rafmacalaba/datause-encoder-data huggingface dataset:
Yeh 2011,2012; Dupas and Robinson (2012);<sup>2,3</sup> women who are not sex workers may not be as forthcoming about their behavior. In addition, transfers made between clients and sex workers are also more salient as they are typically made at the time of the sexual act. However, for women who are not sex workers, the timing of transfers may not correspond with the sexual act. For example, women may have a sexual occurrence with a friend, but receive a transfer from this partner days or weeks later. The data we use for this study (described below) are unique in that they measure sexual behavior, sexually transmitted infections (STIs), and transfers from a sample of women who are not sexworkers and are representative of women in rural Tanzania.

Our study is based on a panel of women in rural Tanzania involved in a conditional cash transfer study aimed at HIV/STI prevention.<sup>4</sup> Four rounds of data, spaced four months apart, were collected. At each round, individuals were tested for four curable STIs<sup>5</sup> , which we use as our main outcome of interest. Compared with self-reported sexual behaviors, these biomarkers have the advantage of not being subject to selfreporting bias. We estimate a relationship between household level negative shocks and sexual behavior incorporating individual and time fixed effects. We find that women experiencing a negative shock are 5 percentage points more likely to be infected with an STI. The magnitude of this increase is both significant and large, as it amounts to a 36% increase in STI risk over a four-month period. In addition, we find suggestive evidence that this effect is stronger among unmarried women and those with the lowest social economic status (SES).

The relationship we establish between shocks and STIs does not necessarily imply that women are responding to shocks by engaging in transactional sex. For example, women experiencing shocks may be unable to afford medical treatment for STIs. They may also have compromised immune systems that make them more susceptible to STIs.

> 2 These studies also have repeated measures of individual level sexual behavior, which allow the use of individual fixed effects.  If self-reporting biased is a time invariant characteristic, these individual FE should account for it.

> 3 A notable exception in the economics literature is Luke (2006) who examines transactional sex amongst unmarried youths in Kenya.

> 4 See de Walque, Dow, Nathan _et al._ (2012)

> 5 The four curable STIs are: Chlamydia, Gonorrhea, Trichomonas, and Mycoplasma.

3
# **VII. Conclusion**

This paper examines the growth and structure of agricultural trade between 1990 and 2007, focusing on the performance of industrial and developing countries and specific commodity groups. Data include 2007 but miss the agricultural price peak in 2008. Despite tremendous change in the past 20 years in global specialization and trade in manufacturing, remarkably little structural change has occurred in global agricultural trade flows.

The developing country share in world agricultural exports increased from 32 percent in 1990/91 to only 42 percent in 2006/07. Most of this gain came from expansion of exports to other developing countries (about 12 percentage points). However, only 47 percent of their agricultural exports are to other developing countries, showing the continuing importance of industrial country markets for their exports. For low-income countries, other developing countries accounted for 51 percent of their exports and 69 percent of imports in 2006/07; up from 27 percent and 57 percent respectively in 1990/91. Thus, other developing countries are now a bigger market for the exports of low income countries than the industrial ones.

There has been some change in the product mix of global agricultural trade. Currently vegetables and cut flowers constitute almost 20 percent of the exports of developing countries, seafood constituting another 20 percent. Temperate zone products such as grains, dairy, and meats constitute another 30 percent. Traditional exports, such as tea, coffee, cocoa, sugar, cotton, nuts, and spices, now constitute a small share of exports at 16 percent. This suggests the need for more attention to global and country policies for non-traditional product groups.

In conclusion, unlike the case with manufacturing, developing countries have not been able to increase their export shares in agriculture as significantly. They have maintained their trade shares by primarily expanding exports to other developing countries. However, like the case with manufacturing and services, trade to output ratios 26
We utilized two main sources of data: COPSA-C administrative data regarding storage and related loan information (take-up, loan amount, and repayment) and independent survey data collected at several stages of implementation (Census, Baseline, Stockage, Destockage, and Endline).

