rafmacalaba/datause-encoder-data huggingface dataset:
# **<<B>>** _Control variables_

In the empirical specification, we include additional controls that are likely to affect agricultural and food policies, as suggested by many previous studies (e.g., Anderson 1995; Beghin and Kherallah 1994; Swinnen et al. 2000; Olper 2007). Specifically, our basic D-in-D specification always includes the following structural controls: the level of development, measured by the log of real per capita GDP; the share of agricultural employment in total employment; the log of agricultural land per capita; and the log of total population. All of these variables are computed from World Bank ( _WDI_ ), FAO, or national statistics.

We also test the robustness of our findings by controlling for several other (macro) covariates, such as different indicators of (aggregate) openness, government expenditures, and economic and political crises (wars and conflicts). Openness indicators (the trade to GDP ratio and the Sachs and Warner (1995) index) and government expenditures are obtained from Wacziarg and Welch (2008)<sup>15</sup> and the Penn World Table, respectively. War and conflict year dummies are based on the UCDP/PRIO Armed Conflict Dataset Version 4-2008 (see Gleditsch _et al._ 2002).

For our matching strategy, we use a limited number of covariates that are likely to influence both regime change and agricultural and food policies. As discussed previously, a shift in agricultural policy may require political reforms of sufficient size (Swinnen _et al._ 2001). Therefore, in our model, we include a variable, _initial polity2_ , that takes the value of our democracy index at the beginning of the sample. This variable is included to take into account that countries with Polity2 values close to zero are more likely to have a political regime change.

To control for the fact that the sample period varies in length across countries and that the length of the sample may be correlated with the probability of changes in the 16
<!-- Start of picture text --> ith 50-year<br>period<br>Wind speeds during ty pho Ne hard<br>| 50 km/h S<br>7] 92 km/h Ne E<br>9) 135 kmyh ; ;<br>1) 178 km/h aT ,<br>HB 220 km a. ;<br>a. 4<br>[[) Vietnam boundary = ie<br>.<br>= . -<br><!-- End of picture text -->

<!-- Start of picture text --> Typhoon with 100-year<br>____-feturn period<br>Vi dsp i<br>| [50% s<br>{ ] 92 km/ E<br>|) 135 km/h<br>1) 178 km/h| f<br>HM 220kmn<br>|<br>[ Vietnam boundary - si<br>i<br>- . -<br><!-- End of picture text -->

<!-- Start of picture text --> Typhoon with 1000-year<br>return period:<br>Wind speeds during typhoon ° ;<br>HE 50 km/h <<br>HE 92 km/h :<br>HB 135 kmjn<br>178 km/h<br>220 km/h :<br>|<br>|) Vietnam boundary<br>; b/ . &<br><!-- End of picture text -->
to draw conclusion why these were so expensive in these regions as compared to other regions and which items - transportation, food at school or lodging, was the driven factor for a such high cost of primary education on these items. And as these were so large items in primary education expense in the Mekong Delta, further study is needed to help understanding better situation in this income poor and educationally poor performance region.

The low level of public budget expenditure on education makes these additional resources essential, but they clearly have serious implications for equity, both in exacerbating existing inequalities in education finance, and in the potential barriers they represent to participation for poor children. Figure 5 shows all public and private education spending per student and by region for both 1993 and 1998. We can see a great variation in both public and private education spending across regions, though the variation in public education spending per student reduced in 1998, compared to that in 1993. In both years, the Southeast had highest level of private spending which was much higher than the rest of the country. Surprisingly, public spending per student was highest in the Southeast, the richest region in the country. This partly due to the fact that this region includes Ho Chi Minh City which has relatively more higher level education institutions than others. Other explanation is due to decentralization effect that the richer cities / provinces have more resource to spend than others, and thus it can spend much more on education. In 1998, the Mekong Delta, the North Central Coast, and The Central Highlands had lowest levels of public spending on education. There may be some link between high performance in terms of enrolment and level of public education spending per student. This can be explored more in the next section by looking at public education spending per student by level and region (Table 14).

