rafmacalaba/datause-encoder-data huggingface dataset:
- Barsky R. B., Kilian L. (2002), “Do We Really Know that Oil Caused the Great Stagflation? A monetary alternative”, in Bernanke B., Rogoff K. (eds.), NBER Macroeconomics Annual 2001, May 2002, 137-183

- Bernanke B. S., Gertler M., Watson M., Sims C.A., Friedman B.M. (1997), “Symmetric Monetary Policy and the Effects of Oil Price Shocks”, Brookings Papers on Economic Activity 1, 91-174

- Bahr R., Hamori S. (2008), “Information Content of Commodity Futures Prices for Monetary Policy”, _Economic Modelling_ , 25, 274-283

- Boughton J.M., Branson W.H. (1990), “The use of Commodity Prices to Forecast Inflation”, Staff Studies for the World Economic Outlook, IMF, 1-18

- Boughton J.M., Bronson W.H. (1991), “Commodity Prices as a Leading Indicator of Inflation”, in Lahiri K., Moore G.H. (eds.), Leading Economic Indicators – New Approaches and Forecasting Records, Cambridge University Press, 305-338

- Browne F., Cronin D. (2007), “Commodity Prices, Money and Inflation”, ECB Working Paper 738, Frankfurt

- Cody, B.J., Mills, L.O. (1991), “The role of commodity prices in formulating monetary policy”, _Review Economics and Statistics_ , 73, 358-65

- Dornbusch R. (1976), “Expectations and Exchange Rate Dynamics”, _Journal of Political Economy_ , 84, 1161-1176

- Frankel J. A. (1986), “Expectations and Commodity Price Dynamics: The Overshooting Model”, _American Journal of Agriculture_ , 68(2), 344-348

- Frankel J. A. (2006), “The Effect of Monetary Policy on Real Commodity Prices”, NBER Working Paper 12713

- Fuhrer J, Moore G. (1992), “Monetary policy rules and the indicator properties of asset prices”, _Journal of Monetary Economics_ , 29(2), 303-336

- Garner, A.C. (1989), “Commodity prices: policy target or information variable?” _Journal of Money, Credit, and Banking_ , 21, 508-514

Hamori, S., (2007), “The information role of commodity prices in formulating monetary policy: some evidence from Japan” _Economics Bulletin_ , 5(13), 1-7

- Hua, P. (1998), “On primary commodity prices: the impact of macroeconomic/monetary shocks”, _Journal of Policy Modeling_ , 20, 767-790

- Johansen S. (1996), “Likelihood Based Inference in Cointegrated Vector Autoregressive Models, 2.ed. Oxford, Oxford University Press

- Johansen S. (2002), “A small sample correction of the test for cointegrating rank of the vector autoregressive model”, _Econometrica_ , 70, 1929-1961

- Juselius K. (2006), “The Cointegrated VAR Model: Econometric Methodology and Macroeconomic Applications, Oxford University Press

Olivera, J.H.G. (1970), “On passive money”, _Journal of Political Economy_ , 78, 805-814

- Marquis, M.H., Cunningham, S. R., (1990), “Is there a role of commodity prices in the design of monetary policy? Some empirical evidence”, _Southern Economic Journal_ , 57, 394-412

- Perron P. (1989), “Testing for a Unit Root in a Time Series with a Changing Mean”, Papers 347, Princeton, Department of Economics – Econometric Research Program

- Pindyck, R.S., Rotemberg, J. J. (1990), “The excess co-movement of commodity prices”, _Economic Journal_ , 100, 1173-1189

14
project teams are instructed to emphasize the need to target the most deserving villages in the municipality. Once those criteria have been agreed upon, the project preparation teams prepare proposals, which are validated in a village meeting.

_Funding_ . Once the proposals are ready, a Municipal Inter Village Forum is set up. During this forum, preparation teams present the proposals and village representatives rank them (i.e., decide on which proposals to fund given the budget allocated to the municipality) based on the agreed criteria. The results of this municipal forum are presented in a village assembly and, in the funded villages, villagers elect the members of the subproject management committee which are responsible for subproject implementation.

In each cycle, all villages in treatment areas go through ’preparation’, while only villages succesful in receiving funds from the project undertake ’funding’. Villages succesful in getting funding during a cycle are still eligible for funding in the subsequent cycles.

