rafmacalaba/datause-encoder-data huggingface dataset:
45

**Table 10**

# **Robustness: endogeneity**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||ED4<br>median|ED4<br>mean|ED4<br>75th|BP5<br>median|BP5<br>mean|BP5<br>75th|
|CA|-2.806<br>[0.228]***|-2.801<br>[0.231]***|-2.799<br>[0.227]***|-1.774<br>[0.213]***|-1.760<br>[0.214]***|-1.771<br>[0.212]***|
|REM|0.047|0.152|0.111|0.087|0.106|0.122|
||[0.485]|[0.497]|[0.513]|[0.416]|[0.428]|[0.429]|
|RES|-1.021|-0.853|-0.983|-1.382|-1.178|-1.210|
||[0.456]**|[0.415]**|[0.399]**|[0.495]***|[0.463]**|[0.441]***|
|EXTD|0.179|0.155|0.195|0.171|0.143|0.182|
|SHORTD|[0.086]**<br>-0.022|[0.084]*<br>-0.029|[0.085]**<br>-0.013|[0.067]**<br>-0.016|[0.066]**<br>-0.030|[0.072]**<br>-0.013|
||[0.144]|[0.144]|[0.146]|[0.107]|[0.108]|[0.109]|
|CREGRO|-0.001|-0.001|-0.001|-0.001|-0.001|-0.001|
||[0.002]|[0.002]|[0.002]|[0.002]|[0.001]|[0.001]|
|PPPGDP|-0.019|-0.021|-0.018|-0.007|-0.009|-0.009|
||[0.025]|[0.025]|[0.025]|[0.025]|[0.025]|[0.025]|
|OPEN|0.200|0.190|0.203|0.231|0.224|0.236|
||[0.106]*|[0.106]*|[0.106]*|[0.096]**|[0.095]**|[0.095]**|
|OT|-0.709|-0.707|-0.723|-0.498|-0.461|-0.498|
||[0.348]**|[0.358]**|[0.344]**|[0.302]*|[0.309]|[0.299]*|
|CONCD|-0.008|-0.015|-0.018|0.035|0.007|0.013|
||[0.113]|[0.114]|[0.113]|[0.102]|[0.103]|[0.102]|
|RES^2|1.640<br>[0.836]*|1.519<br>[0.830]*|1.527<br>[0.806]*|2.228<br>[1.036]**|2.099<br>[1.036]**|2.040<br>[1.003]**|
|EXTD^2|-0.026|-0.024|-0.036|-0.036|-0.034|-0.046|
||[0.025]|[0.024]|[0.021]*|[0.015]**|[0.015]**|[0.017]***|
|RES*DREMave|0.441<br>[0.335]|0.267<br>[0.323]|0.570<br>[0.312]*|0.727<br>[0.321]**|0.563<br>[0.299]*|0.744<br>[0.299]**|
|EXTD*DREMav|-0.075|-0.055|-0.085|-0.074|-0.046|-0.083|
|e|||||||
||[0.045]*|[0.050]|[0.051]*|[0.039]*|[0.044]|[0.046]*|
|Observations|1214|1214|1214|1191|1191|1191|
|R-squared|0.30|0.30|0.30|0.22|0.22|0.22|

_Notes_ : Estimation by linear probability model. Robust standard errors in brackets. All regressions contain country fixed effect and year dummies. The dependent variable BP5 takes the value 1 if a reduction of a current account deficit of at least 5 percentage points of GDP and equal to at least half of the initial current account deficit occurs at time t, and zero otherwise. DREMave is a dummy variable that takes the value 1 if the country mean of workers’ remittances over GDP is above the median or mean or 75<sup>th</sup> percentile value (as specified at the top of each column) of the distribution of country means. All other explanatory variables are described in Table A1 and lagged one period. * significant at 10%; ** significant at 5%; *** significant at 1%
- 20 -

