rafmacalaba/datause-encoder-data huggingface dataset:
# ANNEX 4: COUNdTPRY TYPOLOGY OF ADJUSTMENr

- AL countri (intensely adjusting) have received three or more SALs, or two completed SALs, by 1989, with lending starting in or before 1985 (25 cases).

- Pre-196 countries have received less than three SALs but were included in the program befoi-e 1985 (11 cases).

_Post-1985_ countries received adjustment loans between 1986-1988 (19 cases).

- NAL+ countnies had an increase in average annual per capita GDP growth during the period 1980-87 (17 cases). These countries do not need IMF/World Bank types of adjustment measures, unless the policies they pursue are unsustainable.

- NAL- countries had a decrease in average annual per capita GDP growth during the period 1980-87 (14 cases). These are potential candidates for World Bank adjustment lending, and are proba)ly the closest to the counterfactual nonadjusting countries.

# Public education financing **(%** of total public expenditures)

||Countries<br>|1975<br>|1980<br>|1986|
|---|---|---|---|---|
|IAL<br>|11<br>|14.8<br>|14.8<br>|12.0|
|Pre-1986<br>|2<br>|16.2<br>|15.5<br>|19.0|
|Post-1985<br>|3<br>|9.7<br>|6.7<br>|8.1|
|NAL|12|9.9|10.0|12.1|

# Public education financing (% of GDP)

||Countries<br>|1975|1980|1986|
|---|---|---|---|---|
|IAL<br>|11<br>|3.3|_3.5_|2.9|
|Pre-1986<br>|2|3.0|4.0|4.6|
|Post-1985<br>|3<br>|---|6.7|8.1|
|NAL|12|9.9|10.0|12.1|

# Gross primarv enrollment rates (t)

||Countries<br>|1970<br>|1975|1980|1985|
|---|---|---|---|---|---|
|LAL<br>|25<br>|77.4|83.2|94.2|90.1|
|Pre-1986<br>|9|67.3|70.8|75.5|84.0|
|Post-1985<br>|18<br>|52.4<br>|65.5|68.4|73.7|
|NAL (Total)<br>|33<br>|73.5|80.3|86.0|91.1|
|NAL+<br>|18<br>|76.9|82.4|89.9|98.3|
|NAL-|15|69.4|77.7|81.3|82.6|

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20 Yet as highlighted by Stathers, Lamboll, and Mvumi (2013), these aggregates hide a lot of heterogeneity. In effect, only a minority appears to be affected (only between 7 and 22 percent reports the occurrence of PHL of maize in Malawi, Tanzania and Uganda respectively), when affected, but they suffer substantial losses (between21 and 27 percent of the harvest in Malawi, Tanzania and Uganda respectively). The estimates also fluctuate across years, with the national PHL estimate in Tanzania varying between 4.4 and 2.9 between 2008 and 2010, consistent with the observed changes in marketing behavior and the adoption of improved storage technologies.

More in-depth and causal multivariate analysis of the Tanzania 2008 experience shows that economic incentives, especially the seasonal price gap (and access to the market), substantially decrease the extent of PHL, while climatic factors (particularly the combination of heat and humidity) substantially increases it. PHL only decreases with post primary education (though not with primary education), though households’ wealth position/poverty does not appear to affect it and female headed households tend to experience lower PHL. Some of these factors are likely to work through the adoption of improved storage technologies, an important area for further investigation.

These insights are important to incorporate in the planning, design and targeting of PHL interventions. First, attention is called to a proper contextualization of the widely quoted, high PHL estimates. These concern aggregate estimates of losses along the full chain, while the interventions discussed are typically concentrated on reducing on-farm post-harvest losses only. The latter are much smaller, also in the literature, but the current estimates are still quite high when compared with the self-reported estimates presented here (at least for maize). Yet the self-reported estimates are arguably the more relevant indicators of demand for better storage and handling techniques.

Second, as only a small (though annually changing) proportion of households report a loss, the findings further highlight that “one-size-fits-all” approaches are bound to fail (as highlighted 30 years ago by Lipton) and that solutions and interventions must be compatible with economic incentives.
English hashtag. Hashtags in other languages are also accounted for, albeit in smaller numbers. One prominent example is the hashtag # _Flygskam_ , which was at the center of a large-scale movement to discourage people from flying in order to lower carbon emissions. (Accordingly, this hashtag is assigned to “climate”.) An additional effort was made to screen for hashtags in Arabic, for the same reason we created the MENA variable. In total, our database counts 320 hashtags in English, 111 in Arabic, and 20 in other languages (and 451 hashtags in total).

