rafmacalaba/datause-encoder-data huggingface dataset:
**18**

# "Composite" Conversion Factor

**_25._** The proper use of multiple rates demands a "matrix" weighting approach, that is, a distribution **-f** weights among the multiple exchange rates. What is envisaged here is a system with time series for multiple rates and a corresponding set of time series for weights. Obviously, adjustments to the official and multiple rates require intimate knowledge of the applications of respective rates to specific transactions. Two illustrative examples of IEC's attempt to develop the envisaged system for composite conversion factors are shown in Table 4 for **Egypt,** and Table **_5_** for Syria.

**26.** Table _4_ below illustrates the **Egypt** situation where the multiple rates are from the **IFS** but the weights are from the Fund and Regional staff. Notice that the premium enjoyed **by** each of the rates in the secondary and tertiary markets is very significant.

Table 4: EGYPT **--** Rates and Weights for Computing Average Exchange Rates from Multiple Rates

|**1980**|**1981**|**1982**|**1983**|1984|**1985**|**1986**|**1987**|**1988**|**1989**||
|---|---|---|---|---|---|---|---|---|---|---|
|AlternativeConversionFactors|||||||||||
|**1.**<br>Principal(CentralBank)Rate|**0.7000**|**0.7000**|**0.7000**|**0.7000**|**0.7000**|**0.7000**|**0.7000**|**0.7000**|**0.7000**|**0.8516**|
|2.<br>Secondary (CommercialBank)Rate|**NA**|**0.7390**|**0.8317**|**0.8317**|**0.8317**|**_13010_**|**1.3503**|**_1.5015_**|**2.2128**|**2.5172**|
|**3.**<br>Tertiary(Parallel)Rate|**NA**|**1.0100**|**1.1300**|1.1453|1.2543|**_1.5488_**|**1.8838**|2.1459|**2.5082**|2.6452|
|Wei2hts|||||||||||
|**1.1**Principal(CentralBank)Rate|0.4350|0.4350|0.4350|0.4350|0.4350|0.4350|0.4150|**03690**|**0.3200**|**NA**|
|1.2Secondary(CommercialBank)Rate|**0.3150**|**03150**|**03150**|**03150**|0.1400|0.1400|**0.1830**|**0.2370**|**0.2880**|**NA**|
|<br>**_13_**Tertiary(Parallel)Rate|**0.2500**|**0.2500**|**0.2500**|**0.2500**|0.4250|0.4250|0.4020|0.3940|**0.3920**|**NA**|
|"COMPOSITE"Rate<br>**[(1*1.1)+ **(2*1.2)**+(3*13)]**|**NA**|**0.7898**|0.8490|**0.8528**|0.9540|1.1449|1.2949|1.4596|1.8445|**NA**|
|MemoItems|||||||||||
|**GNP**percapita**(using**PrincipalRate)|**573**|**553**|**637**|**690**|**771**|**847**|**925**|1034|1274|**NA**|
|<br>**GNP**percapita(using"Composite"Rate)|**NA**|1432|**798**|**582**|**611**|**606**|**576**|**561**|**592**|**NA**|
|Ratio:"Composite"Rate/PrincipalRate<br>|**NA**<br>|**1.13**|1.21|1.22|**1.36**|1.64|**1.85**|**2.09**|2.64|**NA**|
|"Composite"percap/"IFSRF'perc|alNA|**2.59**|**1.25**|0.84|**0.79**|**0.72**|**0.62**|0.54|0.46|**NA**|

Source: Multiple rates are from IFS Weights: **1980-85:** Unpublished data from the Fund's Information Notice System; **1986-88:** Regional Staff.
<!-- Start of picture text --> 1000<br>900<br>800<br>700<br>600<br>500<br>400<br>300<br>200<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010<br>Shaded area represents crisis period<br>MYR mn<br><!-- End of picture text -->

# Trend output growth pre- and post-Asian crisis

If there is a negative shock to economic activity, policymakers need to know how the economy would respond so as to decide whether to make a discretionary change in, say, government consumption. In investigating these questions, much of the literature has focused on the advanced countries. From an analytical standpoint documenting empirical similarities and observing whether they are the same across di¤erent levels of income provide an empirical basis for devising ...scal policies that incorporate features and relationships that are particularly important for developing countries.<sup>3</sup> If ...scal policy mainly has demand e¤ects, and shifts out the demand for goods without crowding out private consumption, then clearly there is a role for a countercyclical ...scal policy during recessions, when individuals or ...rms are more likely to be credit constrained. On the other hand, if ...scal policy mainly has a negative wealth e¤ect on labor supply, and a crowding out e¤ect on private investment, then

> 3Much of the literature measuring the size of ...scal multipliers has continued to focus on the advanced economies.

