rafmacalaba/datause-encoder-data huggingface dataset:
# **6. Conclusions**

Following the dramatic decline in inflation in emerging market economies (EMEs) since the mid-1990s, safeguarding the stable price environment has emerged as one of the most important policy priorities. Existing studies have largely focused on the determinants of inflation in EMEs in lieu of the factors triggering the inflation episodes. With a view to enhance the ability of policy makers in EMEs to take appropriate actions in a preemptive manner, we investigate the factors associated with the start of 24 inflation episodes in 15 EMEs between 1980 and 2001 by employing pooled probit analysis. The empirical results show that an increase in GDP growth above trend, agricultural shocks, and expansionary fiscal policy raise the probability of inflation starts in EMEs. The findings, on the other hand, indicate that a more democratic environment and an increase in capital flows relative to GDP reduce the probability of inflation starts in these countries.

Policy implications of this investigation can be summarized as follows. First, our results document the importance of prudent fiscal policy in reducing the probability of inflation starts. In spite of the widespread view that fiscal policy is closely connected with inflation in many EMEs, the empirical evidence pertaining to this link has been rather elusive. In this respect, the findings provide the much-needed additional empirical support concerning the importance of fiscal discipline for maintaining price stability. Second, earlier research has been inconclusive in establishing a close link between inflation and output gap in the context of EMEs.<sup>20</sup> Based on our findings, however, the link between economic activity above trend and inflation starts in EMEs appears to be quite robust, suggesting that central banks in EMEs should closely monitor the current and future path of aggregate demand in the economy.

> 20 See, for instance, IMF (1996) and Lougani and Swagel (2001).

19
Honoré, Bo E. 2002. “Nonlinear Models with Panel Data.” _Portuguese Economic Journal_ 1: 163–179.

Jacoby, Hanan, and Emmanuel Skoufias. 1997. “Risk, Financial Markets, and Human Capital in a Developing Country.” _Review of Economic Studies_ 64 (3): 311–35.

Lahaie, Claudia, Jeffrey Hayes, Tinka Piper, and Jody Heymann. 2009. “Work and Family Divided across Borders: The Impact of Parental Migration on Mexican Children in Transnational Families.” _Community, Work and Family_ 12 (3): 299– 312.

Lang, Kevin, and Jay Zagorsky. 2001, “Does Growing Up with a Parent Absent Really Hurt?” _Journal of Human Resources_ 36 (2): 253–73.

Maluccio, John. 2005. “Coping with the ‘Coffee Crisis’ in Central America: The Role of the Nicaraguan _Red de Protección Social_ .” FCND Discussion Paper 188.

Washington, DC: International Food Policy Research Institute (IFPRI).

Mesnard, Alice. 2009. “Migration, Violence and Welfare Programs in Rural Colombia.” IFS Working Paper 09/19. London: Institute for Fiscal Studies.

Painter, Gary, and David Levine. 2000. “Family Structure and Youths’ Outcomes: Which Correlations are Causal?” _Journal of Human Resources_ 35 (3): 524–49.

Parker, Susan W., and Emmanuel Skoufias. 2006. “Job Loss and Family Adjustments in Work and Schooling during the Mexican Peso Crisis.” _Journal of Population Economics_ 19 (1): 163–81.

30
Table A5: Balance Check: Differences in Employment Outcomes Before Submarine Internet Arrival Between Connected and nonConnected Observations

||**Connected**<br>**Std.Dev**|**Not connected**<br>**Std.Dev**|**Diference**<br>**t-stat**|
|---|---|---|---|
|**DHS**||||
|Currently working|0.57|0.57|0|
||0.5|0.5|1.54|
|Worked during last year|0.71|0.72|0.01|
||0.45|0.45|1.41|
|Low skilled worker|0.63|0.56|-0.07|
||0.48|0.50|-3.95|
|Skilled worker|0.24|0.15|-0.09|
||0.42|0.34|-9.31|
|High skilled worker|0.14|0.08|-0.06|
||0.27|0.35|-8.25|
|Farming is main employment|0.15|0.29|0.15|
||0.36|0.45|1.27|
|Services|0.27|0.18|-0.09|
||0.44|0.39|-8.54|
|**WBES**||||
|Number of full time employee at business start|10.65|12.04|1.38|
||12.65|13.81|1.79|
|Number of full time employee last fscal year|14.91|17.29|2.38|
||31.58|37.45|0.61|
|Number of female full time employee last fscal year|5.39|6.26|0.87|
||23.03|22.03|0.45|
|Number of skilled full time employee last fscal year|13.84|19.93|6.09|
||22.66|29.24|0.64|
|Telecommunication is an obstacle to operations|0.29|0.27|-0.02|
||0.44|0.46|-2.71|
|Losses as percentage of annual sales|8.95|8.58|-0.37|
||14.15|14.68|-1.38|
|Percentage own by the government|0.73|0.75|0.02|
||7.32|6.98|0.12|
|Secured a contract with government in last fscal year|0.20|0.14|-0.06|
||0.40|0.35|-5.57|
|Hours of operations per week in last fscal year|25.67|32.23|6.56|
||29.35|34.49|12.01|
|**LSMS**||||
|Hours worked in last 7 days|6.15<br>17.06|10.03<br>23.64|3.88<br>4.05|

