rafmacalaba/datause-encoder-data huggingface dataset:
# **REFERENCES**

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- Brandt, Loren., Johannes Van Biesebroeck and Yifan Zhang. 2008. ―The Impact of Trade Libralization on Productivity in Chinese Industry‖. Working paper

- Brecher, Richard., and Dias Alejandro, Carlos. ―Tariffs, Foreign Capital and Immiserizing Growth.‖ _Journal of International Economics_ , 1977 (7), pp. 317-322.

- Blalock, Garrick and Paul J. Gertler, "Welfare Gains from Foreign Direct Investment through Technology Transfer to Local Suppliers" _Journal of International Economics_ , March 2008, 74(2), 402–421.

- Blomstrom, M. ―Foreign Investment and Productive Efficiency: The Case of Mexico.‖ _Journal of Industrial Economics_ , 1986 (35), pp. 97-110.

- Borensztein, E., J. De Gregorio and J-W. Lee. ―How does Foreign Direct Investment Affect Economic Growth?‖ _Journal of International Economics_ , 1998, 45, pp. 115-35.

- Caves, Richard E. ―Multinational Firms, Competition and Productivity in Host-Country Markets.‖ _Economica_ , May 1974, 41 (162), pp. 176-93.

- Ethier, Wilfred (1982). ―National and International Returns to Scale in the Model Theory of International Trade.‖ _American Economic Review_ 72, pp. 389-405

- Globerman, S. ―Foreign Direct Investment and  ̳Spillover‘ Efficiency Benefits in Canadian Manufacturing Industries.‖ _Canadian Journal of Economics_ , Feb

30
supplies of tablets and injections were strictly controlled but large quantities of bandages were regularly supplied. Some hospitals used large quantities of intravenous fluids and others less. Antibiotic usage also varied enormously, and whether tablets or injections were given.

Some of the variation between hospitals in the kitchen unit cost might be explained by regional price differences though these seem unlikely to explain it all. The number of inpatient days may well be inaccurate, but it seems unlikely that faulty statistics can explain the very large variation. Chiradzulu and Nkhata Bay, in particular, appear to have reasonably reliable inpatient statistics, but Nkhata Bay's kitchen unit cost is two and a half times that of Chiradzulu (and the unit cost of the food alone almost that).

# District-wide distribution of resources

To a considerable extent, the distribution of resources districtwide is determined by the physical infrastructure of rural facilities. This helps to explain, for exar- 'e, why health centres and PHC absorbed 27% of district recurrent costs in Chiradzulu (which has few rural units that it controls) and 39% in Mulanje (which has many rural units).

Considerable resources are, however, based at the hospital which are expected to serve the district as a whole. This is true, for example, of the MCH staff, the TB, leprosy and ophthalmic assistants, the DHO and possibly other senior staff, and transport services. The extent to which staff could perform district-wide functions was crucially dependent on the availability of transport, 19
_5_ _Figure 2. Ratio of cocoa producer price to FOB price in major producing countries (1995)_ <!-- Start of picture text --> 100%<br>0.89<br>90% 0.82<br>80% 0.71<br>70% -<br>60% -<br>50% 0.48 0.49<br>40%<br>30%<br>20%<br>10%<br>0%<br>Cameroon Cote Ghana Nigeria Indonesia Malaysia<br>d'ivoire<br><!-- End of picture text --> _Source:_ Schreiber and Varangis (1995).

Hanak-Freud and Freud (1996) argue that producer prices are low in Cote d'Ivoire because of high export taxes. This is only partly true, however, as producer prices are still below those in other countries even when export taxes are considered. During the six seasons from 1993/94 through 1997/98, the explicit export tax (the _DUS)_ in Cote d'Ivoire was approximately 22 percent of the FOB price and the average share of producer price to the FOB price was 49 percent, yielding a total of tax plus producer share to the FOB price of 71 percent. This is below producer price shares of the FOB price in other producing countries with the exception of Ghana.