To ensure meaningful participation, an Area Coordination Team is deployed to each municipality two months prior to municipal launch. Area coordinators are selected locally according to their expertise in community mobilization. In addition, community facilitators help mobilize communities and ensure adequate representation, each working with five villages.

The project is active in the 42 poorest provinces of the Philippines out of 82 total provinces. In each province, using municipal poverty mapping methods, only municipalities in the bottom quartile of a provincial poverty ranking (about 177) are eligible to participate. All villages in a municipality are eligible to participate. The poverty ranking was implemented by researchers from the School of Economics at the University of the Philippines relying on census data on the age profile, quality of housing, access to water and electricity, education achievements, distance to center of trade and quality of roads. This data was used to rank municipalities in the province according to a computed poverty index. The poorest one-fourth was then 10
13 included garments, cinemicals (organic and inorganic, polymerization products, wood- and resin-based chemical products), paper and products of paper, textile yams, non-metallic mineral manufactures (cement, lime, fabricated construction materials, mineral manufactures, glassware, pottery), iron and steel products (pig iron, ingots, rails), manufactres of metal (hangars, household equipment of base metals), specialized machinery (machine tools), mechanical handling equipment (cranes, works trucks), household type electrical and non-electrical equipment, non-motorized trailer and other vehicles, and ships and boats.-<sup>3</sup> At first glance, it seems that no major shift in revealed comparative advantage for manufactures occurred in the 198991 period as compared with the 1984-89 period.

Table 4: The **Rlot** of Nuwmfactures in the OECD Export Growth of CEE-3, 1989 to **1991**

||Valu<br>(mill<br>1989|e<br>.<br>US$)<br> <br>1990|<br>1991|Increas<br>(mill.<br>U<br> <br>1989-90|e<br>in value<br> <br>SS)<br>1990-91|Share of Manuf<br>lncrease in To<br>(in percent)<br> <br>1989-90|actures<br>in<br>tal Exports<br>1990-91|
|---|---|---|---|---|---|---|---|
|Czechoslovakia<br>TotalExports<br>Exports of Manufactures|3924<br>2545|45S5<br>3218|6270<br>4788|661<br>673|1685<br>1570|---<br>-<br>102|--<br>-<br>93|
|Hunaarv<br>Total Exports<br>Exports<br>of Manul<br>res|4359<br>2420|5511<br>3347|6451<br>4035|<br>1152<br>927|940<br>686|----<br>81|....<br>73|
|TotalExport<br>Exports of Manufactures|5879<br>2771|8436<br>4390|9546<br>5388|2557<br>1619|1110<br>998|----<br>63|....<br>90|
|Total CEE-<br>Total Exports<br>Exports of Manufactures|14162<br>7736|18531<br>10955|22268<br>14210|<br>4369<br>3219|3737<br>3255|74|80|

SOURCE: See Table I The increase in CEE-3 exports is truly astounding considering their export performance and investment policies in the 1980s, the fact that SOEs were mainly responsible for the increased exports of manufactures, and also that the CEE-3 did not have export supporting institutions similar to Hermes in Germany or Ex-Im Bank in the United States. More detailed analysis is warranted.

# C. Germany: Locomotive of _CEE-S_ Exoort Growth

During the export growth of 1990-91, the geographic pattern of CEE-S exports changed, with a movemen **t** owards European markets in general and Germany in particular. The shift of _CEE-5_ exports to the EC-10 began in the late 1980s and was amplified in the 1990-91 period; the share of the EC-10 in _CE-S_ 13 For a detailed analysis, see Kaminsli, 1993:7-8.
The field implementation of a PMT formula with many variables is expensive and difficult, so some practitioners have opted for stepwise regression to obtain a more parsimonious PMT. We tested a backwards stepwise regression on the extended model to identify the key variables in the PMT. We used a cut-off of _p_ = 0.01. The targeting errors for the more parsimonious regressions are given in the <u>Addendum. We see a modest increase in the targeting errors, which are now back to approximately the</u> same values we found for Basic PMT.

A further methodological change we considered is to include variables that are not as readily available as those in our Extended PMT regressions, but are likely to have extra explanatory power. In one case we used extra data on households’ food security as well as on any shocks the household may have experienced. (Note that these variables are only available for four countries.) We augment the extended PMT model with these food security and shock variables. (The Addendum lists the variables, their means and the regression results for this model.) The R<sup>2</sup> increases slightly for all countries (Table 3). However, this version produced negligible improvement in targeting (Addendum). In another variation on the Extended PMT we included a range of community-level variables; again this was not possible for all countries. And (again) there was only a modest reduction in targeting errors, as can be seen in the Addendum.

