rafmacalaba/datause-encoder-data huggingface dataset:
Next, I examine whether the potential increase in the wife’s inheritance results in partition of joint households by checking for higher rates of migration among couples in reform states married post-reform. Table 6 shows the coefficient for being exposed to the reform on the outcome variable _joint._<sup>23</sup> The first column shows that Hindu women who were ‘treated’ are significantly less likely by about 4.3% to reside in joint families, while the second column shows that there is no such effect for the nonHindu women. A placebo test identical to specification (3), which assumes that the amendments occurred a couple of years prior to their actual implementation, shows no such significant effect for the ‘treated’ Hindu women. This suggests that the significantly lower propensity of Hindu women to reside in joint families post-reform is not simply a function of some such pre-existing trend in the reform states and can in fact be attributed to the amendments.

# (c) _Is part of the effect driven by the negative effect on men’s inheritance?_

Finally, I explore whether this shift in household structure is partially driven by the detrimental effect of the reform on men’s inheritance. These are the men with sisters who were eligible to benefit from the reform. Unfortunately, the NFHS data doesn’t allow me to calculate the number of siblings of each man since I do not observe any siblings who are not residents of the same household. To overcome this shortcoming, I make use of the Rural Economic and Demographic Survey (REDS) conducted in 1999. The REDS data provides information on household structure that is similar to the NFHS but also contains additional information on all the children of household heads who are not residents of the household. In particular, it provides the exact years of marriage of any daughters of the household head, thereby allowing me to identify whether these women, and in turn their brothers, are actually exposed to the reform. The partition channel predicts that men are more likely to leave the household if they have sisters who are treated but not if they have only untreated sisters, in which case their inheritance is not affected. Appendix table S.7 shows results from the REDS data for sons of household heads who are over 18 years of age; Hindu boys are around four percent more likely to reside away from home if they have a treated sister (i.e., a sister who is unmarried or was married after the reform), or if they have a sibling who is entitled to equal inheritance (i.e., a brother or a treated sister) but not if they have an

> 23. The specification for these regressions is the same as before, equation (1).

17
De Gorter et al. (forthcoming) show that the world market prices of soybean, soybean oil, and soybean meal are linked through<sup>5</sup> where _β1_ = 0.19 and _β2_ = 0.81 denote mt of soybean oil and soybean meal, respectively, produced from one mt of soybeans in a representative large country (FAPRI 2012),<sup>6</sup> and _c0s_ denotes the (fixed) processing cost per mt of soybean oil (the crushing margin).

An analogous price link for a small country, which follows from the zero profit condition for soybean crushing, can be written as Because the world soybean price is exogenous to a small country, the price defined by the righthand side of equation (3) does not represent the market price but rather a small country crushers’ willingness to pay for soybeans. Thus, if _PSB_ ' ≥ _PSB_ ,<sup>7</sup> at least some domestic soybean production is crushed because the crushers are willing to pay as much as _PSB_ ' dollars per metric tonne of the feedstock but the market price they have to pay is only _PSB_ ; on the other hand, if _PSB_ ' < _PSB_ , no soybean is processed domestically.

Whether or not small country producers will produce biodiesel (derived from either domestic and/or imported soybean oil) depends on the producers’ willingness to pay for soybean oil compared to its world price. The world soybean oil and biodiesel ( _PB_ ) prices are linked through a zero profit condition for biodiesel production (de Gorter et al., forthcoming):

> 5 We do not use bar signs in equation (2) because the prices are endogenous in world markets.

> 6 These extraction coefficients are reflective of the U.S. market conditions in 2012 and vary over time to a certain extent due, for example, to weather.

> 7 Notice that because _PSB_ ' ≥ _PSB_ implies positive marginal profits, it is possible that not only could all domestic soybean supply be crushed but the commodity could also be imported. While this scenario is theoretically possible under the assumptions we make, in reality, the fixed processing cost is likely to increase (e.g., because of the expansion of the crushing capacity), thus eliminating the gap for positive profits. Alternatively, in competing for the feedstock, the soybean processors could bid up the domestic soybean price above the world price until they earn zero marginal profits (especially when it pays to procure the feedstock domestically as opposed to import it, e.g., due to transportation cost originating from geographical constraints). In this case, the competition for soybeans would result in an implicit soybean production subsidy and would represent a welfare transfer from processors to soybean producers.

5
and because the benefits of these interventions appear to operate at least with a two to three year lag, the potential sample is cut dramatically. The small sample, in turn, makes the estimation quite sensitive to outliers. All that said, with minor corrections for outliers, the available data tell a consistent story across alternative model specifications. However, given so few degrees of freedom, the findings should, perhaps, be viewed with a healthy dose of skepticism. At the least, it should be emphasized that the modeling effort is a work in progress that will be refined as additional Bank projects in the financial sector mature.

