rafmacalaba/datause-encoder-data huggingface dataset:
We also show that our results are robust to spatial correlation in the residuals by replicating the baseline results (Tables 1 and 3) using the Conley (1999) estimator. Results are shown in Tables A3 and A4 in the Appendix.

## **7.2 Sample Extension**

As highlighted in Section 4, our baseline analysis focuses exclusively on the set of countries that got connected to the "second-generation" SMCs to Africa without prior connection to the first-generation SMCs. In this section, we relax this criterion to include countries with a prior connection to an SMC before 2007, while still focusing on the effect of high-speed Internet connectivity via the second-generation SMCs on FDI.<sup>29</sup> Essentially, we classify these countries as though there were not "treated" prior to 2007. While in principle, these countries were already connected to the early SMCs that arrived on the continent, Internet speed in these countries was relatively low due to the low bandwidth capacity of these early cables. Hence, given our focus on "high-speed" Internet connectivity, our focus on the second-generation SMCs should suffice.

To this end, we replicate the baseline analysis in Tables 1 and 3 using the extended sample and show the results in Tables A5 and A6 respectively in the Appendix. Compared to the baseline results (Tables 1 and 3), our new results based on the extended sample are qualitatively and quantitatively similar (Tables A5 and A6) albeit the latter are slightly smaller in magnitude. We also perform our event study analysis using the Borusyak et al. (2021) estimator. Results in Figure A1 also confirm our earlier findings of an increase in FDI after connection to the SMCs. Thus, overall, we find consistent evidence that high-speed Internet connectivity is associated with high FDI inflow, particularly, in the service sector.

# **8 Conclusion**

This paper provides causal evidence of the role of infrastructure quality in driving FDI in developing economies. It explores how the arrival of high-speed Internet in Africa via submarine Internet cables stimulated FDI into the continent. It also examines the influence of complementary infrastructure, such as electricity and road connectivity, in amplifying the impact of Internet access, and also explores potential mechanisms underlying the impact are also explored.

> 29The additional countries included are Senegal, Ghana, Nigeria, Benin, South Africa, Angola, Gabon, Cameroon, and Sudan

20
- Adams, R.H.Jr., J. Page (2003), “International Migration, Remittances and Poverty in Developing Countries”, Policy Research Working Paper No. 3179, The World Bank (Poverty Reduction Group), Washington, D. C.

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- Burgess, R. (1998), “Diversification and Welfare in Rural China”, _mimeo_ , Department of Economics and STICERD, London School of Economics.

- Carr, E.H., R.W. Davies (1971), _A History of Soviet Russia: Foundations of a Planned Economy, 19261929_ (London, Macmillan).

- Chapman, R., R. Tripp (2004), “Background Paper on Rural Livelihood Diversity and Agriculture”, _mimeo_ , AgREN electronic conference on the Implications of Rural Livelihood Diversity for Pro-poor Agricultural Initiatives.

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- Davin, D. (1999), _Internal Migration in Contemporary China_ (New York, St. Martin’s Press, Inc.).

26
different forces or factors are involved, such as consumption patterns change over time. Similarly, vulnerability lines are expected to change over time. Even though, efforts have been made to maintain the characteristics of cohorts fixed over time, in the Colombia case the maximum interval window time was two years for constructing the synthetic panels, some variation is expected when the base year is changed on vulnerability lines over a set of vulnerability indexes.

The sensitivity exercise starts by using the 2008-2010 synthetic panel and estimates the set of vulnerability lines for a range of vulnerability indexes between 22 and 35 percent. **Table 3** shows the main results of the estimation of alternative vulnerability lines for different levels of the vulnerability index for Colombia. The second column shows the share of the vulnerable population in the first period that becomes poor in the second period, or vulnerability index, associated with the vulnerability line expressed in COP in column (3) and in $PPP 2011 in column (4). Column (5) shows the percentage increase in the poverty line corresponding to the value of the vulnerability line, and column (6) shows the percentage of people with consumption below the vulnerability line and above the poverty line in 2008 and 2010.

Repeating the exercise for other pairs of years (i.e. 2010-2012, 2012-2014, 2014-2016) and the same vulnerability indexes, four sets of vulnerability lines are found. **_Figure 3_** presents the combination of vulnerability lines and vulnerability indexes when different couples of years are used. From simple inspection, there is certain convergence of the vulnerability lines over time as the vulnerability index increases. This event seems to happen when the vulnerability index is within 30-32 percent which represents an increment of around two times the poverty line.

