rafmacalaba/datause-encoder-data huggingface dataset:
3 The _bareme_ further deducts the explicit specific export tax (known as the _droit unique de sortie, or D US)_ and CAISTAB's operating costs, including its substantial debt service, from the FOB price. The residual element in the calculation is then the producer price, which is set to satisfy the condition

# _(2) PCIF - maritime transport - DUS - direct domestic marketing costs_

_- CAISTAB operations - Pp_ = _S_ where _Pp_ is the producer price and S is the stabilization margin. In theory _Pp_ is set so that S is zero.

_Table 1. Official cocoa export costs (bareme) in 1997/98_

||CFAF / kg|Percent of<br>FOB|
|---|---|---|
|CIF price_( Pc1 ) _|904|109|
|- maritime transport costs|75|9|
|=FOB price|829|100|
|-Export tax (DUS)|150|18|
|-Direct domestic marketing costs|115|14|
|- CAISTAB operations|49|6|
|-Producer price ( Pp )|455|55|
|=Stabilization margin (S)|61|7|

_Source:_ CAISTAB.

The historical objectives of price policy have been to keep producer prices stable and remunerative. Between 1980 and 1989, the stability objective was met in nominal terms by holding guaranteed nominal producer prices constant across seasons. The official producer price was _set_ at 300 _F/kg from_ 1980 through 1983, rose to 350 in 1984, to 375 in 1985, and was held at 400 from 1986 through 19894.

The goal of inter-annual price stabilization was abandoned in 1990 after the extended decline of world prices had made it impossible to maintain the producer price across seasons<sup>5</sup> . This is consistent with the argument of Deaton (1992) that inter-annual stabilization is difficult because slumps tend to last longer than booms; in such an asymmetrical market, even large stabilization funds go bust. Large funds also imply significant opportunity costs in other public investments as they must be held in

> 4 Similar patterns were followed with coffee (Landell Mills 1996).

> Ruf (1996) discusses the cylical nature of the world cocoa market.
Table 6. Effects of Guerrilla Control on the Size of Plots _Panel A: Size of Plots by Producers Focused on Commercial Activity (Ha)_

||Own Area|Total Area|Cultivated Area|Share of Owned Area|
|---|---|---|---|---|
||(2007)|(2007)|(2007)|(2007)|
||(1)|(2)|(3)|(4)|
|Guerrilla control|-1.100**|-1.255**|-0.543**|-0.0402*|
||(0.538)|(0.541)|(0.231)|(0.0233)|
|Observations|2,021|2,003|2,017|1,838|
|Bandwidth (Km)|2.266|2.266|2.266|2.266|
|Dependent mean|5.135|6.799|2.529|0.352|
|_Panel B: Size of Plots_|_by Producers_|_Focused on Su_|_bsistence Activities_|_(Ha)_|
|Guerrilla control|0.00451|0.0202|0.0133|0.0183|
||(0.0146)|(0.0160)|(0.0124)|(0.0276)|
|Observations|2,309|2,298|2,292|1,677|
|Bandwidth (Km)|2.266|2.266|2.266|2.266|
|Dependent mean|0.228|0.656|0.596|0.678|

_Note:_ The table presents the results of estimating equation 1 for our outcomes related to the size of land used by producers for agricultural activity. Panel A shows the outcomes for the average plot managed by producers focused on commercial activities. Panel B does the same, but for the average plot managed by producers focused on subsistence activities. Column 1 uses as dependent variable the size of the land the producer owns. Column 2 uses the size of the total land the producer manages, which could also include rented land. Column 3 uses the area cultivated by the producer. Column 4 uses the share of the total area managed by the producer that the producer owns. Information in all columns comes from the Agricultural National Census of 2007. The unit of observation in all columns is the census tract. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. The estimates use triangular kernel weights. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