**Figure 5: Per student public and private spending on primary education, 1993 and 1998**

<!-- Start of picture text --> 350 1200<br>300 30 *- Private- | Private -- t99S- "93r 1000 1 000 - UP Publ Rblic ~~~~~~~~~~~~~~~Eivte1998----------- ----- ------<br>250 g -- -- 200 s u FRiblic -- ----------- m8 --- ----- ---- - .00---.... - -80 . .. - ' l.-<br>100--200-.. ..<br>50-~~~~~~~~~~~~~~~~~~~<br>00<br>I I Dl m W Iv v VI vu IIII uV V VI VIl<br><!-- End of picture text -->

_Who benefits from public education spending?_ In the previous sections, we have seen that the overall picture in education is improved from 1993 to 1998, and the improvement occurred in all quintiles and in all regions of Vietnam. In response to increased enrolment, there was a sizable increase in public spending for education in the 1990s, together with intra-sectoral re-allocation towards the primary and lower secondary levels. As policy changes have increased availability of public funds at these levels, in absolute as well as relative terms, public primary and lower secondary spending per student increased. However, public spending on education varied considerably across region as a large part of 15
sample, as expected, the probability of treatment jumps discontinuously from zero to one at the cutoff. These patterns in the conditional probabilities of treatment motivate our selections of partially-fuzzy and sharp RD designs for the phase-3 and phase-4 SLQAT samples, respectively.

_Local smoothness in the density function of SLQAT pass rates_ Rejection of local smoothness in the density function of the assignment variable at the cutoff may suggest manipulation of the assignment variable _z_ (McCrary 2008). Manipulation is plausible in our case as the SLQAT pass rate cutoff for program participation was advance public knowledge, and schools and perhaps even program managers may have an interest in a particular treatment outcome. The pass rate that the school _actually_ received on the SLQAT may be subject to partial manipulation, which is typically benign.<sup>25</sup> However, PEF staff exercise full control over the calculation of SLQAT pass rates, as well as their reporting, thus, introducing the risk of complete manipulation.<sup>26</sup> Given that we use SLQAT pass rates calculated by us based on the individual student test score data as our measure of the assignment variable, and these calculations nearly always match the pass rates calculated by PEF, manipulation would have to occur at the individual student level. Manipulation at this level is less straightforward; nonetheless, we cannot rule out the possibility.

Figures 4 and 5 present frequency histograms of schools with bins equal to the integer values of SLQAT pass rates for the SLQAT and cutoff neighborhood samples, respectively, separately by phase. A simple visual inspection of the SLQAT pass rates for both the phase-3

> 25 The day the SLQAT is offered, the classes which are tested, and the specific SLQAT papers offered at the school are stochastic elements from the perspective of the school and outside its control.

> 26 PEF staff may face incentives to manipulate the pass rates of marginal passers downwards (to just below the cutoff) if, for example, the program budget limits the number of schools that the program can be offered to. Given that PEF reports that they were flush with funds during this period, we can probably discount downward manipulation due to this reason. Alternatively, sympathetic PEF staff might manipulate the pass rate of marginal failers upwards (to just above the cutoff), so that these schools become eligible to participate in the program.

30
# 3.1 Financial Banking Data

The essential ingredient for this project is a micro data set that we built based upon the Basic Statistical Return (BSR)1A, maintained by the Central Bank (RBI). BSR-1A has details of each loan outstanding (above a threshold), reported annually by every branch of every scheduled commercial bank in India. The threshold over which individual account data are reported was Rs. 25,000 until 1998 and Rs. 2 lakh from 1999 onwards (the latter is about $4,000 using historical exchange rates). The universal and comprehensive nature of these financial data is substantially stronger than what can be assembled for most countries, including the United States for example. The BSR data have been used in recent research by Cole (2009), Das et al. (2016), Kumar (2016), and Das et al. (2018).