56
introduced by foreign-owned firms. In other words, local firms do not have the capacity to learn from foreign companies operating in their midst.

- Local companies cannot pay sufficient wages to attract workers from foreignowned companies.  Consequently, this potential spillover channel is negligible.

- Studies in Hungary (Ministry of Economic Affairs and Economic Research Institute of the Hungarian Chamber of Commerce and Industry in 2000), Slovenia and Estonia suggest that foreign-owned companies typically continue to rely on foreign suppliers.  They rarely develop local supplier networks or devote the time and resources needed to improve the technical competence of local suppliers. Thus, while FDI may lead to an increase in the technological sophistication of exports, without specific joint government/industry programs to foster local supplier networks, FDI does not automatically bring local supplier skills and competencies up to international norms.

- Inward FDI does not necessarily help local firms establish linkages with foreign customers. Foreign-owned firms produce mainly for export or for other foreign investment enterprises within the transitional countries.

- Joint R&D projects between foreign-owned firms and domestic companies rarely spring up spontaneously.  In the first place, local firms frequently do not have the local technical skills needed to participate in these projects. And in addition, foreign-owned firms acquire most of their required R&D in the context of their parent company’s global R&D strategy.  Foreign-owned firms have little scope for independent local R&D efforts.

According to a recent comparison of Estonia and Latvia,<sup>18</sup> foreign-based manufacturing companies were attracted to Latvia mainly by an abundance of inexpensive labor. By itself, this is not a bad thing if the initial attraction is just that – a first step in a process that will lure investors to Latvia for reasons other than low wages.

**Table 1  FDI Stock in Knowledge Intensive Sectors, 2001**

||_In million of_<br>_LAT_|_As % of total_<br>_FDI_|
|---|---|---|
|Publishing/printing|1.4|0.1%|
|Chemicals|22.9|1.5%|
|Rubber/Plastic products|4.3|0.3%|
|Electrical/Electronic equipment|4.1|0.3%|
|Precision Instruments|1.1|0.1%|
|Post/telecommunication|117.2|7.8%|
|Computer|9.3|0.6%|
|R&D|1|0.1%|
|Education|0.3|0.0%|
|TOTAL|161.6|10.8%|
|Excl. telecommunication|44.4|3.0%|

_Source_ : FIAS Report

> 18  Cristián Contreras, Gertrud Kasemaa, <u>Mobile Telecommunications Sector In Estonia And Latvia: Drivers Of Development.  Stockholm School of Economics, 2001.</u>

20
the total FDI flows into resource-related flows, which include flows to hydrocarbons, minerals, and agriculture, and non-resource-related FDI flows, which include flows to manufacturing, construction, distribution, and commercial services. FDI flows are measured in millions of US dollars, and because the distribution of these flows is skewed, they are log-transformed, using the logarithm of the inverse hyperbolic sine: ��ln ��� <u>√�</u><sup>�</sup> �1 ).