35
Table S.12: Impacts on households’ willingness to buy solar <u>products (OLS)</u>

|Dependent variable:||One if willin|g to buy||
|---|---|---|---|---|
||any<br>solar<br>products<br>|S2<br>|S10<br>|S250<br>|
|Sample:|All|Those that|were wil|ling to buy|
||(a)|(b)|(c)|(d)|
|**With no HH baseline **|**control**||||
|All products|0.416***|0.198***|0.148**|0.094**|
||(0.062)|(0.062)|(0.060)|(0.044)|
|One product|0.361***|0.043|0.038|0.090*|
||(0.065)|(0.061)|(0.061)|(0.047)|
|All = one (p-values)|0.170|0.014|0.047|0.793|
|All + one = 0 (p-values)|0.000|0.031|0.096|0.048|
|R-squared|0.246|0.187|0.177|0.087|
|No. of obs.|847|492|492|492|
||(e)|(f)|(g)|(i)|
|**With HH baseline con**|**trol**||||
|All products|0.420***|0.199***|0.146**|0.093**|
||(0.061)|(0.064)|(0.058)|(0.045)|
|One product|0.365***|0.049|0.041|0.091*|
||(0.063)|(0.061)|(0.060)|(0.046)|
|All = one (p-values)|0.180|0.011|0.044|0.886|
|All + one = 0 (p-values)|0.000|0.032|0.088|0.048|
|R-squared|0.255|0.197|0.189|0.094|
|No. of obs.|847|492|492|492|
|Village FE|Yes|Yes|Yes|Yes|
|Mean _y_ (control)|0.378|0.094|0.113|0.886|

Notes: (1) Figures ( ) are standard errors. *** denotes significance at 1%, ** at 5%, and * at 10%. (2) Standard errors are robust to heteroskedasticity and clustered residuals within each village. (3) The baseline controls include household size; no. of males; head age (years); head education (years); head gender (dummy); and land size (natural number).

57
We also adopt analogous specifications for the other types of linkages. The results from these specifications are reported in Table 12. Overall, we do not observe a differential effect for supply-chain-linked firms within the US relative to those in other countries. Once we add these interaction terms, the coefficients for buyers and innovation partners remain positive and statistically significant in Column (5) as before, while the coefficients on the interaction terms are not statistically significant. This evidence therefore suggests that our results are not driven solely by firm-to-firm linkages within the US, but hold equally for the impacts of linkages across borders.

Table 12. Firm-to-firm linkages within and outside the US

||Dep v|ariable: Norm|alized share|of technology-|related job ad|verts|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Pioneer|4.630***|4.487***|4.569***|4.486***|3.920***|3.903***|
||(0.398)|(0.394)|(0.394)|(0.394)|(0.852)|(0.853)|
|Pioneer_×_years since emergence|-0.202***|-0.192***|-0.200***|-0.195***|-0.177**|-0.177**|
||(0.036)|(0.035)|(0.035)|(0.036)|(0.079)|(0.079)|
|**Linkages of pioneer frms**|||||||
|Buyer||2.312***||||1.857***|
|||(0.568)||||(0.622)|
|Buyer_×_US||-0.637||||-0.544|
|||(0.666)||||(0.723)|
|Supplier|||0.610**|||-0.098|
||||(0.242)|||(0.303)|
|Supplier_×_US|||0.159|||0.281|
||||(0.335)|||(0.364)|
|Innovation partner||||1.833***||1.245**|
|||||(0.434)||(0.496)|
|Innovation partner_×_US||||-0.260||-0.286|
|||||(0.557)||(0.613)|
|Affliate|||||0.600|0.500|
||||||(0.674)|(0.679)|
|Affliate_×_US|||||0.203|0.076|
||||||(1.103)|(1.105)|
|**Linkages of pioneer frms over time**|||||||
|Buyer_×_years since emergence||-0.061||||-0.041|
|||(0.048)||||(0.054)|
|Buyer_×_years since emergence_×_US||0.015||||-0.015|
|||(0.059)||||(0.064)|
|Supplier_×_years since emergence|||-0.029|||0.009|
||||(0.023)|||(0.028)|
|Supplier_×_years since emergence_×_US|||0.036|||0.023|
||||(0.034)|||(0.036)|
|Innovation partner_×_years since emergence||||-0.072**||-0.051|
|||||(0.033)||(0.041)|
|Innovation partner_×_years since emergence_×_US||||0.053||0.049|
|||||(0.048)||(0.054)|
|Affliate_×_years since emergence|||||0.111*|0.118*|
||||||(0.065)|(0.065)|
|Affliate_×_years since emergence_×_US|||||-0.135|-0.131|
||||||(0.104)|(0.104)|
|Technology FE|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Firms FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Adjusted R-squared|0.041|0.041|0.041|0.041|0.041|0.041|
|Observations|10,072,709|10,072,709|10,072,709|10,072,709|10,072,709|10,072,709|

Notes: Standard errors clustered at the firm level in parentheses.The normalized share is capped at the 99<sup>_th_</sup> percentile of non-zero observations. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 29
developing countries the type of advice that is valid for developed countries; rather, policymakers should be made aware of the risks associated with trade liberalization and incorporate them in their assessment of any trade reform under consideration.

