rafmacalaba/datause-encoder-data huggingface dataset:
types of FDI flows in the face of large changes in their relative magnitude – such as those witnessed over the last decade – could bias the inferences on the relationship of total FDI with investment and growth.

The purpose of this paper is to address these concerns<sup>7</sup> by examining the link between the two components of FDI flows – greenfield and M&A -- and their respective relationship with aggregate investment and growth in a large cross-country time-series data set. The main objective of the analysis is to identify the facts present in the data, rather than exploring the ability of a particular model to explain the empirical regularities. Specifically, the paper focuses on establishing the patterns of time precedence between FDI, investment and growth. Thus, it follows an approach similar to those adopted by recent influential studies that have attempted to determine the patterns of causation between saving, investment and growth (Carroll and Weil 1995; Blomstrom, Lipsey, and Zejan 1996; Attanasio, Picci and Scorcu 2000).

The paper extends the existing literature along two dimensions. First, it provides what to the best of our knowledge is the first exploration of the dynamic relation between greenfield and M&A foreign investment.<sup>8</sup> Second, it uncovers systematic differences between these two components of FDI flows regarding their respective relationship with investment and growth in the destination economies.<sup>9</sup> The paper performs extensive robustness checks by employing a variety of econometric specifications and working with various country samples in order to allow

> 7 Although we will not pursue it here, we should also mention a third concern recently raised by Fernández-Arias and Hausmann (2000), according to which the boom in FDI to developing countries would reflect the sorry state of their markets and institutions which forces domestic investors to sell off their local assets, rather than providing proof of sound economic management, as had been argued in the past.

> 8 There are strands of the FDI literature that focus on other aspects of the separation between greenfield and M&A FDI.  Some of them investigate the determinants of the mode of entry by foreign firms --that is, greenfield vs. M&As-- (e.g. Blömstrom, Kokko, and Zejan 2000 and Görg 2000), while others analyze the welfare implications of various modes of entry on the host economy from a theoretical perspective (e.g. Mattoo, Olarreaga, and Saggi 2001 and Ferrett 2003).

4
- Santaeul`alia-Llopis, R. and Zheng, Y. (2015). The Price of Growth: Consumption Insurance in China 1989-2009. Working Paper, Washington University in St. Louis and City University of Hong Kong.

- Tertilt, M. (2012). The Research Agenda: Mich ́ele Tertilt on Gender Economics. Economic Dynamics Newsletter - November -2012.

- Townsend, R. M. (1994). Risk and Insurance in Village India. _Econometrica_ , 62(3):539–91.

- Udry, C. (1994). Risk and Insurance in a Rural Credit Market: An Empirical Investigation in Nothern Nigeria. _Review of Economic Studies_ , 61(3):495–526.

- Udry, C. (1995). Risk and Saving in Northern Nigeria. _American Economic Review_ , 85(5):1287– 1300.

- Young, A. (2005). The Gift of the Dying: The Tragedy of AIDS and the Welfare of Future African Generations. _Quarterly Journal of Economics_ , (120):423–466.

- Young, A. (2007). In Sorrow to Bring Forth Children: Fertility amidst the Plague of HIV. _Journal of Economic Growth_ , (12):283–327.

- Young, A. (2012). The African Growth Miracle. _Journal of Political Economy_ , 6(120):696–739.

- Young, A. (2013). Inequality, the Urban-Rural Gap and Migration. _The Quarterly Journal of Economics_ .

45
empirical evidence casts doubt on that assumption. Relevant evidence includes:<sup>7</sup> (i) evidence that rejects a unitary model of the household, suggesting new sources of inequality within households; (ii) studies explaining the ‘missing women’ phenomenon; (iii) evidence of discrimination against certain household members such as orphans and widows; and (iv) evidence of unequal exposure to transitory shocks. Heterogeneity in factors influencing individual poverty can also mean that transfers to poor households often miss deprived individuals. It is important for policy makers to know whether standard household data sources can be relied upon to also reach poor individuals.

This paper tries to throw light on how well widely-used household-based measures perform in identifying disadvantaged individuals. Are we reaching such individuals adequately by simply targeting “poor” households? Or do many of them live in households that are not identified as poor? Is it harder or easier to reach vulnerable women and children using household data in settings in which the incidence of individual level disadvantage is high or average income is low?

