rafmacalaba/data-use-mentions-extended huggingface dataset:
Figure 21: Gross external financing requirement (percent of GDP) <!-- Start of picture text --> 18%<br>16%<br>14%<br>12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> Source: Authors’ elaboration using data from the Central Bank of Turkey .

**Financing of the widening CAD since the early 2000s was initially led by inward FDI, but increasingly it has become financed by shorter-term financing which is more volatile.** Sustained, sizeable CADs over the last 15 years have meant that Turkey’s international investment position has been negative and its stock of net foreign liabilities has grown (Figure 20). With continued CADs and growing external debt, the national gross external financing requirement (GEFR) has increased, especially post-GFC (Figure 21). Figure 22 and Figure 23 illustrate the composition of the financial account.<sup>2</sup> Net inward FDI was small until 2001, after which it expanded to reach a peak of 3.5 percent of GDP in 2006 (Figure 23) and declined afterwards. The gap post-2006 was filled by a mix of ‘other’ (bank loans and foreign currency deposits) and portfolio (mostly debt securities) financing. Since capital account liberalization, there have been four portfolio flow reversals leading to net outflows (Figure 22), but otherwise Turkey has generally been a recipient of net inflows. Overall, the use of shorter-term current account financing, which is often associated with greater volatility, increased sharply after the onset of the GFC, aligned with the period when very low interest rates in advanced economies led to inflows into many EMDEs in a ‘search for yield
very strongly correlated with contemporaneous events.

In order to obtain meaningfully‐precise estimates of the multiplier, it is important that disbursements on World Bank loans are also large relative to borrowing‐country GDP. To ensure that this is the case, I focus on a set of aid‐dependent countries where (a) annual disbursements on World Bank loans are available for at least 20 of the 25 years between 1985 and 2009, and (b) total disbursements on World Bank loans as a share of GDP averaged over the sample period 1985‐2009 exceed one percent. This results in a set of 41 mostly low‐income countries where World Bank lending has been an important source of financing for public spending over the past 25 years.

My country sample is further limited by the availability of data on total public spending required to construct ∆�� . My primary source for this data is the IMF’s World Economic Outlook database, which provides information on total general government expenditures, typically beginning in the late 1980s. I supplement this with information taken from the World Bank’s African Development Indicators which also reports data on total general government expenditures, going back to the early 1980s for many 12
# **Sample**

## **District sample**

The eight MESIP disadvantaged districts were selected on the basis of being among the worst-off in the nation in terms of PqTR; female/male teacher ratio; shortages of classrooms; and repetition, promotion, and dropout rates in lower primary. The selection was also conducted in such a way as to ensure the inclusion of at least one school from each of Malawi’s six sub-regional education divisions. Ultimately, the chosen districts were Lilongwe Rural East, and Dedza from the large Central region; Kasungu from the smaller Northern region; and Chikwawa, Machinga, Mangochi, and Phalombe, all from the largest region, Southern.

For our comparison sample, we first identify the 26 districts not targeted by the main MESIP interventions. We remove the four districts into which these interventions were extended by MESIPExtended.<sup>16</sup> Although MESIP-Extended supported similar interventions to MESIP, there were differences in sequencing and implementation modality which make it difficult to robustly and simultaneously evaluate both programs.

We also exclude Malawi’s four urban districts, as the MESIP interventions were designed for implementation in rural areas. Finally, we also exclude Likoma district, a small island in Lake Malawi with a very particular set of challenges relating to schooling stemming from its particular geography.

With these exclusions, we were left with a group of 17 rural education districts as a comparison group. See **Table 1** for a summary of the selection.

