rafmacalaba/datause-encoder-data huggingface dataset:
presents some hurdles (Alderman and Haque 2006). One of these is the ability to scale safety nets up or down, depending on needs. In addition to administrative capabilities, this requires flexible financing. Targeting should also be dynamic. Food price shocks deteriorate the situation of the already-poor net food buyers and also may push into poverty people who initially were not poor enough to be covered by the safety net.

Safety nets are often presented as a good policy alternative to price stabilization policies, because they are targeted, they do not attempt to manipulate food prices, and they do not destabilize world markets. However, these transfers could create pecuniary externalities at world level. Through cash or through in-kind transfers, safety nets protect the purchasing power of the poor from increased food prices and help them maintain their food consumption. If applied, they will reduce the exportable surplus of an exporting country and increase the excess demand of an importing country. So safety nets create pecuniary externalities for other countries by increasing domestic demand for food, and in this respect do not differ much from countercyclical trade policies (Do, Levchenko, and Ravallion 2013), which try to secure local food supply and have been heavily criticized for fueling food crises. Safety nets, however, are advocated as good policy practice. In Section 4, we show that the practical use of these policies creates crucial differences: trade policies tend to over-react to upward price shocks, for example with countries banning exports and accumulating stocks in the midst of the food crisis, while safety nets under-react (Grosh et al. 2011) – probably because of the aforementioned difficulties to adjust them in times of crisis.

# **4 Lessons from historical experience**

This section looks at the effectiveness and limitations of some examples of past food policies. Unfortunately, since statistical evidence on their effects is still limited, it focuses on narratives of stabilization policy successes and failures. As a consequence, even though we can highlight cases where trade and storage policies have been extremely costly or cases where interventions have not led to poverty reduction, or reduced hunger and malnutrition, these interventions cannot be compared to a benchmark situation; there is no counterfactual.

## **4.1 Safety nets during the recent food crises**

Have safety nets protected the poor during the recent food crisis? Although most countries already had some kind of safety net in place, these measures were not always appropriate to protect against rising food prices. And since safety nets are difficult to develop in the timeframe of a food crisis, countries without preexisting and adequate programs have tended to rely on untargeted and distortive policies, such as universal food subsidies or trade policies, import tariff decreases, import subsidies, and export restrictions. The situation is by nature highly heterogeneous among countries. For example, in North African countries, the coverage provided by targeted safety nets is very limited and targeting often inadequate (World Bank 23
<!-- Start of picture text --> Losses Market pri ce<br>100 10<br>50 5<br>0 0 0.001<br>0 10 20 30 40 50 0 10 20 30 40 50 0.003<br>Year Year<br>0.005<br>Total external capital Market supply<br>0.007<br>20 1<br>0.009<br>0 0.5<br>−20 0<br>0 10 20 30 40 50 0 10 20 30 40 50<br><!-- End of picture text --> Figure 8: Sensitivity analysis for the solvency constraint we rst studied the impact of a loss on the market price. To do so, we impose a large loss on a stabilized market of 5 symmetric reinsurers, assuming they were exposed to the expected loss level during the 10 previous years (to remove path dependency from the analysis). Figure 9 represents the price in the year following the shock for the limit default probabilities 0.1%, 0.5%, and 1%. This range is centered on a 200- ~~year risk.~~ <!-- Start of picture text --> 20<br>18<br>16<br>14<br>12<br>0.1%<br>0.25<br>10 0.5%<br>0.75%<br>1%<br>8<br>6<br>4<br>2<br>0<br>0 20 40 60 80 100 120 140 160 180 200<br>Losses amplitude (billion dollars)<br>Price just after shock<br><!-- End of picture text --> Figure 9: Non linearity between price reaction and loss amplitude We rst observe a non linearity between price and the magnitude of the loss. When the regulation is stricter (lower limit probability of default), the price after small losses is higher, because it requires more capital to supply the same amount of reinsurance capacity. So, the market is most of the time less ecient with a strict regulatory constraint. But after a large loss, the market collapse with a weak constraint (e.g., for losses larger than $80 billion if the limit is at 1%), while the loss can be absorbed with a stricter regulation (however at the expense of a large increase in price). Indeed, the highest loss the market is able to absorb is a market loss of $190 bn for a limit probability of default of 0.1% (1000-year loss), $110 bn for a probability of 0.5% (200-year loss) and $80 bn for a probability of 1% (100-year loss).