# **References**

Anderson, K., Martin W., Valenzuela, E., 2006. “The Relative Importance of Global Agricultural Subsidies and Market Access,” _World Trade Review_ 5 (3): 357-76 Copeland, Brian and S. Gulati. 2006. “Trade and the Environment in Developing Countries.” In R. López and M. Toman (eds.) _Economic Development and Environmental Sustainability: New Policy Options,_ Initiative for Policy Dialogue Series, Oxford University Press, New York.

Copeland, B.R. and M.S. Taylor. 1994. “North-South Trade and the Environment.” _Quarterly Journal of Economics_ , 109(3): 755-787.

Diewert, W.E. 1981. “The elasticity of derived net supply function and a generalized Le Chatelier principle,” _Review of economic Studies_ XLVIII, 63-80.

Gordon, H.S., 1954. “The economic theory of a common property resource: the fishery.” _Journal of Political Economy_ 62, 124–142.

Larson, D. F. and J. Nash. 2010. “Resource Management and the Effects of Trade on Vulnerable Places and People: Lessons from Six Case Studies,” Policy Research Working Paper 5258, World Bank (March).

López, R. 1997. “Environmental externalities in traditional agriculture and the impact of trade liberalization: The case of Ghana”. _Journal of Development Economics_ 53 (1), 17–39.

______. 1998. “The tragedy of the commons in Cote d’Ivoire agriculture: empirical evidence and implications for evaluating trade policies.” _The World Bank Economic Review_ 12(1):105-31.

______. Forthcoming. “A Review Essay on the Economics of Forest Resources,” _Journal of Economic Literature_ .

Miranda M., P . Burris P and J.-F. Bingcang. 2003. _Mining and Critical Ecosystems: Mapping the Risks_ . Washington, DC: World Resources Institute.

Schaefer, M.B., 1957. “Some considerations of population dynamics and economics in relation to the management of marine fisheries,” _Journal of the Fisheries Research Board of Canada_ 14: 635–47.

Scott, A. D. (1955). “The Fishery: The Objectives of Sole Ownership”. _Journal of Political Economy_ 63:116-124.

Schiff, M. and A. Valdés. 1992. _The Political Economy of Agricultural Pricing Policy. A Synthesis of the Economics in Developing Countries_ (Vol. 4), Baltimore: Johns Hopkins Univ. Press.

Washington Post, 2009 (February 23, pp. 1-2).

24
||**Appen**|**dix Table A7:**|**Impact on**|**Unrelated Pr**|**actices(ITT)**|||||
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
||Other|Observed|Asked|Advertised|Identified|Kept Annual|Kept Annual|Kept Annual|Kept Annual|
||Business|Products for|Suppliers|the Business|<br>Profit|Profit and|Cash Flow|Balance|Income and|
||Practices|Sale at|about High||Contribution|Loss|Statement|<br>Sheet|Expenses|
||Aggregate|Competing|Demand||of Best|Statement|||Statement|
||Score|Business|Products||Products|||||
|Assigned Handbook|-0.003|0.007|-0.020|-0.003|0.005|0.007|-0.009|0.000|-0.010|
||(0.010)|(0.031)|(0.032)|(0.013)|(0.030)|(0.011)|(0.011)|(0.008)|(0.013)|
|Assigned Handbook & Movie|-0.001|-0.001|-0.036|0.012|0.020|0.002|0.002|-0.002|-0.004|
||(0.010)|(0.031)|(0.033)|(0.015)|(0.029)|(0.012)|(0.012)|(0.008)|(0.013)|
|Assigned Handbook & Assistance|0.005|-0.000|-0.012|-0.005|0.014|0.013|0.013|0.005|0.013|
||(0.010)|(0.031)|(0.032)|(0.013)|(0.029)|(0.011)|(0.013)|(0.009)|(0.014)|
|Assigned All Three|0.005|0.034|0.001|0.021|0.005|0.005|-0.008|-0.007|-0.001|
||(0.009)|(0.032)|(0.032)|(0.014)|(0.029)|(0.012)|(0.010)|(0.007)|(0.013)|
|R-squared|0.183|0.106|0.136|0.087|0.080|0.074|0.066|0.035|0.103|
|N|2204|2204|2203|2204|2204|2204|2204|2204|2204|
|Dependent Variable Mean in Control Group|0.179|0.239|0.294|0.044|0.748|0.024|0.029|0.015|0.040|
|F-test (p-value): Book = Book & Movie|0.809|0.822|0.609|0.321|0.622|0.696|0.327|0.790|0.608|
|F-test (p-value): Book = Book & Assistance|0.413|0.831|0.793|0.829|0.762|0.627|0.078|0.622|0.099|
|F-test (p-value): Book = All Three|0.369|0.403|0.491|0.099|0.983|0.846|0.939|0.354|0.477|
|F-test (p-value): Book & Movie = Book & Assistance|0.576|0.992|0.452|0.232|0.845|0.391|0.406|0.475|0.241|
|F-test(p-value): Book & Move = All Three|0.528|0.286|0.245|0.574|0.603|0.846|0.349|0.545|0.831|