3
- In Kenya, “Cases of misuse of funds were reported in the two sites: according to key informants, in some cases, male recipients have used some of the cash to buy alcohol, although this is relatively rare (only three cases reported, with the majority of the cash being used for consumption and investment)” (Onyango-Ouma and Samuel 2012).

- In Swaziland, a focus group participant reported that “Men don’t return home on paydays; some have found other women to spend the money with” (Devereux and Jere, 2008).

- In Uganda, participants and informants observed that “Some beneficiaries – especially men – have used the cash transfer in over-drinking alcohol” and “Some older men especially drink all the money” (Bukuluki and Watson 2012).

How do we reconcile these anecdotes with the extremely insignificant or even negative effects we observed earlier? First, the results previously discussed do not indicate that no single beneficiary uses his or her transfer on alcohol. For example, the Malawi anecdote above comes from a study that measured the proportion of transfers that were spent on alcohol; the proportion was 0.1%. So although interviewees had “heard of four men” or knew “certain men”, these numbers seem very small. What the quantitative results earlier claim is that, _on average_ , there is no positive impact of transfers on alcohol expenditures.

Second, most of these reports are not with reference to one’s own household, but rather to other individuals who respondents may know who spend the money on alcohol and tobacco. However, multiple respondents may well know the same person in the community who has a reputation for high levels of alcohol or tobacco consumption. These anecdotes can be subject to “saliency bias”, in which individuals pay attention to highly noticeable factors and dramatic events: A village drunkard stands out and is likely to come up disproportionately in discussions.

An alternative possibility is that the respondents in household surveys are unaware of how their household resources are spent. For example, if a husband takes household resources and spends them on alcohol without the wife’s knowledge and the wife is the survey respondent, then such spending might show up in qualitative reports from other households but be missing in the impact estimates. However, it seems unlikely both that (1) the surveys consistently interview the non-drinking member of the household, and (2) this member is consistently ignorant of these expenditures, particularly in low-income households with limited liquid income.

These results underline the importance of complementing qualitative reports with quantitative data and are reconcilable with the earlier quantitative finding that, on average, there is no increase in the consumption of temptation goods.

13
# **Table A.3a: Full Estimates for Panel A of Table 2 in the Manuscript (Same Period World Price** **<u>of Crude Palm Oil)</u>**

|||**Wh**<br>|**olesale-Wor**<br>|**ld Price Mar**<br>|**gins**<br>||
|---|---|---|---|---|---|---|
||Full S|ample|Restricte|Sample|Matched|Sample|
|**Effects of Announcement**|(1)|(2)|(1)|(2)|(1)|(2)|
|Announcement Dummy|-5.341***|-4.771***|-6.504***|-4.575***|-5.350***|-4.767***|
||(-10.10)|(-7.874)|(-12.18)|(-10.41)|(-9.310)|(-8.596)|
|**Announcement*Palm**<br>**(Intercept)**|**35.25****|**24.68***|**37.72****|**24.71***|**32.27****|**22.36**|
||(2.285)|(1.675)|(2.415)|(1.685)|(2.094)|(1.514)|
|**Announcement*Palm* world**<br>**Price (Slope)**|**-0.312**|**-0.221**|**-0.311**|**-0.232**|**-0.312**|**-0.189**|
||(-1.629)|(-1.214)|(-1.605)|(-1.279)|(-1.630)|(-1.030)|
|**Effect of Implementation**|||||||
|Policy Implement. Dummy|-2.486***|-3.047***|-4.012***|-1.674***|-1.222**|-2.484***|
||(-4.379)|(-4.978)|(-6.389)|(-2.743)|(-2.079)|(-3.677)|
|**Policy Implement.*Palm**|||||||
|<br>**(Intercept)**|**25.62*****|**21.10*****|**27.52*****|**19.88*****|**25.95*****|**26.88*****|
||(7.503)|(5.591)|(7.857)|(4.934)|(7.435)|(6.603)|
|**Policy Implement.*Palm* world**<br>**price (Slope)**|**-0.232*****|**-0.210*****|**-0.224*****|**-0.211*****|**-0.273*****|**-0.281*****|
||(-5.333)|(-4.445)|(-5.011)|(-4.301)|(-6.078)|(-5.385)|
|World price*palm|0.224***|-0.198***|0.166***|-0.0209|0.281***|0.0122|
||(5.521)|(-3.807)|(2.804)|(-0.295)|(6.100)|(0.168)|
|World Price|-0.556***|-0.171***|-0.493***|-0.364***|-0.619***|-0.398***|
||(-12.46)|(-3.229)|(-7.646)|(-4.632)|(-12.77)|(-5.363)|
|Palm|17.51***|32.71***|15.72***|29.96***|19.73***|28.83***|
||(16.28)|(17.57)|(14.10)|(15.41)|(13.08)|(10.59)|
|Intercept|20.88***|10.35***|16.96***|28.44***|21.29***|10.08***|
||(22.24)|(3.712)|(10.50)|(4.618)|(20.82)|(3.325)|
|Observations|1,932|1,932|1,374|1,374|1,312|1,312|
|Year and Quarter dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|Ramadan Dummies|No|Yes|No|Yes|No|Yes|
|Relative Prices|No|Yes|No|Yes|No|Yes|
|Fuel Price|No|Yes|No|Yes|No|Yes|
|Dummyif World Price rising|No|Yes|No|Yes|No|Yes|
5 migrant workers are altruistic, more country-level poverty at home should encourage them to remit more money.