Another feature of the _bareme,_ which we cannot quantify precisely, clearly discriminates against farmers. The _bareme_ pays exporters for shipping costs (insurance and freight; I&F) that exceed market rates. Given that the CIF price is competitively determined on world markets, the higher I&F costs thus artificially lower the FOB price which is used to determine producer prices. Based on discussions with shippers and on comparisons with market freight rates, CAISTAB's _bareme_ calculations of freight and handling costs are well above those actually paid by shippers.

# **_II. Are Forward Sales Valuable?_**

Forward sales of cocoa are a linchpin of the Ivorian system in that they permit CAISTAB to fix incomes of producers and other agents. There are two key advantages claimed for forward sales: a) a risk benefit in that forward sales allow the government to offer a fixed annual price to farmers and hence to reduce risks to the latter; and b) an income benefit in that they improve total export revenues because
- **Wu, Edward and Johann Gagnon-Bartsch** , “The loop estimator: Adjusting for covariates in randomized experiments,” _Evaluation Review_ , 2018, _42_ (4), 458–488.

- **W ̈uthrich, Kaspar and Ying Zhu** , “Omitted Variable Bias of Lasso-Based Inference Methods: A Finite Sample Analysis,” _Review of Economics and Statistics_ , 2023, _105_ (4), 982–997.

- **Zou, Hui** , “The adaptive lasso and its oracle properties,” _Journal of the American statistical association_ , 2006, _101_ (476), 1418–1429.

27
Secretariat of the National Public Security Council (SNSP), a federal entity dependent of the Ministry of Interior. This variable has the advantage that aims to be more closely related to “war-on-drugs” spikes of violence that is the motivation of this study. However, this variable was created by individual officials’ assessments of the relatedness to drug trafficking of the given homicide, and thus it is likely to have substantially more measurement error. Thus, although we include it in some models, we use the standard homicide rate variable for our main specification.

# **Demographic Characteristics at the Municipal Level**

Demographic characteristics at the municipality level were obtained from the Censos de Población y Vivienda for 1990, 2000, 2005, and 2010, which include data on total population, share of rural population, share of population between 15 and 29 years of age, share of population over 60, median age, number of households, male to female ratio and fertility rate. For the years between censuses, we interpolated values assuming linear growth.

We have also gathered data on aggregate figures of public expenditures, government transfers, and other public finance variables at the municipal level in Mexico. The data on public expenditures was obtained from the State and Municipal System of Databases (SIMBAD) produced by the National Institute of Statistics, Geography, and Information (INEGI).

# **Data on Education (schooling, enrollment rates)**

The yearly education data from the first data set includes student enrollment (total students), passing rates, and retention rates at the municipality level. These data were obtained from INEGI and are consistently reported from 1994 to 2010. The variables are decomposed by the different levels of schooling, namely for _primaria_ (primary school, grades 1-6), _secundaria_ (lower secondary school, grades 7-9), and, _preparatoria_ or _bachillerato_ (upper secondary school, grades 10-12). Historically in Mexico, grades 1-9 have been compulsory and as of constitutional changes made in 2013 grades 10-12 are now mandatory as well. The education variables considered in this data set include the total number of students for the aforementioned schooling levels (and the log of this number), the passing rate (# passing students / # total students), and the retention rate (# total students / # enrolled students).

While we know the total number of students at each school level, we do not have precise data on the corresponding school age population. Thus, we cannot calculate enrollment rates from this source with reliable precision. In order to include enrollment rates in the analysis, we use data from the Micro-Sample of the Census (2000 and 2010) and the interim census update, the _Conteo de Población_ for 2005. We also use the quarterly _Encuesta Nacional de Ocupación y Empleo_ (ENOE), which provides household level data of participation in schooling and workforce.

When we analyze the effects on the number of students enrolled in the municipality, we only do this at the level of basic and upper secondary school only, and not at the college or beyond levels.

