rafmacalaba/data-use-mentions-extended huggingface dataset:
. ***, ** and * denote significance at the 1, 5 and 10 percent levels, respectively. All equations include time period and group-specific dummies. Data for the transition economies refer only from 1997 onwards. All equations include time period and group-specific dummies.

The estimations confirm our earlier results; see Table 7. In fact, the growth impact of foreign capital in the EU10 and EU candidate countries is even stronger; the magnitude of the coefficient is about three times larger than the previous estimates. Two hypotheses are worth putting forward. First, the impact on growth is not a statistical artifact of the high, but unsustainable, growth episodes of the pre-crisis period among European transition countries. Second, the excesses of the pre-crisis period were not as supportive of medium-term growth. A more detailed assessment, however, is hampered by the small sample size. Still, we believe the evidence is sufficiently clear in that prospective EU membership has been a sound impetus for institutional and economic reforms in the EU10 and the EU candidate economies, and this has made it possible for them to strengthen their capacity to absorb effectively foreign capital. Of course the pre-crisis excesses were also not as supportive of medium-term growth and thus there is much to learn from the case of emerging European countries with more sustainable growth experiences.

20
natural monopoly sectors, and 52 industries are excluded (see Annex Table B1 for the list of partially contestable and natural monopoly sectors).<sup>7</sup>

Table 2: Sector Taxonomy Based on Economic Rationale Justifying SOE Presence

|**Sector taxonomy**|**Definitions**|
|---|---|
|Competitive Sectors|Industries that feature little to no entry barriers and are commercially viable for<br>multiple firms to operate (e.g., manufacturing of food and beverages, and taxi<br>operation). See Annex Table B1 for the list of competitive sectors.|
|Partially contestable sectors|Industries characterized by some form of market failure such as market power<br>resulting from structural barriers (i.e., barriers that relate to industry conditions,<br>such as economies of scale, network economies, fixed costs, and networks), and<br>oligopolistic structures (e.g., aviation, radio broadcasting, and banking). See<br>Annex Table B1 for the list of partially contestable sectors.|
|Natural monopoly sectors|Industries which feature high entry barriers, scale economies, or sub-additivity<br>cost structures (i.e., when a single firm can produce a product at lower cost than<br>the combined cost of two or more firms producing identical products), thus<br>making them more efficient for just one producer to operate (e.g., water utilities,<br>energy transmission, and postal activities with universal obligation). See Annex<br>TableB1 forthelist of natural monopoly sectors.|

Source: The taxonomy was developed by Dall'Olio et al. (2022b) to supplement the World Bank BOS database.

# 4. Empirical Methodology

**This section presents the empirical methodologies behind the main results** . It presents the econometric approaches used to (a) assess the differential performances between the
(2014) compute elasticities of 0.08 and 0.11 pre- and post-2000, respectively, which is comparable to our results.

> 46The availability of quarterly data is critical for relying on Blanchard and Perotti’s (2002) identification method since Blanchard-Perotti assumes that output can respond to government spending within the period but government does not respond to GDP. In other words, all contemporaneous correlation is attributed to fiscal policy affecting GDP. See Ilzetzki et al. (2013) for a detailed discussion.

> 47The sample is too small to further break it down into CE and non-CE. If we computed multipliers for AE and EMDE, the values would be broadly similar (0.67 for AE and 0.57 for EMDE). Ilzetzki et al. (2013) also report multipliers below 1 after 4 quarters for both industrial and developing economies.

> 48 We compute g/y as the average over the sample period for each country.

21
|||||||
|Initial EGI|||0.416*||0.498||0.551||0.606|
||||(0.236)||(0.323)||(0.375)||(0.466)|
|50-100km from Aggl. centerx||||||||||
|InitialEGI|||0.502*||0.672*||0.659*||0.751|
||||(0.293)||(0.404)||(0.395)||(0.477)|
|100-150km from Aggl. center||||||||||
|x InitialEGI|||-0.493||-0.309||-0.155||-0.025|
||||(0.492)||(0.687)||(0.501)||(0.695)|
|Number ofObservations<br>|5063<br>|5258<br>|5258<br>|5258<br>|5258<br>|5210<br>|5210<br>|5210<br>|5210<br>|
|Numberofplants|1065|1116|1116|1116|1116|1104|1104|1104|1104|

