rafmacalaba/datause-encoder-data huggingface dataset:
Models 1 and 3 have a value of 0.42, whereas Model 2 has an RMSE of 0.41. When we turn to the overall consumption aggregate, we note a small difference between the three models. The RMSE values range from 0.53 to 0.58, with Model 3 and Model 1 having the smallest and highest RMSE, respectively. The MAE is quite similar across the three models, within a range from 0.39 (Model 3) to 0.41 (Model 2). These results suggest that no model consistently outperforms the other models.

Tables 3 and 4 apply the model to the three data sets described in the data section (ProGres 2, Targeting 2, and ProGres Targeting data), using the normal linear regression model and the Empirical Error Model and three poverty lines: (i) a poverty line of US$1.9 a day (PPP), which represents the international poverty line for extreme poverty (panel A); (ii) the national poverty line, which corresponds to around US$2.6 (World Bank 2013), represented in panel A; and (iii) the national food poverty line of US$1.8 (PPP), shown in Table 3. These three poverty lines are among the set of the arbitrary poverty lines considered in the general simulation above to evaluate the quality of the prediction.<sup>6</sup> With one exception, the predicted poverty rates are not statistically different from the poverty rates obtained from survey consumption data (henceforth, “survey estimates”). For the case of the food and international poverty lines, this is partly due to the large standard errors of the prediction estimates, but these findings hold for the national poverty line where the standard errors of the predicted values are much smaller.<sup>7</sup> Figure 1 repeats the exercise of Table 5 for the ProGres Targeting data and all the poverty lines between the 66th and 99th quantiles of consumption. Panels A and B, respectively, are the normal linear model and empirical error model. The results suggest that Models 1 and 2 predict the poverty rates for different poverty lines well. The predicted poverty rates are within the 95% confidence interval for all the arbitrary poverty lines considered. The predictions are also very similar across the normal and empirical error models. However, Model 3 overestimates poverty, and the predicted poverty rates provided are outside the 95% confidence interval of the survey-based rates for the set of different poverty lines considered. As Model 3 adds variables related to coping

> 6 See also Table A3 in the annex for the full base model.

> 7 The national and international poverty lines are used for illustrative purposes only. As the consumption aggregate used in this paper is not comparable to a full consumption aggregate as usually derived from nationally representative household surveys, the resulting poverty statistics are not comparable to national or international poverty statistics. In our case, these lines are simply alternative thresholds used to test the sensitivity of poverty estimates to different poverty lines.

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reassigning students into classrooms on the basis of their age.<sup>14</sup> First, I construct the classroom standard deviation of age and the classroom average of age for each classroom by age grouping students. Note that by re-grouping students, classroom standard deviation of age shrinks for all students, but students assigned into a younger classroom will have a lower classroom average age than those assigned into an older classroom. The reduction in classroom standard deviation of age leads to achievement gain, but regrouping lowers the achievement of older students, and increases that of younger students, because the coefficient of classroom average of student age is negative (even though it is statistically insignificant). Second, the differences in the classroom standard deviation of age and classroom average of age between the original age mixing scenario and the new age grouping scenario are then multiplied by the respective point estimates to derive the net achievement gains for mathematics and science. Finally, averages by country are reported in Table 13.

Column (1) of Table 13 shows the standard deviation of age at the grade level for each country. Column (2) reports the average differences of classroom standard deviation between age grouping classrooms and age mixing classrooms. Column (3) reports the average differences of classroom average age between age grouping classrooms and age mixing classrooms. Columns (4) and (5) present the average predicted achievement gain for mathematics and science, respectively. The bottom row reports the averages for the sample of countries.

Table 13 shows that countries with the largest within-grade standard deviation of age also tend to realize the greatest reduction in classroom standard deviation of age if schools were to 14 Estimates reported in Tables 4 and 5 are used because the differential effects of classroom age heterogeneity (between boys and girls or between young and old students) are small in magnitudes. Simulation is conducted using TIMSS 2007 sample.

