rafmacalaba/datause-encoder-data huggingface dataset:
**Table 5: Shifts in Top 15 World Apparel Exporters: 1995, 2000, 2005, & 2007-2008**

[Top 15 by Year; Values in $US Billions; in US Dollars at Current Prices]

|**Country/ Region**|**1995**||**2000**||**2005**||**2007**|**2008**||
|---|---|---|---|---|---|---|---|---|---|
||**Value**|**%**|**Value**|**%**|**Value**|**%**|**Value**|**Value**|**%**|
|**World**|**158.4**||**197.7**||**277.1**||**345.8**|**361.9**||
|China|24.0|15.2|36.1|18.2|74.2|26.8|115.2|120.0|33.2|
|EU-27 (c)|48.5|30.6|56.2|28.4|85.5|30.8|105.1|112.4|31.1|
|Turkey|6.1|3.9|6.5|3.3|11.8|4.3|13.9|13.6|3.8|
|Bangladesh(b)|--||5.1|2.6|6.9|2.5|8.9|10.9|3.0|
|India|4.1|2.6|6.0|3.0|8.6|3.1|9.8|10.9|3.0|
|Vietnam(b)|--||--||4.7|1.7|7.4|9.0|2.5|
|Indonesia|3.4|2.1|4.7|2.4|5.0|1.8|5.9|6.3|1.7|
|Mexico (a)|2.7|1.7|8.6|4.4|7.3|2.6|5.1|4.9|1.4|
|United States|6.7|4.2|8.6|4.4|5.0|1.8|4.3|4.4|1.2|
|Thailand|5.0|3.2|3.8|1.9|4.1|1.5|4.1|4.2|1.2|
|Pakistan|--||--||3.6|1.3|3.8|3.9|1.1|
|Tunisia|2.3|1.5|--||3.1|1.1|3.6|3.8|1.0|
|Cambodia (b)|--||--||--||3.5|3.6|1.0|
|Malaysia|2.3|1.4|--||--||--|3.6|1.0|
|Sri Lanka (b)|--||2.8|1.4|2.9|1.0|--|3.5|1.0|
|HongKong (d)|9.5|6.0|9.9|5.0|7.2|2.6|5.0|--|--|
|Morocco|--||--||2.8|1.0|3.5|--|--|
|Korea,Republic of|5.0|3.1|5.0|2.5|--||--|--|--|
|Taipei, Chinese|3.2|2.0|3.0|1.5|--||--|--|--|
|Dominican Republic|--||2.6|1.3|--||--|--|--|
|Philippines|2.4|1.5|2.5|1.3|--||--|--|--|
|Poland|2.3|1.5|--||--||--|--|--|
|**Top 15 Total and %**|**Share of**|**World**|**Export**|**s**||||||
||**127.5**|**80.5**|**161.5**|**81.7**|**232.6**|**83.9**|**299.1**|**315.0**|**87.0**|

Source: (WTO, 2010); Apparel exports represented by SITC 84 (a) Includes significant shipments through processing zones; (b) Some years include estimates; (c) EU values include intra-EU trade; values only represent EU-15 in 1995; (d) Domestic exports only.

# **i. Capabilities of Leading Global Apparel Exporters**

Table A-3 in the Appendix lists the production capabilities of several of the main apparel exporting countries. As countries such as China, Turkey, and India develop capabilities that permit vertical integration in apparel, their reliance on apparel exports tends to diminish because their upgrading processes facilitate broader industrial diversification. Table A-4 in the Appendix, which provides export dependence ratios for major apparel suppliers, lends support to this argument. Those countries with the greatest apparel export dependence – such as Cambodia (85%), Bangladesh (71%), and Sri Lanka (41%) – emphasize CMT assembly and have limited 8
possibility by examining whether transfers respond to the sexual activities performed that day or to other measures of participation in the sex market. As we discuss further in the discussion of the results, we find no evidence for this alternative.