Notes: Estimations consider HGF dummy for Ethiopian manufacturing using establishment data from 1996 to 2009 from the Large and Medium Manufacturing Industries Survey from the Ethiopian Central Statistical Agency. Explanatory variables of interest include 3-year lagged plant-level TFP computed using the Levinsohn and Petrin methodology. Estimations report standard errors, clustered at the plant level. Agglomeration center is a dummy variable for plant location in top-five towns, which is defined as 1 if plant is located in any of the following cities/towns: Addis Ababa, Awassa, Bahir Dar
pertaining to all crop/livestock/fishing activity):

- (a) only for sale/barter; (b) mainly for sale/barter but some for own/family use; (c) mainly for own/family use but some for sale/barter; or (d) only for own/family use?

There are variations in question design — the Malawi survey asks boundary questions for each of the five most important crops; the Nigeria, Ethiopia, and planned future LSMS-ISA surveys ask one question across all agricultural activities. The data across these surveys reflect similar trends, however. Table 1 shows that in both Malawi and Nigeria, own-use production activity in agriculture is high, and significantly higher for women than men (for Nigeria, in the post-harvest round). Under the 19th ICLS standards, these individuals would no longer be considered self-employed in agriculture — Desiere and Costa (2018) also find that reported employment in agriculture in the Nigeria survey falls considerably under the new standards because of high shares of work in own-use production. Including separate questions in surveys on ownuse production can ensure that the significant time women spend in this work, particularly in poorer agricultural contexts, is not missed in employment surveys.

Table 1. Malawi and Nigeria LSMS-ISA: intended destination of own-farm products in last 7 days, men and women aged 15+ (own-reporting sample)

||Poo|r househ|olds<sup>(b)</sup>|No|n-poor ho|useholds<sup>(b)</sup>|
|---|---|---|---|---|---|---|
||Men<br>(A)|Women<br>(B)|Gender gap<br>(A-B)<sup>(c)</sup>|Men<br>(A)|Women<br>(B)|Gender gap<br>
_Figure 5. Education distribution: Working-age population of Syrians who became -_ _<u>refugees in Lebanon, Jordan, and KRI (2010) and all working age Syrians (2011)</u>_ <!-- Start of picture text --> 80.0<br>70.0<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>Secondary<br>Never<br>Primary or Intermediat (Highschoo Graduate or<br>attended,<br>less e l or more<br>illiterate<br>Vocational)<br>All Syrians, 2010 5.2 45.8 15.8 23.0 10.2<br>Lebanon, attended 13.8 31.2 37.7 14.5 2.8<br>Kurdistan, completed 15.0 49.6 17.1 9.8 8.5<br>Jordan, completed 8.1 70.5 8.3 8.4 4.8<br>SHARE (%)<br><!-- End of picture text --> _<u>Notes</u>_ <u>: Authors’ own tabulation using data from SRHCS.</u> _Figure 6. Distribution of the pre-crisis, working-age, employed population of Syrian_ _<u>refugees (2010) and all Syrians (2011)</u>_ <!-- Start of picture text --> INDUSTRY CATEGORY<br>Syrian refugees, Lebanon Syrian refugees, Kurdistan<br>Syrian refugees, Jordan All Syrians (2011)<br>35.6 34.2<br>25.5 25.0<br>23.0<br>SHARE 15.1 12.0 17.2 13
Ugandan Ministry of Education and Sport and UNESCO IIEP Pôle de Dakar. (2014). _Teacher Issues in_ _Uganda: A shared vision for an effective teachers policy._ United Nations Statistics Division. (2007). International Standard Industrial Classification of All Economic Activities Revision 4, Series M: Miscellaneous Statistical Papers, No. 4 Rev. 4, New York: United Nations. Retrieved from <u>https://unstats.un.org/unsd/classifications/Family/Detail/27</u> UNESCO. (2009). _Universal primary education in Africa: the teacher challenge._ Retrived from <u>https://unesdoc.unesco.org/ark:/48223/pf0000186643.</u> UNESCO. (2020). Institute for Statistics Education Statistics. Retrieved from: <u>http://data.uis.unesco.org/</u> UNESCO. (2016). _The World Needs Almost 69 Million New Teachers to Reach the 2030 Education_ _Goals_ . Retrieved from <u>http://uis.unesco.org/sites/default/files/documents/fs39-the-world-needsalmost-69-million-new-teachers-to-reach-the-2030-education-goals-2016-en.pdf</u> <mark>U.S. Bureau of Labor Statistics. (2018). May 2018 National Occupational Employment and Wage</mark>

