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# **Online Appendix A: Not for Publication**

## **Wage Determination**

We first consider the case where the nonagricultural good is tradable. This simplifies the algebra and help focus on the role played by the migration costs in determining the wage in village labor market. The wage determination in this case is the outcome of the local labor market clearing and the migration equilibrium condition. We normalize the prices to 1, and ignore transport and other costs involved in trading at the international market (we consider transaction costs below when nonagricultural good is assumed to be nontradable).

Given a wage rate _w_ , the demand for labor in different activities can be expressed as: 30
was done using industry-level data we would be concerned about tariffs endogeneity while we think this is not a major issue in our study as we focus on plant-level data.

Moreover, if we analyze the way NAFTA negotiations were carried out we can strenghten our confidence about the Mexican tariffs phase out being exogenous. First, the NAFTA agreement faced Mexico with bilateral negotiations with partners that had more influence and negotiating power. Additionally, the negotiations were carried out in a relatively short period of time which reduces the possibility of external interventions and interferences from individual firms. Moreover, the high degree of uncertainty that surrounded the US Congress approval of NAFTA suggests that Mexican negotiators focused their minds on supporting the negotiating process to increase the chances of NAFTA being approved, rather than trying to defend the interests of individual firms.<sup>32</sup> Indeed, these anecdotal remarks are consistent with the findings of Kowalczyk and Davis (1996) who argue that while for US tariffs there is evidence that higher duties and sectors with lower intra-industry trade were characterized by slower phase out, there is not such evidence when we analyze the phase out of Mexican tariffs except that these appear correlated with US phase-out.<sup>33</sup> Finally, to dispel some remaining doubts we analyze the evolution of Mexican tariffs under NAFTA and observe that, even at a rather disaggregated level (i.e. 6 digits), the rankings of the different tariffs are rather stable. This is shown by table 3 in appendix A where we split the tariffs in deciles and calculate the transition probabilities during the period under analysis. A similar conclusion is reached if we observe table 4 where we calculate the Spearman rank correlation between tariffs. The conclusion from this table is that, no matter which pair of years we consider, we can always reject the null hypothesis that the two tariff schedules are independent. This suggests that whatever the political economy was behind the tariffs structure this did not change much during the NAFTA period, therefore using 6-digits industry fixed effects we control for these time-invariant industry characteristics affecting the political economy of tariffs liberalization.<sup>34</sup> In appendix B.2 we also present a set of additional regressions (see tables 8 and 9) in order to check the robustness of our results.

First of all we exclude from our analysis the “top plants” used to calculate the productive frontier.

> 32NAFTA was only approved by the US Congress only on December 8th,1993, by a narrow majority of 234-200 vote and it took effect after less than one month on January 1st, 1994.

> 33Kowalczyk and Davis (1996) argue that empirical analysis at five-digit SITC level “suggests that from the perspective of Mexican negotiations, many other issues, including the overriding one of obtaining free trade in the near future with its Northern neighbours, were given higher priority than the questions of how to phase duties out”

> 34A similar argument and solution is proposed by Schor (2004) and Goldberg and Pavcnik (2005).

18
<u>Table 6: Additional robustness checks.</u> Low + Low + <u>Middle Low. Mid. Low OLS Tobit Neg. Bin.</u> Entry Cost -0.117* -0.141* -0.207** -0.090** = -0.124*** = -0.133*** [0.064] [0.075] [0.102] [0.035] [0.046] [0.008] Export Cost = -0.329*** = -0.365** = -0.563** = -0.287*** -0.396*** — -0.352*** [0.115] [0.149] [0.233] [0.069] [0.088] [0.023] Distance -0.460*** = -0.359*** 0.228 -0.262** = -0.383*** -0.326*** [0.086] [0.115] [0.342] [0.102] [0.121] [0.024] Tariff (EU) -0.158 -0.539 2.91 2.287 1.634 0.088 [1.643] [1.683] [3.528] [1.383] [1.628] [0.649] Tariff(Own) 0.206 0.01 0.106 0.595** 0.522 0.499*** [0.462] [0.581] [0.707] [0.273] [0.379] [0.161] GDP 0.461*** 0.434*** — 0.520*** 0.352*** —0.485*** — 0.390*** [0.031] [0.045] [0.045] [0.021] [0.026] [0.012] GDPPC 0.113* 0.172* 0.084 0.086** 0.085* 0.101*** [0.066] [0.100] [0.227] [0.040] [0.049] [0.012] Constant 0.591 0.332 -4.376 0.361 0.256 -1.816*** [1.304] [1.594] [4.126] [1.177] [1.499] [0.386] Observations 10656 8256 3744 11328 11328 11328

1. Estimation in columns 1-3 is by Poisson, using data on low and middle income, low and lower-middle income, and low income countries only. Estimation in columns 4-6 is by OLS, Tobit, and negative binomial, using the full sample. All models include fixed effects by 2-digit sector. 2. All independent variable are in logarithms. The dependent variable is in logarithms in columns 4-5 only.

