rafmacalaba/datause-encoder-data huggingface dataset:
during the period 2008-2015, on the average rate of change between 2004 and 2007 of four industry-level performance measures, i.e. total output, employment, TFPQ and markups. We use them as separate regressors and all at once.<sup>18</sup> As a further robustness test, we use the NTM probability only in the first year of the NTMs database (i.e., 2008). The results of these tests are presented in the Appendix Table A1. Odd columns show the simple correlation between dependent and independent variables, while even columns add 2-digit industry fixed effects. Virtually, all the coefficients of the performance measures are statistically indistinguishable from zero, thus providing further evidence against the possible violation of our identifying assumption.<sup>19</sup> Finally, after presenting our baseline estimates in the following sections, we check that the results still hold whenever we exclude large plants - defined as those producing more than 10 percent of a 5-digit industry’s output - or politically connected plants, as they might affect government’s decision on the application of NTMs.<sup>20</sup>

# **4. Impact of NTMs** **_vs_ Import Tariffs on Performance**

Table 2 presents the estimated coefficients of Equation 1 for three dependent variables: TFPQ, markup and TFPR. We do not find any evidence of a statistically significant impact of the aggregate NTM variables on productivity and markups.

The muted impact of NTMs contrasts with the effect of import tariffs, which in line with previous studies have a negative effect on firms’ performance. The coefficient of output tariffs on TFPQ (column 1) is negative, although not statistically significant at conventional levels. On the contrary, output tariffs have a positive and significant impact on markups (column 2). This can be interpreted as a competition effect: by lowering competitive pressure from imports, output tariffs allow plants to charge higher markups, while allowing their inefficiencies to increase. The combination of these 2 effects on TFPQ and markup results in a negative but not significant effect of tariffs on TFPR. The lack of significance might be due to the countervailing effect on TFPQ and markup.

> 18All these variables are taken in logarithmic scale.

> 19These results are robust to the use of different time definitions of both the dependent and independent variables, as well as to the use of an intensive measure of NTM introduction in the referring period. Results of such robustness tests are available upon request.

> 20Politically connected plants are those being identified as having connections with the Suharto regime by Mobarak and Purbasari (2006). There are 246 such plants in our dataset.

16
below the OECD average, both the degree of control exercised by the state over public enterprises and the size of the public enterprise sector are above the OECD average.

53. The indicator for size is the largest in the sample, suggesting that the public sector remains significant in Romania _._ This may, however, be an artifact of the methodology: the approach follows a perpetual inventory method whereby an initial estimate of the public enterprise sector is reduced by an amount equivalent to the value of privatization receipts, as captured in the general government budget.

54. This approach may be creating an upward bias in the computation of the “size” indicator for Romania. This is because a large segment of the Romanian economy was privatized through mass privatization (vouchers scheme) whereby the state did not receive any privatization proceeds. A large number of enterprises were acquired by domestic investors and, oftentimes, the price component of the deal was not the most important. Rather the divestiture of the large public industrial sector, with the quality of its assets often questionable, took primacy in the privatization process. Sometimes the privatized companies were allowed, as part of the privatization contract, to retain part of the privatization proceeds for restructuring and upgrades. In addition, several large privatizations deals, done with strategic investors, came to closure after the period of analysis covered in the present paper.

55. Nevertheless, other sources confirm that the Romanian public enterprise sector remains important. Its size was estimated at around 30% of GDP in 2005 by the Romanian National Institute for Statistics (this has decreased in 2006, as several important privatization deals, especially in banking and energy, went through, such as that of the largest bank, BCR). As to its scope, it is also relatively wide given that the state holds important equity stakes in the largest firms in several sectors, such as electricity, gas, oil, banking (CEC bank), rail and road infrastructure and municipal utilities.

56. Nonetheless, when looking at the scope of the public sector, which captures the extent to which the state holds equity stakes in the largest firm in different sectors, Romania is close to the OECD average and in a better position than several EU15 countries (Figure 12b). Romania’s standing worsens when public ownership is further examined from the point of view of the pervasiveness of the formal control exerted by public bodies on public enterprises (Figure 12c). This result is driven by the presence of a golden share retained by the government in the oil sector. The golden share implies that important decisions can be taken only with a share of 75% of the board votes. The fact that the government controls more than 25% of the votes gives it the power to interfere with decisions concerning mergers or acquisitions, changes in the controlling coalition, acquisition of equity by foreign investors, choice of management or other strategic management decisions.

