rafmacalaba/datause-encoder-data huggingface dataset:
Nevertheless, concerns about adverse revenue effects often figure prominently among explanations of a slow pace of trade liberalization (see Ebrill, Stotsky, and Gropp (1999)). In countries where the share of trade taxes in total revenues is large, trade barriers have often been gradually dismantled due to fiscal constraints. The extent to which total tax revenue fall depends, of course, on what alternative tax bases the government can rely on following a cut in tariffs; but switching to other sources of revenues may entail not only (temporary) switching costs, but also a permanently higher administrative burden—which may be all the more important in countries with a large informal sector.<sup>4</sup> If so, then a cut in tariffs is unlikely to be revenue neutral.

The recent experience of developing countries suggests indeed that trade reforms have often been accompanied by revenue losses. In a study dwelling on data for 111 countries over 25 years, Baunsgaard and Keen (2005) found that high-income countries were able to recover from other sources the revenues that they had lost during previous episodes of trade liberalization. However, for middle-income countries, recovery was on average in the order of 45-60 cents for each dollar of lost trade tax revenue; and for low-income countries (which are those that depend the most on trade tax revenues, as noted earlier), recovery was, at best, no more than about 30 cents of each lost dollar. They also found no evidence that the presence of a value-added tax had, in itself, made it easier to cope with the revenue effects of trade liberalization.<sup>5</sup>

# **1.1.2  Revenue Losses and Spending Cuts**

A fall in revenues associated with a reduction in tariffs may force governments to implement concomitant cuts in expenditure in the short term. If these spending cuts take the form of reductions in social expenditure, they will have a direct effect on poverty, thereby 4For instance, Emran and Stiglitz (2004) have shown that in developing countries with an informal sector in which, say, a VAT cannot be imposed, it is desirable to retain some trade taxes, e.g., to tax imports at a higher rate than domestic production.

> 5By contrast, Agbeyegbe, Stotsky, and WoldeMariam (2006), using panel data for 22 countries in SubSaharan Africa over 1980-96, found evidence that the relationship between trade liberalization and tax revenue is sensitive to the measure used to proxy trade liberalization.

8
**Table 9: Change in value of livestock owned for each AOGCM climate prediction (US$/farm)**

|**Variable**|**Impact per**<br>**small farm**<br>**($/farm)**|**% of livestock**<br>**income**|**Impact per**<br>**large farm**<br>**($/farm)**|**% of livestock**<br>**income**|
|---|---|---|---|---|
|**2020**|||||
|**Change _CCC**|297|21%|-405|-29%|
|**Change _CCSR**|504|36%|-387|-28%|
|**Change _PCM**|-24|-2%|-888|-64%|
|**2060**|||||
|**Change _CCC**|628|45%|-944|-68%|
|**Change _CCSR**|378|27%|-1192|-86%|
|**Change _PCM**|96|7%|-1037|-75%|
|**2100**|||||
|**Change _CCC**|1263|91%|-1276|-92%|
|**Change _CCSR**|1904|137%|-55|-4%|
|**Change _PCM**|446|32%|-1442|-104%|

_Note:_ Small farms have a baseline value of livestock of $1392 per farm. Large farms have a baseline value of livestock of $6596 per farm.

34
**18**

In line with the program coverage, we limit our study sample to households with at least one parcel landholding in their village of residence. In practice, 85% of households have at least one landholding in the same village as their homestead, 9% have all of their landholding(s) outside their village of residence, and 6% have no landholdings (see Table A-3). This yields a working sample of 2,972 households with a total of 6,094 parcels (5,329 of which are located in the household’s village of residence).<sup>24</sup> Although retrospective agronomic data were collected for both rainy seasons in Benin, we limit our analysis to the major rainy season to ensure comparability with northern Benin’s uni-modal rainfall distribution.

# **4.3 Balance**

We perform two classes of balance checks. First, we employ the 2006 EMICoV data from 160 of our sampled villages to establish pre-intervention balance on a range of covariates and outcomes. Table 1 presents differences in means across treatment and control households in the 2006 EMICoV sample. While this balance check does not refer to our study sample, it helps further validate the lottery across the outcome space. We confirm balance across treatment and control communities on a range of key observable characteristics prior to program implementation. The average household head is, however, significantly older by 1.59 years and has 0.22 fewer year of education in the treatment group relative to the control. Although these differences vanish in our full 2011 sample, we control for these observables in all regression models.