# **4.2. An alternative approach to define the vulnerability line**

This section presents the results of implementing the Hertova, Lopez-Calva and Ortiz-Juarez (2010) approach to estimate the vulnerable households. The idea is to shed some light on the robustness of the results found so far. However, it is relevant to notice that the empirical method was adapted to find a vulnerability line.

In recent years, authors like Atkinson and Brandolini (2013) and Lopez-Calva and Ortiz-Juarez (2014) have proposed to study vulnerability anchoring the concept to the risk of falling into poverty. In line with Ravallion (2010), these authors suggest that although during recent decades, the population in developing countries seems to be escaping poverty, some of the households moving beyond the poverty threshold are still highly vulnerable and only marginally better off than their “poor” counterparts. In this context, the key element to define the middle class would be how safe are the income based middle-class citizens from falling back into poverty.

In the case of Colombia, one of the limitations to apply the methodology proposed by Lopez-Calva and Ortiz-Juarez (2014) is that there is no publicly available longitudinal survey that allows to map the 13
- iii -

|||Page No.|
|---|---|---|
|ANNEX ...|............................|28|
|REFERENCE|S.................................<br>...............|30|
|TABLES|||
|Table 1:|Housing Shortages in Selected E.C.E. Countries<br>Countries (Ratios of Households to Dwelling<br>Dwelling Units) ..........**..**<br>0 00...**.**<br>0 .....<br>-.....<br>......<br>.......|.<br>4|
|Table 2:|Recent Trends in Housing Sector Output<br>............|6|
|Table 3:|Housing Investment Relative to GDP in Selected||
||Countries (Percent)<br>.ooo<br>.......... o................0|8|
|Table 4:|Standard Deviation Relative to Average Earnings<br>in the Main and Industrial Sectors in Selected||
||East European Countries, 1976<br>...................|14|
|Table 5:|Average Monthly Wages in Poland, 1985<br>.............|15|
|Table 6:|Results ofHousingInvestment Regression<br>..........|17|
|Table 7:|Results from Net Migration Regressions<br>............|19|
|Table 8:|Results from Earnings Regressions....<br>.............|21|
|FIGURE S|hare of Housing Investment to Gross Domestic<br>Product<br>...........|10|
<!-- Start of picture text --> N<br>CCC 2100, Large Farms ae<br>=<br>— ~~<br>+ apres te<br>Change of Land Value Paws gh Fe<br>HN 200.0004 - 481.0093 Bee a<br>BB(NN 100.00010.0001 - 100.0000- 200.0000 pails¥ Ss<br>[NI -99.9999- 0.0000 sae<br>|] -199.9999 - -100.0000 a<br>[NN -299.9999 --200.0000 ee<br>[ENN -399.9999 - -300.0000<br>[ -549.9999 - 400.0000<br>MS -619.2726 - 550.0000<br>We 619.2727<br>mut Daath NIGGOL SEO, YALE<br><!-- End of picture text -->
The maps for goats and chickens are placed in the Appendix. The maps for goats are similar to the maps for sheep. Goats are chosen more often in West Africa and in the eastern countries such as Kenya and Mozambique. However, in contrast to sheep, goats are owned by many farms in lowland humid zones around Cameroon. Chicken ownership resembles the results for dairy cattle. Chickens are owned widely across Africa, but are favored in wetter places below the Sahel. We do not present a beef cattle map. Beef cattle are highly concentrated in two places: South Africa and the highlands of East Africa.

# **5. Forecasting Livestock Adaptations**

As climate change unfolds over the coming century, farmers are likely to adapt to the changes by adding or subtracting livestock operations and by switching livestock species to minimize the damage and take best advantage of new climate conditions. In this section, we provide an analysis of how farmers would make such changes in the next 100 years. We assume that the cross sectional patterns of behavior we observe today can be used to predict how farmers will adapt over the long run. In making these predictions, we assume that it is only climate that changes over time. Obviously, there will be many other changes as Africa develops including income, technology, and land use. Because we do not take these factors into account the projections should not be interpreted as predictions of what would happen in the future. These many factors need to be taken into account to predict future outcomes. The projections in this study are merely trying to see how farm choices might vary with climate. It is also important to understand that these projections are intended to represent long run adaptations, not changes that farmers make from day to day<sup>12</sup> .

