rafmacalaba/datause-encoder-data huggingface dataset:
# TABLE 111.12. FEDERAL DEBT USES IN percent OF GDP: 1995- 1998

|**Dec-94**|**Dec-98**|**Variation**|
|---|---|---|
|**Federal Net Debt (+ CB)**<br>**12.25%**|**25.34%**|**13.09%**|
|Nominal Interests (Federal Governrment + CB)||13.76%|
|Primary Deficit (Federal Government + CB)||-1.19%|
|Nominal Deficit minus Net Debt Variation of the States and Municipalities||4.17%|
|Nominal Deficit minus Net Debt Variation of the State Owned Enterprises||5.22%|
|Balance Sheet Adjustment Variation||4.56%|
|Privatization Adjustment Variation (-)||-3.44%|
|Adjustment not Computed by the Central Bank||0.87%|
|**Total**||**23.95%**|
|**Assets**<br>**19.78%**|**29.61%**|**9.83%**|
|**1.**Domestic<br>13.67%|**23.71%**|**10.04%**|
|**1.1.**FAT<br>1.88%|3.06%|1.17%|
|1.2. CB's credits to the financial institutions<br>3.82%|5.31%|1.49%|
|1.3. Federal Government's credits (Law 8727 / 93)<br>0.00%|0.42%|0.42%|
|1.4. Debt Renegotiations with the states<br>0.00%|9.49%|9.49%|
|1.5. Others<br>7.96%|5.43%|-2.54%|
|2. Foreign Reserves<br>6.11%|5.90%|-0.21%|
|**Other Debts (-)**<br>**20.81%**|**19.60%**|**-1.22%**|
|**1.**Domestic<br>**8.67%**|**9.44%**|**0.77%**|
|**1.1.**Monetary Base<br>3.29%|4.30%|1.01%|
|1.2. Others<br>5.38%|5.14%|-0.24%|
|2. Foreign<br>12.14%|10.15%|-1.99%|

28
- Oulton, N. (2001). Must the growth rate decline? baumol’s unbalanced growth revisited. _Oxford Economic Papers_ , 53(4):605–627.

- Pavcnik, N. (2002). Trade liberalization, exit, and productivity improvement: Evidence from –

- chilean plants. _Review of Economic Studies_ , 69(1):245 276.

- Rajan, R. G. and Zingales, L. (1998). Financial dependence and growth. _American Economic_ –

- _Review_ , 88(3):559 586.

- Rutherford, T., Tarr, D., and Shepotylo, O. (2005). The impact on russia of wto accession and the doha agenda: The importance of liberalization of barriers against foreign direct investment in services for growth and poverty reduction. _World Bank Policy Research Working Paper No. 3725._

- Van Biesebroeck, J. (2007). Robustness of productivity estimates. _The Journal of Industrial Economics_ , 55(3):529–569.

- Williamson, O. (1973). Markets and hierarchies: some elementary considerations. _The American Economic Review_ , 63(2):316–325.

# **6 Appendix**

## **6.1 Olley-Pakes procedure**

Unobserved productivity follows an exogenous first order Markov process _p_ ( _ωit|Iit_ ) = _p_ ( _ωit|ωit−_ 1), where _Iit_ is the firms information set at time _t_ . Capital accumulated by firms is determined as _kit_ = _kit−_ 1(1 _− δ_ )+ _iit−_ 1, where _i_ is log of investment. Solving a dynamic problem of profit maximization yields the following investment function

37
education in general. Such alternatives include limiting the kind of available products (choices), altering the choice architecture (e.g., Carroll et al. 2009), working with nudges (e.g., Thaler and Benartzi <u>2004; Willis 2011), considering the promotion of commitment devices (e.g., Brune et al. 2016), offering incentives (e.g., Saez 2009), or implementing more rigid consumer financial protection</u> policies (cf. Campbell et al. 2011).