9
24 trade coverage by quantitative restrictions -- measures which general do have important trade constraining effects -- only exceeding 10 percent for three African countries, namely, C6te d'Ivoire, Mauritius and Senegal. All in all, the impression that emerges from Table 8 is that anv general removal of these measures would probably be insufficient to offset African trade losses due to erosion of OECD tariff preferences."

# **Summary and** Conclusions

How much are OECD trade preferences worth to Sub-Saharan Africa? The empirical evidence developed in this study suggests $4 billion in present value terms is an appropriate estimate, and that African export losses associated with an erosion of these preferences within a Uruguay Round agreement would likely exceed any gains from liberalization of nontariff measures. These observations suggest that African countries will probably experience net trade losses as a result of Uruguay Round tariff cuts, although the magnitude of these losses cannot be projected until the depth and structure of MFN tariff cuts are finalized. African export performance has consistently been below that of other countries (see World Bank, 1992) and this performance should worsen further relative to countries like Taiwan (China) and the Republic of Korea that will experience important positive trade gains as a result of the Uruguay Round.

In order for African countries not to experience net economic losses, otfsetting policies 21This conclusion is based on a detailed survey of nontariff measures' estimated ad valorem equivalents published in Laird and Yeats (1991). In most cases, licensing requirements were found to have very low nominal equivalents -- probably just one to two percent for costs of compliance -- while Varangas et. al. (1993) indicate that eco-labeling requirements add less than IG percent to the price of exports. Erzan and Svedberg (1991) come to much the same conclusion, i.e., that NTMs have only a relatively minor effect on African exports, in an independent analysis of the types of trade barriers facing African exporters.
<!-- Start of picture text --> fo}<br>+<br>®<br>2<br>fo)<br>te<br>om<br>2©<br>aa<br>&@ — “—oeeN/r oe<br>SO _ ues -<br>ON ao<br>2 ae<br>©7<br>D=O a ~ —— ee<br>— - —_ —_-— —_—<br>© ve -“—<br>Ss | eect----—- _o<br>°o<br>1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br>year<br>——— migration out of village<br>—-—-—-- migrantion out of county<br>—-—-— migration out of province<br>2003 2005 2007<br>©<br>«<br>\ \. a<br>\ \\. \<br>N “\ \ \ \\<br>2 N SN \a \ ~<br>G ~e 7S ~~<br>~ Q = ~—S =<br><<br>2 2009 2011 2013<br>fe))<br>gf ©<br>=<br>st + 4 a<br>TPN roy ro<br>\i\N<br>N<br>N Nif N\H \\<br>\\ \ \ / \ N<br>= > ><br>Se ~S SO s~<br>16 25 35 45 55 65 75 16 25 35 45 55 65 75 16 25 35 45 55 65 75<br>Age<br><!-- End of picture text -->
11-V _f(T_ _-t)_ where _d,_ =2 Ronn and Verma (1986) model equity as being dividend protected and therefore dividends do not appear in the previous equation. The BlackScholes formula thus defines a one-to-one mapping between the unknown asset value and the observed equity value.

The relationship between the equity and asset volatility, which can be obtained by applying Ito's Lemma to equation (3), is used by Ronn and Verma (1986) as the second restriction where<sup>**_0_**</sup> **_E_** is the standard deviation of equity retums. The derivation of equation (4) can be found in the Appendix.

Since the market value of equity is observable and the equity volatility can be estimated, two nonlinear restrictions are now in place for identifying two unknowns. Using data on total debt (deposits), bank equity, and equity volatility, equations (3) and (4) can be solved simultaneously for V and cJ. Given these values, equation (2) is used to solve for the value of deposit insurance per dollar of deposits, which we interpret as the gross safety net subsidy. In order for this approach to be valid, the time until maturity, T, of the put and call options must be the same. Ronn and Verma (1986) use Merton's (1977) assumption that the time until maturity of the debt is equal to the time until the next audit. They interpret the strike price of the put option to be equal to total debt of the bank instead of total deposits only. This assumes that all the debts of the bank are insured and that they are issued at the risk-free interest rate.