We focus on greenfield investment because it consists of a relatively homogeneous group of investments in new facilities and excludes investments resulting from fire sales (Krugman, 2000). This focus eliminates concerns that heterogeneity of FDI is driving the results and the possibility that investment reflects repairs of facilities associated with prior investments rather than new projects. In developing countries, the inflow of greenfield investments is also considerably greater than the inflow of brownfield investments (Markusen & Stähler, 2011). A comparison of the number of greenfield investments in our data set to all mergers and acquisitions (M&As) registered by the Thomson One data service in the same period reveals that 81.6% were greenfield investments. Moreover, many policy makers are particularly interested in attracting greenfield FDI (UNCTAD, 2013). Finally, data on greenfield investments are more detailed than data on M&As. Although the Thomson One data service includes information on M&As, the size of the investment is missing for approximately 50% of the observations in developing countries for the period under study.<sup>4</sup> Following the political science literature, we measure political conflict using the number of battle-related deaths ( _BRD_ ) per year in a country. The data are obtained from the UCDP/PRIO Battle Related Death database (Pettersson & Wallensteen, 2015) and are gathered using information taken from a selection of publicly available sources, including journals, news agencies, NGO reports and statements of governments. The battle-related deaths variable is a best estimate, based on all information evaluated by UCDP/PRIO. The variable measures fatalities in conflict situations such as conventional battlefield fights, guerrilla attacks on government personnel, and bombardments of military bases, cities, and villages. It only includes battle-related deaths for conflicts with more than 25 battle-related deaths per year. Although this indicator has limitations – e.g., it does not measure non-fatal casualties or damage to property – it is widely available for conflict countries and is considered a good proxy for 18
of explanatory variables given in Table 4 - have an independent, statistically significant correlation with cross-country growth differentials computed both from World Bank and Summers and Heston data.

These results suggest that many popular cross-country growth findings are sensitive to the inclusion of explanatory variables. More fundamentally, they illustrate that it is very difficult to isolate a strong empirical relationship between any particular macroeconomic policy indicator and long-run growth. The subsections that follow systematically examine the robustness of fiscal policy, trade policy, monetary policy, and political stability indicators that have been the focus of past studies. In addition, we study international distortion and tax indicators that have not received much attention.

# B. Fiscal Policy Indicators

The first variables that we analyze using the extreme bounds procedure are fiscal policy indicators. One of the most important and frequently studied issues in economics is the role of fiscal policy in economic development. Empirical attempts to link aggregate measures of fiscal policy with average per capita growth rates in cross-country studies have tended to use (1) measures of the overall size of the government in the economy; (2) disaggregated measures of government expenditures; or (3) measures of the growth rate of government expenditures. In addition to examining these fiscal indicators, we examine the role of government deficits and disaggregated measures of government taxes.

Before presenting our results, it is worth mentioning some problems with these fiscal policy measures. Governments may provide growth-promoting public goods and design taxes to close the gap between private and social costs. On the other hand, governments may waste funds, funnel resources to endeavors that 14
**Table 1. Overview of urea fertilizer quality sampling test results: published studies and reports (reproduced from Hoel et al. (2022))**

|Year<br>sample<br>collected|Country|Acquired from|Authors/study|number<br>of urea<br>samples|Percent of<br>samples out of<br>compliance|Nitrogen OOC<br>shortage<br>severity|
|---|---|---|---|---|---|---|
|2014|Uganda|Retail sellers|Ashour et al.<br>(2019a)|137|All in<br>compliance|n/a|
|2014|Uganda|Retail sellers|Bold et al.<br>(2017)|369|100%|30.0%|
|2017|Uganda|Importers,<br>wholesalers,<br>retailers|Sanabria et al.<br>2018b(IFDC)|38|7%|1.25%|
|2010|Ghana|Retail sellers,<br>gov depots|Sanabriaet al.<br>2013(IFDC)|222|9%|<2.0%<sup>a</sup>|
|2010|Nigeria|Retail sellers,<br>gov depots|Sanabriaet al.<br>2013(IFDC)|147|All in<br>compliance|n/a|
|2010|Côte<br>d’Ivoire|Retail sellers,<br>gov depots|Sanabriaet al.<br>2013(IFDC)|42|All in<br>compliance|n/a|
|2010|Senegal|Retail sellers,<br>gov depots|Sanabriaet al.<br>2013(IFDC)|64|All in<br>compliance|n/a|
|2010|Togo|Retail sellers,<br>gov depots|Sanabriaet al.<br>2013(IFDC)|59|All in<br>compliance|n/a|
|2016|Kenya|Retail sellers|Sanabriaet al.<br>2018a(IFDC)|31|All in<br>compliance|n/a|
|2015-<br>2016|Tanzania|Retail sellers|Michelson et al.<br>(2021)|300|2%|5.0%|
|2016|Tanzania|Farmers|Michelson et al.<br>(2021)|121|4%|4.0%|
|2019|Tanzania|Retail sellers|Michelson et al.<br>(2021)|45|All in<br>compliance|n/a|
|2018|Tanzania|Warehouses|Michelson et al.<br>(2021)|8|All in<br>compliance|n/a|
|2018|Tanzania|Ships at the<br>port in Dar es<br>Salaam|Michelson et al.<br>(2021)|11|All in<br>compliance|n/a|

Notes: a not precisely discernable from the report. 20 samples were out of compliance with nitrogen content between 44% and 45.5%.