As the summary statistics indicated, initial financial sector and macroeconomic conditions had implications for the success of an intervention. Model (1) essentially restates what we know from Table 2.3 -- countries that had relatively under-developed financial systems and low inflation experienced the largest post-intervention improvements as measured by M2/GDP.33 The limitations of the dependent variable should be emphasized. Ideally, one would like to measure the post-intervention health of the banking sector and, perhaps, the efficiency of capital allocation. While financial deepening may often coincide with improved health and efficiency, this need not always be the case. For example, in Tanzania there was much post-intervention financial deepening but, given the lack of serious institutional reform in the banking sector, it is quite unlikely that it 33 The result also holds for improvements in DBPC/GDP, although it is less robust across alternative specifications. In general, the DBPC/GDP results are more fragile than those for M2/GDP. The trade variables, in particular, perform poorly in the DBPC/GDP regressions. The overall fit of those regressions, moreover, fluctuates wildly across alternative specifications, and never approaches that of the M2/GDP regressions. This is somewhat troubling because, on an a priori basis, it is difficult to argue that the M2 data should be any more reliable than the DBPC data as both come from the same source. In any event, we present only the M2/GDP results.

25
are small to begin with, are kept constant throughout at 0.2 percent of GDP and so is the share of domestic borrowing in GDP (0.5 percent).

In this scenario, the average growth rate of real GDP per capita, as well as real private consumption per capita, is about 4 percent. The unemployment rate drops gradually over time, from 6.5 percent in 2009 to 5.1 percent in 2020. The current account deficit remains in the two-digit range throughout, but this is financed by large inflows of foreign direct investment and substantial government borrowing (which reflects a persistent fiscal deficit). The share of tax and nontax revenues in GDP, at about 15 percent, remains relatively low compared to countries at a similar level of development. With total expenditure varying between 22 and 23 percent, and with limited ability to borrow domestically, external debt increases sharply, from about 41 percent in 2009 to almost 72 percent in 2020. This scenario would therefore call eventually for significant fiscal adjustment. It also helps to illustrate the difficult fiscal context in which some countries in the region may operate, as they strive to implement the EPA agreement.

# **4.3 Policy Experiments, 2009-20**

To illustrate the impact of trade reforms, accompanied or not by an aid-for-trade program, several experiments are performed with the model. All of them assume that the main provisions of the EPA can be implemented in 2008-2009. In addition, they all involve a cut in tariffs imposed by the Dominican Republic, as well as an increase in export prices, with the latter reflecting the cut in tariffs on the country’s exports to the EU. Calculations based on data on the composition of the Dominican Republic’s external trade indicate that immediate implementation of the EPA leads to a permanent drop in the average tariff rate of about one percentage point, or equivalently, a permanent revenue loss of about 0.4 percent of GDP, in 2008 and beyond. This drop reflects a fall in duties on final imports originating not only from the EU but also products from other CARIFORUM countries, which also benefit from the cut model used here, see World Bank (2008).

27
25 Table 4: Welfare reduction indexes, OECD Countries, all covered products, 1960 to <u>2007 (percent)</u>

||1960-69|1970-79|1980-89|1990-99|2000-07|
|---|---|---|---|---|---|
|**Western European Countries**||||||
|**European Union**|**114**|**110**|**119**|**62**|**42**|
|**EFTA**|**125**|**111**|**145**|**181**|**148**|
|Austria|92|41|60|83|47|
|Denmark|80|122|128|63|39|
|Finland|133|118|134|133|47|
|France|105|106|120|64|43|
|Germany|144|121|126|66|43|
|Iceland|—|—|274|238|167|
|Ireland|86|142|174|84|59|
|Italy|89|81|96|55|38|
|Netherlands|148|149|148|70|46|
|Norway|—|—|227|201|147|
|Portugal|26|44|50|49|37|
|Spain|44|33|59|51|35|
|Sweden|172|174|150|92|50|
|Switzerland|—|—|268|240|149|
|UK|144|127|132|72|50|
|**Europe's transition**||||||
|**economies**|—|—|—|**40**|**42**|
|Bulgaria|—|—|—|27|25|
|Czech Republic|—|—|—|33|35|
|Estonia|—|—|—|27|28|
|Hungary|—|—|—|34|41|
|Latvia|—|—|—|50|52|
|Lithuania|—|—|—|53|53|
|Poland|—|—|—|28|34|
|Romania|—|—|—|40|60|
|Russia|—|—|—|39|34|
|Slovakia|—|—|—|31|33|
|Slovenia|—|—|—|69|57|
|Turkey|—|—|—|53|53|
|Ukraine|—|—|—|35|26|
|**North America**|**16**|**14**|**35**|**23**|**23**|
|Canada|15|32|83|46|38|
|US|17|12|30|20|22|
|**Japan & Korea**|**77**|**119**|**190**|**221**|**192**|
|<br>Japan|84|130|198|225|190|
|<br>Korea|44|77|153|202|203|
|**Oceania**|**25**|**22**|**20**|**14**|**5**|
|Australia|31|24|17|14|3|
|NewZealand|12|17|27|13|9|