Second, we establish balance on plausibly exogenous characteristics in our 2011 follow-up survey, which is important since the 2006 and 2011 survey samples do not fully overlap. Mean comparisons reported in Table 2 show that households (Panel A) and parcels (Panel B) are balanced across treatment and control groups, with the exception of a marginally significant difference in household size and the presence of sandy soil. We next check for balance in nonvarying village characteristics (see Table A-8). Access to a paved road and the presence of commercial activity are the only potential observable challenges to our identification. Overall, we conclude that the program lotteries yielded balance across the treatment and control sub-samples.

> 24A threat to our identification could stem from differences in migratory patterns across treatment, or from a farming household switching out of agriculture as a result of the land demarcation process. Should this be the case, our sampling frame would not be adequate, and our outcome space would fail to capture relevant changes in investment. While we do not have data on migration patterns over the duration of the program, we do not find that land demarcation activities affected participation in agricultural activities (see Table A-10), and the proportion of parcels cultivated during the twelve months preceding the survey is the same across treated and control villages. Likewise, cultivated landholdings were harvested at the same rate across treatment and control groups.
Mima S. (2016) Model documentation — POLES Mouratiadou, I. et al., 2016: The impact of climate change mitigation on water demand for energy and food: An integrated analysis based on the Shared Socioeconomic Pathways. Environmental Science & Policy, 64, 48–58 Nahmmacher, P., E. Schmid, L. Hirth, B. Knopf (2016) Carpe diem: a novel approach to select representative days for long-term power system modeling, _Energy_ , 112 , pp. 430-442 Nordhaus, W (1992), ‘The 'DICE' Model: Background and Structure of a Dynamic Integrated ClimateEconomy Model of the Economics of Global Warming’, Cowles Foundation Discussion Papers No. 1009, Cowles Foundation for Research in Economics, Yale University.

Nordhaus, W D (2013), The Climate Casino: Risk, Uncertainty, and Economics for a Warming World, New Haven: Yale University Press.

Pietzcker Robert C. Falko Ueckerdt Samuel Carrara Harmen Sytze de Boer Jacques Després Shinichiro Fujimori Nils Johnson Alban Kitousi Yvonne Scholz Patrick Sullivan Gunnar Luderer (2017) System integration of wind and solar power in integrated assessment models: A cross-model evaluation of new approaches, _Energy Economics_ Volume 64, 583-599 Popp, A. et al. (2017) Land-use futures in the shared socio-economic pathways. _Global Environmental Change_ , 42, 331–345.

Rao, S. et al. (2017) Future air pollution in the Shared Socio-economic Pathways. _Global Environmental Change_ , 42, 346–358.

Riahi, K., F. Dentener, D. Gielen, A. Grubler, J. Jewell, Z. Klimont, V. Krey, D. McCollum, S. Pachauri, S. Rao, B. van Ruijven, D.P. Van Vuuren, C. Wilson (2012) Energy pathways for sustainable development, in _Global Energy Assessment — Toward a Sustainable Future_ , Cambridge University Press and the International Institute for Applied Systems Analysis, Cambridge, UK, New York, NY, USA and Laxenburg, Austria, pp. 1203-1306 Sathaye, J. and P.R. Shukla (2013) Methods and Models for Costing Carbon Mitigation, nnual Review of Environment and Resources Vol. 38:137-168.

Stehfest, E. D. van Vuuren, L. Bouwman, T. Kram, et al. (2014), Integrated Assessment of Global Environmental Change With IMAGE 3.0: Model Description and Policy Applications Netherlands Environmental Assessment Agency (PBL) 40
agents (clearance in less than two weeks, in less than a month, etc<sup>16</sup> .). Some seasonality has been observed in these discretionary trends, with a more significant peak around 11 days in the second trimester of the year and conversely a dominance of short dwell times in the last trimester, but the general observance of discrete behaviors is consistent throughout the year<sup>17</sup> . Strata analysis will help better interpret the peaks and test these assumptions.

# **_The need to build logistics families_**

In addition to the latter observation about likely presence of discretionary behavior, shipment level analysis also demonstrates that dwell time averages vary across the sample according to cargo characteristics such as fiscal regime, bulking, density of value and cargo type. Explanatory statistics will later help us measure this correlation, but the main assumption from the following summaries is that logistic families based on cargo characteristics can be defined to explain significant variation of clearance patterns according cargo and shipper characteristics.