# **3.2 Compilation of Twitter Data**

The Twitter Enterprise Full-Archive search API is used to ingest the tweets that feature the selected hashtags. This API granted us access to the full repository of tweets dating back to the inception of the platform. There is however a bandwidth constraint that prevented us from downloading the entire repository. Accordingly, we drew a stratified random sample of tweets.

We stratified by hashtag-year-month for a period of 10 years ranging from January 2014 to June 2023. The ingestion of tweets is done iteratively in batches of up to 5000 tweets each. We start in June 2023 and then work our way back in time towards January 2014, one batch per hashtag-year-month at a time. Upon reaching January 2014 for any given hashtag, we return to June 2023 or the most recent year-month for which our sample of tweets does not yet cover the full set of available tweets, and again work our way back to 2014; one batch for each hashtag-year-month at a time until our bandwidth constraint is exhausted.

The resulting database is an unbalanced panel as different hashtags come in and out of existence at different points in time. Starting in 2014 gives a sufficient amount of data prior to the birth of the # _metoo_ hashtag in 2017, which may mark a structural break in female participation on Twitter, particularly in public discourses that concern 8
up to 50 percent of one of the spouse’s pension balance be transferred to the other spouse’s account as a form of alimony. In our model, we assume that before the reform, divorced individuals only have access to their own pension funds and do not get to keep their former spouse’s pension. After the reform, each spouse gets one-half of the pooled pension savings of the wife and husband. To reduce computational complexity and because separation in old age is relatively rare, we assume that no separation occurs after the woman turns age 60.

# **4.6 Fertility**

The number of children _Nt_ is assumed to evolve stochastically. The probability of having another child is modeled as a logistic model, that depends on the woman’s age, marital status, schooling level and number of children in the previous period.

There are assumed to be no births after the woman turns age 40.<sup>30</sup>

# **4.7 Evolution of other state variables**

The model’s other time-varying state variables, _At_ , _Bt_<sup>_m_,</sup><sup>_B_</sup> _t_<sup>_f_,</sup><sup>_X_</sup> _t_<sup>_m_,</sup><sup>_X_</sup> _t_<sup>_f_aredeterminedbythe</sup> saving, labor supply decisions and asset return shocks. Private savings are assumed to earn the risk-free rate _r_ , assumed to be 5 percent.<sup>31</sup> . The balances on each spouse’s pension account accrue interest stochastically and are augmented by the current period’s contribution. Returns on the pension accounts are modeled as an iid process: _rB ∼ iidN_ ( ̄ _rB, σB_<sup>2).32</sup>

# **4.8 Recursive formulation of the household’s problem**

The optimization problem faced by a single individual of gender _j_ has the following recursive formulation:

> 30This assumption is made in part to reduce computational complexity.

> 31Chile’s 10-year government bond yields oscillated between 4 and 6 percent over the period 2008-2018

> 32Individual returns will differ in part because people can choose different firms to administer their pension funds and choose different funds within those firms. These decisions are not incorporated into the model. Also, allowing for serial correlation in the returns would require adding past returns as additional continuous state variables, which would significantly complicate the model’s numerical solution.

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a proxy variable measuring the gap between actual output and its long-term. trend (independent variables). The estimated model is specified in equation 1 below, where AFX stands for the absolute change in the exchange rate, ?r stands for domestic inflation, **_;us_** stands for the international (US) inflation rate, rg'pis the proxy used for overheating,<sup>54</sup> and c is an error term. The estimation results using monthly data under alternative specifications of the model are reported in Annex 3. The important result drawn from this exercise, as discussed in Annex 3 is that, starting in early 1992, the authorities partly abandoned the policy of correcting the exchange rate for changes in inflation differentials and excessive output growth. In other words, during 1992-96 the policy of keeping an almost constant nominal exchange rate was strengthened, further decreasing the perceived exchange risk and therefore giving more incentives to borrow short-term from abroad.

In sum, the Thai authorities, concerned with preventing an appreciation of the real exchange rate to retain Thailand's export competitiveness, maintained a pegged exchange rate system and intervened in the foreign exchange market to maintain the peg in the face of the large capital inflows. By preventing the nominal exchange rate from departing significantly from the central peg (in particular, by preventing the exchange rate from appreciating), the policy resulted in a fairly predictable nominal exchange rate, which reduced foreign exchange risk faced by investors and increased the incentives of domestic residents to incur in unhedged, short-termn foreign debt. Hence, a more flexible exchange rate policy could have reduced capital inflows and thus overheating pressures, obviating the need to tighten monetary policy in the first place and avoiding the perverse cycle of tight monetary policy, resulting in an increase in interest rates, attracting additional inflows, which result in further tightening of monetary policy.<sup>55</sup>

> 54 The proxy variable for _overheating_ is based on the consumption of electricity in the Bangkok metropolitan area, and is measured as the seasonally adjusted deviation from its long-term trend. The long-term trend is estimated using a simple regression of consumption of electricity on time and time square. The results of this estimation are as follows: elect = c + 10553 time + 32.34 time<sup>2</sup> , where both estimated coefficients resulted significant at I percent levels and both R<sup>2</sup> and adj-R<sup>2</sup> were about 96 percent.