A closer look at the impact of the default probability on the market is provided on Figure 10.

20
# **5 Counterfactuals**

To evaluate the effect of international integration and the income distribution on sustained poverty reduction, we simulate several counterfactual economies and compare them to the status quo (with current levels of international integration and equality). The first is a counterfactual closed economy without international integration, in which _λ_ 2 = _λ_ 3 = 0. This could be understood as the development policy doomsday scenario, in which comparative advantage from trade becomes irrelevant for sustained poverty reduction. The second is a counterfactual maximum equality economy, in which the share of the population in the middle class is calculated using Equation 10 and the current national GDP per capita, but a Gini coefficient of 0.20, the value for Slovakia in 1992, the lowest in the sample. This could be understood as a scenario where national income stays the same, but government redistributes income to about the extent achieved in the Czechoslovak Socialist Republic, which dissolved in 1989. The third is a fully integrated counterfactual, in which the relative population and income of the integrated market are equal to the relative population and income of the whole world. This could be understood as a maximal trade liberalization scenario in which the country signs deep trade agreements establishing and protecting economic integration rights with every country. Table S2 in the Online Supplement reports estimates of the status quo market size for each country period in our data set, as well as each counterfactual market size. All these scenarios should be thought of as polar cases that serve the purpose of helping us quantify the contributions of equality and international market size towards poverty reduction, rather than realistic policies on the table. We note however that in principle, our framework with the detailed information on specific provisions of trade agreements it provides, could be used to also assess the effects of specific liberalization measures (e.g., signing specific provisions of trade agreements with particular countries).

We summarize these results in two figures. Figure 5 shows the estimated status quo market size, calculated as _Mitλ_<sup>ˆ</sup> , and the three counterfactual market sizes for select economies, using data from 2011, the first year of the last window in our sample, to provide a recent view. The blue set of columns correspond to the status quo, in which market size is calculated using the _λ_ reported in column 2 of Table 4. The orange columns show a market size estimate in which _λ_ 2 = _λ_ 3 = 0, so market size is determined only by population and the size of the middle class. The khaki columns show the maximum equality scenario and the dark green columns show the fully integrated scenario.

The five economies in Figure 5, China, Cˆote d’Ivoire, Ghana, Indonesia, and Philippines have large enough markets to support sustained poverty reduction under the status quo. Three of them, Cˆote d’Ivoire, Ghana, and Philippines would not be large enough to sustain poverty reduction as a 25
**Table 3.3 . Government budget in 2012 and for base simulation by period (Percent of nominal GDP)**

|||**2012**|**2017**|**2030**|**2013-17**|**2018-30**|**2013-30**|
|---|---|---|---|---|---|---|---|
|**Receipts**|**Domestic transfers**|1.5|1.5|1.5|1.5|1.5|1.5|
||**Foreign transfers**|21.5|14.4|9.1|16.5|11.1|12.6|
||**Direct taxes**|4.5|14.5|8.5|8.7|10.3|9.9|
||**Import tariffs**|6.9|5.1|5.2|5.8|5.0|5.2|
||**Other indirect taxes**|4.4|3.5|3.2|3.9|3.3|3.4|
||**Domestic borrowing**|-0.2|1.0|1.0|1.0|1.0|1.0|
||**Foreign borrowing**|0.8|2.1|0.9|2.2|1.2|1.5|
||**Total**|39.4|42.2|29.4|39.7|33.5|35.2|
|**Spending**|**Education**|5.0|5.5|4.5|5.2|5.0|5.0|
||**Health**|4.5|4.9|4.0|4.6|4.4|4.4|
||**Water-sanitation**|2.1|1.5|0.4|1.5|0.7|0.9|
||**Energy**|3.7|4.9|2.5|4.4|3.4|3.6|
||**Roads**|4.9|5.1|1.3|4.9|2.4|3.1|
||**Other infrastructure**|2.0|1.8|0.5|1.8|0.9|1.1|
||**Other**|13.8|15.1|12.6|13.9|13.4|13.5|
||**Domestic transfers**|2.9|2.9|2.9|2.9|2.9|2.9|
||**Domestic interest**|0.4|0.4|0.5|0.4|0.4|0.4|
||**Foreign interest**|0.1|0.2|0.3|0.1|0.2|0.2|
||**Total**|39.4|42.2|29.4|39.7|33.5|35.2|

**Note:**

**Columns show values for single years or averages for multiple years (as indicated).**

20
the urban trend, so that overall female labor force participation declined from 22 percent in 1987 to 17 percent in 2011. Note, however, that rural numbers are more likely to suffer from underreporting of women’s work in agricultural activities.