#  **Fiscal regime**

Fiscal pressure seems to play an important role in cargo dwell time. The trend observed is a positive correlation: higher fiscal pressure leads to higher dwell time, with a noticeable exception however for duty free items that have a somewhat high average dwell time despite the absence of dues.

|**Fiscalpressure**|**average dwell time**|**# of containers**|
|---|---|---|
|0% (Duty free)|21.6|5,101|
|0 to 27,8% (necessity goods or duty free)|18.9|3,613|
|27,8% to 33,7 % (raw materials)|19.2|6,676|
|33,7% to 45,7% (semi-finished goods)|21.3|11,992|
|Over 45,7% (finished goods)|22.1|19,119|

**Table 3 – Distribution of average dwell time with respect to Fiscal pressure** (Source: CAMEROONIAN CUSTOMS, 2009)

#  **Bulking of containers**

Bulking of containers seems to play a role also in cargo dwell time patterns: Less-thancontainer load containers take about 3 more days than Full-container-load containers to be cleared from the port. This is paradoxical in the sense that bulking is usually performed by logistics providers that have storage facilities outside the port and who would in theory intend to minimize cargo stay in

> 16 Field investigations have proved this threshold to be the most significant for financial managers and supply chain managers and it is indeed the highest peak. While parking fees (charged by DIT) are quite modest in the first 20 days (5,200 FCFA for a 20 feet container) demurrage costs quickly accrue to more than 50,000 FCFA and most managers aiming at cutting logistics costs set therefore a “0 demurrage fee” objective for their C&F agents and employees.

> 17 There seems to be a better efficiency for Customs operations in the last month of the year, which is consistent with shippers’ feedbacks.

12
28 national market with potential future access to the Soviet market; and (d), stressed in chemicals, less stringent pollution control than in the West."' Table 7. What Polish Firms expect from Foreign Partners

||||Ans|wers in %|||
|---|---|---|---|---|---|---|
||Total|Metallurgy|Elec.|Chemical|Light|Food|
|1. Inflow of financial<br>mcans|27.4|30.0|27.8|29.5|32.6|_15.8_|
|2. Inflow of modern<br>machinery&equipment|19.8|21.3|7.6|17.6|16.8|3C.8|
|3. Access to modem<br>technology|21.3|28.8|24.0|24.7|13.5<br>.|15.8|
|4. Licenses|3.9|2.5|8.9|3.S|-|_5.3_|
|S. Introduction of modem<br>organizational solutions|5.7|1.2|7.6|_5.9_|6.7|7.9|
|6. Introduction of modern<br>informational techniques|2.4|1.2|2.5|1.2|-|7.9|
|7. Help in sales abroad|16.5|10.0|21.6|14.1|27.0|7.9|
|8. Improving qualifications<br>of personnel|3.0|_5.0_<br>I_<br>__<br>__|-<br> <br>.|3.5.<br>I|3.4|7.6|
|TOTAL|100.0|100.0|100.0|100.0|100.0|100.0|

# Role of Managers

In the judgment of the team conducting this enterprise survey, managers are by far the dynamo for change within SOEs, workers playing more of a reactive, ratifying role. The picture that emerges of Polish SOEs is one of tension between managers and the Workers' Council, which manifests itself in three basic ways: emphasis on profits and marketing; slow adjustment of labor force relative to output drop; huge flexibility in real wages and more recently, downward flexibility even in nominal wages with workers willing to tighten the belt when necessary but expecting immediate compensation whenever the situation improves. Although the ideas for change originate largely from managers, they are legally subordinate to the Workers' Council, lack job security and do not have sufficient financial incentive to undertake the gigantic task of restructuring. To make matters worse, few managers have received any degree of formal training in modern management and marketing; and in any event, lack operating experience in a market system. There is need for a big investment in human capital, which must be treated as a public investment problem because the private sector will never have the incentive to impart training on the massive scale (some 100,000 managers) required because training is costly and managers can be lured away by other firms ("free rider" problem).