## **School Sample**

For indicators drawn from EMIS, our sample is all public primary schools in the eight disadvantaged districts and the 17 comparison districts (a total of 4,428 schools).<sup>17</sup> For test scores which are drawn from MLSS, our sample is all public primary schools in the eight disadvantaged districts and the 17 comparison districts which are also part of the MLSS sample and for which baseline and endline data is available (a total of 444
United States and the United Kingdom, also have experienced a significant decline in public listings. On the other hand, trends in developing countries are less obvious. Some countries, like China, have seen rapid growth in both public company listings and private equity activity, while other countries, like Brazil, have seen volatility in private equity activity concurrent with declines in public listings. This paper attempts to explain these changes in global capital markets by exploring three main hypotheses that may explain trends in global PE investment: i) country and industry macroeconomic conditions, ii) financial market development, and iii) institutional and regulatory environment. The hypotheses we examine are not mutually exclusive, and consequently, we seek to also understand the relative importance of different determinants of PE investment. Our ultimate goal is to better understand current and future trends in capital formation through financial intermediation by uncovering the historical determinants of PE investments at the country and industry levels.

Using nearly comprehensive country-industry-level data on international private equity activity, we study the determinants of buyout investments across 61 countries over the period 1990 – 2017.<sup>5</sup> Our results indicate that macroeconomic conditions, financial development, and regulatory environment all play some role in determining the level of buyout activity at the country level. For macroeconomic conditions, we find that buyout activity increases more during economic expansions (as measured by a declining unemployment rate). We also find similar evidence at the industry level: industries receive more buyout investment following expansions in industry-wide

> 5 The countries included in our study are Argentina, Australia, Austria, Belgium, Bulgaria, Brazil, Canada, China, Croatia, Colombia, Costa Rica, Cyprus, Czech Republic, Denmark, the Arab Republic of Egypt, Finland, France, Germany, Greece, Hong Kong SAR, China, Hungary, Indonesia, India, Ireland, Iceland, Israel, Italy, Jamaica, Jordan, Japan, Kazakhstan, Mexico, Malaysia, Nigeria, Netherlands, Norway, New Zealand, Peru, Philippines, Poland
**Figure 4**

<!-- Start of picture text --> Argentina versus Chile<br>Panel A. Real GDP per Capita and Social Indicators<br>Panel A.1. Argentina Panel A.2. Chile<br>120 80 200 40<br>100 100 60<br>160<br>80 120 80<br>120<br>60 140 100<br>80<br>40 160 120<br>40<br>20 180 140<br>0 200 0 160<br>Unemployment Rate Monetary Poverty Unemployment Rate Monetary Poverty<br>1/HDI Real GDP per capita (right axis) 1/HDI Real GDP per capita (right axis)<br>Panel B. Cyclical Component of the Real GDP per Capita and Social Indicators<br>Panel B.1. Argentina Panel B.2. Chile<br>120 -140 200 -300<br>100 -100<br>160 -220<br>80 -60<br>120 -140<br>60 -20<br>80 -60<br>40 20<br>40 20<br>20 60<br>0 100 0 100<br>Unemployment Rate Unemployment Rate<br>Monetary Poverty Monetary Poverty<br>1/HDI 1/HDI<br>Real GDP per capita - Cyclical Component (right axis) Real GDP per capita - Cyclical Component (right axis)<br>Notes: Poverty line of 5.50 dollars per person a day (2011 PPP). The cyclical component of the Real GDP per capita is defined as<br>the cycle over trend.<br>Sources: Authors’ estimations based on SEDLAC (CEDLAS and World Bank), Human Development Report Office (United<br>Nations), and WEO (October 2018).<br>Index (2002=100) Index (2002=100)
imagine an easier way of identifying long run effects as defined here than aggregating estimation at least at the industry level. The reason for this is that most competitive industries seem to be subject to simultaneous entry and exit, for which the spatial analogue would seem to be that new plants are observed to open in a local economy at the same time as others shutdown in the same line of business. Isolating the long run effects while keeping the analysis at the plant level then would mean identifying the effects of business environment on the productivity of the typical new entry and on that of the typical closing plant.