The results suggest that the insurance provided by clients may (on the margin) induce women to enter or remain in the transactional sex market (at least in this part of Kenya). In Kenya, the alternative for young women is typically to marry (or cohabitate). To motivate our empirical approach, we lay out a simple conceptual framework in which women may either get married and earn a relatively low income but receive relatively full insurance from their spouses, or enter transactional sex to receive a higher income and rely upon regular clients for insurance. At the margin, the decision will depend on: i) the level and risk associated with each type of income; ii) the stigma, reputation, and other costs of engaging in sex work; and iii) the insurance that each choice provides against unexpected income shocks. As the amount of insurance provided by clients increases, transactional sex will become relatively more attractive and the number of women who choose to enter the market will rise.

To test this directly, we would like to have a dataset on a broad cross-section of women, and examine whether those facing the most risk (or those who are least able to cope with shocks) are most likely to enter the market. We cannot do this, however, as our dataset includes only sex workers. To provide some descriptive evidence on this issue, we conducted a survey with a separate sample of 99 sex workers in which we asked women to list their reasons for entering or staying in the transactional market and for seeing regular clients. Though only suggestive, a substantial fraction of women report that access to client transfers are an important reason for continuing sex work, and between 87 and 98 percent of women report that they see regular clients in part to access transfers. In addition, 98 percent of women report that regular clients are 5
# **Credit constraints, agricultural productivity, and rural nonfarm participation: Evidence from rural Rwanda**

## **1. Introduction**

The potential negative impact of capital market imperfections and credit rationing in developing countries has long been recognized at a conceptual level (for example, Stiglitz and Weiss, 1981; Eswaran and Kotwal, 1986; Carter, 1988; Kochar, 1997). In an economy with perfect markets, activity choice should be based on households’ comparative advantages and wages independently from their endowments. But with market imperfections, this will no longer hold and households may choose portfolios with low returns just because they are less risky. Many studies document that production organization and resource use in smallholder agriculture are affected by credit market imperfections (Bell, 1988; Carter, 1988; Conning and Udry, 2007). Credit rationing can possibly explain that, in African agriculture, high returns to capital coexist with very low levels of purchased input use (Carter and Wiebe, 1990), possibly because insurance market difficulties reduce households’ willingness to invest in high-return but high-risk activities (Eswaran and Kotwal, 1990). Beyond agricultural productivity, credit constraints may affect rural development more broadly by preventing households from taking up nonagricultural activities which many studies have identified as a key for structural transformation and households’ ability to move out of poverty (Reardon, 1997; Ellis, 2000). However, empirically identifying who is credit constrained poses challenges. Direct elicitation has been suggested to identify credit constrained households and distinguish between supply- and demand-side factors (Boucher et al., 2008).

Against this background, this paper contributes to the literature by using direct elicitation methods for a nationally representative sample from Rwanda, one of Africa’s most densely populated countries, to identify households’ credit constraint status. With credit market imperfections, households’ ability to make the best use of inputs in agriculture or their portfolio of activities will vary significantly between those who are credit constrained and those who are not. For the former, but not for the latter, productivity and the ability to invest in off-farm self-employment will depend on wealth and liquidity. While a number of studies have explored this in the context of agricultural production, to our knowledge, very few empirical works focus on assessing empirically the impact of credit constraints on income diversification choices.

Descriptive evidence from our national sample suggests that formal credit is virtually nonexistent and informal borrowing remains minuscule (USD 34 per year on average). We thus focus on the semi-formal sector (cooperatives, input suppliers, micro-finance institutions, and NGOs) as the relevant source of 2
**<u>Annex 2 : Period Average of Data by Variable and Country, 1994-2003*</u>**