22
is hardly a difference in the effect size whether we use the sample of all firms controlling for their status at baseline—already existing or new—or whether we use the sample of already existing firms at baseline only. The estimated impact on investment corresponds to a modest increase of about 16 percent. In terms of shares, in the group of cash beneficiaries, 35 percent reported some investment (on average XOF 0.882 million or US$1,600), whereas this share was only at 19 percent (on average XOF 0.154 million or US$280) in the control group. So, clearly, more firms in the group of cash beneficiaries invested and they invested on average much higher amounts. It is interesting to see that the average of US$1,600 is not far from the average cash withdrawn reported in the monitoring data for the same firms over the same period. As expected, we do not yet find any effects for beneficiaries of matching grants.

**Figure 4. Rollout of cash and matching grants**

(a) Share of beneficiaries of cash grants who received transfer into their account by month (b) Share of beneficiaries of cash grants who withdrew cash from their account by month <!-- Start of picture text --> 1,0 1<br>0,9 0,9<br>0,8 0,8<br>0,7 0,7<br>0,6 0,6<br>0,5 0,5<br>0,4 0,4<br>0,3 0,3<br>0,2 0,2<br>0,1 0,1<br>0,0 0<br>(c) Share of beneficiaries of matching grants who filed a (d) Share of beneficiaries of matching grants who<br>request to MEBF by month received approval for their request from MEBF by<br>month<br>Nov 2019 Dec 2019 Jan 2020 Feb 2020 March 2020 April 2020 May 2020 June 2020 July 2020 August 2020 Sep 2020 Oct 2020 Nov 2019 Dec 2019 Jan 2020 Feb 2020 March 2020 April 2020 May 2020 June 2020 July 2020 August 2020 Sep 2020 Oct 2020<br><!-- End of picture text --> <!-- Start of picture text --> 1,0 1,0<br>0,9 0,9<br>0,8 0,8<br>0,7 0,7<br>0,6 0,6<br>0,5 0,5<br>0,4 0,4<br>0,3 0,3<br>0,2 0,2<br>0,1 0,1<br>0,0 0,0<br><!-- End of picture text --> _Source:_ Monitoring data.

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An idiosyncratic factor that contributed to the overall decrease (as a proportion of GDP) in the banking system’s commercial credit portfolio has been the ‘de-leveraging’ of Banamex. Grupo Financiero Banamex was purchased by Citigroup in August 2001. At around the same time, Banamex began to significantly lower its commercial credit exposure, a process that lasted until mid-2004 (see figure 20). Banamex’s credit portfolio movement contrasts sharply to the rest of the banking system which grew by almost 40 percent in real terms since 2000, thereby halving its share of performing loans to 7 percent by end-2005.

Figure 20: Real Growth in Bank Commercial Credit for Banamex Versus Rest of Banking System (2000-2005) <!-- Start of picture text --> 50%<br>39%<br>40%<br>30%<br>20%<br>10%<br>0%<br>-10%<br>-20%<br>-30%<br>-40% -29%<br>-50%<br>Total Banks (ex Banamex) Banamex<br>Jul-00 Jul-01 Jul-02 Jul-03 Jul-04 Jul-05<br>Real Growth (%) Mar-00 Nov-00 Mar-01 Nov-01 Mar-02 Nov-02 Mar-03 Nov-03 Mar-04 Nov-04 Mar-05 Nov-05<br><!-- End of picture text --> Source: BOM, CNBV and INEGI.

Note: Some numbers might differ from other Figures because they are based on CNBV data (e.g. performing loans to the private sector shown above exclude the FOBAPROA portfolio) and are deflated using the retail price index as opposed to the GDP deflator.