Source: Authors’ calculations based on data in Anderson and Valenzuela (2008)
# **A single individual.**

39. An individual who used to earn the average wage receives SK5,400 during her first three months of unemployment, and SK4,297 for the rest of the entitlement period.<sup>12</sup> This amounts to 49 and 45 percent of her potential wage in case of re-employment. Once unemployment insurance ends, she receives the minimum subsistence level for a single adult, or 29 percent of her potential labor income.

|**Benefitregime**<br>**Income**|**UI**<br>**First3 months**<br>**(RR 50%)**|**UI**<br>**Afterfirst3 months**<br>**(RR45%)**|**SA/SS**<br>**(Objective)**|
|---|---|---|---|
|**AdultI'sincome(AW)**|5,400<br>(0.49)|4,927<br>(0.45)|3,230<br>(0.29)|
|**Adultl'sincome MW)**|3,230<br>(0.90)|3,230<br>(0.90)|3,230<br>(0.90)|

40. The situation of a worker who was previously making the minimum wage is rather different. Since the unemployment insurance benefit she is entitled to fall below the minimum subsistence level, social assistance/support complements these payments for as long as the worker remains unemployed (and in objective need). In particular, she receives SK3,230 as unemployment insurance and/or social assistance/support, which represents 90 percent of her income after re-employment at the minimum wage.

# A **couple with two small children**

41. We assume that both children are under the age of 6. In this case, the household is entitled to SK1,260/monthl3 in the form of child allowances if their total income is below a 1.36-multiple of the MLS, or SK840/month if it is between a 1.36- and a 1.99-multiple of the MLS, as long as one of the parents stays at home taking care of the children.. The MLS for a family of four of these characteristics is SK8,410.

# **_Adult 2 unemployed or out of the labor force._**

42. We first assume that adult 2 makes no monetary contribution to family income (i.e. he does not work nor receive unemployment insurance). Then adult l's income and replacement ratios are the same as above. Household income falls below the minimum subsistence level, and this implies that the family qualifies for social assistance/support benefits, as well as for the highest possible child allowance. In receiving all payments, family income becomes SK9,670, which represents 79 percent of its potential level were adult 1 to work for the average wage, and a 100 percent were she to work at the minimum wage.

> 12 Notice that UI benefits are capped at a 1.5-multiple of the minimum wage (or, most recently, the subsistence level).

> **13** For both children.

11
# **Appendix. Building Macro SAMs for Guinea**

This appendix demonstrates the construction of macro SAMs in Excel, applied to Guinea, a low‐income country for which it was not possible to construct the three macro SAMs using cross‐country data; most of the data for the SAMs are from a country‐specific source, an IMF report (IMF 2016). The appendix is made up of a set of tables that show (a) 2016 Macro SAMs for Guinea of the three types described in the main text; and (b) the input data and computations that underlie the construction of these SAMs.

More specifically, Table A.1 shows a macro SAM of type I, i.e. with a single sector (one activity and one commodity) and an aggregate treatment of savings and investment. The notation is the same as used in the main text. The data used to build Table A.1 are shown in Table A.2, also indicating the sources used. It is split into two sections. The first covers the data that were entered directly into SAM cells while the second lists the data that were inputs to calculations that produced values for other cells. Table A.3 shows the calculations used for each SAM cell, drawing on the data in Table A.2.