Table 1 provides summary information of the sample. To describe the profile of the villages in which the intervention took place, we show average demographics from the census in column 1. Most households are male-headed. About one-quarter of households are polygamous; thus, households tend to be relatively large, with an average of eight household members. Knowledge of warrantage was high at 81 percent; this is probably a result of the concurrent sensitization activities run by COPSA-C. Columns 2 and 3 present means of selected census and baseline variables for lottery non-participants and lottery participants respectively. “Non-participants” refers to households in the village that were not interested in warrantage and thus chose not to participate in the lottery; participants, on the other hand, are those households that did participate in the lottery (i.e. both treated and control households). Comparing columns 2 and 3 thus sheds light on selection into participation in the warrantage lottery. Column 4 shows the p-value for the pairwise tests of equality between non-participants and participants. Lottery participants tend to come from larger households with slightly younger household heads and are more likely to be familiar with warrantage. Participant households also reported higher hypothetical interest in storing key grains (sorghum and maize) using warrantage. Despite these differences, using an omnibus F-test, we find that the set of variables available at the census presented in Table 1 do not jointly predict participation in the lottery (p-value: 0.152).

> 6 Consent to take part in the lottery was then obtained. The village head held an introductory speech emphasizing the importance of complying with the protocol (including forbidding storing other household members’ bags). Household representatives were asked to declare the number of bags they would be willing to store if allocated storage space; this number was capped at 10. Participants’ names were randomly picked from the _name bag_ to vary the order in which household representatives participated in the lottery. The selected participant was then invited to blindly draw a ball from a basket containing green (win) and blue (lose) balls (the _warrantage bag_ ).

10
<!-- Start of picture text --> Manufacturing Density 2000-2005<br>S<br>x=<br>é-<br>&<br>Ssa<br>e%<br><'<br>oe<br>0 2 4 6 8<br>Manufacturing Density 2000 (logs)<br>Source: NSS and ASI<br><!-- End of picture text --> <!-- Start of picture text --> Services Density 2001-2006<br>wo<br>SB<br>= wo<br>38<br>(o}<br>Ja<br>EO<br><<br>8<br>=<br>0 2 4 6 8<br>Services Density 2001 (logs)<br>Source: NSS<br><!-- End of picture text -->
Puzzolo et al. (2013) finds that high household income favors adoption of ICS, while low household income acts as a barrier.

# 4.2. Behavioral and cultural factors

Behavioral and cultural factors such as household preferences, food tastes, cooking practices and cultural beliefs also influence cooking fuel choice.<sup>10</sup> For example, Masera et al. (2000) finds that people in rural Mexico continue to use fuelwood even when they could afford to use cleaner and modern fuels because cooking "tortillas" on LPG is more time consuming and negatively affects its taste. Likewise, Indian households prefer to use wood cookstoves for baking traditional bread (IEA, 2006). Using 2000 Guatemalan LSMS survey data, Heltberg (2005) argues that traditional cooking practices and food tastes might make people prefer fuelwood, even in situations where fuelwood is as expensive as cleaner alternatives. In another case, Taylor et al. (2011) finds that migrant households in Guatamala often use traditional way of preparing foods despite LPG is available and affordable. Likewise, Narasimha Rao and Reddy (2007) finds that households in rural India with Islamic religion are less likely to use LPG than fuelwood. In Ougadougou, Burkina Faso, Ouedraogo (2006) finds that the frequency of cooking "Tô" − a staple traditional meal made of millet, sorghum or maize − increases the likelihood of using fuelwood. The study also finds that an increased frequency of rice cooking in households reduces the likelihood of using fuelwood.

Many social factors and community interactions also influence the adoption of ICS. For example, Barnes et al. (2012) in Karnataka, India and Person et al. (2012) in rural Kenya, find that the decision to purchase ICS by households was significantly influenced by the experiences of neighbors and relatives who had adopted the stove. Likewise, Miller and Mobarak (2013) and Pine et al. (2011) find that the opinion of leaders within a community also influences the adoption of ICS in rural Bangladesh. Troncoso et al. (2007) find that factors such as the aesthetic appeal and social status gain motivated households in rural Mexico to adopt and use ICS. However, Troncoso et al. (2007, 2011) and Person et al. (2012) also find that lack of suitability of preparing traditional dishes using larger pots and change in cooking habits were associated

> 10 See Masera et al. (2000), Heltberg (2005), Gupta and Köhlin (2006), IEA (2006) and Taylor et al. (2011).

15
Hence, the rural-urban poverty-gap has increased in recent years. Moreover, not only is the income gap to urban areas increasing but the gap between richer and poorer segments of the population in the rural areas is also growing. In 2002, rural Mexico had poverty rates for higher than urban Mexico. Dispersed rural areas had a poverty incidence double that of semi-urban areas. Furthermore, dispersed rural areas had a poverty incidence four times higher than urban areas in Mexico.