Table 3: UNIT ROOT TESTS, ALLOWING FOR A STRUCTURAL BREAK n Regression: Y = a + b DU + cDT + dt + eX + E f X + U t t-1 i=l i t-i t

|Price Series|a|b|c|d|e /a|
|---|---|---|---|---|---|
|Meranti|1.61|-2.64|3.14|0.74|-2.36|
|Teak|0.34|-0.30|0.92|-0.77|-1.67|
|Sapelli|1.23|-0.52|2.08|0.65|-1.44|
|Samba|1.68|-2.49|2.96|0.58|-1.70|
|Niagon|1.71|-1.24|2.11|0.44|-1.86|
|Sawnwood|1.20|-1.80|2.13|0.55|-0.25|

/a The t-statistic reported under e is for the hypothesis e = 1 and it is not distributed according to the standard t-distribution. The critical value for e is -4.24 at 95% the confidence interval (see Perron, (1989) p. 1377, Table VI.B.) Notes: With the exception of the coefficients of the lagged dependent variaDle, for all other coefficients the standard t-distribution critical values can be used.
inflation rate of 10%, real output of $100, an inventory level of 20% and a FIFO accounting system, nominal output would be about $105 and inventory levels, as measured by the above formulas, would be 19% in the beginning of the year and 21% at the end of the year.1<sup>4</sup> We compensate for these problems in part by using both beginning-of-year and end-of-year inventories and also using two years for the same country when available.

**Table 2: Data Availability**

||**Country**|**1973 Survey**|**1983 Survey**|**Country**|**1973 Survey**|**1983 Survey**|
|---|---|---|---|---|---|---|
|**1.**|Australia|1973|*1984|29. Luxembourg|1973||
|2.|Austria|1973|1983|30. Macau||1983|
|3.|Bangladesh||1982|31. Malaysia||1983|
|4.|Barbados||1983|32. Malta||1983|
|_5._|Brazil|*1973||33. Mexico||1983|
|6.|Canada|1973||34. Netherlands|1974||
|7.|Chile|*1973|1983|35. New Zealand||1983|
|8.|Colombia|1973|1983|36. Norway|1973|1983|
|9.|Costa Rica||*1980|37. Panama|1973|1981|
|10.|Cyprus|1972|1981|38. Peru|1973|*1982|
|11.|Czechoslovakia|1973|1983|39. Philippines|1972|1983|
|12.|Denmark|1973|1983|40. Poland||1983|
|13.|Ecuador||1983|41. Portugal|1971||
|14.|Egypt||1979|42. Puerto Rico|1972||
|15.|El Salvador||1983|43. Qatar||1983|
|16.|Fiji||1983|44. Singapore|1973|1983|
|17.|Finland||1983|45.Sweden|1973|1983|
|18.|France||*1983|46. Thailand||1982|
|19.|Guatemala|1974|*1983|47. Turkey|1970|1983|
|20.|Honduras|1975||48. UK|1973|*1983|
|21.|HongKong|1973|1983|49. US|1972|*1982|
|22.|Hungary|1973|1983|_50._Venezuela||1984|
|23.|Iceland||1983|_51._Zambia|1973||
|24.|Iran||1983|52. Zimbabwe||1983|
|_25._|Israel|1972|1982||||
|26.<br>|Japan<br>|1973<br>||*Indicates that only to<br>|tal inventory data w<br>|as available,<br>|
|27.<br>2.|Korea<br>Kra17193inventor|1973<br>y.|1983|rather than both total i|nventory and raw ma|terials|
|28.|Kuwait|1974|1983||||

14 BOY inventory would be 20 and EOY inventory would be 22. 20/105 is about 19% and 22/105 is about 21%.

13
13 combined Soviet transport<sup>and communications sector (not shown) is also above</sup> average.