Corporate insolvency procedures have been significantly improved, but the extent of their contribution to greater bank lending remains unclear. A new law in 2000 (see Box 1) radically changed the commercial insolvency (“ _concurso mercantil_ ”) procedural framework and eliminated the incidence of suspension of payments by insolvent debtors that was prevalent and that delayed enforcement proceedings indefinitely<sup>17</sup> . The record to-date under the new law has been generally positive (around 40 percent of filed cases have already been concluded and out-of-court negotiated settlements have increased), although some doubts have been raised as to the law’s contribution to the recent resurgence of bank credit. Firstly, bankruptcy remains a rarely used option in Mexico (only around 220 cases since the law’s introduction<sup>18</sup> ) and there is no data on the law’s impact on restructuring agreements prior to bankruptcy filing. Secondly, as much as 60 percent of all cases have been initiated by the debtors themselves, which could be an indication of its lack of appeal for bank creditors. On the other hand, the cases that have actually been filed involve a large number of creditors and high value of assets (about

> 17 See Sánchez-Mejorada y Velasco C. (2004 and 2005), Méjan Carrer L.M. (2005), Gamboa-Cavazos M. (January 2005) and Martínez, Algaba, Estrella, de Haro y Galván-Duque (2005).

> 18 By contrast, IMSS figures show that around 3,000 companies were closed just in 2005, although most of these are likely very small and are not obliged to follow the new bankruptcy procedures (the law automatically applies to firms with a minimum debt of 400,000 UDIs).

20
Methodological changes between ICP rounds cast doubt on the comparability of the resulting PPPs over time. This is one reason why users often avoid mixing PPPs between ICP rounds, letting national price data override the ICP data for inter-temporal comparisons. In other words, the PPP conversion is done at the ICP benchmark year. It has been argued that this is the most reasonable practice, given the changes in methodology (Dalgaard and Sørensen, 2002; World Bank, 2008b; Chen and Ravallion, 2010a). Theoretical arguments have been made both for and against this practice. Nuxoll (1994) argues that the real growth rates measured using local deflators better reflect the trade-offs facing decision makers at the country level, and thus have a firmer foundation in the economic theory of index numbers. Of course, this means that the economic aggregates may well lose purchasing power comparability as one goes further back in time from the ICP benchmark year. In the context of studies of economic growth using PWT, Johnson et al. (2009) argue instead that comparisons should only be made between ICP rounds, since only then can one be sure that the economic aggregates are consistently evaluated at purchasing power parity.

All this begs a neglected question: _Can we make sense of the changes in PPPs?_ This paper tries to answer that question by comparing the latest (2005) PPPs with those for 1993 and 1985. Unlike most past empirical studies of PPPs, which have focused on the cross-sectional differences, this paper is concerned with explaining the observed changes over time. I focus on the price level index, defined as the PPP for GDP divided by the exchange rate (US=100%);<sup>3</sup> the inverse of this index is sometimes also called the real exchange rate. For the same reason that one uses PPPs rather than exchange rates for international comparisons, it can be hypothesized here that the PPP will tend to rise relative to the market exchange rate in a growing economy. In the models of Balassa (1964) and Samuelson (1964) this happens if economic growth comes with higher labor productivity in the traded-goods sector (relative to non-traded goods). This can be thought of as a dynamic Penn effect (DPE), corresponding to the widely-observed static Penn effect in which the price level index tends to be higher in richer countries.<sup>4</sup>

> 3 While the term “price level index” is widely used in the literature, it is potentially confusing, since the index is a relative price—the ratio of two nominal prices. But I will stick to common usage.

> 4 The term “Penn effect” appears to be due to Samuelson (1994) and stems from Penn World Tables (PWT) (Summers and Heston, 1991), which provided the data that were used to establish this effect empirically. Evidence of a static Penn effect in data from various rounds of the ICP is reported in Balassa (1964), Rogoff (1996), World Bank (2008), and Deaton and Heston (2010), amongst others. The

3
owner wants. It is fair to say that this criticism ignores the characteristics of a gasoline market, especially those prevailing in Argentina.

In general, the operation of a DODO gasoline station is done by a small firn and usually it is an activity that supports and employs a family. It is seldom the case that a single firm or person owns many gasoline stations. Therefore, it is clear that there are significant asymmetries in the bargaining power of an oil company and the owner of a gasoline station. Thus the likely outcome of contract negotiations is that the owner accepts the conditions imposed by the oil company. Only for those locations with high commercial value the oil company is willing to modify (and shorten) the duration of contracts. Another factor that reduces the bargaining power of the owner of a gasoline station in Argentina is that all the incumbent oil companies have a similar policy with respect to the duration of contracts. As a result, it is possible that a disagreement with the oil company gives a bad reputation to the owner and consequently the other oil companies refuse to deal with her. Moreover, oil companies have the financial capabilities to threaten the owner of a gasoline station to build another station closely located and provide it with better commercial conditions, forcing the owner into bankruptcy.