While the micro-data can only be accessed at the RBI by their staff, we were allowed to aggregate these data for external use. Our aggregations are at the level of the district x industry x year. Districts are administrative subdivisions of Indian states or union territories that provide more-granular distances from the various highway networks. We prepare our platform to resemble those used in prior studies on India's manufacturing sector to facilitate comparability.<sup>10</sup> Accordingly, the core sample contains 311 districts that account for over 90% of manufacturing activity in India. The excluded districts from the full set of 630 districts make very limited contributions to organized manufacturing.

Industry categories are 2-digit NIC for manufacturing and 1-digit for all other industry groups. We have invested substantial time in cleaning and validating the data and ensuring consistency across years. We explicitly designed our aggregations to avoid any kinks due to definitional changes across years. Our data span 1992 to 2013, with our analyses concentrated on the decade from 1999 to 2009 around the GQ reform.

# 3.2 GQ and NS-EW Mapping

We measure the spatial distance of Indian districts to the GQ or NS-EW highway system using official highway maps and ArcMap GIS software. We calculate distances using shortest straight-line metrics measured from the edge of each district. These results are robust to instead measuring distances from district centroids. The Empirical Appendix of Ghani et al. (2016) provides additional details on data sources and preparation, with 10 See Ghani et al. (2016). For additional detail on the manufacturing survey data, see Fernandes and Pakes (2008), Hasan and Jandoc (2010), Kathuria et al. (2010), Nataraj (2011), and Ghani et al. (2014).

7
<!-- Start of picture text --> g Bangladesh:Employment<br>=<br>r=5SL<br>or<br>§<br>g<br>a<br>s<br>—°<br>5<br>Byn<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>*—— Quintile2 '—— Quintile 3<br>-—_ Quintile4 "——— Quintile 5<br>withGenerated vear-ofby bithregressing and state work fixed status effects. on wealth,Standard urban, higher errors Clustered education by state. and interactions<br><!-- End of picture text --> <!-- Start of picture text --> Ps India:Employment<br>ge<br>~=<1g<br>a<br>5°<br>%<br>a<br>G<br>go<br>5<br>oYn<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>— Quintile2 '—— Quintile 3<br>-—— Quinte 4 '——— Quintile 5<br>withGeneratedyear-of-birth by regressing and state fixed work status effects. on Standard wealth, urban, errorshigher clustered education by state. and interactions<br><!-- End of picture text --> <!-- Start of picture text --> gO22 Pakistan:EmploymentPloy<br>a<br>°<br>2<br>ao<br>g'<br>3<br>ke]<br>o<br>a<br>a<br>5<br>2<br>e?<br>w<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>—— Quintile2 '—— Quintile 3<br>-—— Quintile4 '——— Quintile 5<br>withGeneratedyear-of-birth by.regressingand state workfixed statuseffects. on wealth,Standard urban, highererrors clustered educationby state. and interactions<br><!-- End of picture text --> <!-- Start of picture text --> 2® Nepal:Employment<br>Bn<br>=<<br>8<br>3°<br>©<br>3<br>2<br>oN<br>er<br>a<br>es<br>8<br>e¢<br>TM<br>4940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>+— Quintile2 +—— Quintile 3<br>-—— Quintile4 '———— Quintile 5<br>withGeneratedyear-of-birth by regressingand state workfixed statuseffects. on wealth,Standardurban, highererrors clustered educationby state. and interactions<br><!-- End of picture text -->
# **1 Introduction**