- <mark>Estimates United States. Retrieved from</mark> <u><mark>h</mark> ttps://www.bls.gov/oes/current/oes_nat.htm#25-0000</u>

- <mark>U.S. Bureau of Labor Statistics. (2021). Labor Force Statistics from the Current Population Survey.</mark>

<mark>Retrieved from https://www.bls.gov/cps/cpsaat18.htm</mark> <mark>World Bank. (2018).</mark> _<mark>Learning: To Realize Education's Promise<
become multi-topic and multilevel surveys using several questionnaires to measure different aspects of living standards. While unique in their efforts to collect representative information on HHs’ living standards, HCES might unintentionally undersample unregistered or vulnerable populations which are often excluded from the standard census on which HCES often rely for sampling. As a result, certain population groups might be inadvertently omitted or underrepresented from surveys including the homeless, internally displaced people, refugees, informal slum dwellers, or nomadic populations (e.g., CarrHill 2013; Lucci et al. 2018). While the _AHI_ is bound by the available micro-level data, we undertake robustness checks, to account for this potential underrepresentation of e.g., slum areas in the HCES.

7 A draft working paper is available from the authors on request.
This conclusion does not apply to labor earnings in the informal sector though. If data from a representative household survey are used, instead of data from a sample of formal sector firms, a negative association between labor earnings and local unemployment rates can be found. This was shown by Hoddinott (1996) using urban data from the 1985, 1986 and 1987 rounds of the CILSS. Their results also indicate that the negative influence of unemployment on labor earnings is stronger in the case .of young workers, and weaker in the case of public sector employees. Overall, and after controlling for a number of potentially serious econometric problems, most notably unobserved location-specific effects, Hoddinott estimates the wage-unemployment elasticity of Cote d'Ivoire at -0.13. This is similar to the -0.1 elasticity reported by Blanchflower and Oswald (1994) for the US, Britain and other industrial countries.

# b) Nominal rigidity

The case to devalue the CFA Franc rested in part on the premise that formal sector wages were rigid downwards. The conventional wisdom is that the adverse shocks of the late 1970s led to a drop of aggregate demand, hence creating a substantial deflationary pressure. While the labor earnings of those who were not protected by the regulations in force declined, the story goes, workers in the formal sector succeeded in preventing their wages to be cut. Labor costs in formal sector firms thus failed to match the decline in aggregate demand, and this misalignment was at the roots of a deep recession. While this interpretation of the economic developments of the 1980s and early 1990s is plausible, there is little empirical evidence to support it.

If the conventional wisdom was right, over the 1980s and early 1990s there should have been a decline of labor earnings in the informal sector and a relative stability of wages in the formal sector. Moreover, if the downward rigidity resulted, as it is claimed, from labor market policies and institutions, trends in formal sector wages should have reproduced those of the SMIG. This is because the
Kim, L. 1997. Imitation to Innovation: The Dynamics of Korea's Technological Learning. Harvard Business School Press.

Koopman, Robert, Zhi Wang and Shang-jin Wei. 2008. ―How Much of Chinese Export Is Really Made in China: Assessing Domestic Value-Added when Processing Trade Pervasive,‖ NBER Working Paper 14109, National Bureau of Economic Research, Cambridge, Massachusetts.

Krueger, A. O. (1978). Foreign Trade Regimes and Economic Development: Liberalization Attempts and Consequences. Volume X. Ballinger Publishing Company. Cambridge, Massachusetts.

Krueger, A. O. (1983). _Trade and Development in Developing Countries: Synthesis and Conclusions, University of Chicago Press, Chicago and London._ Krueger, A. O. (1997). Korean Industry and Trade over Fifty Years. In D.-S. Cha, K. S. Kim, & D. H. Perkins (Eds.), The Korean Economy 1945-1995: Performance and Vision for the 21st Century (pp. 293338). Korea Development Institute .

Kumagai, Satoru 2008. ―A journey through the Secrete History of the Flying Geese Model‖, Institute of Developing Economies (IDE) Discussion paper 158. June 2008.