To introduce climate change, we examine a set of climate change scenarios predicted by Atmospheric-Oceanic Global Circulation Models (AOGCM’s). We rely on two climate scenarios that bracket the range of outcomes predicted in the most recent IPCC (Intergovernmental Panel on Climate Change) report (IPCC 2007): CCC (Canadian

> 12 Individual producer livestock holdings in Africa have been typically explained by a multi-year risk mitigation where the risks are driven by droughts and diseases (Delgado 1999). This analysis, however, examines livestock holdings as a long term adaptation to existing climate. Hence, if the climate in the year 2002 that our study is based on was substantially different from the normal climate, the estimates in the study suffer from the biases introduced by such discrepancies. However, there is no evidence of such a peculiarity in the year 2002 weather.

12
selection, and promotions but in practice appointments and promotions are based on non-transparent criteria due to the lack of performance assessment.

_Using the private sector to speed up recruitment and deployment_ Where public recruitment systems function poorly the private sector for recruitment can sometimes provide a faster and more effective alternative. In Kenya, it currently takes 1-2 years to fill open public sector positions in the health sector despite the existence of a large pool of unemployed health workers. To address this problem a group including the MOH, the MOE, and the MOF, was created to put together a fast-track hiring and deployment model. Deloitte & Touche Kenya was hired to recruit, deploy, pay, and manage the contracts of 830 recruited health workers later to be transferred to government payroll. Under this arrangement the recruitment process was reduced to less than three months, and, reportedly, retention and satisfaction of workers improved due to timely payment of wages and job orientation programs (Adano 2008). Whether a scaling up of this type of arrangement would be feasible in all settings is not clear but holds promise.

# _Reducing job migration through local recruitment_

Local recruitment of health workers can reduce job migration. In Thailand, local recruitment and training in rural areas of nurses, midwives, and paramedics, then assigned to serve in their home town and only licensed to work in the public sector, have been successful in facilitating recruitment to remote areas and have led to increased staff retention (Lehmann, Dieleman, and Martineau 2008). Similarly, in Indonesia, physicians from remote areas are significantly more willing to serve in remote areas according to surveys where physicians chose among hypothetical assignments (Chomitz et al. 1998).

# _Strengthening accountability to discourage absenteeism_

An example from Venezuela illustrates the importance of accountability for reducing absenteeism. Evidence from 22 urban hospitals in three different states in Venezuela showed that the higher the perceived probability of detection the lower absenteeism for both physicians and nurses, and for nurses a higher probability of penalties for poor performance being imposed was associated with lower absenteeism (Jaén and Paravisini 2001).

# _Hiring contract health workers_

A study examining the effect of different institutional arrangements in four Peruvian hospitals, three public and one private, found that physicians who had “permanent” contracts (civil servants) and enjoyed significant job security had a notably higher absentee rate than “contracted” physicians who can be terminated, lending support to the importance of accountability for reducing absenteeism and raising performance (Alcázar and Andrade 2001). In Ceara, Brazil the state hired primary health care outreach workers under contract and handed responsibility for supervision to municipalities. Local control and renewable contracts helped reduce absenteeism and improve health services (Tendler and Freedheim 1994).

38
11 observe no exchange rate changes. Across the inflation targeters the highest average exchange rate changes are reported in Brazil and, across non-targeters, in Argentina, which was subject to a currency crisis in the early part of the sample (2001 and 2002).

Some stylized facts can also be validated when we compare targeter and non-targeter group averages. For instance, inflation targeters show lower average inflation (3.97%) relative to the noninflation targeters (6.27%). Given this lower inflation, the group of targeters also present lower nominal interest rates (6.41%) than their counterparts (7.15%). Inflation targeters also experience lower economic growth (3.58%) than non-targeters (5.15%). Finally, ITers observe significant lower exchange rate variability (0.28%) than non-inflation targeters (0.73%). This last property makes our sample particularly different from earlier ones, such as Aizenman et al. (2011), who collect quarterly data from 1989 to 2006 for 16 countries (11 inflation targeters and 5 non-inflation targeters) and report average changes in real exchange rates of 2.50% for inflation targeters and -0.49% for noninflation targeters, which yields a close to 3% real exchange rate depreciation for targeters compared to non-targeters. In contrast, in our sample from 2000 to 2015, countries that follow IT practices have their currencies fluctuating by much less when compared to non-IT adopters.<sup>2</sup>

# **3.1 The model**

We employ a panel data approach that allows us to consider an aggregate specification for all countries in the sample. Following Aizenman, Hutchison, and Noy (2011), who run Taylor rule-type