In our analysis we focus on married women in urban India in the age group 25–54. Their labor force participation rate fell slightly from 18.5 percent in 1987 to 17.9 percent in 2011. Breaking down the labor force into different components, one can see in Figure 1 there has been little change in the different types of work and unemployment rates of married women, except for a peak in selfemployment and unpaid family work in 2004.<sup>5</sup> Throughout the period, the labor force participation rate of urban married men in the same age group was stable at around 97 percent and hardly differs by education levels.

# **Figure 1: Urban female labor force participation rate**

<!-- Start of picture text --> 0.25<br>0.20<br>unemployed<br>0.15 casual worker<br>regular employee<br>0.10<br>unpaid<br>0.05<br>self-employed<br>0.00<br>1987 1993 1999 2004 2009 2011<br><!-- End of picture text --> Note: Married women age 25-54. Self-employment includes employers and own account workers. Unpaid refers to unpaid family workers. Regular employees receive salary or wages on a regular basis. Casual workers receive a wage according to the terms of the daily or periodic work contract. _Source:_ NSS Employment and Unemployment Survey 5 We always use the sampling weights provided. There is some question whether differences in sampling strategy between rounds might have a (slight) impact on the comparability of levels in the female labor force participation shown here, particularly also the peak reported in 2004–05. As discussed above, however, they will not affect the observation of low levels, and the stability over time. For a discussion of these issues, see Klonner and Oldiges (2014).

9
# Table 97. Distribution of Manufacturing Establishments Motorcycles for Messengers by Employment Size, Nigeria

## MoTORCYC (OWN NOTORCYCLES FOR MESSENGER) FIRNSIZE CENPLOYEES per FIRM)

<!-- Start of picture text --> Percent<br>ow Pcft<br>Cot Pft 1-19 120-49 150-99 1100-199 1200-499 1500-999 1000 & I<br>I I I I lover I Total<br>.......... . .............. .... +........<br>YES O 6 II 18 8.16 11 S 67<br>0 00 3635 6.15 10.061 8.94 6.15 2.79 37.43<br>O.00 8.96 2667 I.4 2 3.6 16.42 ?.46<br>0.00 17.14 25.00 51.43 61.54 73.33 62.50<br>so 161 291 33 I 171 101 4 1 3 112<br>B 94 | 16.20 18.44 9.S0 5.59 2.23 ' 68 62.57<br>14.29 25.9 29.6 15.18 I 8.93 I 3.57 2 6268 .5<br>100.00 82.86 75.00 48.57 38.461 26.67 37.50j<br>.......... ........ ..... ...... ..........................- .-------<br>Total 16 35 44 35 26 15 a 179<br>8.94 19.55 24.58 19.55 14.53 8.38 4.47 100.00<br><!-- End of picture text -->
**Table 2: Within/Between Decompositions in High, Middle and Low Income Countries**