**Table A.1. Macro SAM I for Guinea in 2016 (billions of current Guinean francs)**

||act|com|f‐lab|f‐cap|hhd|gov|row|tax‐act t|ax‐com t|ax‐imp ta|x‐exp|tax‐dir|sav‐inv dstk|total|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|act||93,654||||||||||||93,654|
|com|42,789||||49,974|6,061|16,357||||||9,842<br>0|125,023|
|f‐lab|17,043||||||0|||||||17,043|
|f‐cap|32,008||||||0|||||||32,008|
|hhd|||17,043|29,583||2,977|716|||||||50,318|
|gov|||||2,065||811|1,814|5,132|2,241|0|1,650||13,713|
|row||23,996|0|2,426|0|0||||||||26,422|
|tax‐act|1,814|||||||||||||1,814|
|tax‐com||5,132||||||||||||5,132|
|tax‐imp||2,241||||||||||||2,241|
|tax‐exp||0||||||||||||0|
|tax‐dir|||||1,650|||||||||1,650|
|sav‐inv|||||‐3,370|4,675|8,537|||||||9,842|
|dstk|||||||||||||0|0|
|total|93,654|125,023|17,043|32,008|50,318|13,713|26,422|1,814|5,132|2,241|0|1,650|9,842<br>0||

Note: Grey cells are raw data; data in green cells are from calculations in Tables A.2 and A.3. Source: Authors’ elaboration.

‐26‐
<!-- Start of picture text --> Triple Diff. Estimates for Log1 cont work<br>-<br>o7-f---------4-------- —- -@ - an<br>9~ |<br>a<br>&RS £xs &xv &A &ab &xO &xo &xi &3<br>Ccoetcent on Rajasthan“Treat"Year with frm FES & 100o 900 as treatment& 900 plus as control<br><!-- End of picture text -->

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<!-- Start of picture text --> Triple Diff. Estimates for Log1 perm work<br>*<br>“0<br>Ofer ro gp<br>s<br>+<br>&sO &xs &NZ &2 &ab &x &xo &x\ &9<br>Ccoetcient on Rajesthan"Treat"Year wih fim FES & 100 10900as treatment & 300 plus as contol<br><!-- End of picture text --> <!-- Start of picture text --> Triple Diff. Estimates for Usage - cont<br>SPT rn a 7<br>I<br>f<br>2<br>&sO &xs &NZ &2 &ab &x &xo &x\ &9<br>Ccoetcient on Rajesthan"Treat"Year wih fim FES & 100 10900as treatment & 300 plus as contol<br><!-- End of picture text -->
**ANNEX 2: Robustness exercise: Italy included in the Eurozone core subsample**

**Table 2A:** Forecast variance decompositions **Eurozone core [1975-2011]:** percent of variation in _CA_ explained by column variable

|Years ahead|_R_|_REER_|_GR_|_YGAP_|_CA_|
|---|---|---|---|---|---|
|2|1.06|1.72|3.14|2.45|91.63|
|4|1.22|1.72|3.19|2.44|91.43|

**Table 2B:** Forecast variance decompositions **Eurozone periphery [1975-2011]:** percent of variation in _CA_ explained by column variable

|Years ahead|_R_|_REER_|_GR_|_YGAP_|_CA_|
|---|---|---|---|---|---|
|2|3.95|4.11|11.40|1.71|78.83|
|4|3.93|4.12|12.01|1.90|78.04|

28
**Table 1.  Sudden Stops, 1991-2002 vs. 2003-214**

||**1991-2002**|**2003-2014**|
|---|---|---|
|# of sudden stops|16|28|
|As percent of available observations|1.8 %<br>(16/903)|2.1 %<br>(28/1354)|
|# of quarters for which the sudden stops last|4.5|3.64|
|Capital flows during Sudden stops (% of<br>GDP), first quarter|-1.61|-1.28|
|Capital flows during sudden stops (% of<br>GDP), average for first four quarters|-1.79|-1.4|
|Capital flows in the four quarters preceding<br>Sudden stops (% of GDP)|1.28|2.1^^|
|Portfolio flows in the four quarters preceding<br>Sudden stops (% of GDP)|.68|.40**|
|Other flows in the four quarters preceding<br>Sudden stops (% of GDP)|.60|1.70^^^|
|Capital flow turnaround: Avg. capital flows<br>during four quarters of sudden stops- Avg.<br>capital flows in the four preceding quarters|-3.06|-3.59*|
|<br>Capital flow turnaround: Avg. Capital flows<br>during all quarters of sudden stops- Avg.<br>capital flows in the four preceding quarters|-2.28|-3.21***|

*, **, *** indicate that the value is significantly lower in the second column, compared to its value in the first column at 10, 5 or 1 percent level of significance (in a one tailed test). ^, ^^, ^^^ indicate that the value is significantly higher in the second column, compared to its value in the first column, at 10, 5 or 1 percent level of significance (in a one tailed test).