Missing data on individual-level poverty present a significant hurdle to examining these issues. However, there is one dimension of individual welfare that can be observed in many surveys, namely nutritional status as indicated by anthropometric measures. Undernutrition can stem from inadequate caloric intakes or deficiencies in protein or micronutrient intakes, or from illness that impedes nutritional absorption. Such nutritional deprivations are of direct and immediate concern, and there is also evidence of longer-term social and economic costs, especially of low birth weight and chronic undernutrition in childhood. Although nutritional status admittedly represents only one dimension of individual poverty, there can be no doubt that it is an important dimension. It is also frequently used as a proxy for individual welfare.

The paper uses undernutrition as the measure of individual welfare to explore the questions posed above. We use data for 30 countries in Sub-Saharan Africa (SSA), where chronic undernutrition among children is a major policy concern. The latest data at the time of writing indicate tha <mark>t the count of stunted children in SSA has risen by 12.5 million since 1990. The incidence of child stunting in SSA today is probably the highest of any of the standard geographic groupings of countries.</mark><sup>8</sup> <mark>W</mark> e draw on anthropometric data for 390,000 women and children from the Demographic and Health Surveys (DHS).

> 7 We provide references on these points later.

> 8 These observations are from the World Bank’s <u>website</u> on nutrition and the latest available estimates compiled by UNICEF. Historically, South Asia has been the region with highest incidence but that region has been making greater progress than SSA in this respect. Also see the discussion in Smith and Haddad (2015). Differences in population growth also affect this shift to SSA of the global share of the undernourished.

3
- 27 -

Figure 12. Predicted Bid Amount per Unit: Water <!-- Start of picture text --> 1200<br>1000<br>800<br>600<br>400<br>Avg. number<br>200 of bidders per Bid amount<br>auction = 6.2<br>± 2 Std. Err.<br>0<br>2 3 4 5 6 7 8 9 10 11 12 13 14 15<br>Number of bidders<br>Source: Author’s calculation.<br>Figure 13. Predicted Bid Amount per Unit: Electricity<br>0.9<br>Bid amount<br>0.8<br>Avg. number of bidders ± 2 Std.<br>0.7 per auction N =4.6 Err<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>2 3 4 5 6 7 8 9 10<br>Number of bidders<br>Source: Author’s calculation.<br>(million US$)<br>Predicted bid amount per million m3 per day<br>Predicted bid amount per MW (million US$)<br><!-- End of picture text --> In addition, the predicted unit cost is significantly affected by contract design, especially the size of contract. When the estimated equilibrium bid function for road projects is evaluated with different lengths of roads, it is evident that a road of less than 10 km would be extremely expensive (Figure 14). Hence, how to design lot packages is an important issue. As expected, large electricity projects have a lower unit cost, because of economies of scale. This fact calls for close collaboration among subnational governments—or communities, or neighboring countries, depending on the individual context—at the project preparation stage. Similarly, small projects with less than 100,000 m<sup>3</sup> of water treatment capacity or less than 100 MW of electricity installed capacity will be expensive disproportionally (Figures 15 and 16). However, large contracts will create another problem that only a small number of firms can apply for such auctions. There is a tradeoff between competition intensity at auctions and economies of scale in procurement.
# **6. Conclusions**

An ongoing and large-scale civil war in Tigray has resulted in significant human suffering and economic disruptions, which are still being experienced at the time of writing. To make matters worse, besides the direct effects of the war on lives and livelihoods, what the UN calls a de facto blockade of the region has obstructed humanitarian assistance while also limiting our understanding of the breadth and consequences of the armed conflict.<sup><u>34</u></sup> One fortuitous aspect of this situation, however, has been the opportunity to repurpose a high frequency phone survey (HFPS), originally developed by the World Bank to monitor the COVID-19 situation and impacts in Ethiopia and continued to monitor household food security and economic activities amid an armed conflict. Using these survey data, in tandem with armed conflict location and events data (ACLED), we show evidence of significant disruptions to livelihoods, with negative impacts on household food security and access to food in the first few months of the war covered by the HFPS data. We find that, as a result of the conflict, moderate or severe food insecurity increased by 38 percentage points. Using the ACLED granular data on households’ exposure to violent conflict, we show that exposure to an additional battle within 20 km radius leads to 1 percentage point increase in the probability of moderate to severe food insecurity. Similarly, affected households report a 10 percentage point reduction in non-farm activities and 15 percent reduction in wage employment within two months of the start of the war. Farming, on the other hand, was notably resilient to impacts of the war, with participation in farm activities remaining relatively steady.

