetus to the possibility of exchanging services between countries.

In this paper we explore the way in which digital technologies affect world trade in services, by looking specifically at the case of international tourism. Within services, tourism stands out as having a sizable share of internationally traded services. Worldwide, in 2017, travel services stood at 25.6% of all imported services, having grown steadily since the onset of the Great Recession. While the latter figure is blurred by the relative performance of other internationally traded services — in particular, by the performance of financial services trade before and after 2008 — traveler flows attest to the rapid growth of tourism services and reached 1,341 million visitors in 2017.

The advent of digital tools has had a noticeable impact on tourism. A growing number of travelers plan their trips relying on online travel agencies (OTAs), digitally user‐generated content (UGC) and other digital tools. It is estimated that, in 2014, 59% of trips by EU residents traveling internationally relied on digital tools to book accommodations, and 67% for air transportation.<sup>3</sup> In the United Kingdom, the share of travelers using online accommodation services grew from 42% in 2007 to 52% in 2017.<sup>4</sup> Digital travel sales are expected to grow from US$471 billion to US$818 billion by 2020.<sup>5</sup> The flip side of the use of digital tools to plan trips and purchase travel services can be seen in the sharp decline in the number of physical travel agencies. For example, in the United States, the number of travel agencies declined from 25,975 establishments in 2000 to 14,797 in 2016, with a concomitant fall in employment from 183,143 to 108,984.<sup>6</sup>

> 2 All figures are from the World Development Indicators, World Bank Group.

> 3 Source: European Commission, _Statistics on ICT use in tourism_ . Online publication.

> 4 Source: Statista.

> 5 Idem.

> 6 Source: 2000 and 2016 _County Business Patterns_ , US Census Bureau.

Page **2** of **34**
# **<u>Initial land ownership</u>**

## **Table A25: Second stage: Effects of refugee presence on household welfare disaggregated by initial landownership, with analysis at cluster level and the distance -** **<u>weighted refugee index at 50km from the clusters (Panel 2009 2012)</u>**

|||||L|og of welfare|(consumption|aggregate per a|dult equivalen|t)||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
||||||<br>Panel A|<br>(Own <=medi|<br>an land size“2|<br>.5 acres”)|||||
|Log of refugees (50km) predicted by Log of<br>Distance IV (mean border distance)|0.07818|0.07681|0.08618|0.09122*|||||||||
||(0.05174)|(0.05145)|(0.05350)|(0.05023)|||||||||
|Log of refugees (50km) predicted by Log of<br>Distance IV (square of mean border distance)|||||0.10015*<br>(0.05656)|0.09973*<br>(0.05647)|0.10804*<br>(0.05942)|0.11007**<br>(0.05611)|||||
|Log of refugees (50km) predicted by Log of<br>Distance IV (square-root of mean border<br>distance)|||||||||0.06218<br>(0.05683)|0.05974<br>(0.05643)|0.07027<br>(0.05660)|0.07976<br>(0.05329)|
|Observations|2,124|2,124|1,981|1,980|2,124|2,124|1,981|1,980|2,124|2,124|1,981|1,980|
|Kleibergen-Paap rk Wald F|11.99|11.96|12.08|11.94|11.29|11.27|11.37|11.19|10.32|10.29|10.48|10.39|
|Root MSE|0.526|0.526|0.506|0.378|0.528|0.527|0.507|0.380|0.526|0.525|0.505|0.378|
||||||Panel B|(Own > medi|an land size“2.|5 acres”)|||||
|Log of refugees (50km) predicted by Log of<br>Distance IV (mean border distance)|0.01440<br>(0.05408)|0.02057<br>(0.05455)|0.02718<br>(0.05567)|0.03440<br>(0.05698)|||||||||
|Log of refugees (50km) predicted by Log of<br>Distance IV (square of mean border distance)|||||0.02504|0.03104|0.03487|0.04439|||||
||||||(0.05746)|(0.05758)|(0.05876)|(0.05870)|||||
|Log of refugees (50km) predicted by Log of<br>Distance IV (square-root of mean border<br>distance)|||||||||0.02396<br>(0.05874)|0.03090<br>(0.05963)|0.04013<br>(0.06174)|0.04849<br>(0.06387)|
|Observations|2,170|2,168|2,124|2,122|2,170|2,168|2,124|2,122|2,170|2,168|2,124|2,122|
|Kleibergen-Paap rk Wald F|13.41|13.38|13.09|13.08|12.09|12.07|12.09|12.10|12.29|12.26|12.01|12|
|Root MSE|0.508|0.505|0.493|0.340|0.508|0.506|0.493|0.341|0.508|0.506|0.493|0.341|
|**_Included controls_**|||||||||||||

41
pooor growth is the average across all percentiles of the income distribution of the product of (a) the growth rate of income in the p<sup>th</sup> percentile _relative to average income growth_ , and (b) the sensitivity of poverty to growth in that percentile. For example, if the poverty measure of interest is very sensitive to growth among the poorest, and if the income of the poorest grows faster than average incomes, then poverty will fall.

Equation (3) is useful for thinking about the various definitions and sources of pro-poor growth. For example, the Kakwani and Pernia (2000) definition of pro-poor growth states that growth is pro-poor if and only if the second term in Equation (3) is negative, i.e. the pattern of growth in relative incomes is such that the poverty measure falls. A broader definition of pro-poor growth suggested by Ravallion and Chen (2003) is that growth is pro-poor if the poverty measure of interest falls. According to this definition, there are three potential sources of pro-poor growth: (a) rapid growth in average incomes; (b) a high sensitivity of poverty to growth in average incomes; and (c) a poverty-reducing pattern of growth in relative incomes.