**Notes:** This table presents impact analysis (Intent-to-Treat) for business practices we measured in baseline, midline and endline surveys but did not cover in the handbook, hence did not treat. Regressions use an ANCOVA specification, where a midline survey round dummy, baseline value of the outcome variable as well as stratification controls are included on RHSs and standard errors are clustered at retailer level. Statistically significant p-values are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level).  Details on business practices can be found in Appendix Table A10.
_“Earlier fathers used to say ‘what is the use of educating girls....they will go to another house’. But now, fathers send both daughters and sons to school and college.” School going adolescent girl, Mymensingh_ _“ Mothers of the earlier generation used to advise their daughters to learn house-work and get education up to primary; now mothers are telling their daughters to get at least secondary school certificate”. School going adolescent boy, Satkhira_ _Source:_ World Bank (2007) Social norms and attitudes are often an indicator of social trends and the demand for various goods and services. They are also often pointers to the trajectory of social change. It is therefore not surprising that norms and attitudes have been studied by scholars for now several decades. The literature on norms and their transformation is rich in the US in particular. During the 1970s Mason et al (1976) looked at changing attitudes to women’s work and their domestic roles at a time when the women’s movement in the US was gaining strength and women were also entering the labor market in large numbers. This was followed by other scholars trying to assess the importance of education in changing “sex role attitudes.” Still others asked how norms and values change, whether behaviors precede norms-change or vice versa.

We aim to add to this body of work by looking at change in attitudes to some aspects of gender equality in Bangladesh during a period of rapid social transformation. This work we believe is of particular significance because, while data sets in developed countries have allowed for analysis of norms and attitudes to emerge, those from developing countries have been few or restricted to small samples and to attitudes to reproductive decision-making, sex preferences for children and to violence against women. Also, for the most part the work on developing countries has focused on using
Table 3a

# Accumulated Pension Fund Balances* (per cent of total wage bill)

|||Growth|Rate of|Real Wage|s||
|---|---|---|---|---|---|---|
|Real Interest Rate<br>0%|0%<br>300|1%<br>257|2%<br>223|3%<br>196|4%<br>174|5%<br>156|
|1%|355|300|257|224|196|174|
|2%|423|354|300|258|224|197|
|3%|511|422|353|300|258|225|
|4%|624|508|420|353|300|258|
|5%|769|619|506|419|352|300|
|10%|2499|1903|1468|1148|910|731|

* Pensions indexed to wages, k=10%, n=40, m=20, pillar demographic structure (0% population growth), dependency rate 50%.

# Table 3b

Accumulated Pension Fund Balances* (per cent of total wage bill)

|||Growth|Rate of|Real Wage|s||
|---|---|---|---|---|---|---|
||0%t|1%|2%|3%|4%|5%|
|Real Interest Rate<br>0%|188|163|143|127|114|103|
|1%|219|188|163|143|127|114|
|2%|258|219|188|163|144|128|
|3%|308|258|218|188|164|144|
|4%|371|306|257|218|188|164|
|5%|451|368|305|256|218|188|
|10%|1383|1065|831|658|528|430|

* Pensions indexed to wages, k=10%, n=40, m=20, pyramidal demographic structure (1.6% population growth), dependency rate 30%.