||**197**|**0-80**|**198**|**0-90**||
|---|---|---|---|---|---|
||%ΔSn|% Within|%ΔSn|% Within|**Notes**|
|**HIGHINCOMEGROUP**||||||
|US|0.19|86|0.51|76||
|**Australia**|**0.06**|**52**|**0.42**|**92**|**1970, 80 ,87**|
|Sweden|0.38|81|0.07|25||
|**Norway**|**0.33**|**76**|||**1970, 80, na**|
|West Germany|0.67|95|0.42|83|1970, 79, 90|
|**Luxembourg**|**0.90**|**95**|**0.73**|**123**||
|Denmark|0.12|42|0.64|89|1973, 80, 89|
|**Belgium**|**0.77**|**86**|**-0.06**|**92**|**1973, 80, 85**|
|Finland|0.27|82|0.70|83||
|**Austria**|**0.69**|**93**|**0.36**|**76**|**1970, 81, 90**|
|UK|0.39|91|0.62|92||
|**Japan**|**0.37**|**98**|**0.14**|**98**|**1969-75, 78-90**|
|**MIDDLEINCOMEGROUP**||||||
|Venezuela|0.78|141|0.56|62|1970, 81, 91|
|**Spain**|||**0.70**|**92**|**na, 80, 90**|
|Ireland|0.02|25|0.58|75|1970, 80, 89|
|**Greece**|**0.38**|**104**|**0.93**|**90**||
|Cyprus|||-0.07|108|na, 81, 91|
|**Uruguay**|||**0.17**|**51**|**na, 80, 88**|
|Hungary|||0.93|96|na, 80, 90|
|**Portugal**|**-0.97**|**96**|**0.48**|**90**|**1972, 80, 87**|
|<br>Malta|-0.26|43|0.72|76|<br>1970, 80, 88|
|**Poland**|||**0.06**|**80**|**1970, 80, 89**|
|Chile|1.12|92|0.05|153||
|**Czechoslovakia**|**0.06**|**61**|**0.22**|**92**|**1970, 80, 89**|
|Malaysia|||-0.35|86|na, 83, 90|
|**Korea**|||**0.08**|**36**|**1973, 80, 90**|
|Colombia|-0.13|145|0.66|84|1972, 80, 90|
|**Peru**|**0.13**|**-247**|**1.38**|**103**|**1972, 80, 88**|
|Turkey|||0.60|79|na, 83, 90|
|**Guatemala**|**0.36**|**50**|**0.96**|**69**|**1973, 80, 87**|
|**LOWINCOMEGROUP**||||||
|Philippines|0.68|46|||1970, 77, na|
|<br>**Egypt**|**0.23**|**49**|**0.44**|**83**|<br>**1971, 80, 88**|
|<br>Pakistan|||0.50|72|<br>na, 80, 88|
|**Bangladesh**|**0.21**|**152**|**0.32**|**20**|**1970, 80, 88**|
|India|0.19|-11|-0.08|303|1970, 80, 88|
|**Tanzania**|**-0.43**|**93**|**0.65**|**84**|**1970, 80, 85**|
|Ethiopia|||0.58|91|na,80,88|

**Note:** ΔSn is the change in non-manual wage-bill share×100. The percentage “within” is the percentage of the change due to within industry increases. Data are from the United Nations General Industrial Statistics program. See Berman and Machin (2000) and Berman, Bound and Machin (1998) for data definitions and detailed descriptions of the data.

8
analysis.

## **5.4 Heterogeneity**

Next, we examine the effects of the riots by religion. The following equation identifies the effect of the riots by religion: where _Muslimi_ is equal to 1 if woman _i_ belongs to the Muslim community. The coefficient of interest, _γ_ identifies the additional effect of the riots on Muslim women’s marital outcomes relative to the outcomes of women of all other regions. We cluster all our standard errors at the state level or the state-year level in alternative specifications.

# **6 Results**

In this section, we discuss the effects of the Hindu-Muslim riots of Gujarat in 2002 on women’s age at marriage and their post-marital social and economic status.

## **6.1 Women’s Marriage Age**

Using data from NFHS-IV and specification 1, Table 3 presents the changes in women’s age at marriage, probability of child marriage, age at cohabitation, age at consummation, and age at first birth post the 2002 riots compared to the rest of India. Columns (1) and (2) suggest that women’s age at marriage declined by 0.33 years or 4 months after the riots, and the probability of marriage before the age of 18 increased by 4.4 percentage points or 14 percent compared with the rest of India or the control mean. Columns (3) and (4) in Table 3 indicate that the age at cohabitation and age at first sex also declined after the riots. The estimates in the first four columns of Table 3 are significant at the 1 percent level. According to column (5) of Table 3, while statistically insignificant, the effect of violence on women’s age at first birth is negative.

Figures 1(a) and 1(b) present coefficients using the event study specification in 2. Figure 1(a) suggests that the age at marriage decreased for women married after the Gujarat riots of 2002. Figure 1(b) suggests that the probability of child marriage increased for women married after 2002. Nevertheless, these effects are not immediate and are prominent after 10
Distortions in this framework can be viewed as taxes. Thus, in equations (9) and (10) the after-tax marginal revenue products of capital and labor are equalized across firms. The before-tax marginal revenue products must be higher in firms that face disincentives, and can be lower in firms that benefit from subsidies.

