rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Payment Systems<br>Reform<br>Currency Holding Excess Reserves Holding<br>Financial Intermediation Financial Intermediation<br><!-- End of picture text --> Figure 1: Transmission Mechanisms (deposit channel). Second, innovations in wholesale payment systems help establish wellfunctioning interbank markets for end-of-day funds. This may reduce the need for banks to hold excess reserves (outside money) to self-ensure against end-of-day out‡ows and thus helps credit creation (reserves channel).

It is important to emphasize, however, that neither the deposit nor the reserves channel needs to hold by virtue of a balance sheet identity (see equation 1).

For example, even if the payments reform leads to an increase in deposits () at the expense of cash, this may result in an increase in reserves (), or other liquid assets (), leaving unchanged the level of loans () to the non-...nancial sector. The extent to which an increase in deposits results in an increase in loans is therefore an empirical question which needs to be examined carefully.

Likewise, even if reform leads to a reduction in reserves (), this can be matched by a reduction in the total amount of interbank deposits (), or an increase in other liquid assets (), rather than an increase in loans to the non-...nancial sector ()<sup>4</sup> . Whether or not a reduction in reserves has an e¤ect on loans supplied by the banking system is therefore again a question that needs to be addressed empirically.

To examine whether payment system developments help credit creation and which, if any mechanism underpins this e¤ect, we use as our laboratory a sample of transition

> 4Moreover, as emphasized by Disyatat (2008), central banks will in practice tend to accommodate ‡uctuations in desired reserves, e.g. by reducing the amount of securities taken on repo in open market operations (reducing reserve repos ), in order to prevent such ‡uctuations to have an e¤ect on interest rates.

4
# **2. Data**

The data we use are from the Demographic and Health Surveys (DHS). The DHS are nationally representative household surveys that have been conducted in more than 70 lowand middle-income countries since 1985. Most countries have multiple survey rounds, with an average spacing of about 5 years between surveys. Calculating the maximum absolute child mortality reduction achievable with investments in water and sanitation infrastructure requires recent estimates of water and sanitation coverage and child mortality rates. Since it does not appear plausible to assess current infrastructure gaps and investment needs based on data from the 1980s and 1990s, we exclude surveys prior to the year 2000. In countries where two surveys are available after 2000, we use the most recent survey. As summarized in Table 1, this leaves us with 38 surveys collected between 2000 and 2006, a sample more up-to-date but also smaller than the full DHS sample analyzed in the preceding paper in this series (Günther and Fink, 2010).

**Table 1: Survey List**

|**#**|**Country**|**Year**|**#**|**Country**|**Year**|**#**|**Country**|**Year**|
|---|---|---|---|---|---|---|---|---|
|1|Azerbaijan|2006|14|Haiti|2005|27|Nicaragua|2001|
|2|Bangladesh|2004|15|India|2005|28|Niger|2006|
|3|Benin|2006|16|Indonesia|2002|29|Nigeria|2003|
|4|Bolivia|2003|17|Kenya|2003|30|Pakistan|2006|
|5|Burkina|2003|18|Lesotho|2004|31|Peru|2003|
|6|Cambodia|2000|29|Liberia|2006|32|Philippines|2003|
|7|Cameroon|2004|20|Madagasc|2003|33|Senegal|2005|
|8|Chad|2004|21|Malawi|2004|34|Swaziland|2006|
|9|Congo,|2005|22|Mali|2006|35|Tanzania|2004|
|10|Dominican|2002|23|Morocco|2003|36|Uganda|2006|
|11|Gabon|2000|24|Mozambi|2003|37|Zambia|2001|
|12|Ghana|2003|25|Namibia|2006|38|Zimbabwe|2005|
|13|Guinea|2005|26|Nepal|2006||||

Out of the 38 countries in this simple, 25 are located in Sub-Saharan Africa; the rest of the sample is fairly evenly distributed across South- and South-East Asia as well as Central and South America.