> 11 This second form of endogeneity is also discussed in Dollar et al (2004) and has  to be addressed in analysis based on industry aggregates as well

7
**Table 4:** Effects of AIA on investment and cloud types

|||Panel|A: Investment|||
|---|---|---|---|---|---|
|Regressions|(1)|(2)|(3)|(4)|(5)|
|Dependent variables|Hardware<br>Investment|Software<br>Investment|Plant & Mach.<br>Investment|Vehicles<br>Investment|Land & building<br>Investment|
|AIA Dummy|0.346***|0.246***|0.549***|0.214***|-0.012|
||(0.079)|(0.070)|(0.100)|(0.072)|(0.086)|
|Observations|17,651|24,313|22,569|18,583|22,327|
|||Panel B:|Cloud Adoption|||
|Regressions|(6)|(7)|(8)|(9)|(10)|
|Ddtibl<br>24|Cloud|Cloud|Cloud|Cloud|Cloud|
|epenen varaes<br>|Databases|Storage|CRM|Ofce|Email|
|AIA Dummy|-0.094***|-0.097***|-0.091***|0.001|-0.045|
||(0.040)|(0.035)|(0.037)|(0.027)|(0.032)|
|Observations|2,200|2,200|2,200|2,200|2,200|

Note: Time Period: Investment regressions use annual data for the years 2007 to 2013. Cloud
**-** **<u>Table 3: Comparing PSG and non PSG participants based on farm survey data</u>**

|||<br>**PSG pr**|<br>**ogram ben**|<br>**eficiaries**||**Eligible**|**non-benef.**|_Δ_**Sig**|
|---|---|---|---|---|---|---|---|---|
||**To**|**tal**||**Size class**||**T**|**otal**||
||**2022**|**2023**|**<20**|**20-50**|**50-120**|**2022**|**2023**||
|**Panel A: Farm characteristics**|||||||||
|Total area cultivated (ha)|68.7|78.4|14.0|50.6|107.0|55.4|70.0||
|Members working full time on farm|1.51|1.76|1.86|1.68|1.74|1.64|2.01||
|Members working part-time on farm|0.83|1.04|1.19|1.10|0.98|0.86|1.07||
|Permanent employees|0.89|1.08|0.17|1.81|1.20|2.11|2.45||
|<br>Have tractor < 40 hp||0.22|0.28|0
- (2) The surveys were conducted no earlier than 2010. If there were multiple surveys available for the same country, we chose the most recent one. This allows us to provide a contemporary characterization of teacher pay across several African countries, especially when describing current levels and comparing those to other professions.

- (3) The survey data were accessible.

The resulting set of countries includes Côte d’Ivoire, Namibia, Sierra Leone, Tanzania and Zambia with data from national labor force surveys; Burkina Faso, Malawi, Niger, Nigeria and Uganda with data from Living Standards Measurement Surveys (LSMS) with an employment module; and the Democratic Republic of Congo, The Gambia, Ghana, Liberia and Senegal with data from national household surveys with a labor force module. Table 1 provides background information on these surveys: the countries, the source of the survey data and the year in which the survey was conducted.

The focus of our analysis is primary and secondary school teachers. This choice was driven by the fact that teachers in the basic education cycle make up the bulk of the teaching workforce. Additionally, their salaries are often set centrally and typically paid directly out of government expenditure on education, which makes cross-country comparison possible and meaningful. From each of these surveys, we identified primary and secondary teachers using occupation codes listed in interviewer manuals. Most of the surveys used the International Standard Classification of Occupations (ISCO) or a variant form of it. The occupation codes allowed us to differentiate primary and secondary teachers from other educators such as pre-primary teachers, special education teachers, and university lectures and professors. Exceptions are Malawi where the data classifies teachers at all levels in the same group. (Detailed data processing notes can be found in Appendix Table A1.) We retained Malawi in our analysis, but our conclusions are similar if we exclude it. We excluded educators other than primary and secondary teachers from our analytical samples since their jobs and pay determination may be quite different from primary and secondary teachers.
municipality.

The remainder of this paper is organized as follows. In Section 2, we provide further institutional details on procurement and budget execution in Brazil and present some statistics from our datasets. We then proceed to validate the quality of our budget execution data in Section 3, producing metrics of internal consistency as well as comparing aggregates constructed from microdata to information available at the SICONFI. In Section 4, we illustrate two applications of this new dataset. Finally, we conclude in Section 5 by discussing other avenues of research using these new data.