In short, (a) accountability and authority at the firm-level are vague. At the moment, neither principal nor agent is clearly defined; (b) firm-level incentives in terms of managerial job "Although one would expect that this will change, Polish environmental standards in many cases exceeding thosw<sup>in</sup> the EC.
# **Table 3: Training and Self-Employment assistance**

|**Crisis and Context**|**Authors**|**Methodology**|**Main Findings**<br>**Comments**|
|---|---|---|---|
|**Korea (date crisis began Oct**<br>**2007)**<br>Training programs for the<br>unemployed.|Kang and<br>others (2001)|(Descriptive) Comparison of participants and<br>non-participants to determine effect of training<br>on the unemployed + hazard model.|Re-employment Rate: No significant differences between<br>the two groups: 49.6% of participants v/s 53% of non-<br>participants have been re-employed and 38% of<br>participants v/s 40% of non-participants were employed at<br>time of study.<br><br>Performance after active job search: Training<br>participants were more successful in finding jobs: one-<br>third was re-employed within a month and 54.3%<br>within the first three months and 23.4% found re-<br>employment after seven months (compared to rates of<br>, 21% s and 60% for non participants).<br><br>Participants spent on average 4.3 months before re-<br>employment compared to an average of 8.6 months<br>for the rest.<br><br>However a higher percentage of participants held full-<br>time wage employment compared to non-<br>participants, while a larger percentage of non-<br>participants held part-time jobs.<br><br>However, participation in training increases the<br>probability of re-employment by 28% and the impact<br>was particularly significant for women, training<br>participation increases the probability of re-<br>|
||||employment(63% higher).|
|**China (1998-2000)**<br>Retraining programs for laid-off<br>workers in the cities of<br>Shenyang and Wuhan.|Bidani et al.<br>(2005)|Propensity score matching in conjunction with<br>regression methods.<br>The training and comparison group was<br>chosen from official 1998 census data. The<br>final sample was administered in 2000.|<br>Positive impact on employment in Wuhan. Little<br>impact in Shenyang but impact on earnings was<br>reversed<br><br>Participation higher for unemployed younger females,<br>previously working in non-state owned manufacturing<br>who have visited an employment service center in the<br>past (indicating targeting was effective).|
|**Argentina (1994)**<br>Programa Joven.|Aedo et al.<br>(2001)|Data for individuals who registered and<br>qualified for training programs (March 1996 –<br>December 1997).<br>Propensity Scores Matching Estimators and<br>Cost benefit analysis.|<br>Impact on earnings statistically significant but only for<br>young males and adult females.<br><br>Impact on employment only statistically significant for<br>adult females.<br><br>Statistically significant impacts not sensitive to<br>different specification ( propensity score matching) but<br>cost-benefit analysis indicates it takes 9 years for the<br>netpresent value to becomepositive.|

22
# **1 Introduction**

Information asymmetries about workers’ skills are prevalent in labor markets, especially in the market for low-skill and entry-level jobs.<sup>1</sup> Hiring firms can reduce these asymmetries through reference letters from previous employers. However, in various contexts (particularly in the developing world) this practice is largely absent and most firms resort instead to informal referrals, such as those from their existing workforce.<sup>2</sup> This has potential adverse effects on match quality as it limits the pool of candidates and as current employees may refer close friends or family members rather than their most qualified peers (Loury, 2006; Beaman and Magruder, 2012). In addition, informal referral systems may exacerbate inequity as they disadvantage less connected groups (Montgomery, 1991).

We conduct three experiments in cooperation with the South African Department of Labour (DoL) to investigate the value and usage of standardized reference letters among young job seekers. Specifically, we design a reference letter template and encourage young job seekers to have a former employer complete it. Belot et al. (2016) note that there are only a few studies in the literature that focus exclusively on the impacts of reducing information asymmetries in the labor market. In particular, the large literature on active labor market policies consists mostly of evaluations of programs that combine a variety of elements, such as advice, monitoring, and/or training. To our knowledge, this is the first study on the use and value of reference letters.<sup>3</sup> Our first experiment (Experiment 1) is an audit study where we submit applications with and without reference letters on behalf of job seekers to vacancies and compare firm responses. Importantly, we conduct the study with actual job seekers who visit the Labour Centres. This addresses the criticism that application materials designed by researchers may not be realistic or include all relevant information (Heckman, 1998) as well as ethical concerns about sending fictitious applications (Riach and Rich, 2004).

> 1In these markets, job seekers often have limited work experience and lack educational degrees to signal skills. Firms are less likely to invest in costly screening as employment relationships are often short-term (Autor and Scarborough, 2008).