> 2 In Aizenman et al. (2011), real GDP growth was relatively close across samples: 1.11% versus 1%. However, their figures were similar to ours on monetary policy: inflation targeters show lower average inflation (5.40%) relative to the non-inflation targeters (9.60%) and the group of targeters present lower nominal interest rates (8.98%) than their counterparts (12.68%). Therefore, in addition to slower economic growth for ITers in our sample, the rate of change of exchange rates is much lower for ITers on average: 0.28% versus 0.73%.
factors. However, these scaling factors have a differential impact on individuals according to their _personal_ employment histories; high real earnings growth and low rates of return make earnings in later life relatively more significant in terms of capital accumulation than earnings early in life. Figures 5 and 6 give an indication of this effect for an individual retiring at 65, with the average LIFEM_JD full-time male age-specific earnings, but with 15-year earnings gaps early (age 28-42) and late(age 49-63) in life. The figures present the highest cost (real earnings growth [dY] 2%, real rate of return [i] 2%) and lowest cost (real earnings growth 0%, real rate of return 4%) scenarios from Table 7. Although the absolute value of the capital sum accumulated by age 65 will be different in each case, the data is presented in proportional terms for ready comparison. The figures show that, with an earnings gap at young ages, the capital accumulation profile is highei when i = 4% and dY = 0%, than when both i and dY = 2%. However, if the earnings gap appears at older ages, the leverage from past earnings growth when i and dY =2% dominates the effect of past real interest rates at 4% in the absence of any real earnings growth. For individuals who have lifetime earnings profiles that diverge from the average, therefore, the actual rates of return and real earnings growth that they experience will affect their _relative_ as well as their absolute pension fund accumulation outcomes.

From LIFEMOD we can determine how many people have their _relative_ pension accumulation altered by the interaction of different rates of earnings growth and investment returns. Table 11 shows the extent to which there is movement by indiv duals between deciles of the pension fund distributions based on actual individual earnings profiles and the alternatives of i = 2%, dY = 2%, and i = 4%, dY = 0%. First, LIFEMOD individuals who survive to age 65 have been ranked according to their decile position in the pension fund accumulation distribution on the basis of i=2%, dY=2%, and then re-ranked on the basis of i=4%, dY=0%. The percentage who remain in or move between deciles in these two distribution is reported in the cells of table 11.

48
different participants in this debate refer to different things.<sup>19</sup> Below we argue that the deforestation impact of technological progress depends critically on three sets of factors:

- (a) The sector in which technological progress is occurring;

- (b) The market context;

- (c) The type of technological progress.

We first discuss technological progress when considering only one agricultural sector, and then interactions between two sectors. The different market contexts are discussed under each of them: the perfect market case, and then effects at the farm household level and at the market level (general equilibrium effects).

The impact of technological changes is in most contexts greatly conditioned by the nature of technological change, a key element being the factor intensities. Table 2 provides a classification of technologies based on that.

|**Type of technology**|**Yield (y)**|**Labor per ha (l)**|**Capital per ha (k)**|
|---|---|---|---|
|Labor intensive|+|+|?|
|Labor saving|?|–|?|
|Capital intensive|+|?|+|
|Capital saving|?|?|–|
|Pure yield increasing (Hicks neutral)|+|0|0|
|Yield increasing and input intensive<br>(‘land saving’)|+|+|+|

**Table 2. Classification of technologies based on change in yield and factor intensities.** _Source: Angelsen et al. (2001)._

## **_2.3.1 One agricultural sector_**

# **_Perfect markets_**

The perfect market case, as used in this paper, refers to a situation where all relevant prices are given and does not change following changes in the agricultural sector (output demand and input supplies are perfectly elastic). Thus farmers can sell any amount of the produce at a given price, they can hire or sell labor in any amount at a given market wage rate, and they can get as much credit as they want at a given interest rate to buy any inputs they want at a given price. While unrealistic if taken literally, it may give a reasonable approximation of a situation where the sector experiencing technological progress is relatively small (low market share), and the farmers are well integrated into markets.

The impact of technological progress is pretty straightforward in this case: it boosts agricultural land rent, and therefore forest conversion. This can be read directly out of the basic land rent equation: higher yield ( _y_ ) and/or reduced input use ( _l_ and _k_ ) will lift up the agricultural rent curve. The effect does _not_ depend on the type of technological progress.

While simple and straightforward, this conclusion is an important one and is present in all cases discussed below. Technological progress in agriculture at the forest margin increases the 19 See Angelsen and Kaimowitz (2001) for a general overview of this debate. Angelsen and Kaimowitz (2004) discusses this with particular reference to agroforestry, while Kaimowitz and Angelsen (2006, forthcoming) reviews this debate with reference to livestock intensification in Latin America.

19