Second, this paper uses macro-economic data on the skill composition of migrants to examine how the type of workers sent abroad by a country – educated or uneducated, skilled or unskilled – affects the level of remittances received by that country. Until recently, the lack of country-level data on the skill composition of migrants has prevented much meaningful work on this issue.<sup>2</sup> At the micro-economic level, some past studies have suggested that (holding all other factors constant) more educated migrants remit more because they typically earn more (Banerjee, 1984). However, other microeconomic studies have found that more educated migrants remit less because they have lower propensities to return to their origin country (Rapoport and Docquier, 2004).

Using country-level data on the skill composition of migrants can help clarify which of these predictions is correct.

# 3.  New Data on the Determinants of International Remittances

As noted above, this study is based on a new data set that includes information on remittances, poverty, country income, the skill level of migrants and other economic and financial variables for 76 “low-income and middle-income” developing countries.<sup>3</sup> These countries were selected because it was possible to find relevant data on all of these variables for the period 1995 to 2001. Since it was not easy to assemble this data set, and several problems still plague this (and all other) data sets on international remittances, it is important to spell out how this information was assembled.
firms in the dataset have paid water levies, and therefore, was not complying with at least one regulatory standard.

**Table 1 Water discharge characteristics in 1997**

<!-- Start of picture text --> Average discharges (kg!ear)<br>TSS (total suspended solids) 47,861<br>(530)<br>COD(chemical oxygen demand) 48,591<br>(626)<br>Average concentration (mg)<br>TSS (total suspended solids) 99<br>(503)<br>COD (chemical oxygen demand) 280<br>(507)<br>Proportion of firms paying 45 %<br>levy<br><!-- End of picture text --> In 1997, the 640 enterprises were the object of 5,287 inspections, most of them (99.4%) performed by the Zhenjiang Environmental Protection Bureau. These enterprises were also the object of 78 water related complaints, and 163 air related complaints.

Table 2 provides the mean and coefficient of variation of the variables of interest in this analysis. Some remarks are useful before commenting the statistics of interest. First, given the presence of missing observations, the number of observations used to compute these statistics is not necessarily the same for all variables. Second, the coefficient of variation instead of the standard deviation has been used as a measure of dispersion to compare variables with different means and different units of measurement. Noteworthy are the mean values of the number of inspections, the ratio of total refund to the levy paid in the previous year as well as the ratio of total profit to total employment. On average a plant got inspected 8 times a year. In fact, the highest number of inspections recorded in a single plant was 61 in one single year. This is quite an impressive number. Also observe that the variable "refund" is important since on average 14
find an association between male LFP and having a washing machine. The variables capturing social norms and attitudes towards women and public policies are, in general, not statistically significant. Exceptions include the share of women in high-paying positions in Central Government, the logarithm of public spending in health interacted with having a family member 60 years old or more, and the municipality road accessibility measure.