Business **and** consumer services.<sup>Figures 5 and 6 show that the Soviet trade</sup> share in value added is far below<sup>OECD norms, where the latter are defined with</sup> and without restaurants and<sup>hotels. The share of Soviet banking and insurance</sup> in value added is close to zero,<sup>as shown in Figure 7. Figures 8 and 9 show a</sup> comparison of consumer services<sup>(repair, recreation, culture). Some OECD data</sup> include housing, and some<sup>do not.</sup> Therefore, USSR data are shown<sup>with and</sup> without housing. When housing<sup>is included, the USSR lies above the regression</sup> line; but when housing is excluded,<sup>it lies below the line.</sup> This is because the housing share in value added is<sup>large in the USSR--about 5 percent of GDP.</sup> Public services. Figures 10,<sup>11, 12, and 13 compare the share of public</sup> services value added in the<sup>USSR and OECD. Figure 10, which includes wages of</sup> military personnel for the USSR and<sup>most OECD countries, shows that the Soviet</sup> share of public services was<sup>higher than OECD norms in the 1960s but that it</sup> dropped slightly below international<sup>norms in the 1970s and 1980s. The exclusion</sup> of military personnel (Figure<sup>11), science personnnel (Figure 12) and both</sup> military and science personnel<sup>(Figure 13) in the Soviet data substantially</sup> reduces the Soviet public<sup>services share.</sup> Conclusion: Soviet value added<sup>shares of trade, communication, banking and</sup> finance, consumer services<sup>excluding housing, and public services excluding</sup> science/military personnel are<sup>substantially below- -and in some cases far below</sup> -- international norms. As a result, even though some<sup>activities, such as</sup> transport and housing, have higher<sup>value added shares, total services play amuch</sup> smaller role in the Soviet economy<sup>than they do in OECD countries. Above-norm</sup>
real-world protection adds to the discrimination against exporters of manufactures and processed agricultural products resulting from their greater dependence on intermediate inputs.

|Table4. Share|s of intermedia<br>|teinputs <br>|andEffective <br>|Rates of Pr<br>|otection fore<br>|xporters,1997<br>|
|---|---|---|---|---|---|---|
||Agriculture|Ag.<br>Proc.|Resources|L Manuf|K Manuf|Services|
||%|%|%|%|%|%|
|**_Input shares_**|||||||
|Argentina|21.8|61.9|11.9|58.9|57.7|24.2|
|Chile|40.8|76.8|46.3|65.5|65.2|39.6|
|China|42.9|80.0|48.4|74.0|78.3|61.3|
|India|32.0|82.3|27.6|69.6|76.8|40.6|
|Malawi|40.3|58.2|35.7|55.9|50.9|30.3|
|Morocco|34.9|82.7|35.6|62.2|75.7|52.1|
|Pakistan|35.0|84.2|18.7|72.2|79.3|41.2|
|World|44.5|72.2|37.3|64.5|68.0|39.7|
|**_ERP-X _**|||||||
|Argentina|-2.7|-13.6|-0.8|-16.2|-13.7|-2.9|
|Chile|-5.2|-22.5|-5.6|-11.2|-13.2|-2.6|
|China|-15.1|-54.0|-7.3|-34.8|-27.9|-13.7|
|India|-5.4|-38.5|-3.3|-22.6|-34.8|-6.3|
|Malawi|-7.3|-16.4|-5.0|-15.0|-8.9|-3.9|
|Morocco|-8.5|-50.4|-1.9|-27.5|-17.9|-8.1|
|Pakistan|-8.4|-45.4|-5.6|-40.5|-54.0|-12.2|
|World|-7.2|-25.0|-1.0|-8.5|-5.7|-1.4|
|Source: GTAP 5<br>protection on inte<br>based on the com<br>Maldives.|database(www.g<br>rmediate inputs u<br>posite region ‘Oth|tap.org<br>). E<br>nder the ass<br>er South A|RP-X measures<br>umption of hom<br>sia” which also|the reduction i<br>ogeneous prod<br>includes Afgha|n Value Added<br>ucts. Results fo<br>nistan, Nepal,|caused by<br>r Pakistan are<br>Bhutan and the|

Where the negative effective rates of protection seen in the lower panel of Table 4 the structure of protection is clearly a daunting problem for putative exports of manufactures or processed agricultural products, particularly if there are fixed costs involved in entering export markets. However, it is clear that this problem is much more manageable in many countries than it was in the early 1980s. If we triple China’s protection level from its 1997 base to align it with the tariff rates that applied in China in the early 1990s, we find negative ERP-X’s of –78 percent for processed food and –62 percent for labor-intensive manufactures. And this is before the direct adverse impacts of 24
values, although as we shall see below the output elasticities contain the other parameters and often are highly significant.