19
# **3.1 Experimental task**

We first asked participants to review records for five fictitious patients in different stages of labor and then to identify all the clinical actions that are appropriate for each patient. The clinical record—called a partograph—is a standard tool that is “strongly recommended” by the World Health Organization and used by Nigeria’s Federal Ministry of Health to train health workers providing maternal health services (WHO, 2014; White Ribbon Alliance, 2015). The correct use of partographs is also often part of PFP programs for health care in LMICs, although not in the Nigerian trial that our experiment was embedded in (Fritsche et al., 2014). We designed five partograph cases based on examples from medical training materials and hired two medical professionals to independently identify actions that would be clinically indicated or unnecessary based on standard clinical guidelines in the management of labor and delivery. Unnecessary actions at best serve no medical purpose and may even be potentially harmful to the patient.

We fielded two types of partograph cases that represent typical scenarios in our settings. In the two “simple” cases, we asked participants to assess whether a single action suggested by an unnamed colleague is correct or incorrect. In the three “complex” cases, the respondent is described as being in charge of the patient and is asked to name all actions that she deems clinically indicated for that patient. Table A.1 presents all possible actions for the three complex tasks and notes whether a given action is indicated or unnecessary. Which actions are indicated varies across cases but is invariant across participants. We scored as “correct” actions that participants correctly identified as indicated as well as those that participants did not identify as indicated and are, in fact, clinically unnecessary.

Table 1 presents the prices and proportion of correct responses for each action, disaggregated by whether the action is listed with or without incentive or is unlisted.<sup>2</sup> There is considerable variation in performance across actions as well as an overlap in the range of performance for listed and unlisted actions. We observe relatively high levels of performance for many listed and unlisted actions. For example, health workers correctly identify the need for referral to a higher-level facility (an incentivized action) 75 percent of the time and correctly recommend administering magnesium

> 2 Table A.2 disaggregates performance on each of these actions for the three complex cases.

10
potential source of mistakes. While the advent of computer-assisted personal interviewing (CAPI), holds the potential to reduce such errors (Caeyers et al. 2012), in the short-run, for a number of reasons, paper will continue to be a common survey method used in low-income countries.

# _UNIT CONVERSION ERROR_

Throughout most of the developing world, households do not typically purchase, harvest, or consume their food in standardized units (kilograms or liters). Some surveys force reporting in standardized units (an example would be the Tanzania National Panel survey) but there are doubts about the accuracy of these reports when made by people who never transact in metric units. A typical HCES consumption module will allow the respondent to report in local units, such as bunches, heaps, tins, buckets, or bundles. In order to quantify food consumption, local units must be converted into standardized units. There is no systematic approach across countries to convert to standard units.

# _FOOD HETEROGENEITY ERROR_

Having obtained estimates of weight or volume of each food item, pre-existing food tables can be used to determine energy content. This happens in two steps. Frist the edible proportion of the food item is estimated, subtracting from the total the expected amount of stems, peels, bones or other inedible parts of the food. Then, the energy content of the edible part of the food can be calculated. For this study we relied on Lukmanj et al. (2008) and USDA (2002) to calculate the edible portion within each quantity and the amount of energy it contained, expressed in KCal. An important feature here is the level of specificity of food item in the list. To the extent the list of food items contains grouped foods, calories may be measured with error.<sup>8</sup>

# _ERRORS IN CALORIE REQUIREMENTS_

Once the food reported in an HCES is converted into calories, the household’s calorie intake is compared to its need. This estimation presents another potential source of error. James and Schofield (1990) and FAO/WHO/UNU (1985) note how energy requirements will depend on a wide range of factors such as metabolism, age, gender, weight, height, activity level and for women on whether or not they are

> 8 Behrman and Deolalikar (1987) find that estimates of nutrient availability based on expenditure patterns can be upwardly biased when there is a high level of aggregation in food groups in expenditure surveys. Households replace cheap calories with more expensive ones as income goes up -- even though this extra expenditure yields no increase in calories (instead increasing attributes like taste and convenience). This increased expenditure can wrongly be equated with more calories if this replacement happens within a food group