Now, HK shows that industry productivity ( _TFPs_ ) is a function of output and factor distortions. HK uses the distinction between _revenue productivity_ and _physical productivity_ initially developed in Foster et al. (2008) and denoted respectively by _TFPR_ and _TFPQ_ .<sup>9</sup> The use of a firm-specific deflator yields TFPQ, whereas using an industry deflator gives TFPR: In this framework, TFPR does not vary across firms within an industry unless plants face output and/or factor distortions.

In the absence of distortions, more factors of production should be allocated to firms with higher TFPQ to the point where all firms have the same TFPR (this comes from a property of this model that higher output results in a lower price). From the marginal revenue products of labor and capital we obtain: Thus, we show first that TFPR is a function of output and factor distortions. Second, high firm TFPR is a sign that the firm confronts barriers that raise the firm’s marginal products of capital and labor, rendering the firm smaller than optimal.

Having defined TFPR and TFPQ (≜ _A_ ) we can obtain the expression of industry total productivity ( _TFPs_ ):<sup>10</sup> 9TFPQ reflects the quality and variety of a plants products, not just its physical productivity (see HK for the demonstration).

> 10HK shows that a Lucas span-of-control model (diminishing returns in production rather than utility) and monopolistic competition are isomorphic for aggregate TFP if the number of firms is proportional to labor input.

10
# Trends in Duration of Stay and Resource Utilization

The results of the study show that, overall, patients treated for incomplete abortion with MVA spent less time in the hospital and consumed fewer resources than similarly diagnosed patients treated with D&C.

Figure 7 shows the percentage of monetary and time resources saved (or needed) by the implementation of MVA in place of D&C for the treatment of first-trimester incomplete abortion. Grouped data must be interpreted cautiously because of differences in the duration of stay and cost associated with a given procedure at different hospitals within the same country. These differences may be due to variations in administrative level of the hospital, staff efficiency, caseload and/c patient management. However, these grouped data show similar trends in resource savings as data from the individual study sites.

Figure 7 illustrates grouped patient data from hospitals in Kenya (KEN), Mexico (MEXa -- ward patients, and MEXb -- ambulatory-surgery patients) and Ecuador (ECU).

<!-- Start of picture text --> Rgure 7. Trends In Duration of Stay and Resource Utilizatlon<br>for MVA Patients<br>(Peroentage of Resources Saved or Addiional Resources<br>% Needed for Patients Treated with MVA)<br>X Average Duration of Patient Stay Average Patient Cost<br>640%<br>4.0%-<br>KEME IEXII iIE iE KENU<br>2Do% "A ~~~~~~~~~~~~~~~~~A .A MX<br>-40<br>'Based on comparisons of mighted means aculat dforp atttreted wth O&C and MVA<br><!-- End of picture text --> 15
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- 12 -

The Primary School Curriculum (Dottrens, 1962: prepared under UNESCO auspices using data pnblished by the International Bureau of Education, 1958). Several regional supplemeniary sources were also used: for Central America (Waggoner and Waggoner, 1971), for Asia (rNIER and UNESCO, 1970; Japanese Ministry of Education, 1964) and for Africa (Sasnett and Sepmeyer, 1955; Hawes, 1979).

From the official curricular timetables we located in these sources, we coded two important pieces of information: first, a list of the subjects to be taught during the elementary school cycle (usually, 5-,u years) and second, the number of periods (or hours) to be devoted to each subject during a typical week of primary schooling. We used this information to indicate the presence or absence of topics in the official curriculum and the relative emphasis on each -- these are the two main dependent variables reported in the analyses below.

A number of technical and s.ubstantive points may help clarify properties of the data: (1) Data Frame: First, the data cover national entities -- both sovereign countries and a few dependent colonies -- with distinct national educational systems. In the current period (1970-86), almost all data refer to formally independent nation-states. Second, when we have mcre than one timetable for a given country during a period, we report results for the data nearest the target years 1960 and 1985. Third, we report data for most of the countries in the world with mass education systems, but we do not have a representative sample. Naturally, the data tend to underrepresent polities and educational systems most weakly linked to the world educational network. Thus our results may overemphasize world conformity and underrepresent local idiosyncrasies or particularly unusual curricular adaptations. In the contemporary period, however, the data appear to be sufficiently extensive to minimize this problem.

(2) Coding Decisions: First, listed topical categories almost always fit into conventional educational categories. When minor variations are found, the topic can ordinarily be classified into such a category. More significant variations are coded as "other." Second,