|**Country**|_it_<br>_Y_ (Tax/GDP)|_it_<br>_Y_ (Rev/GDP)|_it_<br>_GDP_|_it_<br>_POP_|_it_<br>_TRADE_|_it_<br>_AGR_|_it_<br>_CORR_|_it_<br>_BUREAU_|
|---|---|---|---|---|---|---|---|---|
|Albania|14.6|18.6|1,067.2|-0.4|59.0|36.7|-4.4|-3.1|
|Algeria|30.3|32.6|1,734.4|1.7|54.9|10.8|-3.8|-4.0|
|Argentina|15.0|16.5|7,391.5|1.2|25.0|6.7|-4.5|-6.5|
|Armenia|17.3|18.9|597.3|-1.1|85.4|33.0|-2.9|-2.5|
|Australia|23.5|26.0|19,529.3|1.2|40.6|3.5|-8.2|-10.0|
|Austria|35.5|37.9|22,665.0|0.4|81.2|2.4|-7.8|-10.0|
|Azerbaijan|17.3|18.0|677.3|1.0|100.7|21.4|-3.3|-2.5|
|<br>Bahrain|7.0|28.0|11,754.0|2.6|151.7|0.9|-5.0|-5.7|
|Bangladesh|7.9|10.0|334.9|2.1|30.3|25.1|-2.8|-3.8|
|Belarus|29.0|30.5|1,227.7|-0.3|129.2|14.9|-4.6|-2.5|
|Belgium|42.1|43.4|20,953.5|0.3|147.7|1.6|-6.7|-10.0|
|<br>Belize|19.5|21.9|3,147.0|2.9|111.8|17.0|...|...|
|Bhutan|7.2|18.1|566.4|2.9|76.2|37.7|...|...|
|Bolivia|15.4|19.1|979.6|2.1|49.3|15.8|-4.3|-3.7|
|Bosnia And Herzegovina|36.9|42.0|1,002.1|-0.5|92.2|18.3|...|...|
|Botswana|20.6|44.8|2,810.7|1.4|89.1|3.6|-5.1|-5.0|
|Brazil|19.1|24.6|3,360.6|1.5|22.1|8.5|-5.0|-6.0|
|Bulgaria|27.1|34.3|1,574.9|-0.8|104.9|14.9|-5.6|-5.0|
|Burundi|15.6|17.5|113.5|1.8|30.9|51.1|...|...|
|Cambodia|8.4|10.5|265.3|2.4|89.5|41.8|...|...|
|Cameroon|9.8|13.7|585.1|2.2|48.3|40.4|-4.0|-4.5|
|Canada|19.3|20.9|21,558.5|1.0|74.5|2.7|-9.3|-10.0|
|Chile|17.7|21.5|4,636.8|1.4|59.9|6.3|-5.9|-6.7|
|China|5.8|6.2|852.2|0.9|45.5|16.9|-4.0|-5.3|
|Colombia|13.2|17.6|2,023.1|1.8|37.8|13.7|-4.2|-5.9|
|Congo, Dem. Rep.|4.1|4.9|104.4|2.7|42.6|52.7|-1.2|-1.0|
|Congo, Rep.|9.9|31.7|968.0|3.2|126.7|8.5|-5.1|-2.5|
|Costa Rica|18.9|20.9|3,830.2|2.3|87.1|11.3|-7.1|-5.1|
|CoteD'Ivoire|17.5|18.2|612.7|2.4|73.3|24.9|-4.8|-3.9|

- 22 -
models with individual fixed effects have an incidental parameters problem, and are generally biased (Greene 2003). We thus report results from both pooled Tobit, without unobserved effects, and random effects Tobit.<sup>35</sup> Table 7 presents the results. Columns (1) and (2) show the marginal effects at the sample mean from pooled Tobit estimation of tariffs on imports of capital goods shares and columns (3) and (4) report the results from random-effects Tobits. The coefficient of interest on input tariffs is negative and significant in all specifications implying that input-trade liberalization increases the share of imports of capital goods.

Table 7: Input-trade liberalization and the intensive margin of imports of capital goods

|Dependent vari|able: the share of imported capital goods over total import|s of the frm_i_in_t_.||
|---|---|---|---|
||(1)<br>(2)|(3)|(4)|
||Pooled Tobit|Random e|ffects Tobit|
|Input tariff(s)(t-1)|-0.378***<br>-0.362***|-0.218***|-0.225***|
||(0.066)<br>(0.066)|(0.064)|(0.064)|
|Capital goods and output tariff(s)(t-1)|Yes<br>Yes|Yes|Yes|
|Random effects||Yes|Yes|
|Industry fxed effects||Yes|Yes|
|Year fxed effects|Yes<br>Yes|Yes|Yes|
|Observations|10,800<br>10,800|10,800|10,800|
|log likelihood|-5533<br>-5412|-4457|-4436|
|Sigma u|0.352<br>0.348|0.267|0.261|
|Sigma e||0.264|0.264|

_Notes_ : The dependent variable is the share of imported capital goods over total sales of the firm _i_ in year _t_ . All specifications include capital goods and output tariffs and the Herfindahl index. Columns (2) and (4) also include firm-level controls. All control variables are defined in table 3. Heteroskedasticity-robust standards errors are reported in parentheses. Errors are corrected for clustering at the 3-digit industry level.<sup>_∗∗∗_</sup> ,<sup>_∗∗_</sup> , and<sup>_∗_</sup> indicate significance at the 1, 5 and 10 percent levels respectively.