Kuznets, Simon. 1930. _Secular Movements in Production and Prices._ Kuznets, S., 1966. _Modern Economic Growth: Rate, Structure and Spread, New Haven, CT and London: Yale University Press._ Kwan, C.H. (2002), ―The Rise of China and Asia‘s Flying-Geese Pattern of Economic Development: An Empirical Analysis Based on US Import Statistics‖ The Research Institute of Economy, Trade and Industry (RIETI) Discussion Paper Series 02-E-009, July.

Lim, W. (2011). Joint Discovery and Upgrading of Comparative Advantage: Lessons from Korea's Development Experience. In S. Fardoust, Y. Kim, & C. Sepulveda (Eds.), Postcrisis Growth and Development (pp. 173-226). Washington DC
01|15.04|
|November||3.20<br>22.01|-.36|
|December||16.80<br>25.55|48.39|
|January|1986|<br>32.96<br>30.56|-10.35|
|February||7.95<br>-14.43|11.67|
|March||.07<br>2.39|4.13|
|April||3.59<br>-1.76|8.57|
|May||.97<br>1.88|10.53|
|June||4.26<br>-.90|2.93|
|July||1.76<br>-.69|6.91|
|August||.66<br>.06|-.07|
|September||2.28<br>-.52|9.06|
|October||.58<br>.57|9.19|
|November||-.11<br>.14|10.36|
|December||.65<br>6.1|8.25|

# Source: Morales (1987.b)

/a Month to month changes of monthly average levels.

/b End of month to end of month changes.
input data file, (2) the number of subgroups, (3) the number of records, (4) the type of data configuration (codes 1 through 8), and (5) the DOS name for the output file. Once this input has been received for each of the two specifications of the Lorenz curve underlying the simulation tool, the program provides an estimate of the Lorenz curve, along with relevant statistical summary measures. It prompts the user to supply a poverty line and a different estimate of the mean of the distribution (if he/she does not want to use the estimate based on the data).

# **Box 1: Lorenz Curve Approach: POVCAL Assessing Poverty Dynamics in Madagascar**

Essama-Nssah (1997) uses POVCAL to analyze the dynamics of poverty in Madagascar between 1962 and 1980. Over this period, different governments showed various levels of concern over poverty issues. Some of the policy choices targeted either the rural or the urban sector. From the early 1960s to the mid-1970s, public policy favored the rural sector by lifting the poll and cattle tax applicable to the sector and by providing farmers free access to agricultural inputs. In 1977, an urban bias was introduced in public policy when the government increased the minimum wage and decided to subsidize basic items such as rice, edible oils, and condensed milk.

The study is based on two published aggregated data sets on income distribution in the rural and urban sectors. These data are in the form of class intervals with the associated frequency and mean incomes. The analysis through POVCAL revealed that, while poverty in Madagascar remained a predominantly rural phenomenon, poverty generally increased between 1962 and 1980 in both rural and urban areas. A decomposition of the poverty outcomes into growth and inequality components showed that increased income inequality in the rural sector was the major cause of the observed increase in rural poverty (see the table below). In the urban sector, however, increased poverty was most likely due to the lack of economic growth.
# **Table 11**

# **FG and financial recoupling: Robustness tests and causality**

This table presents estimations of the relationship between financial recoupling and financial globalization. All results are from Arellano and Bond difference estimator. FG Flows are absolute values of net inflows to a country by global and global emerging mutual funds normalized by market capitalization. Dependent variables are dlog returns from EM assets (MSCI country indexes, sovereign spreads, and exchange rate, LCU/USD). Sample goes from 2005-09/2010 due to data availability. Normal and turmoil divides the sample between positive (or zero) and negative returns from the global asset respectively. Different type of instruments were used. External indicates that instruments for country i were FG flows to EM minus FG flows to country i. Internal indicates that the first lag of the dependent variables were used as instruments. Robust standard errors in parentheses. *, **, and *** denotes significance at the 10, 5 and 1% level respectively. Source: MSCI, GEM World Bank, Bloomberg, US Treasury, EPFR, Barclays Capital, GEM World Bank, IMF IFS Statistics.