Since 1945, more than 70 civil wars have occurred around the world, resulting in approximately 20 million deaths and displacing more than 67 million people.<sup>1</sup> Understanding the causes of the onset and continuation of such conflicts is therefore of great importance, and the analysis of the determinants of civil conflict has been the subject of a recent and growing literature in economics.<sup>2</sup> There have been several cross-country studies of the proximate correlates of civil war: Collier and Hoeffler (2004) and Fearon and Laitin (2003) both find that poorer countries face a greater risk of civil conflict. Miguel et al. (2004) use instrumental variable analysis to corroborate this view that lower economic growth leads to more conflict. However, this finding is subject to varying interpretations: while Collier and Hoeffler (2004) interpret poverty as proxying for the opportunity cost of rebel recruitment, Fearon and Laitin (2003) emphasize the lack of state capacity in poor countries. These studies also differ in their findings on the impact of other factors: Collier and Hoeffler find that civil conflicts are more likely in countries which are dominated by one ethnic group and which have a larger stock of easily expropriated primary commodities, while the other two studies find no impact of ethnic divisions or primary commodities on the likelihood of conflict.

The results from cross-country studies are subject to the caveat that civil conflict in one country might have very different causes and characteristics than conflict in another.<sup>3</sup> Further, the data on civil conflicts and other variables may not be strictly comparable across countries (Sambanis, 2004). There have been a few within-country studies of civil conflict. Barron et al. (2004) examine village-level conflicts in Indonesia, while Deininger (2004) examines the causes and consequences of civil strife in Uganda. However, both these studies focus on a local level of conflict and construct measures of conflict based on subjective interviews. They do not study organized conflict like insurgencies or civil wars, and hence are not strictly comparable to the cross-country studies. The set of case studies in Collier and Sambanis (2005)

> 1Collier and Sambanis (2005) pp xiii.

> 2See Humphreys (2003) for a review of this literature.

> 3Sambanis (2001) analyses ethnic and non-ethnic conflicts separately and finds that they have somewhat causes.

1
**Figure 3 – Female Labor Participation by Five Years Age Cohorts (World, MENA, Morocco)**

<!-- Start of picture text --> World MENA Morocco<br>20 30 40 50 60 20 30 40 50 60 20 30 40 50 60<br>age age age<br>1990 2000 1990 2000 1990 2000<br>2010 2010 2010<br>80 80 80<br>60 60 60<br>40 40 40<br>(mean) FPR (mean) FPR<br>20 20 20<br>0 0 0<br><!-- End of picture text --> Source: ILO-EAPEP database (Female LFP) One of the main arguments behind the U-shape hypothesis is the structural transformation of the economy from agriculture to industry and services. Figure 2 showed that the MENA region performed above the world average in terms of reduction of the share of agricultural output over the period 1990-2010 and this is line with the U-shape hypothesis. However, for the upward trend of the curve to occur, it is important that sectors that typically attract women such as manufacturing and services increase in output as opposed to sectors that are less likely to attract women such as heavy industry or mining. Gaddis and Klasen (2013) recently noted that it is important to break down the sectors’ shifts in GDP to understand FPRs. If we break down the development of value added shares across sectors and for the world regions, we can see that the MENA region stands out as the only region where the share of agriculture, services and manufacturing all declined between the last decade of the 1990s and the first decade of the 2000s (Table 1, see also Diop et al., 2012). Therefore, the economic restructuring away from agriculture has not favored sectors that are more likely to hire females. The table below shows that this is really a peculiarity of the MENA region as compared to other world regions.