There are two arguments in favor of implementing financial education. First, the small average effect comes with low average intensity. More than 70 percent of our considered studies invest no more than two days in education, indicating that these measures may have only small effects, but also low costs. Second, the average small effect of financial education is accompanied by large heterogeneity, indicating that those offering financial education measures can still learn from best practice experiences, a development that is ongoing as evidenced by time trend of slowly increasing effectiveness documented in rigorous impact evaluation studies.

24
- 31 -

Table 3 and Graph 2 show the results of simulations for the period 1988-1991 with a view to obtaining a fiscal policy framework consistent with the objective of debt/GDP stabilization. The simulations are based on World Bank projections for the ratio of budget interest payments to GDP and for the inflation and growth rates. 121 The model assumes no change in the income velocity of reserve money and no change in the real effective exchange rate over the period. The results indicate that the sustainable governament cash deficit is slightly lower than the projected deficit, implying a somewhat greater fiscal effort to achieve the stabilization goal. The required average deficit reduction over the period is 0.9 percent of GDP.

Table 4 looks at the same issue by asking a different question: suppose that the government wishes to adhere to the projected cash deficit, and further suppose that the interest payments assumption turns out correct; what would be the growth and inflation rates required to achieve a constant debt/GDP ratio? The fundamental difference between this and the earlier calculation is that here the warranted growth and inflation rates (as well as the implied monetary base, the inflation tax and seignorage) are all calculated simultaneously. In this respect, the results of Table 4 are internally consistent. Note, however, that there is still a lack of a behavioral framework that would provide an unequivocal answer to the 12/ Kenya: Public Expenditure Review, Main Report and Annex Volume, Report 7508-KE, November 21, 1988.
The year of the last state election ranges from 2010 through 2016 for respondents in the NFHS data. We merge the NFHS data with electoral data for statewide elections obtained from the ECI. Since the election data are at the level of the constituency—a smaller geographical unit than a district—we follow Bhalotra and Clots-Figueras (2014) and aggregate election data to the district level before merging it with the NFHS data using the election year and respondent’s district of residence. Districts in our data have between 1 and 40 electoral constituencies, and the median district has 6 constituencies.

The electoral data comprise candidate-level information within each state assembly constituency that allows us to identify the gender of winners and runners-up as well as the victory margins. We construct a district-level aggregated variable that measures the fraction of constituencies in district _d_ in state _s_ that were won by female politicians— both overall ( _Fds_ ) and in close elections against a male runner-up ( _FCds_ )—in any given election year. We define an election to be close if the margin of victory in terms of vote share was less than 3 percent.<sup>9</sup> To analyze effects on fertility and birth spacing, we reshape the cross-sectional NFHS dataset to construct a retrospective woman–year panel spanning the time period between the last state election and the year of the survey. Moreover, we use the 2012–13 DLHS to examine whether female leaders influence village-level health infrastructure. The DLHS is merged with the election data using the last round of state elections and district information. Finally, we utilize data from the Socioeconomic High-Resolution Rural–Urban Geographic Dataset on India (SHRUG) (Asher et al. 2021) to obtain information on district characteristics, as per the 2011 census of India.

Table A.2 describes the characteristics of the 628 districts covered by our NFHS estimation sample using election and census data. On average, 9 percent of constituencies in a district had a female winner; 3 percent of constituencies had a close election between a male and a female candidate; and 1 percent of constituencies had both. Table A.3 presents summary statistics for the 245 districts included in our DLHS sample and describes the DLHS data on public health infrastructure in 7,726 villages.

individuals aged 25 years or older surveyed in the 2008 National Sample Survey of India, 89 percent of women and 93 percent of men lived in the state where they were born.

> 9In Section 4.1, we show that our results are robust to alternative bandwidth selections for close elections.

5
Sánchez-Martín, Escribano and de Arce, 2014. “How regional integration and transnational energy networks have boosted FDI in Turkey” mainly to trade clauses, and persisting technical barriers and obstacles to investment. Therefore, despite its theoretically advantageous location to serve Europe, Turkey had still constraints hampering the attraction of foreign investors in the second half of the 1990s.