6
**Table 24 Regression of the Logarithm of Household Expenditure per Capita** _(t-statistics in parentheses)_

|_Dummy_|_Household ex_|_penditure per capita_|_<100, 000 CFAF_|_Household ex_|_penditure per capita_|_2100,000 CFAF_|
|---|---|---|---|---|---|---|
|_variablesa)_|_April-May_|_June-November_|_December-March_|_April-May_|_June-November_|_December-March_|
|Abidjan|0.144<br>(1.377)|-0.022<br>(-0.730)|-0.044<br>(-0.433)|-0.043<br>(-1.867)|-0.005<br>_(-0.455)_|0.041<br>(2.191)|
|Other Cities|-0.042<br>(-0.618)|-0.020<br>(-1.333)|0.073<br>(1.876)|0.072<br>(2.466)|-0.020<br>(-2.289)|0.013<br>(0.578)|
|East Forest|-0.072<br>(-1.433)|0.024<br>(1.128)|-0.001<br>(-0.036)|-0.034<br>(-0.797)|0.025<br>(1.536)|-0.026<br>(-1.017)|
|West Forest|0.009<br>(0.106)|0.048<br>(1.493)|-0.080<br>(1.686)|-0.021<br>(-0.474)|0.000<br>(0.019)|0.015<br>(0.470)|
|Savannah|-0.001<br>(-0.024)|0.025<br>(0.928)|-0.029<br>(-0.906)|-0.019<br>(-0.450)|-0.003<br>(-0.148)|0.010<br>(0.444)|
|Cotton|-0.169<br>(-2.975)|-0.014<br>(-0.436)|0.119<br>(3.143)|0.022<br>(0.586)|-0.042<br>(-2.610)|0.057<br>(2.323)|
|Coffee/Cocoa|-0.024<br>(-0.440)|-0.034<br>(-1.610)|0.071<br>(2.135)|0.022<br>(0.586)|-0.042<br>(-2.610)|0.057<br>(2.323)|
|High consumption of<br>h dtib|0.071|0.006|-0.046|0.043|-0.006|-0.009|
|ome proucon)|(1.693)|(0.313)|(1.674)|(1.170)|(-0.399)|(-0.411)|
|R'<br>N||0.7420<br>1160|||0.6660<br>5173||

a) Dummy variables shown in this table are interacted with the dunmny for the specified time period in the table.

b) Defined as the ratio of consumption of home production to production to purchased food 2 0.6.

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Figure 4. Average Growth, 1979-85 <!-- Start of picture text --> 7 )<br>4<br>3-<br>2<br>-2<br>1979 ~ ~ ~ ni1913 1960<br>0 mIahoduMr3 4 n*n-reschduJegu<br><!-- End of picture text --> Reschedulers (countries from World Debt<sup>Tables categorized as Major Borrowers</sup> or Highly Indebted Countries, excluding sub-Saharan Africa, which had not rescheduled prior to 1983 but did reschedule<sup>10% or more of their total debt</sup> in any of the years 1983-85, thereby restricting<sup>attention to countries</sup> that rescheduled only after the 1982 crisis) include Argentina,<sup>Brazil,</sup> Chile, Costa Rica, Ecuador, Mexico,<sup>Morrocco, Peru, Philippines,</sup> Uruguay, and Yugoslavia. Non-reschedulers include Colombia, Egypt, India, Indonesia, Israel, Korea, Malaysia and Venezuela.

**Source: International Financial Statistics.**

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WPS4037 <mark>B</mark> ACKGROUND <mark>P</mark> APER TO THE <mark>2007 W</mark> ORLD <mark>D</mark> EVELOPMENT <mark>R</mark> EPORT

# **Conscription and Crime**<sup>*****</sup>

Sebastian Galiani _Washington University in St. Louis_ Martín Rossi _Universidad de San Andrés_ and Ernesto Schargrodsky _Universidad Torcuato Di Tella_

## **Abstract**

The initiation in criminal activities is, typically, a young phenomenon. The study of the determinants of entry into criminal activities should pay attention to major events affecting youth. In many countries, one of these important events is mandatory participation in military service. The objective of this study is to estimate the causal relationship between mandatory participation in military service and crime. We exploit the random assignment through a draft lottery of young men to conscription in Argentina to identify this causal effect. Our results suggest that participation in military service increased the likelihood of developing a criminal record in adulthood (in particular, for property and weapon-related crimes).