11
||(0.02)|(0.01)|(0.02)|(0.02)|(0.02)|(0.0)|(0.0)|(0.0)|
|---|---|---|---|---|---|---|---|---|
|Hhld bil|0.33|0.37|0.31|0.37|0.34|0.1**|0.0|-0.0|
|ouseo owns a cyce|(0.03)|(0.01)|(0.02)|(0.03)|(0.03)|(0.0)|(0.0)|(0.0)|
|Hhld bil h|0.89|0.88|0.87|0.90|0.89|0.0|0.0|-0.0|
|ouseo owns a moe pone|(0.01)|(0.01)|(0.02)|(0.02)|(0.02)|(0.0)|(0.0)|(0.0)|
|Hhld id/DVD|0.18|0.14|0.12|0.13|0.12|0.0|-0.0|-0.0|
|ouseo owns a veo|(0.02)|(0.01)|(0.02)|(0.02)|(0.02)|(0.0)|(0.0)|(0.0)|
|Hhld lii|0.25|0.28|0.28|0.28|0.27|0.0|-0.0|-0.0|
|ouseo owns a teevson|(0.02)|(0.01)|(0.03)|(0.03)|(0.03)|(0.0)|(0.0)|(0.0)|
|Hhld t|0.02|0.03|0.03|0.03|0.04|-0.0|0.0|0.0|
|ouseo owns a compuer|(0.01)|(0.00)|(0.01)|(0.01)|(0.01)|(0.0)|(0.0)|(0.0)|
|Household owns a|0.08|0.09|0.08|0.10|0.09|0.0|0.0|-0.0|
|refrigerator/freezer|(0.01)|(0.01)|(0.02)|(0.02)|(0.01)|(0.0)|(0.0)|(0.0)|
|Hhld AC/f|0.07|0.07|0.07|0.07|0.07|-0.0|0.0|0.0|
|ouseo owns an an|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.0)|(0.0)|(0.0)|
|Hhld di|0.49|0.44|0.42|0.46|0.39|0.0|-0.0|-0.1**|
|ouseo owns a rao|(0.03)|(0.01)|(0.02)|(0.03)|(0.03)|(0.0)|(0.0)|(0.0)|
|Hhld it t|0.85|0.85|0.83|0.86|0.86|0.0|0.0|-0.0|
|ouseo owns a mosquo ne|(0.02)|(0.01)|(0.02)|(0.02)|(0.02)|(0.0)|(0.0)|(0.0)|
||0.44|0.45|0.46|0.48|0.48|0.0*|0.0|-0.0|
|Log of per capita residential area|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.0)|(0.0)|(0.0)|
|Roof is made of concrete/metal|0.85|0.84|0.83|0.85|0.85|0.0|0.0|-0.0|
|sheets/tiles|(0.02)|(0.01)|(0.02)|(0.03)|(0.02)|(0.0)|(0.0)|(0.0)|
|Wall is made of burnt|0.62|0.55|0.60|0.56|0.56|-0.0|-0.0|-0.0|
|bricks/concrete|(0.03)|(0.02)|(0.03)|(0.04)|(0.03)|(0.0)|(0.0)|(0.0)|
|Floor is made of|0.51|0.52|0.48|0.47|0.50|-0.0|0.0|0.0|
|concrete/cement/tiles/timber|(0.03)|(0.01)|(0.03)|(0.03)|(0.03)|(0.0)|(0.0)|(0.0)|
|Source of drinking water: piped|0.41|0.44|0.40|0.38|0.34|-0.0|-0.1**|-0.0|
|water/truck|(0.04)|(0.02)|(0.04)|(0.04)|(0.04)|(0.0)|(0.0)|(0.0)|
||0.33|0.35|0.36|0.35|0.34|-0.0|-0.0|-0.0|
|Toilet: flush/VIP|(0.03)|(0.01)|(0.03)|(0.03)|(0.03)|(0.0)|(0.0)|(0.0)|
||<br>0.25|<br>0.28|<br>0.25|<br>0.24|<br>0.27|<br>-0.0|<br>0.0*|<br>0.0**|
|Residence: Urban|(0.02)|(0.01)|(0.04)|(0.04)|(0.04)|(0.0)|(0.0)|(0.0)|
||0.75|0.72|0.75|0.76|0.73|0.0|-0.0*|-0.0**|
|Residence: Rural|(0.02)|(0.01)|(0.04)|(0.04)|(0.04)|(0.0)|(0.0)|(0.0)|
|**Number of observations**|**1179**|**4644**|**711**|**701**|**698**|**1412**|**1409**|**1399**|

**Note:** The standard errors are in parentheses, and the differences are estimated considering the complex survey design. * p < 0.1, ** p < 0.05, *** p < 0.01. The population weights are applied. The consumption data are expressed in monthly basis and are spatially and temporarily deflated within each round. The consumption data in NPS 2019/20 and in NPS 2020/21 are deflated to 2022 prices using the annual WB deflator.