**Figure 3. Expected impact of regional integration agreements on FDI.**

<!-- Start of picture text --> Locational advantages<br>+ Positive Negative<br>1 2<br>Mexico<br>NAFTA<br>Brazil, Arg Paraguay, Uruguay<br>Turkey 2005<br>MERCOSUR MERCOSUR<br>EU accession<br>Canada<br>CUSFTA<br>3 4<br>Turkey 1996<br>Customs Union<br>-<br>Strong<br>Environmental change<br>Weak<br><!-- End of picture text --> Sources: Elaborated following Blomström and Kokko (1997a) On the other hand, the beginning of the EU accession negotiations in 2005 was accompanied by a wider and deeper set of reforms in different fields such as business, economics, politics or even human rights. This wider and more credible “environmental change” has helped improving both the investment climate and business expectations in Turkey. Therefore, now it can be argued that the Turkey-EU accession partnership should be located in quadrant one, together with other regional agreements contributing positively to FDI. In addition, Turkey moved a little bit to the left hand side of the graph, reflecting increasing locational advantages, as ten Eastern European countries joined the EU in 2004.

Furthermore, we will argue that, given that Turkey is not a member of the EU yet, the relative position of the Accession Negotiations agreement may change dynamically over time, and weaker membership prospects would mean that the expected environmental and “doing business” change in Turkey could be narrower than initially expected. This could result in international investors, aiming at serving the EU market, less prone to establish their facilities in Turkey.

15
# **II. Research Strategy, Data, and Preliminary Comparisons**

We aim to understand how differences in business environments contribute to the gap in economic performance between China and India using firm-level data. Given the importance of productivity in income determination, we use total factor productivity (TFP) to measure firm performance. Indeed, TFP has been shown to be a major source of output growth (Solow 1957; Jorgenson and Griliches 1967). Moreover, TFP levels are highly correlated with income per capita (Islam 1995; Hall and Jones 1999). In a survey of economic growth, Helpman (2004, p. 33) states, ―There is convincing evidence that total factor productivity plays a major role in accounting for the observed cross-country variations in income per worker and patterns of economic growth. We therefore need to understand what drives the differences in total factor productivity.‖ For this paper, we focus on understanding the TFP differences at the firm level between the two countries. For robustness checks, we also used labor productivity, measured by value added per employee in logarithmic scale, as an alternative dependent variable and we found similar results. In our data, TFP measures are closely correlated with labor productivity, with a statistically significant correlation coefficient of 0.61.

We proceed in three steps. First, we estimate total factor productivity for each firm in our sample. We then use available data to construct proxies for known business environmental factors that affect firms. At the last step, we estimate the effects of the proxies of the business environment on firm productivity using regression analysis. Our presumption is that even firms in the same country face different business environments at the local level. A caveat is that our results are only as representative as our sample. For example, our data do not reveal policy impacts on firm entry and exits. Also, because our data cover only manufacturing firms, our results do not apply to nonmanufacturing sectors. Finally, our sample does not contain what some view as the most productive sector in India—the software industry—which may bias our estimates and overstate China’s productivity advantage.

# **_Data_**

We draw our firm-level data from World Bank surveys on the two countries’ investment climate in 2003. The two surveys are similar in sample design and survey instruments. However, some differences remain. The India survey covered 1,860 manufacturing establishments, sampled 5
# **Graph (4): Stability Test (One Step and N Down Chow Test) of - 3 Lags VAR- Closed Economy VAR**

<!-- Start of picture text --> 2020 2020 2020 2020<br>2000 2000 2000 2000<br>1% 1%<br>1% 1%<br>1980 1980 1980 1980<br>1up INFL 1up CHOWs Ndn INFL Ndn CHOWs<br>1.0 0.5 0.0 1.0 0.5 1.0 0.5 0.0 1.0 0.5<br>2020 2020 2020 2020<br>2000 2000 2000 2000<br>1% 1% 1% 1%<br>1980 1980 1980 1980<br>1up rgdp 1up DISC Ndn rgdp Ndn DISC<br>1.0 0.5 0.0 1.0 0.5 0.0 1.0 0.5 1.0 0.5 0.0<br>2020 2020 2020 2020<br>2000 1% 2000 2000 1% 2000<br>1% 1%<br>1980 1980 1980 1980<br>1up rm2 1up Interestrt Ndn rm2 Ndn Interestrt<br>1.0 0.5 0.0 1.0 0.5 0.0 1.0 0.5 0.0 1.0 0.5 0.0<br><!-- End of picture text --> 57
- 6 -