From equations (2)-(4) it follows that the implicit safety net subsidies can be expressed as a function of only three known variables: the equity volatility, the ratio of equity to deposits, and the dividend yield. Table 1 in the Appendix presents this relationship for a range of equity volatility and equity-to-deposits values.

Duan (1994) shows that Ronn and Verma's (1986) method is problematic from a statistical point of view, since they estimate instantaneous equity volatility by the sample standard deviation of daily equity retums. This estimator is, however, not efficient and 7
Figure 4.c. Simulation of a marginal increase in skilled emigration with incentive e¤ects (Polity2) <!-- Start of picture text -->
-40 -30 -20 -10 0<br>  zh<br>kdensity zh (delta=0.01) kdensity zh (delta=0.05)<br>kdensity zh ( delta =0.1) kdensity zh ( delta =0.23)<br>2<br>1.5<br>1<br>.5<br>0<br><!-- End of picture text -->

Legend: Kernel densities of zh computed with �=(0.01;0.05;0.10; 0.23) Vertical continuous lines = mean value of <u>�2</u><sup>Verticaldashedlines=con...denceintervalat90percent(thicklinesforunbalancedpanel,thinlinesfor</sup> �1<sup>.</sup> balanced panel) Figure 4.d. Simulation of a marginal increase in skilled emigration with incentive e¤ects (EFW) <!-- Start of picture text -->
-40 -30 -20 -10 0<br>  zh<br>kdensity zh (delta=0.01) kdensity zh (delta=0.05)<br>kdensity zh ( delta =0.1) kdensity zh ( delta =0.23)<br>2<br>1.5<br>1<br>.5<br>0<br><!-- End of picture text -->

Legend: Kernel densities of zh computed with �=(0.01;0.05;0.10; 0.23) Vertical continuous lines = mean value of <u>�2</u><sup>Verticaldashedlines=con...denceintervalat90percentobtainedwiththeunbalancedpanel.</sup> �1<sup>.</sup> 35
they may see little reason to search for a wage job. If they have no work on the family farm or in the household business, they stay home, doing chores or waiting for an opportunity.

**Figure 3: A model of the developing country labor market**

<!-- Start of picture text --> DEMAND<br>SUPPLY *<br>SUPPLY<br>A B C<br>Q1 Q2 Q3<br>Employment (Quantityof Labor)<br>W*<br>Wage Rate<br><!-- End of picture text --> Figure 3 is especially important for understanding the youth employment challenge, as it illustrates the market for entry‐level jobs almost perfectly. To an employer, new entrants to the labor market mostly look alike (assuming that they all have the required minimum education). The employer is indifferent between those workers in the rectangle bordered by Q1 (group A) and those in the trapezoid bordered by Q1 and Q2 (group B), who were not at the front of the B queue so did not get hired. If, through some kind of job matching intervention, those in group B move to the front of the line and get hired, some of group A will not get hired. But total employment will not go up. The only way total employment goes up is if firms start hiring more workers than before (through the expansion of production by existing firms, or the creation of new firms), so that the demand curve shifts out. From this perspective, the youth employment problem is really just a special case of the overall labor market challenge—an oversupply of workers relative to the number of firms. The evidence from enterprise and household surveys suggests that as wage employment increases, youth employment expands proportionately (Filmer and Fox, 2014).