The HFPS sample is a subsample of a nationally representative sample of households drawn for an in-person survey in 2019. It covers households who reported phone ownership in the in-person survey. Due to this non-random sample and additional complication of war related nonresponse and displacements of households during the survey period, the survey is likely to include those who are relatively less exposed and less vulnerable to the vicissitudes of the war. While we try to account for systematic non-response and attrition using sample weights constructed using a long list of observables, the actual negative impacts of the conflict are likely to be larger on average, and it is difficult to fully quantify the extent to which this is the case. Moreover, we note that these impacts refer to the first few months after the onset of the crisis but also in months relatively closer to the harvest season. We know that even in normal times food

> 34 <u>https://www.reuters.com/world/un-warns-catastrophe-looms-ethiopias-north-urges-government-end-de-facto-aid2021-09-02/</u>

38
**Figure 12: Electronic Business Registration, by Region**

<!-- Start of picture text --> Countries with e-registration Countries with e-distribution<br>100%<br>14/16<br>16/20 13/16<br>80% 53/75<br>10/17 7/11<br>6/11<br>60%<br>32/75<br>6/17 4/11<br>40% 3/11 6/20<br>20%<br>0%<br>Africa and Middle Asia Eastern Europe Latin America Industrialized Total<br>East and Central Asia and the countries<br>Caribbean<br><!-- End of picture text --> Source: World Bank Group Entrepreneurship Database, 2007.

Moreover, the registries were questioned on the information businesses were required to file, as well as if they registered any other information besides business incorporations. We find deep disparities among regions (not shown). When it comes to the information the companies are required to register, the majority of them oblige businesses to report closures and annual financial statements. Nevertheless, not all countries have the mechanisms to enforce these requirements. In addition, while business registries in industrialized countries tend to stand alone, and only in some cases register internet domains, developing countries tend to have registries where businesses, real state, internet domains and patent registrations coexist.

# **_7.2   Impact of E-Registry on the Ease of Doing Business_**

Once the typology of the registry is defined, the survey aimed at understanding how different typologies impact entrepreneurship and the ease of doing business. In this regard, the data show that countries with e-registries tend to have shorter incorporation time frames, with less bureaucratic and cheaper procedures. For instance, the number of days necessary to incorporate a business is on average 21% lower in countries with e-registries, and the number of procedures is 23% lower, as shown in Figure 13.

28
Our data are subject to all the concerns that bothered Kuznets, other economists, and the present authors. Our income inequality statistics exploit fragile measures of annual household income, without adjustment for taxes and transfers, life-cycle patterns, or household composition. None of our ancient inequality observations would rate a “1” on the Deininger-Squire scale. Yet, like Gregory King in the 1690s and Simon Kuznets in the 1950s, we must start somewhere. Section 2 begins by introducing some new concepts that we use for the analysis -- the _inequality possibility frontier_ and the _inequality extraction ratio_ , measures of the extent to which the elite extract the maximum feasible inequality. These new measures open the door to fresh interpretations of inequality in the very long run. The next section presents our ancient inequality evidence. Section 4 examines income gaps between top and bottom, and the extent to which observed inequality change over the very long run is driven by those gaps as opposed to the distribution of income among those at the top or the top’s income share. Section 5 explores how the stylized facts are changed when conventional annual income measures are replaced by lifetime income measures. We conclude with a research agenda.