World Bank Policy Research Working Paper 4037, October 2006 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> * Sebastian Galiani, Department of Economics, Washington University in St Louis, Campus Box 1208, St Louis, MO 63130-4899, US, <u>galiani@economics.wustl.edu; Martín Rossi, Universidad de San</u> Andres, Vito Dumas 284, B1644BID, Victoria, Provincia de Buenos Aires, Argentina, <u>mrossi@udesa.edu.ar; Ernesto Schargrodsky, Universidad Torcuato Di Tella, Saenz Valiente 1010,</u> C1428BIJ, Buenos Aires, Argentina, <u>eschargr@utdt.edu. We are grateful to Horacio Tarelli and</u> Fernando Michelena for crucial help throughout this study. We also thank Rut Diamint and the Argentine Army for their cooperation. Maximiliano Appendino and Florencia Borrescio Higa provided excellent research assistance; and the World Bank provided financial support.

1
By considering the feasible distance for migration and the "back-tracks"', it is estimated that resistant Heliothis from Guntur can affect crops in the districts of Nalgonda, Mahbubnagar, Rangareddy and Medak/Sangareddy (refer to Map 2). Note that detailed resistance monitoring and migration studies are required to get a more reliable idea of the potentially affected areas.

Table 5, reports the output and value of crops grown in these districts as well as in Guntur which are vulnerable to Heliothis attack. The total value of the vulnerable crops (including cotton) outside Guntur was Rs.350 crores (about $210 m), in 1989/90. In Guntur the total value of susceptible crops (excluding cotton) was about Rs.320 ($192 m) crores. The total amount of Rs.670 crores ($400 m), which potentially could have been affected was about two and a half times the value of cotton grown in Guntur.

# Continuing CroR Losses

As has been repeatedly stressed, in this section and elsewhere, we need to estimate the continuing losses due to an increase in the general level of resistance as well as the (expected) catastrophic losses due to an increase in the probability of field failure.

4 As stated earlier, back tracks indicate the migration path for Heliothis moths, based on moth catches at ICRISAT and on the prevailing wind patterns. Pedgley et al. (Pedgley et al., 1987) find that their back tracks come mostly from the Andhra Pradesh coast, East or South-East of ICRISAT. Map 2 gives an idea of the districts that can be potentially affected by moths migrating from Guntur.

61
and Pakistan). In several of the Western and Central African countries where the absolute gap is less than 10 percentage points, the ratio is large (that is, between 1.24 and 1.58 in Burkina Faso, Mali, Niger, and Senegal). There are exceptions however, in Ghana the gap is only 2.5 percentage points and the ratio is 1.03, and in Cameroon and Togo it is close to 9 percentage points and the ratio of about 1.15. Perhaps surprisingly, in Bangladesh in the most recent year (1996-97) there is no female disadvantage (and there is even a small female advantage).

Although the regional patterns are strong, there is still within-region variability.

In most of the other countries covered by the DHS data, there is close to no gender gap in the youngest age group, and in many cases there is a female advantage. There are exceptions however, such as Comoros, Guatemala, Mozambique, and Turkey.

When moving to the slightly older age group, ages 12 to 14, the pattern remains much the same. In most countries where there was a gender disadvantage among 6 to 11 year olds, it is exacerbated both as an absolute and relative measure (although this doesn't hold for Egypt and Morocco). The male-female gap reaches over twenty percentage points in Benin, C.A.R., Chad, India, Nepal, Pakistan and Togo. The male/female ratio was as high as 2.06 in Niger although it has gone down since 1992. In Benin the ratio was 1.92 in 1996 with 56 percent of boys enrolled but only 29 percent of girls. Again, in the rest of the world, Comoros, Guatemala, Mozambique and Turkey stand out as having a large female disadvantage. In two of the countries that did not have a large disadvantage among 6 to 11 year olds, Bolivia and Uganda the male/female ratio is 1.11 forages 12to 14.

The bulk of this paper will focus on disparities in enrollment of 6 to 14 year olds, but Table 3 also reports levels, gaps, and ratios for the percentage of a recent cohort -those aged 15 to 19 -that have completed grade 5. This is a surmmary measure that captures both the share of (1999).

11
cheaper capital over this period, either domestically through government-subsidized credit or, more likely, through lower-cost international financing, which would make their equity more attractive. Finally, it is possible that trade and other reforms that occurred in many of these countries could have benefitted large firms more than their smaller counterparts.