10
Previous work on allocative efficiency has largely avoided periods of crisis, and work on crises has rarely examined firm responses. By using firm-level data to examine the impact of a major crisis on resource allocation this paper combines and contributes to several strands of literature that have evolved fairly separately up until this point. The available evidence on the impact of crises on resource allocation typically relies on aggregate data. The few previous attempts to empirically validate the predictions of the cleansing paradigm using firm-level data – discussed in detail in section 2 – have either failed to examine changes in the reallocative process itself or suffer methodological shortcomings. Second, existing plant-level studies of reallocation dynamics have demonstrated that business cycles are important determinants of both the pattern and pace of reallocation (see the surveys by Bartelsman & Doms, 2000, Caves, 1998, and Syverson, 2010) but typically deliberately exclude crisis-periods. It is therefore not clear to what extent the conclusions based on them generalize to crisis times. Third, cross-country studies based on aggregate data suggest that plant-dynamics are a key determinant of the depth and duration of crises (Bergoeing et al. 2005, Collier and Goderis 2009), but do not examine how these dynamics matter. Fourth, micro-studies of the impact of crises on labor market outcomes mostly rely on household and labor market data (see e.g. McKenzie 2003, 2004; Fallon & Lucas, 2002; Manning, 2000, Beegle et al. 1999), which are ill-suited for analyzing the impact of crises on reallocation dynamics and labor demand. Firm-level data are better suited for this purpose and enable us to document heterogeneity in firm vulnerability and adjustment patterns.

Examining the impact of the East Asian crisis on the Indonesian manufacturing sector provides an interesting case study of how financial crises reverberate through the real economy. Lessons could be relevant for recovery from the current global crisis, which is also characterized by a sharp contraction of demand, reduced access to credit, and uncertainty (although with less dramatic depreciations). In addition, our analysis may be relevant for those concerned with the impact of transitions to democracy, such as those currently taking place in the Middle East, on 3
30 estimate an annualized return. We then take the mean of the annualized returns across the seven bins to obtain the _mean annualized return_ throughout the experience distribution (remember that we use any available survey data during the 1990-2017 period).<sup>43</sup> The results in Col. (4) of Panel A, Table 9 show that urban returns to education might be lower in NRXGDP and DEINDU countries, however not significantly so. If we use the product of the years of schooling and the returns, to obtain an imperfect measure of the _value_ of education, we see a substantially lower value of schooling in DEINDU countries, but not in NRXGDP countries (col. (5)).

In Panel B and the first column of Panel C of Table 9, we investigate the same correlations for the urban returns to education or their interactions with the urban number of years of education when also including country-year-sample-location fixed effects in eq. (13). Locations correspond to macro regional areas (“Reg 01”), first level administrative areas (“Reg 02”), or primary sampling units, hence survey clusters (“PSU”).<sup>44</sup> Doing so allows us to compare individuals residing in the same regional or local urban labor market. While the number of available country estimates decreases, we tend to find lower returns to education in NRXGDP and DEINDU countries, and no significant differences for the interactions with the number of years (not shown). Finally, we tend to find lower returns to experience in NRXGDP and DEINDU countries, but not all the estimated differences are significant (see the last 4 columns, Panel C).

We then use data from IPUMS to obtain the urban number of of education for years adults (ages 25 to 65) in 54 countries. We end up with 151 country-years. Since we select countries with at least two years of data, the sample size decreases to 104 observations and therefore we cannot include lags. We run panel regressions with country and year FEs, regressing the education variable in _t_ on MFGSERV in _t_ , NRXGDP in _t_ -10, DEINDU between 1980 and _t_ (= 0 before 1980), the urban share in _t_ , and log population in _t_ and its square. We again do not find significantly different correlations for MFGFIRE, NRXGDP, and DEINDU. The results are similar for younger cohorts (ages 25 to 45).<sup>45</sup>

> 43 More precisely, for individuals belonging to bin _e_ , we obtain the _bin-specific annualized return_ as _rete_ = (( _βe_ +1)<sup>(1</sup><sup>_/e_)</sup> _−_ 1) _∗_ 100, with _βe_ being the estimated coefficient for bin _e_ in eq. (13). For this subgroup of individuals, it tells us by how many percentage points wages increased on average for each extra year of experience. We then take the average of these seven bin-specific annualized returns so that each bin is _equally_ represented. Therefore, the overall return _re_ is equal to (<sup>�7</sup> _e_ =1<sup>_rete_)</sup><sup>_÷_7.</sup>

> 44For example, “Reg 01” and “Reg 02” correspond to regions and states in the U.S., respectively. “PSUs” mostly likely correspond to neighborhoods or census blocks in the urban areas. 45All these results are available upon request.
**4.**

<!-- Start of picture text --> Policy Research<br>World Development Report<br><!-- End of picture text --> WPS 809 This paper, a background paper for the _1991 World Development Report,_ is a product of the **Office** of the Vice President, Development [:conomics. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact the World Development Report office, room P4-001, extension 31393 (34 pages). November 1991.