23
Figure 6: Distribution of share of local suppliers across different states <!-- Start of picture text --> MG PR<br>40<br>75<br>50<br>20<br>25<br>0 0<br>0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7<br>RS PB<br>40 30<br>20<br>20<br>10<br>0 0<br>0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7<br>CE PE<br>30<br>20<br>20<br>10<br>10<br>0 0<br>0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7<br>Share of same-municipality suppliers Share of same-municipality suppliers<br>Frequency<br>Frequency<br>Frequency<br><!-- End of picture text -->

**Notes:** This figure presents the distribution of the percentage of suppliers located within the same municipality where the tender process occurs, regardless of whether it is a headquarters or a branch, for each state. Data are drawn from the tender-participant table, and encompass all types of purchases, including tender waivers, for both products and services. Here, we consider only the winners’ (suppliers) information. We match this dataset with the _Cadastro Nacional de Pessoas Jurídicas_ (CNPJ), a dataset provided by the Ministry of the Economy that contains information on every firm registered in Brazil (we use this information as of 2019, the earliest available year). Red dotted line marks the average value of the distribution. The temporal coverage of the data ranges from 2014 to 2021. See Table 1 for more details on our data coverage.

26
Abosedra, S., and H. Baghestani. 2004. “On the Predictive Accuracy of Crude Oil Futures Prices.” _Energy Policy_ 32 (12): 1389–1393.

Ahmad, W., M. Aamir, U. Khalil, M. Ishaq, N. Iqbal, and M. Khan. 2021. “A New Approach for Forecasting Crude Oil Prices Using Median Ensemble Empirical Mode Decomposition and Group Method of Data Handling.” _Mathematical Problems in Engineering_ 2021: 1389–1393.

Alipour, A., A. Khodaiari, and A. Jafari. 2019. “Modeling and Prediction of Time-Series of Monthly Copper Prices.” _Int. J. Min. Geo-Eng_ 53 (1): 91–97.

Alquist, R., and E. Arbatli. 2010. “Crude Oil Futures: A Crystal Ball?” _Bank of Canada Review_ 2010 (Spring): 3-11.

Alquist, R., and L. Kilian. 2010. “What Do We Learn from the Price of Crude Oil Futures?” _Journal of Applied Econometrics_ 25 (4): 539-573.

Alquist, R., L. Kilian, and R. J. Vigfusson. 2013. “Forecasting the Price of Oil.” In _Handbook of Economic Forecasting_ , edited by G. Elliott, C. W. J. Granger, and A. Timmermann, 2 (A): 427507. Amsterdam: Elsevier.

Astudillo, G., R. Carrasco, C. Fernández-Campusano, and M. Chacón. 2020. “Copper Price Prediction Using Support Vector Regression Technique.” _Applied Sciences_ 10 (19): 1-12.

Baffes, J., and P. Nagle. 2022. _Commodity Markets: Evolution, Challenges and Policies_ . Washington, DC: World Bank.

Baumeister, C. 2022. “Measuring Market Expectations.” In _Handbook of Economic Expectations_ , edited by Bachmann, R., G. Topa, and W. van der Klaauw, November (2022): 413-442. Academic Press: Elsevier.

Baumeister, C., D. Korobilis and T. Lee. 2022. “Energy Markets and Global Economic Conditions.” _Review of Economics and Statistics_ 104 (4): 828-844.

Baumeister, C., and L. Kilian. 2012. “Real-Time Forecasts of the Real Price of Oil.” _Journal of Business and Economic Statistics_ 30 (2): 326–36.

Baumeister, C., and L. Kilian. 2014. “What Central Bankers Need to Know about Forecasting Oil Prices.” _International Economic Review_ 55 (3): 869-889.

Baumeister, C., and L. Kilian. 2015. “Forecasting the Real Price of Oil in A Changing World: A Forecast Combination Approach.” _Journal of Business and Economic Statistics_ 33 (3): 338-351.

Behmiri, N. B., and J. R. P. Manso. 2013. “Crude Oil Price Forecasting Techniques: A Comprehensive Review of Literature.” _Alternative Investment Analyst Review_ 2013 (2): 29-48.