We also tried other versions of PMT that might be of interest. In one case we used quantile regression at the median (in both the Basic and Extended PMTs). In another we used log consumption per equivalent single adult as the dependent variable. The <u>Addendum gives the results. There was little</u> improvement in the targeting performance of the PMT.

So far we have focused solely on the inclusion and exclusion rates as the measures of targeting performance. These appear to be the most popular measures in the literature, though others have been proposed and used in some studies. Probably the most promising example of the latter when the policy objective is poverty reduction is the targeting differential (Ravallion, 2000, 2009). Recall that the normalized _TD_ is in the range [-1, 1], with zero corresponding to a uniform (un-targeted) transfer.

Table 10 gives summary statistics on the normalized targeting differential using both the Basic and Extended PMTs. The mean _NTD_ for Basic PMT is 0.21, meaning that if program participation was based on the PMT scores the participation rate for the poor would be 21% points higher than that for the non-poor. Using Basic PMT, in four of the five countries with panel data, the _NTD_ is higher using timemean consumption; this rises to five out of five using the Extended PMT. Returning to the cross-section surveys, the poverty-quantile method yields the highest _NTD_ , at around 0.49 on average for Basic PMT, 21
# **The size of enterprises plays a pivotal role in determining whether employees earn the minimum wage**

**or higher compensation.** Typically, the average wage increases with the firm's size (as discussed in the literature by Oi & Idson, 1999). This trend has also been confirmed in other countries of the region, such as Hungary and Poland (Harasztosi & Lindner, 2019; Chrostek et al., 2022). This pattern is indeed evident in our dataset. In January 2020, the mean gross salary was as follows: 2,311 RON in micro enterprises (nearly aligned with the minimum wage), 3,521 RON in small enterprises, 4,812 RON in medium-sized enterprises, and 5,988 RON in large enterprises.

**As the size of an enterprise increases, the proportion of its employees earning the minimum wage diminishes.** Specifically, within microenterprises (employing fewer than ten individuals), a substantial 67% of taxpayers fall into the minimum wage category. In small enterprises (with 10 to 49 employees), this percentage is reduced to 39% of minimum wage earners. In medium-sized enterprises (employing 50 to 249 individuals), the figure drops to 17% of employees earning the minimum wage. Finally, in large enterprises (comprising over 250 employees), only 6% are in the minimum wage bracket. Figure 6 shows the share of minimum wage earners across different enterprise sizes.

**Figure 6. Share of minimum wage earners (as share of Total Earners) by enterprise size.**

<!-- Start of picture text --> 0.80<br>0.67<br>0.60<br>0.39<br>0.40<br>0.17<br>0.20<br>0.06<br>0.00<br>Micro Small Medium Large<br><!-- End of picture text --> _Source: Own estimates based on administrative tax data. The definition are as follows: micro enterprises employ fewer than 10 persons, small enterprises employ 10 to 49 persons, and medium-sized enterprises employ 50 – 249 persons. Large enterprises employ 250 and more employees._

**Among employees, the highest percentage of minimum wage earners is among the oldest and**

**youngest age groups** . A prevalent misconception suggests that individuals earning extremely low wages, those who would experience an increase if the minimum wage were raised, predominantly consist of young workers. Our results show that older workers are also overrepresented in Romania. The disparity in the proportion of minimum wage earners among the youngest employees is particularly interesting. Notably, the percentage of minimum wage earners escalates from 27% among taxpayers born in 1994 (26 years old in 2020) to 43% among those born in 2001 (19 years old in 2020). Figure 7 shows the share of minimum wage earners across birth cohorts.

30
including the latter three is that tangibility might be spuriously capturing an industry characteristics that provides a comparative advantage. If importers are highly dependent on the goods of certain industries in crisis situations they will be more likely to support them by extending additional trade credit or providing advance payments. On the other hand, in industries that are more easily substitutable it is likely that importers will switch to other suppliers rather than accepting less favorable payment conditions. If this was really the case, the positive and significant coefficient on the tangibility interaction with crisis would be spurious. However, inclusion of any of these additional interactions does not weaken the baseline result and also none of them enters significantly.