> 55 A wider band would have reduced the incentives for short-term borrowing since these flows are more sensitive than longer term flows to fluctuations around the central parity. Long-term flows are determined by perceptions regarding the medium- to long-term sustainability of the central parity.
<!-- Start of picture text --> 2750 14<br>2500 13<br>12<br>2250 a<br>2000 10<br>1750 9<br>1500 8<br>7<br>1250 6<br>1000 5<br>750 4<br>500 3<br>2<br>250 1<br>0 0<br>2019 2021 2023 2025 2027 2029 2019 2021 2023 2025 2027 ~ 2029<br>90% 70% m40% 20% 90% 70% m40% 20%<br><!-- End of picture text --> <!-- Start of picture text --> 14<br>13<br>12<br>a<br>10<br>9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>2019 2021 2023 2025 2027 ~ 2029<br>90% 70% m40% 20%<br><!-- End of picture text --> <!-- Start of picture text --> — — +0.8 «(— -— oO — )<br><!-- End of picture text -->
autonomy, accountability systems, tracking or the privately operated share of an education system (Hanushek and Kimko, 2000; Barro and Lee, 2001; Hanushek and Woessman, 2006). As an example, high levels of school autonomy and competition between publicly and privately operated education systems characterize the highest ranked countries on international assessments. Thus, policy reforms favoring these systemic features seem to boost achievement. Yet, it might be premature to draw conclusions from simple cross-sectional comparisons of countries for two main reasons. First, it is likely that time-varying factors bias these regressions. Second, it is possible that these factors are subject to omitted variable bias and are therefore not entirely causal; For example, a third factor, such as governance indicators, might be driving school autonomy as well as achievement. If we exclude this factor, it seems as though school autonomy is driving higher test scores, where in fact governance indicators are driving both and is our main input of interest.

Beyond these econometric and methodological shortcomings, even if associations between systemic features and cognitive skill were causal, existing results are only valid for countries included in specific samples. Since mostly industrialized nations participate in international achievement tests, these findings are significantly less relevant for developing countries. This is an issue since these poorly performing countries demand the most rigorous and effective interventions. In particular, there exist many unanswered research questions pertaining to education quality in developing countries. For example, while a large gap in economic growth between the industrialized world and developing countries is evident, it is not _apriori_ clear whether this is due to differences in human capital endowments, policies or institutions. Such underlying differences in a country’s educational performance have important implications. One might think that education systems are tremendously underdeveloped in such countries. If this is the case, they require fundamental support in the form of basic resources and infrastructure rather than school autonomy, accountability or tracking.

In order to address these issues, we build on studies conducted by Hanushek and Woessmann (2009b) as well as Altinok and Murseli (2007) in order to link regional assessments to international assessments. Indeed, while many developing countries do not participate in international tests, Latin America and Africa have at least participated in regional achievement tests carried out during the 1990s and recent years. These tests include the UNESCO Laboratorio 6"
and providing policy relevant perspectives for improving the existing incentive structure of the mechanism.

The CDM provides an incentive to Annex B countries for meeting their targets at lower costs. For measurable and verifiable emissions reductions that are additional to what would have occurred without the CDM project, an Annex B country earns certified emission reduction (CER) credits, each equivalent to one ton of CO2 equivalent (tCO2e hereafter) abatement. The Annex B country is allowed to use the earned CERs to meet part of its emission reduction targets under the Kyoto Protocol or sell the credits to other parties. Stimulating sustainable development through technology transfer and foreign direct investments, the CDM also provides a way for developing countries to contribute to emissions reduction efforts.

Both industrialized and developing countries have responded to the incentives provided through the CDM. As of December 2010, there were 6,700 projects at some stage of the CDM project cycle.<sup>4</sup> If all of these projects were validated by the Executive Board (EB) and implemented to their full potentials, they would generate emissions reductions totaling 3.51 billion tCO2e and generate an equivalent number of CERs by the end of the first commitment period of the Kyoto Protocol in 2012 (UNEP Risoe CDM/JI Pipeline Analysis and Database, 01 January 2011).

While the rapid increase in the number of CDM projects indicates that this provision aligns the incentives of the Annex B and non-Annex B parties, the role of cost as a motive for investment is less well understood. Improving on this understanding is crucial for policy. This is because most numerical analysis of how the CDM affects the cost of meeting the Kyoto Treaty objectives are based on specified abatement cost curves and the assumption that capital will seek out least-cost projects.<sup>5</sup> This same approach also leads to prediction of the sectors and regions likely to benefit from project investment flows. However, project costs are not synonymous with abatement costs and there are additional

> 4 See Larson et al. (2008) for a discussion of CDM implementation rules and the CDM project cycle.