**Figure 3.1: Extreme Poverty (P0) in Mexico Measured,** **<u>Selected years 1996-2002</u>**

<!-- Start of picture text --> 70.0<br>National<br>Urban<br>60.0<br>Rural (0-15,000)<br>localities with 1 to 2,500<br>50.0 localities with 2,501 to 15,000<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>1992 1996 2002<br>Source: calculations based on ENIGH 1996, 1996, and 2002.<br><!-- End of picture text --> Mexico has steep gradients in conditions of living from more developed urban areas, through the urban periphery and smaller towns, through to the more remote rural areas. Figure 3.1 shows that this pattern has not changed much over the last decade and in rural localities with less than 2,500 people more than 40 percent were extremely poor compared with those localities with 2,500-15,000 people where 21 percent were poor in 2002. This translates into 13.2 million people in poverty. Most of the rural poor live in more dispersed rural areas, as 2.8 million were poor in semi-urban areas and 10.4 million poor in dispersed rural areas (information on population in rural areas in Appendix A).

Geographic factors are important when analyzing poverty in Mexico. Living in a poor area can make a profound difference to well-being and life prospects. There are large differences in income poverty between different regions, with a generalized gradient from North to South (Figure 3.2 and Table 3.1).<sup>5</sup> In 2002, the headcount poverty rate in rural areas in the North region with less than 15,000 inhabitants reached 14 percent, a third of that in the South region where 48 percent were poor.

> 5 ENIGH is not designed to be fully representative at regional level, but differences between regions are statistically significant (also Word Bank 2004).

9
Table 2a: Log-normal Hazard Model for Duration of Education: Albanian Males in comparison with Serbs

||Albania|n**Males:Ag**|**e16-26**|**Albania**|**nMales: Ag**|**e27-35**|**SerbMales**|**&Females**|**:Age:16-35**|
|---|---|---|---|---|---|---|---|---|---|
||Coefficient|Z|Mean|Coefficient|Z|Mean|Coefficient|Z|Mean|
|Ageis16years|-0.263|-20.867|0.133|||||||
|Ageis24years<br>|0.048|3.451|0.102||||-|-||
|Ageis27-35years<br>|-||-||||0.074<br>|5.104<br>|0.329<br>|
|Females|-||-|-|-|-|0.013|0.967|0.517|
|Serbs andothers|0.016|0.615|0.029|-0.030|-0.863|0.038|0.414|2.577|0.002|
|BorninUrbanareas|0.015|1.750|0.419|0.024|1.714|0.357|-0.020|-1.271|0.369|
|BornoutsideFormerYugoslavia<br>ResidenceoutsideFormer|0.181|2.162|0.003|-|-|0.000|-0.118|-1.461|0.010|
|Yugoslaviabeforeconflict|-0.007|-0.248|0.019|-0.029|-0.970|0.049|-|-|-|
|Fatherseducation(inyears)|0.003|2.286|11.183|0.003|1.776|9.310|0.004|2.079|10.971|
|ValueofHousehold durables||||||||||
|over5yearsold(log)|0.000|-0.130|6.526|-0.003|-0.552|6.944|0.018|2.204|7.286|
|AreaofResponsibility||||||||||
|France|-0.028|-2.155|0.122|-0.082|-3.862|0.099|-0.009|-0.529|0.515|
|Germany|-0.028|-1.920|0.148|-0.050|-2.402|0.184|-0.226|-3.502|0.004|
|Italy|-0.029|-2.219|0.169|-0.033|-1.538|0.138|-0.002|-0.027|0.012|
|USA|-0.003|-0.240|0.204|-0.048|-2.388|0.200|0.011|0.540|0.215|
|Constant|2.453|97.105|-|2.566|63.212|-|2.292|34.911|-|
|N|793|||337|||430|||
|Loglikelihood|583.885|||239.323|||241.874|||

> Note: The variable "Serbs and others" is modified to "Albanians and others" in the Serb model.

27
where _TOT_ , the changes in the terms of trade (i.e., changes in a country’s export prices relative to its import prices), and _WEV_ , the changes in world export values (i.e., changes in a country’s export prices relative to the world’s), are defined as: _TOT_ and _WEV_ gains or losses affect _real_ domestic demand by changing the purchasing power of a country’s output. As can be seen in equations 12-14, the TOT valuation effects are captured in the EL term, whereas the _WEV_ valuation effects are captured in the DR term. Instead, the real external pull ( _EP_ ) reflects only volume effects, as it is obtained by subtracting _WEV_ from the nominal export pull ( _NEP_ ).