# **5. Robustness Checks**

Our results are robust to different samples. First, we expand the sample to include women aged 25 through 55 years old. Table 2 presents results when controlling for individual and household characteristics, local labor market drivers, and the model that interacts the normalized number of public preprimary centers with having kids in the 4-6 age range, respectively. Findings are aligned with previous estimates: (i) there is a negative association between having children and FLFP and the penalization is larger the younger the kids, and (ii) a larger number of public preprimary centers in the municipality where a woman lives is positively related to FLFP of women having children in the 4-6 age range.

Our second robustness explores alternative measures of access to preprimary centers using information from the census. Using census data, we compute a measure for each woman in our sample indicating whether at least one of her children ages 4 to 6 attends a preprimary center. Column 3 in Table 2 presents the results of our main specification when including this variable and restricting the sample to women that are mothers of children aged 4 to 6 years old. Results indicate that access to a preprimary center is still strongly and positively associated with FLFP. Considering that most preprimary centers are public, we interpret this finding as reinforcing our previous results. It also suggests the importance of public infrastructure to facilitate the entrance of mothers into the labor market.

We also propose different measures of social norms and attitudes towards women in the society using data from 2018 Latinobarómetro. The first captures the share of males considering that a team of women and men will have better results than a team of only men. The second captures the opinions towards women and men being equally skilled in science and technology. Results reported in column (4) of Table 2 show a that the positive association holds (but its non-significant with the second measure).

Finally, column (5) of Table 2 reports the main findings when the sample is restricted to urban areas. The direction of the main findings remains the same. However, the penalty associated to young children is larger than in the baseline estimates. In urban areas, women having an additional child in the 0-3 age range are 6 percentage points less likely to participate in the labor market (the 16
Table 1 shows the CE coefficients used to convert crop and animal products into cereal equivalents.

Table 1. Sample Cereal Equivalent (CE) Coefficients

|Crop pro|ducts|Animalproducts<sup>a</sup>|
|---|---|---|
|Products|Coefficients|Products<br>Coefficients|
|Cereals|1.00|Bovine meat<br>19.8|
|Fruits|0.14|Pig meat<br>8.5|
|Pulses|1.06|Poultry meat<br>4.7|
|Starchy roots|0.25|Mutton & Goat meat<br>19.8|
|Sugar, sweeteners|1.08|Eggs<br>3.8|
|Tree nuts|0.74|Milk<br>1.2|
|Vegetable oils|2.73||
|Vegetables|0.07||

Source: Rask and Rask (2011) and authors’ calculation. aMeat coefficients represent animal carcass weight to conform with FAOSTAT definition of meat consumption.

The coefficients in Table 1 reflect the high resource costs of producing animal products relative to cereals, and illustrate the great differences among animal products. The CE coefficient of 19.8 for carcass beef, for instance, takes into account the large amount of feed used directly to produce beef; the relatively low dressing weight percentage for live cattle (0.59); and feed for breeding cows and young calves needed to supply production animals and replacement breeding stock.<sup>13</sup> Pork, poultry and fish are more efficient both because of generally higher feeding efficiencies and the lower costs involved in maintaining their breeding stock. Within crop products, CE coefficients range from 2.7 for vegetable oils to 0.07 for vegetables.

The magnitudes of the CE coefficients appear to be broadly consistent with other estimates of land requirements in the literature (e.g., Gerbens-Leenes et al., 2002ab; Williams et

> favorable soil condition and restoration of ecosystems and habitats. Wirsenius et al. (2010) show that the global use of the non-edible type feeds is substantial.

> 13 Using beef as an example, Rask and Rask (2011) use a feed conversion ratio of 11.7 for producing live cattle, taking into account feed for both animals slaughtered and for breeding animals. This figure is converted to carcass weight using a dressing weight percentage of 59 percent, which gives rise to the final value of 19.8 (11.7/0.59). Carcass weight is used in order to conform to FAOSTAT meat consumption coefficients which are presented in carcass weight format (e-mail communication with Norman Rask).

10
15 schooling or more as skilled, and all results were qualitatively robust. Since workers with completed university accounted for 4.5% of the labor force in 1988; while workers with complete high school and above represented 16.4%, we chose the latter classification as more meaningful for Brazil. See Gonzaga et al. (2006) for a similar discussion of this classification.