|Sample|Full Sample|Turmoil|Normal|Full Sample|Turmoil|Normal|
|---|---|---|---|---|---|---|
|**Instrument Type**|**External**|**External**|**External**|**Internal**|**Internal**|**Internal**|
|||**Equity**|||||
|S&P Returns|0.255***|0.047|0.503***|0.474***|0.432***|0.564***|
||(0.089)|(0.140)|(0
by public investment** . A vector autoregression is conducted to estimate crowding‐in of private investment by public investment for eight EMDEs with available data for 1998Q1‐2016Q2. A decomposition of investment into private and public investment is only available for a restricted sample of EMDEs. The sample includes Bulgaria, Czech Republic, Hungary, Mexico, Poland, Romania, Slovak Republic, and Turkey. These countries are highly open and rank above the EMDE average in the World Bank Doing Business indicators. Variables included are, in this ordering: real government investment, real GDP, real private investment, current account balance, and the real effective exchange rate. The results are statistically significant within the usual 16‐84 percent confidence bands.

**E. Investment growth and reforms** . Values in columns of Figure 19 are based on a panel data regression in which the dependent variable is real investment growth. A spurt (setback) is defined as a two‐year increase (decrease) by two standard deviations in one or more of the following four measures of the Worldwide Governance Index (WGI): regulatory quality, government effectiveness, rule of law, and control of corruption. The WGI indicators are principal components of a wide range of survey based and other indicators. For each index, the standard deviation is measured as the average of the standard errors of the WGI Index in the beginning and at the end of each two‐year interval. Episodes in which there were improvements in one measure and simultaneous setbacks in another are excluded. The sample spans 97 EMDEs over 1996 ‐ 2015, and excludes EMDEs with populations less than 3 million.

Let t denote the end of a two‐year spurt or setback. The coefficients are dummy variables for spurts and setbacks over the [t‐3, t+2] window around these episodes. In Figure 19, “Reform” denotes the t=[‐1,0] window (i.e., around the two years of improvement/deterioration
_∗_</sup> _i_<sup>.IntheOLSmodel,incontrast,apositivevalueof</sup><sup>_βk_impliesthat</sup> an individual with a higher value of _xik_ adopts later in expectation.

From the model of the previous section, one key explanatory variable of interest is the income gain that an individual expects from the transition. Recall that, in our simple model, an individual starts saving to finance the new technology as quickly as possible only if the expected net gain from adoption is positive. Unfortunately, the researcher does not observe individual _expected_ net gain but only a measure of _realized_ net gain, which � � can be retrieved from _μFi_ and _μCi_ . We interpret realized net gain as a proxy for expected net gain. More specifically, when individual expectations are unbiased, realized net gain equals expected net gain plus a random error term which has expectation zero. Define 19
of my medical tests”, and “the doctor should have given me a different or additional treatment or drug”. We aggregate these into three standardized indices following Kling et al. (2007): an overall satisfaction index, which includes measures of satisfaction with tests, medications received, and overall care, as well as two sub-indices that focus on satisfaction with tests and medications separately.<sup>22</sup> Table 5 reports treatment effects. Surprisingly, the extended training had significant negative effects on patient satisfaction: column 1 shows that patients in the extended training arm are 0.092 standard deviation units _less_ satisfied (significant at the 5 percent level), with declines in satisfaction with both tests (column 2, significant at the 1 percent level) and medications (column 3, not significant, but similar in magnitude).<sup>23</sup> Figure 5 graphs CDFs of the satisfaction indices by provider training arm; the distribution of satisfaction in the basic training arm first-order stochastically dominates that in the extended training arm. Figure A1 uses local linear regression to explore how treatment effects on the three patient satisfaction indices vary with predicted malaria risk. The graphs show that patient satisfaction is relatively constant across the risk distribution, with satisfaction for patients visiting extended-training clinics consistently lower than satisfaction among patients visiting basic training clinics.

By contrast, Table 5 shows that the patient information treatment had no effect on patient satisfaction. In other words, the additional information about the proper malaria testing protocols did not change their views about the care they received at the clinic despite the overall high rates of treatment misallocation.

**Mechanisms for the Decline in Patient Satisfaction.** The extended training improved quality of care and increased patients’ ability to predict their home RDT results. Why did patient satisfaction nonetheless decline? One plausible mechanism is that patients themselves are skeptical of malaria RDTs and dislike providers relying on them. Since the extended training increased RDT (only) testing rates, it may be that learning