Summarizing, oil companies decide the optimal allocation of contractual arrangements by solving a principal-agent problem. The most efficient allocation of company-owned and dealer-owned gasoline stations in a network depends on the ability of oil companies to provide incentives and control the managers of gasoline stations. Besides the efficiency considerations, oil companies can use the network as a strategic barrier to entry, deterring potential competitors or hindering the growth of smaller competitors. Thus, oil companies will decide the size and contractual composition of the network of retail outlets taking into account an efficiency (cost minimizing) dimension as well as a strategic competitive dimension.

# 6. **The study of the SDCyC and its policy recommendations**

The objectives of the study conducted by the SDCyC were the identification of market characteristics that hinder the development of a competitive gasoline market and suggest policy recommendations.

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# **Key Points**

- **South Africa’s real exchange rate depreciation is large and persistent.** The persistent deviation from the law of one price is only partly explained by a traditional BalassaSamuelson effect. Nevertheless, the Balassa-Samuelson analysis directs attention toward differentials in sectoral productivity.

- **Productivity in the traded sector is inversely related to the degree of openness of the economy** . The intuition of modern trade theory is that countries with high barriers to entry in international markets and low competitiveness in the domestic economy will have larger deviations from the law of one price. South Africa shows all the characteristics of a relatively closed economy, with high barriers to entry in international markets, and quite protected internal sectors.

- **Low productivity growth in the manufacturing and mining sectors is coupled with growing productivity in some service sectors, especially the ones with high skill intensity** . The picture of traded goods stagnation is matched by dynamism in some service sectors. The increase in productivity in some service sectors makes the services relatively cheaper than goods in less productive sectors. This is reflected by sectoral differences in rates.

- **The Balassa-Samuelson logic is reversed.** The increase in productivity in the service sector has a spillover effect into the goods sector in the form of an increase in wages: this logic is reflected in the increase in the unit labor cost in the manufacturing sector being largely decoupled from productivity increases.

- **Firms in the traded sectors reduce employment or require protection from external competition.** Firms in the manufacturing sector will respond to the increase in labor costs either by reducing their labor force or by demanding higher levels of protection against competition from more productive external competitors. The negative spiral of low productivity and low competitiveness is thus self-reinforcing, with the increasingly monopolistic nature

2
of land is demarcated and claimed to be his property, the plot enters the formal system and therefore the 1985 law becomes enforceable. For women whose husband is Akan, the Intestate Law makes it strategic to have more children to claim the largest share of inheritance (the law provides 3/16 for the surviving wife and 9/16 to the surviving children).<sup>21</sup> This is in contrast with women whose husband is not Akan and are thus part of a patrilineal system: for these women, having children already translated to strengthened control over land if their husband passed away, and so we would expect lower fertility effects as a result of the Intestate Law.

We see this effect in Table 13: women whose husbands are Akan have on average 0.7 more children because of land titling, which represents a 20% increase over the control group. In line with the old-age security motive, Appendix Table A10 suggests that women who are most likely to be vulnerable in old age—due to their lack of ownership of at least one plot in their own name—are driving the effect.

Table 13: Heterogeneity by Matrilineal Status (Ghana)

||Number of child|ren|
|---|---|---|
||Husband is not Akan<br>(1)|Husband is Akan<br>(2)|
|Treatment follow-up 1|0.141<br>(0.090)|0.744***<br>(0.189)|
|Treatment follow-up 2|-0.001<br>(0.140)|0.381<br>(0.362)|
|Observations|1,269|307|
|Control group mean|3.405|3.796|
|p-val—follow-up 1|0.000||
|p-val—follow-up 2|0.329||

Notes: This table displays heterogeneous treatment effect of the intervention on the number of children at each follow-up depending on whether the woman’s husband is part of the Akan ethnic group. Standard errors in parentheses are clustered at the village level. Controls include: number of children at baseline, marital status, woman’s education, woman’s age, age square, whether woman is household head and household type. *, **, *** denote significance at the 10, 5 and 1 percent levels respectively.