The second data set used for this study comprises the trade-related variables for 22 industries, across the 1987-1999 period. Six trade-related variables are used: nominal tariffs and effective rates of protection come from Kume et al. (2000), as reported in Abreu (2004); import penetration and export shares by industry come from Muendler (2003); and import-weighted and export-weighted industry-specific exchange rates were constructed by the authors, based on the methodology suggested by Goldberg (2004), and using data from the World Bank’s World Development Indicators and the UN’s COMTRADE.<sup>19</sup> Table 1 presents initial (1988) and terminal (1995) values for each of these variables for the 22 industries into which we have grouped Brazilian firms.<sup>20</sup> As in Pavcnik et al. (2004), our use of household survey and trade data with different industry definitions necessitated a concordance between the various datasets, mapping the more disaggregated industry classifications in the trade data to the broader PNAD classifications. In addition to the standard _Nível 80_ to _Nível 100_ concordance,<sup>21</sup> we developed concordances of our own. The main one is based on (but not identical to) Pavcnik et al. (2004), and is presented in Table 2.<sup>22</sup> A more detailed description of the steps taken to clean, construct and match the data is included in the data appendix.

> 19 As Goldberg (2004) notes, “At the national level, analyses of exchange rate moves often rely on aggregate trade-weighted exchange rates. However, aggregate indexes can be less effective than industryspecific indexes in capturing changes in industry competitive conditions induced by moves in specific bilateral exchange rates.” (p.1)

> 20 Although we use data until 1999 in our estimation stage, the key initial and terminal years for our wage decomposition analysis are 1988 (at the onset of trade liberalization) and 1995 (the year in which it was completed.

> 21 Available on Marc Muendler’s excellent website of Brazilian data resources

> (http://www.econ.ucsd.edu/muendler/html/brazil.html). Nível 80 and 100 are official Brazilian industry classifications.

> 22 We are grateful to Nina Pavcnik and Norbert Schady for graciously making the details of their concordance available to us.  A further concordance between Nível 100 and the trade categories used by Kume et al (2000) was also constructed, but is too detailed to present here.
||(0.0284)|(0.0280)|(0.0235)|(0.0229)|
|---|---|---|---|---|
|Has university degree|0.491**|0.404**|0.403**|0.322**|
||(0.0457)|(0.0425)|(0.0343)|(0.0314)|
|In full-time work|0.174**|0.133**|0.139**|0.105**|
||(0.0341)|(0.0353)|(0.0284)|(0.0281)|
|In good health|0.348**|0.300**|0.269**|0.227**|
||(0.0195)|(0.0189)|(0.0158)|(0.0157)|
|Not married|-0.243**|-0.245**|-0.193**|-0.189**|
||(0.0352)|(0.0338)|(0.0270)|(0.0258)|
|Ln(Household size)|0.428**|0.0208|0.325**|0.0157|
||(0.0535)|(0.0410)|(0.0409)|(0.0308)|
|Share of children in HH|-0.192**|-0.221**|-0.149**|-0.164**|
||(0.0613)|(0.0736)|(0.0508)|(0.0585)|
|Share of female in HH|-0.0227|-0.00974|-0.0127|0.00541|
||(0.0487)|(0.0488)|(0.0390)|(0.0384)|
|Share of elderly in HH|0.125**|0.0525|0.102**|0.0465|
||(0.0513)|(0.0507)|(0.0386)|(0.0367)|
|Believes in free market|0.138**|0.126**|0.106**|0.0922**|
||(0.0343)|(0.0344)|(0.0272)|(0.0268)|
|Reason for success:|||||
|Efort and hard work (ref.)|||||
|Intelligence|0.123**|0.101**|0.0969**|0.0733**|
||(0.0343)|(0.0334)|(0.0276)|(0.0254)|
|Political connections|-0.115**|-0.105**|-0.0784**|-0.0733**|
||(0.0473)|(0.0463)|(0.0385)|(0.0367)|
|Breaking the law|-0.302**<br>(0.0488)|-0.294**<br>(0.0505)|-0.224**<br>(0.0391)|-0.217**<br>(0.0389)|
|Other|-0.390**|-0.370**|-0.296**|-0.277**|
||(0.108)|(0.102)|(0.0800)|(0.0748)|
|Cause of need in society:|||||
|No luck (ref.)|||||
|Laziness|0.166**|0.133**|0.145**|0.118**|
||(0.0543)|(0.0533)|(0.0431)|(0.0404)|

18