27
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specific drought or flood. Nevertheless, the opportunity to assess the relative importance of idiosyncratic and covariate shocks in different regions of a country is particularly useful for determining insurance priorities and targeting social protection programs.<sup>12</sup>

# 4. Data

## **Data source and poverty measurement**

The main sources of data used in this paper are the twinned Household Consumption Expenditure Survey (HCES) and the Welfare Monitoring Survey (WMS) of 2016. HCES and WMS are the official poverty and welfare monitoring surveys collected by the Central Statistics Agency (CSA) of Ethiopia every five years; HCES focuses on monetary welfare as measured by consumption expenditure while the WMS covers non-monetary welfare including education, health, access to services and exposure to various risks among other things. The two surveys cover the same sample households and the sample is large enough for sub-national analyses including at the regional level and urban and rural areas within a region. Coverage of pastoral areas of Afar and Somali was sparse until the 2016 survey round because in prior rounds the zones (administrative units below region) in Afar and Somali where pastoralists are prevalent were not included in the surveys. Therefore, the prior rounds of the surveys systematically under-represented pastoralists rendering them unsuitable for vulnerability analyses focusing on drought-prone and pastoral areas. In 2016 additonal zones were included in the HCES/WMS survey in Afar and Somali to increase the representativeness of the surveys to pastoral areas.<sup>13</sup> Consumption expenditure includes both food and non-food items, regardless of whether these items are purchased on the market, come from own production, or were received as gifts. For ownconsumption and gifts, the quantities consumed are valued at prevailing prices in the enumeration area. To capture the effect of seasonal variations, the data were collected over a 12-month span (Hamle 1 to Sene 30, i.e. July 8 to July 7), by randomly allocating sampled households to different

> 12 Idiosyncratic shocks are, by definition, not correlated and more likely to be mutually insured within communities where information asymmetries and enforcement limitations may be smaller than across communities. On the other hand, covariate shocks that affect all households in a community more or less in the same manner are less likely to be insured by mutual insurance mechanisms _within_ communities or even by mutual insurance mechanisms _across_ communities.

> 13 While the HCES/WMS sample covered only two administrative zones in Afar and three in Somali in 2011, it included five zones and eight zones respectively in Afar and Somali in 2016.

11
find that firms having customers paying early are more likely to remit early to their suppliers. This result provides further support to our idea that firms do not simply try to increase as much as possible the amount of payables, but they time the payment structure in the down-stream and up-stream market.

Our novel set of results is important to rationalize two stylized facts for which the extant literature still lacks a clear understanding. While most of the related theoretical and empirical literature has stressed the importance of credit constraints as a motivation to take credit (Biais and Gollier, 1997; Burkart and Ellingsen 2004; Petersen and Rajan, 1995; Burkart, Ellingsen, and Giannetti 2006 among others), the existence of credit constraints does not explain (a) the large use of trade credit by unconstrained firms (Marotta 2005) or by listed companies, which generally have access to both public and private financial markets (Demirguc-Kunt and Maksimovic 2002), and (b) that small or credit constrained firms are more likely to both grant and receive trade credit than large unconstrained firms (see McMillan and Woodruff, 1999 for evidence on Vietnamese firms and Marotta 2005 for the Italian manufacturing sector).<sup>3</sup> Our argument is that the matching story would provide an explanation for these two stylized facts. Small and credit-constrained firms are able to offer trade credit (needed to compete in the product market) as long as they can use payables. Large and unconstrained firms might decide to take trade credit to optimally hedge receivables risk.

Our paper is also related to the more recent literature analyzing the role of trade credit as an informal source of finance in developing economies where formal financing

> 3 Using the complete _World Bank Enterprise Surveys_ database, which includes a sample of over 40,000 mostly small and medium sized firms surveyed in over 50 mostly developing countries around the world, we find that 69% of all firms report selling goods on credit, while 51% use trade credit financing (compared to 62% of firms that have access to bank financing).These are authors’ estimations.

7