18
<!-- Start of picture text --> 0.8<br>0.6 agriculture<br>construction<br>0.4 + pvt. traded<br>—pvt. non-traded<br>0.2 —mineral—other govt.<br>=-total<br>0.0<br>02<br>-0.4<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 2.0%<br>—Scenario with only labor input<br>1.5%<br>—Scenario with only inte: lary<br>inputs<br>0.5%<br>0.0%<br>-0.5%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 3% * + agriculture<br>construction<br>2% =e vadable bie<br>—other govt.<br>1% —total (ex Specific inputs)<br>0%<br>-1% prrcaaeee<br>-2%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> —Scenario with only labor inputs<br>7% , —Scenario with only intermediary inputs<br>6%<br>5%<br>4%<br>3%<br>2%<br>0%<br>-1%<br>2012 2015 942018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 2%<br>0%<br>-2%<br>agriculture<br>4%<br>6% pvt. non-traded<br>8% —other govt.<br>-10%<br>-12%<br>-14%<br>-16%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 4% +» agriculture —construction<br>—pvt. tradable —>pvt. non-tradable<br>2% —other govt. —total<br>0% t T ee<br>-2%<br>0%<br>-8%<br>2012 2015 2018 2021 2024<br><!-- End of picture text -->
12 tortilla taxes and subsidies; the second column shows the effects of a gradual change where maize moves linearly to world prices over 5 years (so that in the sixth year domestic and world prices are equal).

Table 1: Welfare and Efficiency Effects

||Maize 1Y<br>no CNA|Maize 5Y<br>no CNA|Maize 1Y<br>GPA|Maize 5Y<br>CNA|Maize SY<br>CNA|Maize 6Y<br>CNA early|
|---|---|---|---|---|---|---|
||no F&V|no F&V|no F&V|no F&V|F&V|F6V<br>l|
|Subsistence Farmer'|0.967|0.971|1.007|1.011|1.013|1.015|
|Landless Rural Workere|0.984|0.985|0.993|0.995|1.000|1.001|
|Rain-Fed Farmer"|0.943|0.949|0.996|1.001|1.000|1.003|
|Irrigated Farmere|1.028|1.024|1.019|1.015|1.028|1.025|
|Urban Worker|0.984|0.986|0.993|0.995|1.000|1.001|
|Urban Capitalist"|1.018|1.017|1.013|1.012|1.007|1.006|
|Efficiency Gainsw|42.44|40.08|51.96|49.57|44.81|43.18|
|Cumulated Fiscal Gaine|23.17|21.94|18.04|16.76|13.64|12.50|

a/Measured as a percentage of the reference case.

b/1989 USS billion; Mexico's GDP was 207 billion in 1989.

Instantaneous liberalization leads to very large efficiency gains. The PDV of these gains is US$ 42.4 billion. With a growth-adjusted discount rate of about 3%A , these efficiency gains translate into US$ 1.22 billion of 1/ We assume a (risk-adjusted) world real interest rate of 7%, and long term rates of technical progress and population growth such that steady-state growth is 4X. The growth-adjusted discount rate thus is 2.91 (-(1.07/1.041)*100), implying a growth-adjusted discount factor of 0.972.
# **Figure 10a. Changes in Log Wages for Stayers in Tradable Industries Grouped by the Magnitude of the Direct Export Shock, 2007-2009**

<!-- Start of picture text --> Average Change in Log Nominal Wage for Stayers<br>(industries grouped by size of shock to exports)<br>time<br>Ave change in log wage in sectors with small shocks<br>Ave change in log wage in sectors with medium shocks<br>Ave change in log wage in sectors with large shocks<br>Note: groups defined by % drop in ln(exports) from Oct - Nov 2008<br>.05<br>.04<br>.03<br>.02<br>.01<br>0<br>2007:2 2007:3 2007:4 2008:1 2008:2 2008:3 2008:4 2009:1 2009:2 2009:3 2009:4<br><!-- End of picture text -->

# **Figure 10b. Changes in Log Wages for Stayers in Tradable Industries Grouped by the Magnitude of the Related Export Shock, 2007-2009**

<!-- Start of picture text --> Average Change in Log Nominal Wage for Stayers<br>(industries grouped by size of shock to exports in related industries)<br>time<br>Ave change in log wage in sectors with small related shocks<br>Ave change in log wage in sectors with medium related shocks<br>Ave change in log wage in sectors with large related shocks<br>Note: groups defined by % drop in ln(exports) in related inds from Oct - Nov 2008<br>.06<br>.04<br>.02<br>0<br>-.02<br>2007:2 2007:3 2007:4 2008:1 2008:2 2008:3 2008:4 2009:1 2009:2 2009:3 2009:4<br><!-- End of picture text --> 43