Suppose that an employer wants entry‐level workers to have some work experience. In this case the employer would prefer not to hire youth who have recently left school. The employer wants to hire entry‐level workers who have been out of school for a few years, and who have worked in temporary jobs or in the family farm or business. Only after this pool is exhausted will the younger job seekers be hired. So, to help the younger individuals get a job, the government finances an apprenticeship or internship program, possibly combined with vocational training, so that youth entering the labor market can compete effectively with those already looking for jobs, who have some work experience. The effect of this intervention is to move the supply curve out to S*. Now the number of people eligible for an entry‐level wage job has expanded to Q3 and includes groups A, B, and C. But the total number of jobs has not expanded. The intervention simply moved some youth into group A, but bounced some other youth or some slightly older adults still looking for an entry‐level job into groups B and C. Without a shift in the demand curve, the intervention effect was only _displacement_ . Younger job seekers displaced older job seekers in line for jobs.

7
However, for our empirical analysis this is likely to be too simplistic as each location has access to a number of city/town markets of different size. It is also important to recognize that all of the areas located at a given arc distance from a city do not have the same market access. For instance, an area located close to an intersection of multiple trading routes tends to have a much better access than an area located on a major route but far from the intersection. This distinction is more consequential in countries such as Mali and Burkina Faso which are predominantly agricultural and trade routes run through areas which are rural and isolated from the urban areas.

Instead of defining a band around a route, we compute a measure of market access suggested by Redding and Turner (2015). The market access variable is computed as: where _dih_ is the distance of commune _i_ to city _h_ and _Nh_ is the total population of city _h_ . The market access as defined above is the gravity measure widely used in the trade literature; it is equal to the weighted sum of the extent of the urban markets proxied by their population. The weight is defined by the reciprocal of distance squared. By definition, markets farther away are given lower weights.

# **(6.3) Heterogeneity w.r.t. Pre-war Market Access and Determining the Autarkic Comparison Areas**

A substantial and mature literature shows that economic density in a location is correlated with the access to market,<sup>19</sup> and the theoretical analysis above suggests that trade/transport shocks can have differential impacts depending on the extent of market access of a commune/department during the pre-war period. Perhaps, most important for our empirical analysis is the identification of the autarkic comparison areas through a sequential search process noted earlier. To conduct this analysis over the distribution of

> 19For excellent recent surveys, please see Donaldson (2015), Redding and Turner (2015), Duranton and Venables (2018).

21
|||Full Sample||Trimming Tail 5%|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|_Power Structure_|||||
|YRD|-0.357**|-0.266*|-0.268*|-0.186|
||(0.151)|(0.140)|(0.156)|(0.149)|
|_Initial Conditions_|||||
|Ln_GVOPC_52||-0.317*|-0.319*|-0.254|
|||(0.160)|(0.175)|(0.168)|
|Ln_pop_52||-0.206**|-0.212**|-0.147|
|||(0.083)|(0.101)|(0.090)|
|_Geography_|||||
|Share of Plain (%)|||0.001|-0.001|
||||(0.008)|(0.008)|
|Distance to Xiamen|||-0.000|0.000|
|(Km)|||(0.001)|(0.001)|
|Obs.|53|52|52|48|
|_R_<sup>2</sup>|0.062|0.147|0.110|0.037|

Table 15: The Effect of Factions on Average Expenditure-Revenue Ratios between 1950 and 1957.

|||Full Sample||Trimming Tail 5%|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|_Power Structure_|||||
|YRD|4.297***|2.864***|3.072***|2.844***|
||(1.027)|(1.024)|(0.932)|(0.723)|
|_Initial Conditions_|||||
|Ln_GVOPC_78||-5.515***|-5.425***|-3.474***|
|||(1.752)|(1.478)|(0.900)|
|Ln_pop_78||1.466|-1.138|-0.001|
|||(1.238)|(1.297)|(0.907)|
|_Geography_|||||
|Share of Plain (%)|||0.193***|0.150***|
||||(0.066)|(0.056)|
|Distance to Xiamen|||-0.018***|-0.019***|
|(Km)|||(0.005)|(0.005)|
|Obs.|58|57|57|53|
|Adjusted-_R_<sup>2</sup>|0.083|0.405|0.597|0.623|

Table 16: The Effects of Factions on Growth Rates in Counties in Fujian, 1984-1998.

42