In the empirical section of this paper, I will use data on income distributions and average incomes for a large sample of developing countries to construct these three sources of pro-poor growth, document their relative importance, and investigate their determinants. Before doing so, however, it is useful to examine the key ingredients in Equation (3) in more detail: the pattern of growth in relative incomes, gt (p) − <sup></sup> dμ t ⋅ 1 <sup></sup> ,  dt μ t  and the function summarizing the sensitivity of poverty to growth in each percentile, ηt(p).

Figure 1 graphs two examples of the pattern of growth in relative incomes, for China over the period 1990-1998, and for Indonesia over the period 1996-1999. In China, according to the household survey average incomes grew at 14 percent per year, and the dollar-a-day headcount measure of poverty fell from 51 percent to 33 percent of the population. However, there was also a sharp increase in inequality during this period, with the Gini coefficient rising from 34 to 40. The pattern of relative income growth rates shown in the relative growth incidence curve highlights this pattern of increased inequality. Growth in the poorest 80 percentiles of the population was below average growth, while the richest 20 percent of the population saw above-average 7
∂F<sup>0</sup> where ⋅ ρ is the multiplier of the constraint η⋅a=k-v⋅e+v*⋅e*+l; and κ1, κ2 and κ3 ∂a are the multipliers associated with the constraints that k≥e, e≥0 and e*≥0, respectively. The usual Kuhn-Tucker complementary-slack conditions apply: κ1⋅(ke)=0, κ2⋅e=0 and κ3⋅e*=0. It is straightforward to verify that F0 = 1 ⋅ ln a + f 0 (X) and δ functions and the first-order conditions, it follows that (A5)-(A9) correspond to Equations (4), (5) and (22)-(24) in the text. Equations (6)-(8) follow immediately by setting α=λ=0 in Equations (22)-(24), and Equations (15)-(17) follow from setting λ=0 in Equations (22)-(24).

# **References**

- Albuquerque, Rui (2003).  “The Composition of International Captial Flows:  Risk Sharing Through Foreign Direct Investment”.  Journal of International <u>Economics</u> 61:353-383.

- Hall, Robert and Charles Jones (1999).  “Why Do Some Countries Produce So Much More Output Per Worker Than Others?”.  Quarterly Journal of Economics. 114(1):83-116.

- International Monetary Fund (1992).  “Report on the Measurement of International Capital Flows”.  Washington.

- Kraay, Aart and Jaume Ventura, “Current Accounts in Debtor and Creditor Countries” <u>Quarterly Journal of Economics, 115(4):1137-1166.</u>

- Kraay, Aart, Norman Loayza, Luis Serven and Jaume Ventura (2000).  “Country Portfolios”.  NBER Working Paper 7795.

- Lane, Philip and Gian Maria Milesi-Ferretti (2001) , “The External Wealth of Nations: Measures of Foreign Assets and Liabilities for Industrial and Developing Countries,”.  Journal of International Economics 55:263-294.

- Lane, Philip and Gian Maria Milesi-Ferretti (2001) in Horst Siebert (ed)  The World’s <u>New Financial Landscape:  Challenges for Economic Policy.  Berlin:  Springer-</u> Verlag.

33
# **I. Introduction**

Recent empirical work highlights high rates of entry and exit into exporting, and explores the way in which exporters access foreign markets.<sup>1</sup> While the standard heterogeneous-firm model (Melitz 2003) does an excellent job of explaining empirical findings on exporters’ characteristics,<sup>2</sup> it is less equipped to explain these entry and exit patterns and how foreign markets are accessed. It uses comparative statics to explain entry and exit, and as a result, it cannot explain why a firm would enter and then immediately exit exporting with no change in trade costs, as is observed frequently. It also cannot explain how firms develop new products or enter new markets. **S** tudying these factors is important, especially in dynamic sectors, where these entries account for a large share of export growth.

In this paper, we explore the role of idiosyncratic uncertainty and sunk entry costs in explaining why many firms enter the export sector and then exit almost immediately. We also examine entry and exit into new products and markets, and describe characteristics of the pioneers. While some of our results are consistent with earlier work, our contribution is to uncover precisely why entry and exit in exporting is so

> 1 Eaton, Kortum and Kramarz (2004) examine French data and find that most firms sell to only one market, typically the most popular one; while some firms that export widely serve the less popular markets.  Eaton et al. (2008) examine data from Colombia and find extremely high entry and exit rates into exporting—total entrants in a given year exceed the number of continuing firms and most entrants exit after one year. Alvarez and Lopez (2008) use data from Chile and also find high rates of entry and exit. Volpe Martincus and Carballo (2008) examine exports from Peru from 2001 to 2005 and find that large firms export more products to more markets.  Iacovone and Javorcik (2010) examine data from Mexico from 1994-2003 and find that new exporters tend to start small and that there is a lot of churning of products within firms. 2 A number of studies find that exporting firms are bigger, more productive, pay higher wages and offer better working conditions than otherwise similar import-competing firms. Bernard and Jensen (1995) report detailed statistics for the United States.  A number of papers followed their approach and find similar results in both developing and developed economies.  Shank, Schnabel and Wagner (2007) provide a summary of these papers, and offer similar evidence for Germany. Bernard, Jensen, Redding, and Schott (2008) also provide a summary.

2