The goodness of fit statistics are reported in Table 4 indicate an excellent fit of the data with R- squared coefficients ranging from 0.97 to 0.99. The Durbin-Watson statistics indicate first-order autocorrelation, which is why we allow a first order moving average correction in the GMM estimation. An explicit structural correction for autocorrelation is not pursued because it could introduce specification error and would violate the conditions that allow the input demand functions to be integrated back to the cost function. Given the relatively large sample size used in this study, the theoretical result that the GMM estimates are asymptotically efficient seems reasonable.

Two sets of elasticities can be computed from the restricted cost function and the input demand functions. The elasticities of demand holding levels of the quasi-fixed factors are equivalent to short-run elasticities often defined in the literature. Given that we are estimating a separable cost sub-function holding capital and labor fixed, this study uses the term partial adjustment for these elasticities, which are reported in Table 5.<sup>6</sup> The demands for carbon permits and fuels are essentially perfectly inelastic assuming levels of nuclear, hydroelectric, and renewable resources are fixed. In some sense, these elasticities are an artifact of this extremely restrictive ceteris paribus measurement and would explain the violation of the concavity conditions for these partial adjustment elasticities. Nonetheless, the other elasticities reported in Table 5 show that greater levels of nuclear and renewable resources reduce the demands for fuels and carbon, as one would expect. Indeed, the convexity conditions are satisfied for all observations. Likewise, predicted marginal cost is positive

> 6 Using annual data on installed capacity and monthly estimates of replacement costs for new capacity, we generated monthly time series for capital stocks. Likewise, using quarterly data for employment levels in the electric generating and transmission sector and quarterly data on wages, we generated price and quantities for labor. We then estimated a model using (4) and (5) with carbon, labor, and energy as variable inputs and non-carbon energy and capital as quasi-fixed inputs. The test of the overidentifying restrictions was decisively rejected, suggesting that the extrapolated data could be introducing measurement errors.  This finding verifies our approach to base our analysis on reported data, which admittedly can only allow measurement of the short-run flexibility of the power grid to switch generation sources in response to relative prices and output.

15
<u>Figure 1: Unemployment Rates and Share of Formal Employment (Trends and Cycle): Mexico an Brazil</u> Mexico Brazil <!-- Start of picture text --> 1983q1 1987q3 1992q1 1996q3 2001q1<br>1987q1 1991q3 1996q1 2000q3 2005q1<br>F/E U/U+E F/E U/U+E<br>1983q1 1987q3 1992q1 1996q3 2001q1<br>1987q1 1991q3 1996q1 2000q3 2005q1<br>F/E (HP) U/U+E (HP) F/E (HP) U/U+E (HP)<br>.65 .65 .08<br>.07<br>.07<br>.06<br>.6 .6<br>.06<br>.05<br>F/E U/U+E F/E U/U+E<br>.05<br>.04 .55<br>.55<br>.04<br>.03<br>.5 .03<br>.5 .02<br>.02 .4 .2<br>.02<br>.01 .1<br>.2<br>0<br>0<br>0<br>F/E (HP) -.01 U/U+E (HP) F/E (HP) U/U+E (HP)<br>0 -.1<br>-.02<br>-.02 -.2<br>-.03 -.2<br><!-- End of picture text --> Notes: The share of formal employment, F/E, is constructed as the number of formal workers over total employment. Unemployment rate, U/(U+E), corresponds to the number of unemployed workers over the total labour force. The series have been smoothed using a 4 quarter moving average to remove high frequency fluctuations. The bottom panels shows the series logged and de-trended using an HP filter with lambda 1600. Data for Mexico (left panels) is drawn from the quarterly National Urban Labor Survey (ENEU) from 1987:Q1 to 2004:Q4. Data for Brazil (right panels) is drawn from the Monthly Employment Survey (PME), quarterly averaged from 1983:Q1 to 2001:Q2. Shaded areas indicate recessions.