61
i 5

**rta.e 4;** irE fRGV'U(&J'N FUNCTION

||**(l-X)**|**a**|-75J/<br>D75d/|IT2<br>2|**Stability**<br>(1)**c/**|Period of<br>Estimation|**_Bi-aMi_**<br> <br>Year|
|---|---|---|---|---|---|---|---|
|Ch;l<br>/ (2)|**.83**<br>(7.89)|**.64**<br>(2.34)|**.36**<br> <br>(2.8)|**.94**|U|82-81|81|
|Colombia (2)|**.89**<br>(8.79)|**.53**<br>(.92)||**1.0**|S|8d-85|84|
|**Ghana**(4)|**.82**<br>**(7.68)**|**.2S**<br>(.78)||.71|S|61-86|81|
|**Jamaica (8)**|**.84**<br>(18.94)|**.36**<br>(3.13)||**.94**|**S**|**61-88**|**SC**|
|C2te**d'Ivoire**<br>**(fixed)**b/)|**(3).89**<br>(20.31)|**.32**<br>(2.27)||**.98**|U|**88-79**|79|
|**Kenya (3)**|**.96**<br>(29.8)|**.24**<br>**(1.88)**||**.99**|S|**88-88**|81|
|**Korea**(3)|**.84**<br>(8.50)|**.60**<br>(2.48)||**1.0**|**S**|**el-88**|**So**|
|4aiawi<br>(4)|**.94**<br>(32.56)|.28<br>2. 91)||.99|U|61-80|80|
|4exico<br>(2)|.79<br>(8.22)|**.83**<br>(2.93)||**1.0**|U|62-81|81|
|Vorocco (3)|**.96**<br>**(17.53)**|29<br>(1.47)||S9|S|81-86|82|
|Pakistan**(4)**|.97<br>(26.13)|**.84**<br>(2.88)||**1.0**|S|81-88|79|
|Philippines<br>(3)|<br>**.90**<br>(5.90)|**.45**<br>(1.14)||**1.0**|U|81-79|79|
|Thailand<br>(2)|**.9e**<br>(64.7)|.37<br>(4.85)||1.0|S|81-86|81|
|Zambia (3)|_.90_<br>(16.94)|0o<br>(1.09)||.92|S|61-8B|83|

**Notes:** t-statistics in **parenthes,s**

- **a/** Number of SALs **and** SECALS n :aSertress.

- b/ For **all countries, gross** domestic neestmert **was used** for **It ksee** eqat **rn** 5) except. for **CSte** d'Ivoire **where** fixed **nvestment was sed.**

- _/_ **S: stable** equation (the **break year** is pr or to **the** first **Bank adj.s "e't** carn) **U: unstable** equation.

- d,' Dummy **variable equal** to 1 .or the year !975 ard zero ctherw.se.
Each student taking the test was asked to answer a number of questions relating to their family and parental background. Detailed data on personal characteristics and the history of pre-secondary schooling (such as types of primary and pre-primary school attended) were also collected. For each school, the head teacher was interviewed to gather data on various aspects of the school including arsenic contamination of tube wells at school. If the head teacher was absent, the teacher-in-charge was interviewed. Additionally, school registers were accessed to collect data on student performance in school final examination in grade 7 in the previous year. This way, retrospectively, we collected data on 7-th grade final math, English and total test scores for all sample students.

To identify arsenic-affected children, we asked head-teachers and sample pupils whether tube wells at school and home were arsenic contaminated respectively. To be precise, we have used information on arsenic poisoning of the primary source of drinking water—tube wells--to ascertain arsenic exposure. This information has been gathered both at householdlevel and at school. This is because, children spend a substantial amount of their total time at school and a significant number of tube wells located in school compounds in the affected region are arsenic contaminated<sup>8</sup> . Therefore, we can simultaneously isolate arsenic affected and unaffected children at school and individual level.

Appendix Table 1 summarizes key variables of interest<sup>9</sup> . Our working sample therefore comprises of 7710 students for whom we have complete data on test score and arsenic contamination of tube wells at home. The main source of drinking water in 98% (316) of our sample schools is tube well. Of these, 30.22% (or 97 schools) are contaminated with arsenic. On the other hand, 13% of sample children belong to households with arsenic contaminated tube wells. In general, level of student achievement is very low in our sample. On average, students could correctly answer only 38% of secondary-standard math questions. When looking at test score by arsenic pollution status of tube well at home, we find that children from affected households have systematically lower math scores (both in secondary and primary-standard tests) and subjective well-being (happiness) (see Appendix Table 2). We

> 8 Rosenborm (2004) in a study of arsenic contamination in 15 upazilas find that in 72% of all schools all wells exceeded the permissible concentration of arsenic.

> 9 13% of the actual sample observations were discarded due to missing data problem leading to a working simple of 7482 students.

9