# **4.7.4 Country exclusions**

We also perform several robustness checks to see whether our results are driven by a specific country or group of countries. First, to test whether the results are spuriously driven by one country, we run our baseline model given by equation (1) excluding one of the crisis countries at the time. We do this for both, the external finance dependence and tangibility measures. In both sets of 21 unreported regressions the coefficient on our variable of interest stays significant with the sign and magnitude almost unchanged throughout the experiment.

Second, we exclude the poorest and the richest countries (Table 15) to see whether one of these particular groups is driving the results. We find that in both cases the coefficient on the interaction of the external dependence measure with financial crisis remains negative and statistically significant. Given that the result is stronger after excluding the rich countries, this is also consistent with our previous conclusion that poorer countries were hurt more by the banking crises. Similarly, the estimated coefficient on the tangibility interaction with financial crisis is positive and significant in both cases, implying the importance of collateral in crisis rises regardless of the level of economic development with the effect being slightly stronger in richer countries.

Finally, we look at whether having countries in the sample that have experienced more than one crisis might have affected our results. In Table 16 we show that the coefficients on the main interactions get slightly weaker after the exclusion. This result is not very strong but it seems to suggest that in countries that have already experienced a crisis the effect of the credit crunch gets especially pronounced.

# **4.7.5 Addressing endogeneity issues**

Even though, our identification strategy built on Rajan and Zingales (1998) is much less exposed to endogeneity concerns than typical cross-country models some concerns still remain. In our 19
data from the randomized experiment and supplement their findings with results from regressionadjusted difference-in-difference estimators. Parker et al. (2008) provide an up-to-date and indepth review of the state of the literature. Therefore, in this paper I provide only a brief overview of the strand of the literature relevant to the current analysis and then turn to program details.

The _Progresa_ program has been evaluated on a range of outcomes: enrollment (Schultz, 2000 and 2004), labor force participation (Skoufias and Parker, 2001), morbidity, height, prevalence of anemia (Gertler, 2004), food consumption (Hoddinott and Skoufias, 2004), intrahousehold allocation of expenditure (Martinelli and Parker, 2008), and investment behavior (Gertler et al. 2006). The effectiveness of _Progresa_ is well-documented along virtually all of these dimensions. Subsequent evaluations on the schooling effects of Progresa have sought to identify how the effects of the program vary: via endogenous peer effects (Bobonis and Finan 2006), via within family effects (Parker et al. 2005) and family network effects (Angelucci et al. 2008).

What is absent from these evaluations is an examination of the role school location and infrastructure might play in increasing enrollment. Do schools with better infrastructure register larger increases in enrollment than do schools without such infrastructure? Are urban schools more easily able to attract students than rural ones? Maluccio and Flores (2004) find increases in food expenditures, school enrollment, nutritional status and participation in health care in their evaluation of Nicaragua’s _Red de Protección Social_ program. When this program was shut down because it was too expensive an examination of any long run effects was ruled out. This begs the question, do CCT programs sustain their early successes?

4
context indicate that synergism (super-additivity) of impacts must remain a source of concern. Each threat when considered in isolation is less significant than the combined impacts of pressures, so that the total impact is greater than the sum of the individual effects. This suggests practical and conceptual challenges for policy making which typically considers impacts and issues separately and by sector. For instance debates on the volume and impact of tourism and tourist infrastructure, seldom consider effects emanating from the agriculture and land-use, or connectivity and vice-versa. Moreover the analysis finds that the conventional instruments of conservation policy (fines, quotas, etc) are less effective in reversing the damage once carrying capacity has been impaired.

The CGE analysis suggests that economic impacts that eventuate from changes in tourism and bushmeat hunting in the Serengeti have diffuse economy-wide impacts that outweigh (by orders of magnitude) the effects of the alternative activities considered. To guard against overstatement, ecosystem impacts (such as tourism revenues) are undervalued. These results appear to indicate that linkages to the natural resource sector (backward and forward multipliers) are important and neglecting these may lead to underestimates of the economic significance.