- The Baht was devalued by nearly 15 percent in nominal effective terms and then pegged against an u.ndisclosed basket that weighted heavily the US Dollar. As the Dollar lost value vs. the Yen during the second half of the 1980s, the Baht, in turn, continued to depreciate in nominal terms vs. other East Asian currencies. These changes, in combination with the tight financial policies, reversed the appreciating trend of the REER during the early 1 980s and led to a lasting real depreciation of the Bhat, which by 1987 had depreciated by _25.5_ percent compared to its level in 1984.

- Monetary policy was tightened significantly starting in 1985. Real credit growth declined significantly in 1985 and 1986 as compared to the previous three years,<sup>5</sup> while real interest rates increased to their highest levels in the 1980s (Kochhar and others, 1996).

- Fiscal policy, however, was adjusted only with a one-year lag with the adoption of the 1985/86 budget in late 1985. Following a period of large deficits and no clear trend for the fiscal stance, between 1985/86 and 1987/88 the central government's fiscal balance went from a deficit of 5.3 percent of GDP to a surplus of 0.7 percent (Figure 1). Hence, fiscal policy became sharply contractionary starting in 1986 as illustrated by the large and negative estimates for the fiscal impulse.

**Figure 1. The Stance** of Fiscal Policy: 1980-87

<!-- Start of picture text --> 4.0%<br>2.0%<br>02.0%<br>-4.0%<br>-6.0%<br><!-- End of picture text --> <!-- Start of picture text --> 1979/80 1980/81 1981/82 1982/83 1983/84 1984/85 1985/86 1986/87 1987/88<br><!-- End of picture text -->

* Overall BalanceM Fiscal Impulse

Source: IMF: GFS. Authors' estimates.

- _5_ It is difficult, however, to clisentangle the extent to which the decline in credit growth reflects tight supply conditions or a decline in the demand for credit, in turn, reflecting the downtum in aggregate demand.
# **1 Introduction**

According to the _United Nations Refugee Agency (UNHCR, 2019)_ , 70.8 million people are currently forcibly displaced worldwide, due to a variety of reasons including armed conflict, economic deprivation and environmental disasters.<sup>1</sup> These events entail the loss of many lives and huge human suffering among those directly affected. In addition, neighboring countries are also affected because, willingly or not, they end up hosting a large part of the displaced populations.

It is important to study the economic consequences of receiving large inflows of forcibly displaced migrants. Identifying which groups in the hosting regions are bearing the largest economic burden, and adopting policies to alleviate it is crucial in order to avoid backlash and foster integration. Obtaining and analyzing data in a timely fashion poses an important challenge to researchers and policy makers because the typical sources, population censuses and labor force surveys, are ill-suited to collect information pertaining to recently arrived migrants that are still in the process of settling down. As a result, researchers have to develop new strategies to address these shortcomings by using new data sources.<sup>2</sup> Our focus in this paper is on the Venezuelan exodus that started around year 2015. As of September 2019, it is estimated that more than 4.3 million Venezuelans have left the country since in the last 5 years. About 80% of these migrants have settled in Latin America and the Caribbean, with Colombia (about 1.5 million) and Peru (about 1 million) as the main destinations.

Our analysis focuses on Ecuador, which started receiving significant inflows of Venezuelans around 2016 and is currently hosting over 400,000 Venezuelan migrants (as of 2019). More specifically, our main goals are to analyze the determinants of the location choices of Venezuelan migrants within Ecuador and how they might affect the labor markets of the host regions. Crucially, we employ novel data based on mobile phone records to measure the geographical distribution of Venezuelan migrants across Ecuador’s regions ( _cantons_ ), which we merge with Ecuador’s household labor force survey ( _ENEMDU_ ).