PBV has a significant effect on asset returns in only six of the countries examined; in three of those countries the effect is positive. In these three cases, the findings are contrary to those of Fama and French (1992), who examine the ratio of book value of equity to market value of equity (BE/ME) and find a positive relationship.'<sup>0</sup> Our findings are closer to those of Kothari, Shanken and Sloan (1995), who find little evidence in support of a PBV effect in their study of the U.S. market.

EP ratios are significantly different from zero in only 7 countries, with six of those positive. For these six countries, the results agree with the findings of Fama and French (1992). For the remaining twelve countries we find little relationship between E/P and returns, which is similar to the CHL (1991) results for Japan.

Dividend yield played an important explanatory role in 7 of the countries; in four cases that role was negative. By comparison, the after-tax CAPM of Brennan (1970) predicts that the coefficient on dividend yield in a cross-sectional relationship represents a weighted average of the tax rates of investors and, consequently, should be positive. A subsequent extension of that model (Litzenberger and Ramaswamy (1979)) allows the coefficient to be either positive or 10 Fama and French (1992) thus examine the inverse of our ratio. They report results using the natural logarithm of the ratio. CHL (1991) employ the ratio of book value of equity to market value of equity, as do Fama and French, but without employing the natural logarithm. CHL also find a positive relationship between that ratio and returns.

13
Table A.4. Impacts of PEP on Economic Resilience

||Economic<br>Resilience<br>Index|A Household<br>member had not to<br>skip a meal|Days that ate<br>protein in the<br>household|Did not<br>face housing<br>eviction|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|1[_RAMVijk_ = 1]|0.422|0.156|0.640|0.105|
||(0.048)|(0.022)|(0.108)|(0.024)|
|R-squared|0.136|2,334|2,334|1,519|
|Observations|2,337|0.101|0.083|0.332|
|Mean values(Non-RAMV)|0.000|0.507|4.253|0.667|

_Notes:_ Dependent Variables: (i) Economic Resilience Index is constructed using the outcome variables of columns (i) to (iv) of Table A.3 using the methodology of Kling et al. (2007); (ii) A household member had to skip a meal is an indicator [=1] if any member of the household have to skipped a meal because there were no food in the last month; (iii) Days that ate protein in the household is the number of days that the members of the household ate protein in the last week; (iv) Housing Eviction is an indicator [=1] if the household was evicted from their home due to COVID-19 crisis. All columns include department (Antioquia, Atl ́antico, Bogot ́a, and Norte de Santander), wave, sampling-city fixed effects, and controls for the interaction between wave dummies and the set of individual controls listed in Table A.5. Robust standard errors are reported in parentheses.

10
WPS3890

# **Migration in Towns in China, a Tale of Three Provinces: Evidence from Preliminary Tabulations of the 2000 Census**

Anqing Shi* Development Research Group The World Bank

# **Abstract**

There is a concern that the growth of towns in China has been stalled recently and with it, the creation of non-farm jobs in rural industries. This study uses the 2000 census tabulations to look at this issue by examining in-migration in towns in three provinces in China, Zhejiang, Henan, and Sichuan, their educational attainment, original place, and occupational composition. In addition to the diversified patterns of town in-migrants revealed in the three provinces, the paper finds that town in-migrants generally possess a higher level of educational attainment than the local population in towns, especially in the less-developed western and central regions. This inflow of human capital could foster development in towns. There is also evidence that as economic opportunity increases in towns, such as in the richer coastal province of Zhejiang, better educated people in rural areas are likely to shift their jobs from the farm to the non-farm sector in towns nearby, instead of leaving the countryside to migrate to other provinces. This could reduce migration pressure on big cities. Finally, the labor market in towns in the less-developed west and central regions is more flexible in accommodating in-migrants, whereas in the developed province of Zhejiang the labor market is segregated between migrants and the local population.