# **2. The Inequality Possibility Frontier**

The workhorse for our empirical analysis of ancient inequalities is a concept we call the _inequality possibility frontier_ . While the idea is simple enough, it has surprisingly been overlooked by past authors. Suppose that each society, including ancient nonindustrial societies, has to distribute income in such a way as to guarantee subsistence minimum for its poorer classes. The remainder of the total income is the surplus that is 5
# **INTRODUCTION**

At first glance, unemployment does not seem to be a large problem in Moldova. After all, the unemployment rate is low, lower than in most transition economies in Central and Eastern Europe (CEE). Does this mean that the labor market performs well? Is job creation high, making it easy to find a new job and escape unemployment? Or does low unemployment mean slow enterprise restructuring, and thus limited job destruction and low inflows into unemployment? This paper attempts to answer these questions by examining labor market dynamics in Moldova, and by specifically focusing on job creation and job destruction. The paper has three objectives. The first is to examine more closely a broader spectrum of measures of labor market performance, focusing on job creation and job destruction, and thus to move beyond the unemployment rate. The second goal is to identify the main factors behind observed labor market outcomes. And the final objective is to recommend actions to improve labor market performance.

The paper finds that despite the low unemployment rate, the labor market in Moldova is depressed and labor resources are substantially under-utilized. The main source of the problem is a low job creation rate, rather than a high job destruction rate. A primary factor contributing to low job creation is the high cost of doing business in Moldova. Improving the business environment is thus the key for better labor market performance, employment growth and ultimately poverty reduction.

The labor market in Moldova has not been analyzed much, with one probable reason being the scarcity of data. The most comprehensive existing study is that by Lindauer (1999), however the author was able to use only aggregate data on major labor market trends and patterns. This paper is the first that utilizes firm-level data coming from the Annual Survey of Enterprises (known as ASA), which made it possible to
# TABLE OF CONTENTS

# Abstract

|I.|Introduction. ...<br>............ .............<br>1|........|
|---|---|---|
|II.|A Systems Model for Skills Development ....................|. 4|
|III.|Linking Economic Policies to VET<br>............. ...............|10|
||Economic Policies Involving Factor Markets<br>............<br>The Market for Labor ...............<br>..........<br>The Market for Capital..................<br>.......<br>Economic Policies Involving Product Markets<br>...........|11<br>11<br>22<br>..<br>25|
|IV.|Concluding Thoughts andaResearch Agenda<br>.................|. 28|
||Technology Choices<br>.................................<br>et.....<br>Developmen**t**<br>S rategies .....<br>............<br>.........<br>.<br>t.......|.<br>31<br>. 32|
||Wage<br>Compression..........................<br>O.*.<br>ET Financinge|34<br>35|

References
25 Figure 22 <!-- Start of picture text --> Generalized Impulse Response(s) to one S.E. shock in the equation for<br>LGOVCONP<br>0.02<br>0.01<br>0.00<br>-0.01 LGDPPC<br>-0.02<br>-0.03<br>-0.04<br>0 1 2 3 4 5 6 7 8 9 10<br>Horizon<br> Generalized Impulse Response(s) to one S.E. shock in the equation for<br>LGOVCONP<br>0.008<br>0.006<br>0.004<br> TOTTAXGDP<br>0.002<br>0.000<br>-0.002<br>0 1 2 3 4 5 6 7 8 9 10<br>Horizon<br><!-- End of picture text --> Figure 23 Figure 24 <!-- Start of picture text --> Generalized Impulse Response(s) to one S.E. shock in the equation for<br>LGOVCONP<br>0.02<br>0.01<br>0.00<br>-0.01<br> LKSTPRPC<br>-0.02<br>-0.03<br>-0.04<br>-0.05<br>0 1 2 3 4 5 6 7 8 9 10<br>Horizon<br><!-- End of picture text -->
**Table 5: Impact on Employment**