Methodologically, we create a wide range of regional labor market indicators, including participation and employment rates, along with measures of employment quality, informality and earnings. We also classify these regions ( _cantons_ ) according to the den-

> 1In the years prior to 2017, about 2.4 million people fled South Sudan. Likewise, it is estimated that 6.3 million people were displaced from the Syrian Arab Republic (Syria) between 2011 and 2017.

> 2For instance, Hausmann et al. (2018) used _Twitter_ data to estimate that close to 3 million Venezuelans left the country in year 2017.

2
23

# **USPTO Utility Patent Counts**

An alternative patent data set is that of Hall, Jaffe and Trajtenberg (2001), which covers all utility patents that were granted between January 1963 and December 1999. Utility patents are patents that are granted to persons who invent or discover any new and useful process, machine, article of manufacture, or compositions of matters, or any new useful improvement thereof. Thus this data set focuses on only one category of patents but provides data with a substantially longer time dimension. Also note that utility patents are the largest category of patents. In 1999, the number of utility patents granted reached 153,486 versus a total number of patents of 169,134. Hence, utility patents constitute more than 90 percent of all patents. As with the total patent count, we use the annual number of utility patents granted by the USPTO for a country as a measure of the level of domestic innovation activity existing within that country.

# **USPTO Utility Patent Citations Received**

It is widely accepted that innovations tend to vary enormously in terms of their technological and economic importance, significance, or commercial value. While the patenting process requires innovations to be assessed for their novelty and commercial value, patented innovations still tend to exhibit a large variance in terms of quality and economic importance. One method of accounting for the large difference in the quality and value of patents is to use patent citation data that are included in the details of each patent. These citations are to previous patents and to the scientific literature, and they enable the tracing of multiple linkages between inventions, inventors, firms, geographical locations, etc. In particular, citations received may be telling of the importance of the cited patent and thus the number of citations an individual patent receives acts as a simple indicator of the significance of cited patents, this allowing one to capture the enormous heterogeneity in the value of patents.

The idea of using patent citations to control for the quality of patented innovations has been affirmed by the USTPO: “ _If a single document is cited in numerous patents, the technology revealed in that document is apparently involved in many developmental efforts. Thus, the number of times a patent document is cited may be a measure of its technological significance_ .” (Office of technology Assessment and Forecast, USPTO, 1976, p. 167; as quoted in Hall, Jaffe and Trajtenberg (2001)).

One inherent shortcoming with patent citation data is the truncation problem. More specifically, given that patents receive citations from subsequent ones over a long period of time (up to several decades), at any given point in time when the data are
<!-- Start of picture text --> of Firm Size Size Labor Productivity Premium of Firm Sales per Worker Premium of Firm<br>Size Size<br>gg 30% ag 20%<br>— E 45%<br>5 25% 4 40x<br>a= 20% 25 35%<br>2 2 30%<br>° ®<br>B 15% BZ 25%<br>3 % 20%<br>=E 10% £E 15%<br>2 2 10%<br>— 5% €<br>a 0% a 0%<br>Large Small Medium Large Small Medium Large<br>Size Group Size Group<br><!-- End of picture text --> <!-- Start of picture text --> Revenue TFP Premium of Firm Size Size<br>16%<br>2 14%<br>€<br>5 12%<br>=10%<br>2<br>Z 8%<br>&<br>z 6%<br>5 4%<br>&<br>o<br>0%<br>Small Medium Large<br>Size Group<br><!-- End of picture text --> <!-- Start of picture text --> Revenue TFP Premium of Firm Age Labor Productivity Premium of Firm Age Sales per Worker Premium of Firm Age<br>8.0% 20.0% 25.0%<br>F cox “© 18.0% “2<br>s ‘Ss ‘s<br>© n0% £ 16.0% £ 20.0%<br>rsESv 2.0% ES%v 14.0% ”v$<br>2Sox 2= 12.0% £¥ 15.0%<br>é E 10.0% E<br>S 2.0% 2 °°<br>3 5 8.0% = 10.0%<br>2= gyS 6.0% 2®<br>5 cox =E 4.0% &E 5.0%<br>£ -2.0% 5 2.0% 5<br>a a a<br>-10.0% 0.0% 0.0%<br>59 10-14 15-19 >=20 59 10-14 15-19 >=20 59 10-14 15-19 >=20<br>‘Age Group Age Group Age Group<br><!-- End of picture text --> ij,t it ijt
**Table A6: Comparing household and HH head indicators in the short panel (2009-2012) dataset between non-refugee hosting and refugee hosting districts (including Kampala)**