# **5.2.3 Investing Directly in Assets**

Recall that we find no average treatment effect of the land titling program on fertility in Rwanda. Appendix Table A11 shows that this null result holds regardless of whether women have a son, while Appendix Table A12 shows that this is the case regardless of whether the woman has rivals over her inheritance. One plausible explanation for these null effects is that the intervention in Rwanda guaranteed women the right to own and use the land owned by the household even in the event of their husband’s death. In Benin and Ghana,

> 21One-eighth goes to the surviving parent and one-eighth is determined by customary law.

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|||**Grade**|**4**||**Grade**|**8**|
|---|---|---|---|---|---|---|
||**Male**|**Female**||**Male**|**Female**||
|**Class/school-level variables**|**%**|**%**|**_φ/φc_**|**%**|**%**|**_φ/φc_**|
|_School location_|||0.068***|||0.247***|
|urban|61.5|58.7||66.4|47.9||
|suburban/medium size city or large town|27.5|27.4||15.0|33.7||
|small town or village/remote rural|11.0|13.9||18.6|18.4||
|_Teacher age (mathematics teacher)_|||0.073***|||0.243***|
|29 years or younger|9.7|4.8||28.3|3.9||
|30–39 years|39.6|47.0||48.2|62.1||
|<br>40 years or older|50.8|48.2||23.5|34.0||
|_Teacher age (science teacher)_|||0.047**|||0.126***|
|29 years or younger|12.7|6.5||10.2|2.1||
|30–39 years|38.9|46.8||56.2|50.7||
|40 years or older<br>|48.4|46.7||33.6|47.2||
|_Time assigned to mathematics homework_|||0.147***|||0.119***|
|15 minutes or less|52.7|67.2||58.4|62.8||
|16 minutes or more|47.3|32.8||41.6|37.2||
|_Time assigned to science homework_|||0.054**|||0.180***|
|15 minutes or less|77.3|69.8||66.8|81.4||
|16 minutes or more|22.7|30.2||33.2|18.6||
|_Poor teacher timekeeping_|||0.157***|||0.165***|
|not a problem|42.0|52.1||39.3|56.6||
|<br>minor problem|35.6|34.0||42.2|26.5||
|<br>moderate or serious problem|22.4|13.9||18.5|16.9||
|_Teacher absenteeism_|||0.211***|||0.066***|
|not a problem|45.0|33.1||47.8|42.8||
|minor problem|28.6|48.9||31.8|32.0||
|moderate or serious problem|26.4|18.0||20.4|25.2||
|_Teacher highest level of education (mathematics teacher)_||||||0.174***|
|Up to ISCED level 6 — bachelor’s or equivalent level<br>||||97.7|100.0||
|(grade 8)|||||||
|ISCED levels 7 & 8 — master’s or doctorate degree<br>(grade 8)||||2.3|0.0||
|_Teacher highest level of education (science teacher)_||||||0.038**|
|Up to ISCED level 6 — bachelor’s or equivalent level<br>(grade 8)||||91.3|98.0||
|ISCED levels 7 & 8 — master’s or doctorate degree<br>(grade 8)||||8.7|2.0||
|_Teacher major area of study (mathematics teacher)_|||0.222***|||0.209***|
|education and mathematics (grade 4)|40.7|21.0|||||
|<br>mathematics but not education (grade 4)|47.4|59.6|||||
|mathematics and mathematics education (grade 8)||||24.9|11.6||
|mathematics but not mathematics education (grade 8)<br>||||68.3<br>|70.6<br>||
|all other majors|11.9|19.4||6.8|17.8||
|<br>_Teacher major area of study (science teacher)_|||0.245***|||0.167***|
|<br>education and science (grade 4)|40.9|15.8|||||
|<br>science but not education (grade 4)|49.1|66.9|||||
|science and science education (grade 8)||||27.2|10.3||
|science but not science education (grade 8)||||65.6|85.0||
|all other majors|10.0|17.3||7.2|4.7||

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