||Before|During|A|fter Subsid|y|p‐value|p‐value|
|---|---|---|---|---|---|---|---|
|Sample size|Subsidy|Subsidy|Year1|Year2|Year3+|equality|all zero|
|**Panel A: Number of Paid Workers**||||||||
|Assigned to Treatment<br>4824|‐0.077|0.204***|0.124|0.047|‐0.023|0.043|0.027|
||(0.082)|(0.075)|(0.078)|(0.080)|(0.084)|||
|Control Mean|0.48|0.48|0.45|0.51|0.60|||
|**Panel B: Any Paid Worker**||||||||
|Assigned to Treatment<br>4824|‐0.014|0.140***|0.111***|0.024|‐0.002|0.000|0.000|
||(0.036)|(0.035)|(0.034)|(0.035)|(0.032)|||
|Control Mean|0.25|0.27|0.24|0.28|0.29|||
|**Panel C: Added a Worker Between Survey Rou**|**nds**|||||||
|Assigned to Treatment<br>4614|‐0.008|0.109***|0.015|‐0.041**|‐0.003|0.000|0.000|
||(0.036)|(0.024)|(0.020)|(0.020)|(0.017)|||
|Control Mean|0.19|0.11|0.10|0.14|0.13|||
|**Panel D: Subtracted a Worker Between Survey**|**Rounds**|||||||
|Assigned to Treatment<br>4614|‐0.022|‐0.007|0.017|0.028|‐0.005|0.413|0.546|
||(0.016)|(0.019)|(0.022)|(0.020)|(0.016)|||
|Control Mean|0.05|0.11|0.14|0.10|0.11|||
|**Panel E: Own Hours Worked in the Business**||||||||
|Assigned to Treatment<br>4882|‐0.580|0.664|2.919|3.932*|4.440**|0.423|0.189|
||(2.261)|(1.910)|(1.978)|(2.076)|(1.960)|||
|Control Mean|47.3|51.3|44.0|45.0|42.8|||
|**Panel F: Number of Unpaid Workers in Busines**|**s**|||||||
|Assigned to Treatment<br>4785|‐0.045|0.008|‐0.026|0.022|0.008|0.646|0.776|
||(0.047)|(0.035)|(0.044)|(0.038)|(0.037)|||
|Control Mean|0.21|0.18|0.26|0.17|0.22|||
|Notes: robust standard errors in parentheses,<br>strata, variables used for re‐randomization, th<br>survey round dummies. *, **, *** indicate sign<br>Regressions are unconditional, and assign zero<br>p‐values are for test that the treatment effect i<br>and that the treatment effect is zero in all four|clustered a<br>e baseline<br>ificance at<br>to the out<br>s equal in<br>periods.|t the firm l<br>value (exce<br>the 10, 5, a<br>come for fi<br>the during,|evel. All re<br>pt for pan<br>nd 1 perce<br>rms not op<br>year 1, yea|gressions c<br>els C and D<br>nt levels r<br>erating.<br>r 2, and ye|ontrol for<br>on churn<br>espectively<br>ars 3 to 4|randomiz<br>), and con<br>.<br>periods;|ation<br>tain|

41
19 middle child mortality improve their coverag.<sup>on average from 57 to 75 per</sup> cent for tuberculosis, from 47 to 70 for diphtheria<sup>and tetanus, from 46 to 74</sup> per cent for poliomyelitis, and from 40 to 59<sup>per cent for measles.15</sup> Data compiled from the WHO Expanded Program on<sup>Immunization (Tables</sup> Health 3 to 6) indicate a continuous improvement<sup>of immunization coverage in</sup> the second part of the 1980s for all regions.<sup>Yet, coverage against every</sup> disease as of 1985 was still below 50 per cent<sup>for Africa. For South Asia, it</sup> was below 50 per cent for all diseases but<sup>Diphtheria, Pertussis and Tetanus</sup> (DPT), but it would have been for DPT too if<sup>it were not for India. East Asia</sup> is well above 50 per cent for each disease, and<sup>China is above 75 per cent for</sup> each too. ENENA and IAC countries immunize<sup>on average two thirds of their</sup> children. Eastern Europe reports rates of coverage<sup>close to 100 per cent for</sup> all diseases. However, recent information<sup>of the health status of Eastern</sup> European children indicate that these<sup>figures might have been overestimated.</sup> When compared to the 1970s and early 1980s,<sup>the immunization data show</sup> the dramatic speeding up of the pace of coverage<sup>increase. Although the</sup> reduced s.ze of the time frame does not permit<sup>much intertemporal comparisons,</sup> there is no doub' as to the improvement<sup>of the global situation. Africa alone</sup> jumps by 350 per cent between 1985 and 1986<sup>which was declared African</sup> Immunization Year. According to EPI<sup>experts, a[p]rogress achieved in</sup> different [rlegions largely reflects differences<sup>in socioeconomic development</sup> 15UNICEF (1989), op.cit. p.98-99.