# **_b) Interpretation and causality_**

_G_ measures the changes in a country’s growth relative to world growth. _EP_ is the country’s _real export pull_ , which can be interpreted as the traction exerted by the country’s exports on its growth. EP should grow when the country becomes more efficient in producing the exports that the world demands (a favorable local supply response to world demand patterns). _EL_ is the country’s _real external leverage_ , which captures the impact on the current account of the country’s real export growth relative to its real import growth, hence its use of external (debt and equity) financing. _DR_ reflects the country’s real output growth relative to the growth of its imports. It can be interpreted as the _real domestic response_ to a change in real imports brought about by changes in the country’s combined export pull and external leverage. _DR_ rises when the country’s GDP grows faster than its imports.

Since _EL_ is a function of the current account disequilibrium of the balance of payments, it should reflect the impact on the external gap (the difference between imports and exports) of deviations between domestic aggregate demand and output. Under stable real exchange rates, the external gap and the output gap (the difference between potential and actual output) should be closely related. Hence, _EL_ should be a good proxy for excess aggregate demand. Strong real exchange rate depreciations, however, could channel aggregate demand away from tradables toward non-tradables, in which case the (positive or negative) growth impact of domestic demand would be distributed in the _EL_ and _DR_ terms. But otherwise _DR_ should mainly reflect supplybased (rather than demand-based) factors, such as changes in productivity that allow a country to produce more for a given level of imports, or changes in trade policies that restrain or promote imports for a given level of output.<sup>2</sup>

> 2 Indeed, in the case of advanced economies for which good output gap historical series are available, _EL_ is generally highly correlated to the output gap, which is not the case for _DR_ .

6
_COTE D’IVOIRE: Growth, Specialization and the Terms of Trade_

# Cote d’Ivoire: Growth, Conflict, Specialization, And the Terms of Trade

**Real GDP per capita and capital stock in Cote d’Ivoire grew strongly from 1960 to 1979, but have declined ever since, for twenty-five years; this paper explores underlying, long-term factors of this development record.** As a result, Cote d’Ivoire’s real GDP per capita in 2003 is about the same as in early 1960s and the capital stock only somewhat above that in the immediate aftermath of independence.1 Poverty rate increased from 32.3 percent in 1993 to an estimated 42-44 percent in 2003 in the immediate aftermath of the conflict.2 Therefore, the country has traveled a full circle from economic success to failure in little more than a generation.

**This paper aims to present some long-term stylized facts on Cote d’Ivoire’s growth performance and then explore long-term factors behind that performance using deterministic and stochastic total factor productivity approaches.** First, we present long-term economic and demographic trends in the 1960-2002 period and some crosscountry comparisons, the macroeconomic and social impact of the 2002-03 conflict, and the key role of cocoa played in the Cote d’Ivoire’s economy. Second, we discuss briefly the macroeconomic impact of the most recent 2002-03 crisis as the latest episode in a long-term economic decline that began over two decades ago. Third, we explain the crucial role of cocoa and coffee sectors, cocoa and coffee prices, and, more broadly, terms of trade, which influence both short term and long term performance of Cote d’Ivoire’s economy.  Finally, we present the findings of the total factor productivity analysis in order to ascertain the roles of factor accumulation and productivity movements in explaining Cote d’Ivoire’s long-term growth. Annexes detail the methodologies for calculating human capital and physical capital stocks, deterministic and stochastic estimates of TFP (Annex 1), and further discussion of the role of the cocoa/coffee sector in the Ivorian and the world economy.

## **1. Long Term Economic and Demographic Trends**

**There are two distinct periods in recent economic history of Cote d’Ivoire: the “boom” period of 1960 to (about) 1979 and the period of economic decline from 1980 to 2002 (Figures 1-2).** This pattern is apparent whether one looks at broad trends in the overall, per capita real output or its components, as well as population, and labor force (Table 1). GDP, consumption, and investment peaked in 1979, then fell in the early 1980s. Thereafter, the three variables recovered and began to climb, but at a slower rate

> 1 Using the World Bank’s SIMA database, in 2002, real GDP per capita in 1995 US dollars fell to US$776 per capita, the lowest seen since it declined to $764 in 1994. GDP per capita first broached US$800 in 1964 when it hit US$849. It thereupon faltered to $792 per head in 1965, then grew steadily to $1,376 in 1978, but declining thereafter.