Key words: migration, town development, Chinese census, labor segregation World Bank Policy Research Working Paper 3890, April 2006 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at_ _<u>http://econ.worldbank.org.</u>_

*I thank Bert Hofman, David R. Dollar, Luis Serven, and Songsu Choi for very helpful comments on the earlier version of this paper.
<!-- Start of picture text --> Peru<br>10000 35000<br>sogg 30000<br>sooo<br>Food 25000<br>6000 20000<br>5000<br>aood 15000<br>000 10000<br>2000<br>soo0<br>1000 - (Government Expenditure Tax Revenues ——Private Consumption fright scale}<br>in in<br>=—_ ca co =r =—_ cv oD =r =—_ cA oD = —_ cA oo = —_ cA oo =r —_ ca co<br>a a a) a oi a oi a oi oi oi oi a oi oi oi oi ai oi i a i ai<br>co = co on wo on lel oo ao =— co cd wo ot re on co =—_ co on wo ot re<br>oo oo oo oo co oo on oo a a a a oo a oo a fo] fo] fo] fo] fo] fo] fo]<br>oneeon on on oa on oa on oa oa oa oa oo oa oo oa fony fony fonyee fonya fonyeo fonyee fony|<br><!-- End of picture text -->

<!-- Start of picture text --> Fiscal Stimulus: Cumulative Impact on Real Demand{LCU 1 Unit Innovation<br>in Fiscal Variable}<br>U2 | |] a<br>03 [|<br>0.6<br>-1.5<br>m Gov Expenditure Shock @& Tax Revenues Shock<br>-1.8<br>“2.5<br>4 Quarters & Quarters 12 Quarters<br><!-- End of picture text -->
**Figure 2: Unconditional Impulse Responses**

<!-- Start of picture text -->
1.20 A. Government Consumption 0.08 B. GDP<br>1.00 0.06<br>0.04<br>0.80<br>0.02<br>0.60<br>0.00<br>0.40<br>-0.02<br>0.20<br>-0.04<br>0.00 -0.06<br>-0.20 -0.08<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br><!-- End of picture text -->

_Note:_ The graphs show the unconditional impulse responses (percentage points) to a positive shock to government consumption. These are based on estimates from the SVAR model of Ilzetzki, Mendoza, and Vegh (2013) that features no interaction terms. Solid lines represent the median, and dotted lines are the 16-84 percent confidence bands.

17
the 2004-05 NSS round 61, I obtain the expenditure shares _νA_ and _νS_ , along with the baseline welfare (per capita expenditure) level, for each household. Figure 3 plots these shares by per capita consumption percentiles, showing, as might be expected, opposite patterns for food and nontradables (services).<sup>31</sup> In addition to the parameters that have already been discussed, I need income shares for each household to compute Ω _._ Appendix B.3 describes the construction of the income components from NSS household data and Figure 4 shows how the component shares vary along the wealth distribution. Not surprisingly, manual labor earnings as a share of total income decline steadily by percentile, whereas earnings from nonmanual labor and land increase.

After combining all of these elements, I plot each household’s value of _ε_ against its per capita expenditure percentile in 2004-05 to obtain the solid curve in Figure 5. The salient feature of this welfare elasticity profile is that it is everywhere positive. Thus, rural Indian households, both rich and poor, would _benefit_ from higher food prices. But the profile is also increasing in household wealth, paralleling the pattern seen in the proportion of income derived from land (cf., Figure 4). So, while the welfare gain to the poorest is 0 _._ 25 percent for every one percent increase in the agricultural price index, the welfare gain to the richest is around 0 _._ 4 percent.<sup>32</sup> How important are assumptions regarding intersectoral labor mobility? The lower dashed curve in Figure 5, which plots _ε_<sup>_′_</sup> by percentile, provides the answer. In moving to a segmented labor market, roughly a third of the gains to the very poorest households of higher agricultural prices are wiped away, whereas the structure of labor markets hardly affects the welfare gains of the richest households at all. The reason for this can be gleaned from Figure 4.

> 31Nontraded goods expenditure categories include: firewood and other local fuel, transport and travel, tailoring expenses, house rental and related expenses, medical treatment expenses, education expenses, remittances and gifts, recreation and leisure expenses, and taxes and fees.

> 32Results are qualitatively similar if I restrict price changes to rice and wheat, the dominant foodgrains of India. In this case, _εRW_ = Ω _sRW − νRW_ , where _sRW_ and _νRW_ are the shares of rice and wheat in production and consumption, respectively. Appendix Figure C.1 shows that the benefits from higher prices are not quite as great when the increases are confined to rice and wheat and, in particular, are substantially less advantageous to the wealthy.

24