# **2 Institutional context and data**

Our dataset on public procurement and budget execution of Brazilian municipalities is constructed from data that TCEs assemble. The mission of these audit courts is to oversee the fiscal policy of states and municipalities, which includes taxation, spending, and budget execution. TCEs are present in each of the twenty-six Brazilian states plus the Federal District.<sup>8</sup>

> 7The Public Procurement Law (Law 8,666) contains most of the public procurement regulation. Article 40 describes the rules that govern payment conditions. See also here for a summary of payment rules.

> 8In the states of Rio de Janeiro and São Paulo, the TCEs oversee all municipalities apart from the capitals, which have their own audit court. Three states – Bahia, Goiás, and Pará – have two audit courts: one that oversees the state government and one that oversees all municipal governments within the state.

4
Cumming (2008) find a strong link between bankruptcy laws and entrepreneurship in their study of 15 countries in Europe and North America, suggesting that the legal environment is an important factor for VC activity. Similarly, we hypothesize that there is more buyout activity in countries with stronger institutions and better governance. As buyout transactions typically involve a large transfer of ownership and private contracting, investor protection and contract enforcement would be particularly important for PE investors. Hence, we use investor protection and contract enforcement reforms in addition to rule of law to measure the overall strength of the regulatory and institutional environment in a country.<sup>10</sup>

# **3 Data and Descriptive Statistics**

The PE investment data come from Burgiss, a financial services company providing record keeping and performance analysis support to large institutional investors. There are two major advantages of this data set over others. First, Burgiss sources its data exclusively from limited partners, as opposed to general partners (GP), so the typical biases associated with GP-sourced data sets are not present.<sup>11</sup> Second, Burgiss has provided us a complete data set of investments, by country, by industry, by year for their entire database of funds and these data are unavailable from any other source.

The primary variable from Burgiss data for our study is the annual amount of buyout capital invested (measured in US$) at the country level for 61 countries over the period 1990 to 2017.

> 10 The use of reforms rather than other traditional measures of regulatory quality, which are typically highly correlated with rule of law, also allows us to perform a difference-in-differences analysis where we compare buyout activity pre- versus post-reform in countries that implemented a reform versus others that did not.

> 11 GP-sourced databases on private equity may have significant biases as GPs strategically stop reporting. In many cases, Burgiss cross-checks data across different investors in the same fund, which leads to a high level of data integrity and completeness. Recently,
Across the 64 parameter estimates in the two tables, the correlation is negative as expected (the indicators are constructed to take the value of one for the outcome assumed to be associated with improved economic wellbeing). Within each country, as the rankings of years of schooling and the asset index increase, the likelihood of being classified as poor under either allocation is negative. The same expected relationship holds for the indicator variables (e.g., households that have electricity, or have a head that is literate, are less likely to be poor under either allocation). All estimates are statistically significantly different from zero – all are significant at = 0.01 except for one. The variance estimates underpinning the significance measures account for the complex αα designs of each of the country random samples. The metadata on the primary sampling units (PSUs) are used to correct for within-PSU correlation (i.e., violations of the independent and identically distributed assumption), and leveraging the fact that each country survey is an independent operation, each country in our pooled sample is treated as a stratum. It is expected that most of the correlations take the expected sign. That all are negative and statistically significant is somewhat surprising (these are sample data and presumably also contain measurement error concerns) but certainly aligns with the view that both the per-capita and squareroot allocation of household resources produce a classification of poverty that contains useful signal.

Furthermore, we carry out 32 tests of equality between the pairs of estimated 1 coefficients. The differences between the estimated coefficients from the per-capita and squareroot regressions are statistically significant for all 32 comparisons (all comparisons in Tables 8 ββ and 9). The p-value is less than 0.01 for all comparisons except two (which have p-values less than 21
>Low-bound elasticity Medium elasticity Upper-bound elasticity<br>Income Gains (%)<br><!-- End of picture text --> _Source:_ Estimates based on Russia Longitudinal Monitoring Survey–HSE 2016. _Note:_ Estimates assume a price shock of 25 percent.