3. Robust standard errors, adjusted for clustering by exporter, are in square brackets. Statistical significance is indicated by * (10%), ** (5%), and *** (1%).

38
**Table 8: Firm Investment amount and Purpose of Mobile Money Use**

|**Dependent variable: Log of Purchase of Fixed Assets**<br>**(amount in USD)**||**O**|**LS**||
|---|---|---|---|---|
||**coef/se**|**coef/se**|**coef/se**|**coef/se**|
|Mobile money to pay employee Y:1 N:0(0 if not adopted)|1.364||||
||(0.882)||||
|Mobile money to pay supplier Y:1 N:0(0 if not adopted)||2.368***|||
|||(0.495)|||
|Mobile money to pay utility bills Y:1 N:0(0 if not<br>adopted)|||0.554<br>(0.609)||
|Mobile money to receive payments from customers Y:1<br>N:0(0 if not adopted)||||1.725***|
|||||(0.480)|
|Log of size|1.114***|1.159***|1.103***|1.191***|
||(0.280)|(0.254)|(0.281)|(0.263)|
|Top manager experience in sector (years)|-0.030|-0.041*|-0.033|-0.041*|
||(0.022)|(0.021)|(0.022)|(0.021)|
|Log of sales per worker (outliers removed)|0.088|0.155|0.084|0.105|
||(0.129)|(0.123)|(0.133)|(0.131)|
|Log of age of firm|-0.428|-0.376|-0.399|-0.260|
||(0.415)|(0.411)|(0.423)|(0.390)|
|Firm is part of a larger firm Y:1 N:0|-0.305|-0.347|-0.360|-0.215|
||(0.892)|(0.786)|(0.880)|(0.834)|
|Exports 10% or more of sales Y:1 N:0|-0.031|-0.005|0.006|0.062|
||(0.852)|(0.752)|(0.837)|(0.808)|
|Foreign ownership Y:1 N:0|0.420|0.553|0.399|0.468|
||(0.907)|(0.878)|(0.923)|(0.898)|
|Firm with unfavorable credit record Y:1 N:0|0.026|-0.091|0.037|-0.008|
||(0.496)|(0.468)|(0.506)|(0.483)|
|Establishment has a line of credit or loan at this time Y:1<br>N:0|1.506***|1.365**|1.494***|1.400**|
||(0.563)|(0.545)|(0.568)|(0.563)|
|Firms use email to interact with clients/suppliers Y:1 N:0|0.812|0.769|0.941*|0.936*|
||(0.526)|(0.490)|(0.545)|(0.525)|
|Establishment has checking or savings account at this<br>time Y:1 N:0|-0.251|-0.317|-0.414|-0.477|
||(0.631)|(0.523)|(0.621)|(0.561)|

30
-6-

context that provides the underlying basis for most market integration studies, in particular, Ravallion's (1986) dynamic application.

While t;Le advantage of a dynamic model pertains to any spatial models of market integration, even those which consider a single commodity, the introduction of the speculative nature of ttade and storage into a price formation model offers additional advantages in a multi-commodity model. In particular, it provides the basis for the test of the efficiency of information flows referred to in the introduction. Appeal to basic demand theory should be sufficient ti. indicate that prices of supplements and complements enter into the formation of current prices.a This, however, pertains to equilibrium values and does not say how markets forecast changes in prices. If a market is efficient with respect to some information set _0_ then it is impossible to make economic profits by trading on the basis of 0. Past prices are clearly a plausible candidate for p. As such, changes in prices would be white noise; tomorrow's price change would reflect tomorrow's news but current information would be fully incorporated in today's price.' If prices of a second commodity improve the forecast of the first then they clearly provide news today and the basis for economic profits. As such, one would expect that in an efficient market the second series would be redundant. Granger and Escribano state this hypothesis in terms of drift of price series and provide the basis for the cointegration model presented below.