30
plete integration frameworks, respectively. The analysis in these sections begins with a case with no international borrowing, and then extends the framework to include the possibility of international debt. Section 5 introduces the productivity shocks under both integration schemes. Results in this section are driven by using numerical simulations as analytical solutions do not exist. Finally, section 6 offers some concluding remarks.

# **2 A General Model**

We consider the simplest framework that can capture our main points, which consists of an economic union with two members: country 1 and country 2. Each country is inhabited by households/firms (whose mass is normalized to unity) that live for two periods. In the first period agents in country _i_ receive an endowment income _y_ 1 _i_ which can be used for both consumption and investment purposes. In the second period of their lives they have access to the following production technology to produce a good _y_ 2 _i_ , where _A_ , _k_ , and _g_ denote a productivity parameter, a private input (to be interpreted as either produced or non-produced in what follows), and public goods, respectively. The formulation allows for spillovers across jurisdictions such that public goods provided by country _j_ affect output in country _i_ . We define _γ_ such that _γ_ = _θβ_ with _θ ≤_ 1 to capture the notion that these spillovers are less than one-to-one across economies.<sup>7</sup> Following Besley and Coate (2003), we abstract from mobility of labor and capital across jurisdictions.

Public investment goods are produced using the following production technology where Ω and _z_ denote the productivity parameter and the level of input into the public good production process in each country. We suppose that _μ ≥_ 0 but not necessarily below unity to capture any returns to scale in the production process.

Households in both economies maximize lifetime utility

> 7Among others, Cohen and Paul (2004) provide evidence for the existence of such inter-state productivity spillovers. Using the manufacturing series in US States between 1982 and 1996, they show that infrastructure investments in neighboring states increase the value of own-state infrastructure investments a well as reducing the manufacturing costs potentially through cost savings in transportation.

7
correlations between monthly international price and domestic prices at each of the different coffee producing centers are above 0.9.

Figure 1: Monthly international coffee price index <!-- Start of picture text --> 2006m1 2008m1 2010m1 2012m1 2014m1 2016m1<br>Price index (adjusted for nominal exchange rate)<br>International price index<br>5000 300<br>4000 250<br>3000 200<br>International price index<br>2000 150<br>Price index (adjusted for nominal exchange rate)<br>1000 100<br><!-- End of picture text --> Note: This figure plots coffee price index for the variety of coffee known as _Brazilian Naturals_ , which is Arabica species that is dried inside the fruit rather than after the fruit has been removed. It is one of the most widely traded coffee type, with Ethiopia as one of the major suppliers. In addition to the international price index, I also plot the index adjusted for nominal exchange rate of Birr/USD to facilitate comparison with domestic coffee prices below. The gap between the international price index and the index adjusted for nominal exchange rate is expanding after 2012 due to depreciation of Birr against USD. The vertical red lines indicate the survey windows. Data Source: International Coffee Organization.

# **2.2 Descriptive statistics**

In this subsection, I provide descriptive statistics of key variables from my dataset. Table 1 provides descriptive statistics of most of the variables used in analysis. I provide descriptive statistics for the whole sample and for two different groups of households: households that allocate at least 10% of their farmland to coffee and those who allocate less than 10% of their farmland to coffee. While the 10% threshold is arbitrary, it can be argued that farmers who allocate at least 10% of their farmland to coffee are likely to be significantly affected by coffee price fluctuation, and can be considered coffee producers. These households account for 11% of the sample size, and they allocate about 40% of their farmland to coffee, on average (see table 2). There are some notable differences in the summary statistics across the two groups.

7
Figure 3: Net Impact of the Ports Measure and Net Exports in the Presence of Threshold Effects <!-- Start of picture text --> Net NX and Ports Impact<br>0.08<br>0.06<br>0.04<br>0.02<br>0<br>-0.02<br>-0.04<br>-0.06<br>-0.08<br>-0.1<br>-0.12<br>-0.14<br>Year<br>NX Ports<br>19701971 1972 19731974 197519761977 1978197919801981 1982 19831984 198519861987 19881989199019911992 19931994 199519961997<br>Net Impact<br><!-- End of picture text --> 46
# **Box 1: ILLUSTRATION OF REDISTRIBUTION METHODS**

To further illustrate the two methods for redistribution (relative size vs. relative value), the following examples offer a sense of how these two redistribution techniques are carried out.