32
salaried work are consistent with findings of substantially less voluntary entry (Perry et. al. 2007).

That said, the informal sector is clearly very heterogeneous and other data from the Mexican ENEU that is suggestive that there is a component of informality that does correspond to disguised unemployment. Figure 4d plots the proportion of workers who respond positively to the question “Have you been looking for a job over the last two months” and who had not changed employment status from the quarter before as a possible proxy for the degree of dissatisfaction with the current job coupled with the availably of alternative jobs. Search intensity is generally higher in the informal sector, perhaps, reflecting the relative youth of the informal salaried sector, although the magnitudes (and hence differences) are not large: in the upturns of mid-1990 and to 2000 search rates were equal across sectors at roughly 1-2%. However, the share searching is strongly countercyclical implying that as the labor market becomes slack and the acess ot the formal sector from all sectors decreases, the dissatisfaction increases. This is especially true in the informal sectors where the percentage searching for better jobs peaks at just under 7% during the 1995 crisis, a gap of slightly over 4% points over the formal sector, suggesting that, in fact, the sector contained more workers who were forced into bad matches. This would make sense if, during the crisis, only the informal sector was hiring: though the job finding rate in the sector is reasonably acyclical, the fact that unemployment is increasing does imply that it informality is absorbing more unemployed as a share of the workforce than during booms. Consistent with the transition patterns, however, the percentage dissatisfied is a relative minority of the sample.

# _Flows among Non-Employment Types: Unemployment and Inactivity_

Finally, figure 5 and table 5 depicts how inactivity and unemployment interact over the business cycle. Perhaps the most remarkable fact is the strong negative correlation between output and the inflow rate into unemployment from inactivity (- 0.52 in Mexico and -0.72 in Brazil). This suggests that in recessions, inactive workers enter the market, increasing the number of unemployed. This is reinforced by the fact that fewer unemployed leave the labor force. Together, these imply a further increase in 18
- 16 -

female education in fertility reduction.

4.7 The extent to which female education, interacting with the mean level of family planning services, reduces fertility can also be seen from the predicted values of total fertility rate, also derived from equation 4, Table 1. Figure 3 presents the predicted values of total fertility rate in 1985 for varying levels of female enrollments in 1975 and family planning services in 1982. The fertility rate falls steadily with increases in female secondary enrollments. The rate of decline in fertility appears steep initially, but tapers off after female enrollment levels reach 40 percent. Family planning services also reduce fertility but by a slower rate. Even with the maximum family planning service score of 100 (quadruple the mean of 25 percent in 1982), fertility does not reach the same level as with 40 percent female secondary enrollment. It would appear that mere expansion of family planning services by itself affects fertility much less than if it were accompanied by expansion of female secondary enrollment." _1"_ Several micro-level studies based in househol **d** ata sets have reached similar conclusions. For example, one recent cameful study based on Indonesian household-level data set confirms the small contribution of family planning program effort to fefility decline (P. Gcrtler and J. Molineaux, 1992). In this study, the authors show that over the period 1982-87, the combined educational and economic impacts, workdng though increases in contraceptive use, accounted for 87 per cent of fertlity decline over the period. By contrast, measures of family planning program inputs were rsponsible for only 4 to 8 per cent of the decline. Of course, this study uses a fixed effects model which relates changsg during 1982-86 in family planning inputs and socio-economic variables to chanIzes in contracepive use and ferility. Since family planning was already quite well established in 1982, these results may reflec **t** his context.