Harrison draws together a variety of measures of openness to test the association between growth and openness.

Although the correlation across measures is sometimes weak, there is generally a positive association between all these measures and GDP growth. The strength of the association generally depends on whether analysts use cross-section or panel data.

For industrializing countries, trade policies have varied too much over time to make the long-ran averages used in cross-section estimates very meaningful.

in the many specifications Harrison explores, she always finds that _greater_ openness is associated with higher growth. Tests of the sensitivity of these results to country size do not change the results.

Harrison highlights two issues interesting for future research: Does openness cause growth? Or is it **ihe** other w - v around?

And is it possible to disentangle short-run from long-run effects without throwing away annual data?

In many respects, the results are surprisingly robust. When openness _is_ statistically significant ThePolicy Research Working PaperScries disseminates the findings of work under way in theBank. Anobjectiveof the series **is** to get these findings ouL quickly, cven if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not ncccssarily rcprcscnt official Bank policy.

Produced by the Policy Research Dissemination Center
Policy Research Working Paper 7447

# **Abstract**

The number of countries that regularly participate in international large-scale assessments has increased sharply over the past 15 years, with the share of countries participating in the Programme for International Student Assessment growing from one-fifth of countries in 2000 to over one-third of countries in 2015. What accounts for this increase? This paper explores the evidence for three broad explanations: globalization of assessments, increasing technical capacity for conducting assessments, and increased demand for the microeconomic and macroeconomic data from these assessments. Data were compiled from more than 200 countries for this analysis, for six time periods between 2000 and 2015, yielding more than 1,200 observations. The data cover each country’s participation in each of six cycles of PISA as it relates to the country’s level of economic development, region, prior experience with assessment, and OECD membership. The results indicate that the odds of participation in PISA are markedly higher for OECD member countries, countries in the Europe and Central Asia region, high- and upper-middle-income countries, and countries with previous national and international assessment experience; the paper also finds that regional assessment experience is unrelated to PISA participation.

This paper is a product of the Education Global Practice 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 at http://econ.worldbank.org. The author may be contacted directly at lockheed@princeton.edu or through Marguerite Clarke, at mclarke2@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
capital, with scarce resources being diverted from high productivity growth T sectors to a booming but low productivity growth NT sector. For an explanation of Nigeria’s poor growth record we will have to look elsewhere.

**Figure 8 Capacity Utilization and Unemployment**

<!-- Start of picture text --> Unemployment Rate (%, Nigeria Definition) Manufacturing Capacity Utilization (%)<br>25 80<br>70<br>20<br>60<br>15<br>50<br>10 40<br>30<br>5<br>20<br>0 10<br>0<br>NATIONAL URBAN RURAL<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br>1999 2000 2001 2002 2003 2004 2005<br><!-- End of picture text --> There maybe another exchange rate related problem in the presence of large increases in oil revenues, related to capital markets rather than goods markets. There could be capital-flow induced overvaluation, if financial markets anticipate future oil wealth but expenditure is slow to catch up (Neary and van Wijnbergen 1986). But Nigeria is not integrated in world capital markets and anyhow was not slow to catch up, on the contrary. The Neary-van Wijnbergen (1986) argument really applies to countries that have discovered oil but cannot get it out yet, like the UK in the 1970s).

# _III.2 Why the Poor Growth Record: Volatility and Growth_

Another line of reasoning looks at the volatility of spending, rather than at its level, as a factor impeding growth. Commodity prices and revenues from natural resources tend to be volatile and may translate into macroeconomic instability and a highly volatile real exchange rate. Volatility can be seen as a tax on investment. Investment requires irreversible decisions: capital once installed cannot be moved to other sectors. Highly volatile relative prices discourage the irreversible commitments to specific sectors that capital investment implies (van Wijnbergen (1986)).

Aghion e.a. (2006) have shown empirically that high volatility slows down productivity growth by a substantial margin in countries with a relatively underdeveloped financial sector, like Nigeria. In their 12