||**Mean**||**_In the base_**<br>|**_wave=2_**|**_009/10_**||**Mean**||**_In the last_**<br>|**_wave=2_**|**_012_**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**(Non-**<br>**hosting)**|**Obs.**|**Mean**<br>**(Hosting)**|**Obs.**|**Mean Diff.**|**Std.**<br>**Error**|**(Non-**<br>**hosting)**|**Obs.**|**Mean**<br>**(Hosting)**|**Obs.**|**Mean Diff.**|**Std. Error**|
|HH welfare(consumption<br>aggregate per adult equivalent)|56666.446|1835|82389.101|394|-25722.655***|3317.328|60848.386|2008|88149.285|430|-<br>27300.899*<br>**|6505.562|
|Rural locations|0.823|2026|0.565|432|0.258***|0.021|0.835|2026|0.593|432|0.243***|0.021|
|HH size|6.239|2026|5.720|432|0.519***|0.167|6.239|2026|5.720|432|0.519***|0.167|
|Age of HH head|46.089|2023|44.456|432|1.633**|0.801|47.518|2025|46.134|432|1.384*|0.788|
|HH head male|0.720|2026|0.734|432|-0.014|0.024|0.687|2026|0.715|432|-0.029|0.024|
|HH head female|0.280|2026|0.266|432|0.014|0.024|0.313|2026|0.285|432|0.029|0.024|
|**_HH head education level_**|||||||||||||
|No formal education|0.198|1908|0.141|404|0.057***|0.021|0.194|1979|0.155|420|0.039*|0.021|
|Didn't complete primary|0.429|1908|0.369|404|0.060**|0.027|0.411|1979|0.350|420|0.061**|0.026|
|Completed primary|0.266|1908|0.290|404|-0.023|0.024|0.280|1979|0.293|420|-0.012|0.024|
|Secondary and above|0.106|1908|0.200|404|-0.094***|0.018|0.114|1979|0.202|420|-0.088***|0.018|
|**_HH head marital status_**|||||||||||||
|Married monogamously|0.559|2021|0.546|432|0.013|0.026|0.537|2025|0.549|432|-0.012|0.026|
|Married polygamously|0.185|2021|0.227|432|-0.042**|0.021|0.195|2025|0.236|432|-0.042*|0.021|
|Separated(divorced/widowed)|0.237|2021|0.199|432|0.038*|0.022|0.248|2025|0.190|432|0.059***|0.023|
|Never married|0.019|2021|0.028|432|-0.008|0.008|0.020|2025|0.025|432|-0.005|0.008|
|**_HH main source of income_**|||||||||||||
|Subsistence farming|0.535|1946|0.436|399|0.099***|0.027|0.566|1856|0.437|382|0.129***|0.028|
|Commercial farming|0.028|1946|0.000|399|0.028***|0.008|0.012|1856|0.005|382|0.007|0.006|
|Wage employment|0.160|1946|0.211|399|-0.051**|0.021|0.129|1856|0.199|382|-0.070***|0.020|
|Non-agric self-employment|0.205|1946|0.248|399|-0.044*|0.022|0.213|1856|0.264|382|-0.052**|0.023|
|Remittances & Others|0.072|1946|0.105|399|-0.033**|0.015|0.080|1856|0.094|382|-0.014|0.015|

6