> 2 The World Bank (2003).

1
# **B Rates of co-residence**

This table shows the co-residency rate by country for different ages. The co-residence rate is the total number of individuals that co-reside with at least one member of an immediately older generation in the household divided by the total number of individuals in the age group. The sample considers only individuals for whom their own educational attainment and the relationship to household head are observed.

**Table A3:** Co-residence rates

|||Ra|te||Obse|rvations|(thousa|nds)|
|---|---|---|---|---|---|---|---|---|
||14-18|18-25|21-25|20-23|14-18|18-25|21-25|20-23|
|Argentina|95.7|72.1|63.1|72.2|1246|1746|1067|870|
|Bolivia|86.7|57.6|48.8|56.4|263|358|218|180|
|Brazil|93.7|63.0|51.7|62.0|12292|16695|10015|8312|
|Chile|95.4|72.7|63.8|73.3|410|570|351|285|
|Colombia|93.4|68.4|59.6|68.2|1086|1451|888|717|
|Costa Rica|94.5|68.3|58.8|68.0|122|173|105|87|
|Cuba|91.6|74.6|68.7|74.8|141|217|136|107|
|Dominican Republic|89.0|63.4|54.1|62.7|222|307|182|153|
|Ecuador|92.8|64.8|55.2|64.2|451|621|378|311|
|El Salvador|90.8|66.8|57.8|66.1|110|138|82|68|
|Guatemala|92.8|63.4|52.8|62.6|286|363|214|180|
|Haiti|94.4|71.6|60.3|71.1|123|158|88|76|
|Honduras|91.1|62.3|52.1|60.9|136|168|98|83|
|Jamaica|90.5|65.2|55.2|64.7|58|76|45|37|
|Mexico|93.8|69.1|59.4|68.5|3363|4318|2536|2112|
|Nicaragua|92.4|67.7|59.1|67.2|120|156|93|78|
|Panama|92.5|66.8|57.7|66.3|108|150|91|74|
|Paraguay|94.7|67.4|57.4|67.2|136|177|107|89|
|Peru|93.3|69.8|61.8|69.4|436|604|371|301|
|Saint Lucia|94.7|66.3|55.7|65.2|2|3|2|2|
|Suriname|95.7|81.2|75.6|82.2|4|5|3|3|
|Trinidad and Tobago|96.1|78.1|70.4|78.8|47|66|40|32|
|Uruguay|95.4|68.9|59.2|68.6|125|175|107|87|
|Venezuela|92.6|67.7|59.0|67.1|630|858|518|428|

37
**Table 2: Exports After Outward Proceig (Share In Total exports to EU)**

||**CEEC**||**MEN**|**A**|
|---|---|---|---|---|
||**1989**|**1993**|**1989**|**1993**|
|**Total**|**10.4**|17.9|**1.6**|1.7|
|**Leather**|38.9|34.5|8.0|**8.5**|
|**Ganments**|60.8|**74.5**|**15.6**|11.1|
|**Machinery**|**8.1**|**14.4**|**_5.4_**|**2.6**|
|<br>**Transport**|12.3|4.7|**4.5**|2.3|
|<br>**Instruments**|**6.4**|11.9|**6.5**|**2.5**|
|**Furniture**|26.5|13.9|1.2|**_1.5_**|

Source: EUROSTAT, COMEXT database.

The data reveal clearly that the CEECs are moving rapidly to exploit their geographic proximity to the EU, which in conjunction with their relatively low wages and significant stocks of human capital makes them formidable competitors for the MENA region. The geographic advantage that the MENA region used to have--because Eastern Europe was effectively closed to open exchange with the West--has now disappeared. MENA firms must now compete head-to-head with companies located in the CEECs. This is indeed a challenge, not least because relative labor costs **in the CEECs** and MENA are not that different. Per capita incomes in the CEECs--one proxy for such costs-are close to MENA levels (World Bank, 1995a). Although factor endowments differ in important respects, it is also noteworthy that some of the CEECs are producing and exporting similar products to the EU. Correlation coefficients between revealed comparative advantage indices' of MENA and CEEC countries are sometimes significant. Table 3 reports those cases where such correlation coefficients are above 0.1.

'The RCA is defined as: <!-- Start of picture text --> x'j/XJ<br>.,.I X/Elff,, X<br><!-- End of picture text --> where x, are exports of commodity i by country **_j, X,_** are country j's total exports, and N is the number of countries.

11