3 Prices of crops which are complements or supplements in production also influence market clearing prices.

4 This abstracts from any forecastable changes in risks and transaction costs (Granger and Escribano).
has yields of between 700-1100 kg/ha. These tend to be larger-scale farmers with average plantings of 100-200 tareas. The third type is a CONOCADO producer who produces organic cocoa with good techniques where yields vary between 700-1300 kg/ha. The wide range in estimates reflects different levels of management.

# _Cocoa Production and Household Income_

Because of its pronounced seasonal labor and cash flows and the common practice of interplanting cocoa plantings with other trees and crops, cocoa production is often a part of a producer household’s diversified income generating activities. The degree of diversification is important for household decisions on labor and capital allocation to different enterprises and also affects self-financing and management of price and yield risks. Following the cocoa price declines of the 1990s and Hurricane Georges, many small-scale producers moved their families near urban centers in search of more remunerative economic prospects. Some returned to their farms following rehabilitation efforts and the subsequent spike in world prices, but many still live outside the areas of production and manage their farms on an irregular basis. It will be interesting to observe how these “part-time” producers manage their farms.

In the CRMG survey, over 50% of cocoa producers indicated they received 50% or more of their on-farm income from cocoa sales, while 44% of the producers received 50% or more of total household income from on-farm activities. This indicates a fairly high degree of income diversification. The survey indicates that smaller cocoa producers are likely to receive about 50% of their income from the farm, with the larger and the very smallest producers being less diversified and receiving higher shares of income from cocoa.

The CRMG survey results are comparable with the results of a survey of cocoa producers conducted by a leading cocoa export firm Nazario Rizek that indicate cocoa producing households who market with Rizek have diversified sources of income. At almost 39% average share of household gross income, cocoa represents the single most important source of income for cocoa producing households, but clearly other sources of income are important (see table IV.9).

**<u>Table IV.9  Cocoa as Share of Producer’s Income</u>**

||RevenuesRD$|Percent|
|---|---|---|
|Cocoa Sales|25,629|38.6|
|OtherCrops|12,959|19.5|
|Livestock|3,471|5.2|
|Remittances|6,035|9.1|
|Colmado (PettyTrading)|2,647|4.0|
|Moto-Taxi|1,059|1.6|
|Salaries/Labor Income|3,706|5.6|
|Cash in BankorCoop|2,029|3.1|
|Money Owed|6,529|9.8|
|Other|2,412|3.6|
|**Total**|66,476|100%|

Source: Nazario Rizek; 107 Producers Surveyed in 2002, Average Farm Size 5.7 Ha. Note: Estimated value in US$ based on mid-July 2002 exchange rate of 1US$/17$RD$.