> 5 Metz et al. (2007) provide a careful discussion of abatement cost curves in top-down and bottom-up models of mitigation costs and how the models are used to inform policy.

3
**Figure 6:** Share of the growth of LE in the growth of _PALE_ 1 and _PALE_ 0 _._ 5, 19902019

<!-- Start of picture text --> Year<br>LE growth contribution, θ = 1 LE growth contribution, θ = 0.5<br>80<br>70<br>60<br>50<br>%<br>40<br>30<br>20<br>10<br>0<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020<br><!-- End of picture text --> Reading: in 1991, the growth of life expectancy contributed to 10% of the growth of _PALE_ 1 and to 25% to that of _PALE_ 0 _._ 5.

Turning now to deprivation as captured by the _EDθa_ ˆ index, Figure 7 reports the evolution of _ED_ 1 _,_ 70 in the world during the same period.<sup>24</sup> The evolution of _ED_ 1 _,_ 70 illustrates the vast progress made against deprivation at the world level. While a newborn expected to have 58% of its normative lifespan either lost to mortality or spent in poverty in 1990, this expected loss falls to 18% in 2019. Unlike PALE, ED can be directly decomposed into each of its components, as indicated in the graph. In particular, the share of the lifespan deprivation component increased over time: from 30% in 1990 to 51% in 2019. As a result, the neglect of mortality in most poverty measures amounts to missing an increasing and significant share of total deprivation.

# **4.3 Improving on comparisons based on the headcount only**

Our indices also allow for comparing countries in which life expectancy and poverty evolve in opposite direction, as in the Pakistan-Bangladesh comparison presented in Table 2 or in the comparison between the years 1990 and 2007 in South Africa (Figure 4). However, the choice of _θ_ may not be innocuous in these comparisons. We now focus on these cases. For these cases, we discuss the extent to which _PALEθ_ offers comparisons that are robust, that is, for which the ranking is not affected by the choice of _θ_ . Note that if the ranking proposed by _PALEθ_ is robust to the choice of _θ_ , this implies that _PALE_ 0 and _PALE_ 1 yield the same ranking. Since _PALE_ 0 corresponds to life expectancy, a measure solely based on the headcount provides a

> 24We take 70 as the lifespan deprivation threshold since world life expectancy is 71 years in 2019. This threshold is therefore a reasonable choice given our maximalist perspective: dying before this age means dying below the average expected age of death at the world level.

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reported that 66 percent of all new jobs generated during the 1960s and early 1970s, came from the smaller firms that were less than five years old and had twenty or fewer employees. They also show that almost 70 percent of all employees work for companies employing less than 250 people. The message of this seminal work is that the engines of change are small and select, and we must gain a better understanding of those small but effective economics units. At the same time the most remarkable social development of the late 1970s and early 1980s, in the U.S., is the reemergence of entrepreneurship. The creation of new enterprises has increased from 90.000 a year in the late qns to more than 600.000/year by the early 80s. It is not a siw coinc lence that such growth has occurred in the country .ich records the greatest growth in the venture capital industry, which 'as provided essential support to a group of the most dynamic start-ups.

A reinforcing effect has taken place between venture capital and entrepreneurship, in the last two decades. While the existence of venture capital even in its initial stages, has encouraged many potential entrepreneurs to take the leap and try their ideas in the marketplace and has substantially increased the number of people looking for, and receiving, financing to prove their capabilities, the increase of entrepreneurship and ideas has &timulated governments and private investors to contribute resources to those new enterprises. Governments and investors around the world are looking with increased interest towards the U.S. experience. Their attitude towards venture capital are now more favorable than in the past due to the increased visibility of the innovation and productivity benefits of venture capital.

# 2. The Venture Capital Industry Today

# 2.1 Size of the Industry Today

Recent surveys on venture capital have reported operations in more than 20 countries, in 1985-86 (Table 1). While more than half of them record as their first year of startup 1978 or later they show an important growth in recent years. According to those surveys, th a were over 1,100 venture capital institutions operating in the world, in 1985, and a total capital pool of nearly US$30.0 billion, of which US$19.6 billion were managed by U.S. companies, although not necessarily invested in the U.S. market. A more recent survey on venture capital limited to European countries (Peat & Marwick, 1986), reports the existence, in Europe alone, of 458 companies, of which 170 are registered members of the European Venture Capital Association. Their pool of capital was estimated to be over ECU1O.0 billion (US$8.9 billion) of which U.K., France, and the Netherlands represent 71 percent and the U.K., alone, nearly 50 percent.