> 2In developed economies about 50% of jobs are found through informal network (Topa, 2011). In South Africa, where our study is based, about 68% of jobs are found through informal referrals (Schoer et al., 2014).

> 3There is relatively little research on reference letters, defined as a “description or evaluation of an applicant that is completed by an observer and used as a source of information for personnel selection” (McCarthy and Goffin, 2001), despite its ubiquity in the selection process (Aamodt, 2015). Existing research focuses on the ability of reference letters to predict future performance. One exception is Kaas and Manger (2012), who find through an audit study that reference letters do not increase overall employer responses but may benefit applicants from minority groups.

1
**<u>Table 9: Determinants of self-reported willingness to pay to have land transactions registered</u>**

||**All parcels**|**Parcels in cell of residence**|
|---|---|---|
|Ln age of head|-0.433|-0.495*|
||(0.267)|(0.275)|
|Female headed household|0.057<br>|0.091<br>|
||(0.167)|(0.201)|
|Number of years of schooling, head|0.029<br>|0.022<br>|
||(0.024)|(0.025)|
|Household size|0.005|0.019|
||(0.032)|(0.035)|
|Ln sale value of assets, USD|0.151**|0.134*|
||(0.066)|(0.074)|
|Head/Spouse knows about registration process|0.377|0.425*|
||(0.236)|(0.249)|
|Legal knowledge on registration, computed ind.|-0.047|-0.070|
||(0.071)|(0.068)|
|Parcel purchased|0.050|0.101|
||(0.138)|(0.154)|
|Parcel allocated by Gov.|-1.379***|-1.728***|
||(0.305)|(0.293)|
|Parcel jointly owned|0.154|0.150|
||(0.191)|(0.221)|
|Parcel main use : residential|0.113|0.130|
||(0.141)|(0.158)|
|Ln size of parcel, Ha.|0.114**|0.110**|
||(0.047)|(0.048)|
|Swamp/wetland|-0.001|0.049|
||(0.233)|(0.220)|
|Parcel irrigated|0.215|0.110|
||(0.195)|(0.167)|
|High quality soil|0.013|-0.013|
||(0.113)|(0.128)|
|No. months since acquisition|-0.007*|-0.005|
||(0.004)|(0.005)|
|Share of parcels certified in Sector|0.364|0.320|
||(0.396)|(0.411)|
|Ln. dist. to DLO, km|-0.213**|-0.357***|
||(0.095)|(0.082)|
|SLM in place at time of int.|0.385|0.584**|
||(0.273)|(0.277)|
|Land Week event|0.170|0.086|
||(0.136)|(0.135)|
|West|0.107<br>|0.167<br>|
||(0.183)|(0.194)|
|North|<br>0.323*|<br>0.369**|
||(0.176)|(0.182)|
|East|0.274*|0.401***|
||(0.154)|(0.155)|
|Constant|9.068***|9.677***|
||(1.163)|(1.190)|
|Number of observations|447|377|
|R2|0.177|0.209|

_Note:_ As explained in more detail in the text, the sample comprises rural land users who had acquired land in the 2012-15 period. Dependent variable is the log of the self-reported willingness to pay (in RwF) for formal registration. . Robust standard errors clustered at the sector level in parenthesis: *** significant at 1%; significant at 5%; * significant at 10%.

24
Shinoj, P., Raju S.S., and Joshi P.K. (2011). “India’s Biofuel Production Programme: Need for Prioritizing the Alternative Options.” _Indian Journal of Agricultural Sciences_ 81(5): 391-397.

# **Appendix 1. Market Effects of an Exogenous Diesel Price under a Binding Blend Mandate and One Oilseed Feedstock**

The equilibrium in the soybean–biodiesel market with a binding blend mandate, _α_ , and an exogenous diesel price is given by The first equation gives the fuel price as the weighted average of biodiesel and diesel prices, taking into account the volumetric fuel tax (the term _t_ λ represents the tax on biodiesel in $/DEEG). The second equation represents the requirement that _α_ [x100 percent] of the final fuel blend has to come from biodiesel. The interpretation of the remaining equations is the same as presented for the binding tax credit.

Substituting the last identity in (A1.1) into the second equation, totally differentiating the ensuing system of equations, and solving, we obtain 15