In the case of redistribution by _relative size_ , take the example of a landowner who owns 1,000m<sup>2</sup> of undeveloped land. The municipality is entitled to 30 percent of the landowner’s holdings to use for public infrastructure. In this case, the landowner should receive 700m<sup>2</sup> of her property back, which is now fully serviced. If the municipality takes more than 30 percent and say, returns only 600m<sup>2</sup> then the municipality must compensate the landowner with a payment equal to the new market value (valuation methods in Germany are discussed just after these examples) of the additional 100m<sup>2</sup> . Similarly, if the municipality does not need the full 30 percent for readjustment, and say, for example, the landowner receives 800m<sup>2</sup> back, then the landowner now owes the municipality the new market value of the additional 100m<sup>2</sup> (examples adapted from Müller-Jökel 2004).

In the case of redistribution by _relative value_ , we return to the example of a landowner who owns 1,000m<sup>2</sup> of undeveloped land. In this case, we are concerned with two values: 1) the input value of the land before readjustment; and 2) the post-development value of land once readjustment has taken place. If the input value is 100USD/m<sup>2</sup> then the input value is 100,000USD. Let us assume that the value post-development is calculated to be 200USD/m<sup>2</sup> . If municipality returns 600m<sup>2</sup> of land to the landowner, then the value of that land after redevelopment is 600m<sup>2</sup> *200USD = 120,000USD. Thus, the landowner has received a benefit of the value of the post-development land she receives minus her original input value of her land, so 120,000USD - 10,000USD = 20,000USD. The following table illustrates this example.

|Table. An Example of Redi<br>|stribution byRelativ<br> <sup>2</sup>|e Value<br> <sup>2</sup>||
|---|---|---|---|
|**Land Status**|**Land Value**/m|**Land Size**(m)|**Total Land Value**<br>(land value*land size)|
|Input land|100USD|1000|100,000USD|
|Post-development land|200USD|600|120,000USD|
|Compensation (post-deve<br>• Payment from lando<br>• Payment from muni|lopment value – inp<br>wner to the munici<br>cipality to landowne|ut value):<br>pality if positive value<br>r if negative value|20,000USD|

13
# _3.2. Indicators of Financial Development and Structure_

# 3.2.1 Indicators of Financial Development

To test our hypotheses, we need appropriate indicators of the efficiency with which financial intermediaries and markets reveal information and exert corporate control, and therefore channel external resources to industries that need them most. While the perfect indicators certainly do not exist, the recent literature has developed indicators that proxy relatively well for financial intermediary and stock market development across countries. We use newly constructed data in Beck, Demirgu,c -Kunt, and Levine (2000) and Levine, Loayza, and Beck (2000) to measure overall financial development. We also construct new measures of financial development, isolating private financial intermediaries, and discuss these results in the sensitivity section below.

_Finance-Activityis_ a measure of the overall activity of the financial intermediaries and markets. It is defined as the log of the product of Private Credit, the value of credits by financial intermediaries to the private sector divided by GDP, and Value Traded, the value of total shares traded on the stock market exchange divided by GDP. Private Credit is the most comprehensive indicator of the activity of financial intermediaries by including both bank and nonbank intermediaries. Recent work shows that Private Credit exerts a large, positive, robust influence on economic growth [Levine, Loayza, and Beck 2000; and Beck, Levine, and Loayza 20001. Value Traded measures the activity of the stock market trading volume as a share of national output and thus indicates the degree of liquidity that stock markets provide to economic agents.

> **8** Levine and Zervos (1998) point out a potential pitfall of Value Traded. If forward-looking stock markets anticipate large corporate profits and therefore higher economic growth, this will boost stock prices and therefore boost Value Traded. Thus, a positive relationship between Value Traded and growth might reflect a spurious correlation due to this price effect. This price effect, however, does not arise in our model, since we focus on within-country, acrossindustry growth rates. If markets anticipate higher growth in one industry, the resulting larger value of Value Traded

13