There are of course a number of caveats and limitations of the analysis that need to be considered in future research. First it would be useful to develop a regional and spatially differentiated CGE and SAM, though this would be a large and resource intensive exercise. Additionally the bioeconomic model could also need to be enriched by including labor supply in the household decision making. Our preliminary analysis suggests that this adds algebraic complexity without changing the key results (summarized in Lemmas 1 – 3). Finally the biological model could be improved by adding a stochastic component to represent population dependence on rainfall, climate change as well as predator-prey dynamics other forms of species interactions.

23
# **4 The data, stylized facts and results from empirical valueadded models**

The data used in this study was collected for a randomized controlled field experiment with the objective to establish the causal effects of Indonesia’s teacher certification program, which included a doubling of a teacher’s base pay (De Ree, Muralidharan, Pradhan, and Rogers 2015). 360 schools were sampled using a two-step sampling strategy. First, 20 districts were selected from 10 broader geographical strata, and second, within each district, 12 primary and 6 junior secondary schools were selected.<sup>17</sup> The data on the 240 primary schools are used in this paper. Within these 240 schools, all class teachers ( _N ≈_ 1700) were tested and interviewed.<sup>18</sup> In addition, all students ( _N ≈_ 45 _,_ 000) in the 240 sample schools were tested as well. The multiple choice tests for teachers and students consisted of a mathematics, a science and an Indonesian language component.<sup>19</sup> At the back of the answering sheets, students also filled out a brief questionnaire on wealth indicators and on parent education.<sup>20</sup> Further selections on the data were made prior to the analysis. First, the data from one of the districts ( _Maluku Tenggara Barat_ ) were dropped from the analysis, because baseline data was collected about 6 months later as for the rest of the sample. Second, students who repeated class at least once are not used in the analysis. Third, because the statistical model requires three periods of measurement on test score data, we only rely on the grade levels which we can track for three consecutive years, i.e. 2 _→_ 3 _→_ 4, 3 _→_ 4 _→_ 5 and 4 _→_ 5 _→_ 6. Fourth, we only use classrooms for which we can match students to teachers, for which we have testing information for both students and teachers, and for which the class has a single teacher for

> 17See World Bank (2015) and De Ree, Muralidharan, Pradhan, and Rogers (2015) for more details on the selection of schools.

> 18In general, all “core subject teachers” were tested an interviewed, which are class teachers in primary schools, and mathematics, Indonesian language, Physics, Biology, and English language teachers in Junior secondary schools.

> 19Tests for teachers were different from tests for students, and grade appropriate tests were used to test students.

> 20The information on parent education was not used in this paper as it was found that the wealth indicators correlate more strongly to the student’s test scores than the information on parental education. One reason for this might be that young children might not know the education levels of their parents. About 90% of the students in our data reported to have a TV at home. They are however much less likely to have a fridge (50%) or a car (15%). Similarly, parents are quite likely to have some level of education, but generally not more than secondary school.

20
Table A6: Net present value of increased learning (all interventions)

|**Country**|**Intervention**<br>|**Effect**<br>**Sizes**|**Country**<br>**GNI Per**<br>**Capita,**<br>**2015 PPP**<br>**Structured pe**|**Labor**<br>**Share of**<br>**Income,**<br>**latest**<br>**available**<br>**year**<br>**dagogy**|**Average**<br>**income,**<br>**2015**<br>**PPP**|**Return to**<br>**literacy**|**Percentage**<br>**change in**<br>**earnings**|**NPV$**|
|---|---|---|---|---|---|---|---|---|
||Northern Uganda Literacy||||||||
|Uganda|<br>Project (NULP) NGO<br>treatment arm|‐0.14|1,780|0.25|447|0.59|‐0.08|‐596|
|Uganda|Northern Uganda Literacy<br>Project (NULP): Gov<br>treatment arm|‐0.1|1,780|0.25|447|0.59|‐0.06|‐426|
|Chile|The Collaborative Language<br>and Literacy Instruction<br>Project (CLLIP)|‐0.1|21,740|0.38|8,235|0.72|‐0.07|‐9,385|
|Uganda|Northern Uganda Literacy<br>Project (NULP): Gov<br>treatment arm|‐0.09|1,780|0.25|447|0.59|‐0.05|‐383|
|Chile|The Collaborative Language<br>and Literacy Instruction<br>Project (CLLIP)|‐0.06|21,740|0.38|8,235|0.72|‐0.04|‐5,631|
|Kenya|Reading to Learn (RtL)|0.02|3,060|0.35|1,079|0.59|0.01|206|

43