# **Income gains deriving from an increase in working life years**

We estimate the cost of the working life lost because of tobacco consumption, assuming that the impact of lower tobacco use on health and work-generated income is direct. The 6.0 million years of life lost attributed to tobacco consumption are distributed using the occurrence of mortality profile. For each death, working years lost are divided across deciles proportionately to the number of households that consume tobacco in each income group. The results show that the reduction in tobacco consumption and the expected reduction in work years lost have a positive impact on welfare that represents a transversal income bump among all decile groups (table 8 and figure 4).

**Table 8 – Years of Working Life Lost (%), by Decile**

|_Price shock scenario_|_1_|_2_|_3_|_4_|_5_|_6_|_7_|_8_|_9_|_10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Low-bound elasticity|0.35|0.36|0.32|0.32|0.31|0.38|0.32|0.31|0.31|0.27|
|Medium elasticity|0.49|0.53|0.49|0.51|0.51|0.63|0.54|0.55|0.55|0.53|
|Upper-bound elasticity|0.64|0.70|0.66|0.70|
Policy Research Working Paper 6113

# **Abstract**

The authors use a new dataset on export transactions for a large set of Latin American and Caribbean and comparator countries to assess the extent of “export entrepreneurship” during periods of fast export growth (2005–2007) and depressed external demand (2008– 2009). Export entrepreneurship is equated with the extensive margin of exports, namely the advent of new exporting firms, new export products, and new export market destinations. The main findings are: (1) annual exporter entry, exit, and survival rates in Latin America and the Caribbean are quite similar to what is observed in other countries, and entry rates across sectors are quite similar but survival rates appear to be highest in agriculture; (2) the relative size of entrants into export markets (relative to incumbents) tended to be lower for natural resource-abundant countries during 2005–2007, but less so during the crisis years of 2008-2009; (3) entry rates tend to be lower in sectors in which a country has revealed comparative advantage, however, exit rates and survival rates of new exporters are higher in those sectors; and (4) the low growth of exports during the global recession of 2008–2009 in Latin America and the Caribbean was due to lower growth in exports of incumbent firms’ pre-existing products and destinations, while new products and destinations tended to attenuate the recession’s effects. Overall, the data suggest that the Latin American and Caribbean region appears to be no less entrepreneurial in terms of the extensive margins of exports than comparator countries.

This paper a joint product of the International Trade Department, Poverty Reduction and Economic Management Network; Office of the Chief Economist, Latin America and the Caribbean Region: and the Trade and International Integration Unit, Development Research 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
policy. Assessing and improving the degree of anchoring of inflation expectations are thus critical tasks for monetary policy makers.

# 4.1. Evolution of inflation expectations

In both advanced economies and EMDEs, long-term (five-year-ahead) inflation expectations have fallen during the past two to three decades. After declining rapidly 18 Estimations that allow for time-varying coefficients indicate that, although inflation expectations are better anchored in advanced economies than in EMDEs, anchoring has improved in both groups over time (IMF 2016). Other studies offer similar findings. Capistrán and Ramos-Francia (2010) and Mehrotra and Yetman (forthcoming) conclude from data for a large sample of EMDEs that inflation targeting has affected inflation expectations. Studies of Mexico, Brazil, and South Africa find that the adoption of inflation targeting has helped anchor expectations in each case (Carrasco and Ferreiro 2013; Cerisola and Gelos 2009; and Reid 2009, respectively). However, Kabundi, Schaling, and Some (2015) show that, in South Africa, even with inflation targeting, expectations of price and wage setters (businesses and trade unions) were higher than the upper bound of the official target band, while expectations of analysts were within the target band. This study also finds that expectations of price and wage setters were substantially influenced by lagged inflation, but that those of analysts were not.

> 19 IMF (2018) focuses on four measures: absolute deviation of three-year-ahead inflation forecast from target, variability of inflation forecasts, dispersion of inflation forecasts, and sensitivity to inflation shocks. In the context of a small macroeconomic model, IMF (2018) also shows that better-anchored inflation expectations reduce inflation persistence and limit the pass-through of currency movements to domestic prices.

> 20 Using a dynamic stochastic general equilibrium model calibrated to the United States, Eusepi and Preston (2018a, 2018b) conclude that government liabilities can reduce the effectiveness of monetary policy in controlling inflation in economies with high government debt under imperfect knowledge.

10