60
<mark>types of surveys such as consumption and labor force surveys (e.g., Douidich et al. 2016). Newhouse</mark> et al. (2014 <mark>) and Dang et al. (2019) offer recent summaries of previous imputation studies that highlight the main advantages, debate different approaches, and provide useful insights about welfare imputation practices. More recently, Dang and Verme (2019) is the first study to propose the use of a cross-survey imputation method in the context of the Syrian refugees living in Jordan, using survey and administrative data provided by the</mark> United Nations High Commissioner for Refugees <mark>(UNHCR). Their findings suggest that in the absence of actual household consumption survey data, their cross-survey imputation method can provide a secondbest alternative to address the data challenge.</mark> <mark>In this paper, we make several new contributions to the nascent literature on poverty measurement for refugees. First, we apply the cross-survey imputation method to a sample of refugees based in Chad. Since this country is in Sub-Saharan Africa and one of the poorest countries in the world, its differences from Jordan in both geographical location and income levels represent a new setting to apply and further validate this imputation method. Second, we exploit a richer and more diverse set of data than the Jordan study, which includes r</mark> egistration data, census-type targeting data, and a household consumption survey. We also rigorously examine the imputation method against different poverty lines, including the food poverty line, the national poverty line, the international poverty line, and various other simulated values. Finally <mark>, in addition to the poverty imputation exercise, we test the performance of the proposed method for targeting purposes. In particular, we compare the targeting performance of our method with the targeting method currently used in Chad to administer cash assistance to refugees as well as with the global experience.</mark> <mark>The estimation results indicate that the limited set of variables available in the UNHCR registration data predict household consumption (welfare) reasonably well. Estimates from the three sets of data available for the analysis produce similar welfare figures. Th</mark> e <mark>current targeting strategy in Chad, which is used jointly by the N</mark> ational Commission on the Welcoming and Resettlement of Refugees <mark>(CNARR), UNHCR, and World Food Programme (WFP), is accurate in predicting household welfare. However, our results suggest that this targeting strategy could be further improved by reducing the inclusion and exclusion errors. If these encouraging results are replicated</mark> 3
optimal in the sense that it minimizes information losses in moving from a prior distribution assumption to a posterior distribution estimate.<sup>5</sup> Whereas forecasters disagree in assigning prior distributions based on their judgment and personal information, these disagreements are in part a reflection of underlying uncertainty (Al-Najjar and Shmaya 2015). The BCEF procedure is flexible to exploit heterogeneity in beliefs embedded in assumed prior probability distributions of the parameters defining the study variable. We can get BCEF estimates under an almost infinite number of prior distributions, which, in most cases, the results will converge to similar posterior estimates.<sup>6</sup> A critical characteristic of our BCEF method is that it uses ex-ante information to generate density forecasts. The used ex-ante information is extracted from surveys on expectations and implied statistics of forecasting models. While the use of ex-ante information to predict uncertainty in future outcomes is becoming more common, there is still some disagreement regarding its predictive power. Rich and Tracy (2010) build up uncertainty statistics to analyze inflation forecasts from the Survey of Professional Forecasters (SPF). Their results found little evidence that disagreement among forecasters is a good proxy for inflation uncertainty. Using the same SPF data, in contrast, Giordani and Söderlind (2003) find that difference among forecasters is a good measure for inflation and output growth uncertainties. More recent literature uses ex-ante forecasts errors based on SPF sources to improve the accuracy of uncertainty estimates around macroeconomic forecasts (Clark, McCracken, and Mertens 2018).

The empirical application of the introduced BCEF method generates monthly density forecasts of world GDP growth based on ex-post information spanning the period of October 2005–August 2015. The BCFE density forecasts for world GDP growth assess three annual horizons. The accuracy of the BCEF density forecasts is evaluated ex-post using the continuous

> 5 In the language of Bayesian inference, the BCEF procedure capture a measure of the information gained when one revises one’s beliefs from the prior probability distribution to the posterior probability distribution. 6 Using incorrect priors will result in solutions that may be far from the underlying truth. In statistical terms, the solution will be biased (Golan 2017, p. 314).

4
the Balance Sheet adjustment. Note that this expansion effect from the Balance Sheet adjustment (23.9%) was substantially weakened by the privatization's contractionary effect (-18.5%). As we have already discussed, none of these items constitutes exactly the public - deficit. According to the definition of the federal deficit, neither of the other items related to states and municipalities (6.2% of item #1's growth) and the state owned - enterprises (19.3%) should be included, since this item (#1) refers only to the federal level.<sup>23</sup> The federal govemment and Central Bank's primary deficit had a contractionist impact during this period (-5.8%).

TABLE 111.8. MAKING THE FEDERAL NET DEBT STATISTICS AND THE FEDERAL NOMINAL DEFICIT COMPATIBLE: 1995-1998

||R$ (millions)|Percentage Share|
|---|---|---|
|=Federal Net Debt Variation (+ CB)|_165,422_||
|+Nominal Interest (Federal Government + CB)|117,430|71.0%|
|+Primary Deficit (Federal Government + CB)|-9,589|-5.8%|
|+Nominal Deficit minus Net Debt Variation of the<br>States and Municipalities|10,196|6.2%|
|+ Nominal Deficit minus Net Debt Variation of the<br>State Owned Enterprises|31,894|19.3%|
|+ Patrimonial Adjustrnent Variation|39,516|23.9%|
|- Privatization Adjustment Variation|-30,656|-18.5%|
|+ Adjustment not Computed by the Central Bank|6,646|4.0%|
|=TOTAL|165,437|100.0%|

**_1112_** _Accumulation of assets_

Table 111.9 decomposes the accumulation of assets during this period. Note that domestic assets growth (194.42%) was substantially greater than the foreign assets' growth (64.04%).

> **23** Thus, it would be expected that the nominal deficit were equal to the net debt variation, for both the states and municipalities as for the state-owned enterprises. Therefore, elucidating these items is still another<sup>work</sup>

22