> 35In the random effects Tobit, firm unobserved heterogeneity is assumed to be part of the composite error. Random-effects Tobits are unbiased if firm characteristics are exogenous (uncorrelated with the regressors). Honore (1992) has developed a semiparametric method dealing with this issue which captures unobserved time-invariant individual heterogeneity. He proposes a trimmed least squares estimator of censored regression models. Nevertheless, this semiparametric estimator for fixed-effect Tobits is not suitable here due to the relatively small sample size.

45
Spadaro 2006): (1) a micro-dataset containing information on observed economic and socio-demographic characteristics of individuals or households; (2) the policy rules to be evaluated; (3) a theoretical model of individual response to policy.

Standard economic analysis relies on the optimization principle and the market system to explain individual behavior and social interaction. Each agent attempts to implement the best feasible course of action. Modeling optimizing behavior thus entails the specification of (1) actions that a socioeconomic unit can take, (2) the constraints it faces, and (3) the objective function used to evaluate such actions (Varian 1984).

Two basic approaches, both relying on the optimization principle, are commonly used to construct microsimulation models. The first represents optimal behavior by an _envelope function_ also known as the maximum value function (Dixit 1990). This is the maximum attainable value of the objective function given the prevailing constraints. The second approach is based on _discrete choice models_ . These models seek to explain the behavioral process that leads to the choice made by an agent facing a discrete choice set<sup>40</sup> (Train 2003). The behavioral process is represented by a function linking the agent’s choice to observed and unobserved factors. The unobserved terms are taken to be random variables. This randomness implies that the analysis must be conducted in terms of the probability of choosing an option from the choice set.

The usefulness of the microsimulation approach to policy evaluation is better appreciated when compared with the alternative based on representative agents. For instance policy analysis based on two representative households such as rural and urban or even on six or ten regional groups can hide much of heterogeneity in agent’s behavior that explains variation in outcomes. The fact that the microsimulation approach uses a micro-data set offers the possibility to fully account for the observed heterogeneity of socioeconomic agents (Bourguignon and Spadaro 2006). Thus the approach provides a more precise way of identifying likely winners and losers from a policy reform. It also improves the accuracy in evaluating the impact of a policy on the government budget. We now consider the use of microsimulation models in fiscal incidence analysis.

> 40 A choice set is said to be discrete if it contains a finite number of all possible alternatives that are also mutually exclusive from the point of view of the decision maker (Train 2003).  When a choice set contains all possible alternatives, we say that the set is exhaustive.

42
<!-- Start of picture text --> Intermittent — Inadequate training, Cultural norms relating to<br>Supply Fseater Lack of latrines managementcultural norms and hand washinghygiene practices and personal<br>nosInadequatequantity water Poor water quality ;Inadequatedisposal excreta a>ao ‘managementie& general) (medical. Lackproducts of cleaning aackshowers)of sinks (and Lackbehaviours of7 handwashing Tackhandwashingpersonalalternativesof soap hygiene& andsoapfor<br>] ax >]<br>ee Co<br>[re] are<br>Unsafe water<br>storage<br>Limited<br>capacity for<br>water storage)<br>No drinking Fake pace Failure to protect fortes Failureworkersof health to practice care Failure of patients and igen<br>Kb Froplahed iaton &frommedicalcontaminationequipment handwashingtimes & asepticat critical caregivershandwashing to practice pekineal igen<br>techniq<br>= Po<br>Po<br>a SS ee<br>elderly,Dehydrationpatients__children)mothers° labouring (esp. &in;in_) \————!feciesaos ay5 ) <CBEe838"od~_|y >||SScontaminationIncreasestaff handsrisk; ofof) |2“patient ——\ contaminationIncrease handstisk,eeofof |)| <<V {contamination(eurfaces,(floors)(Increasefomtespate risk beds of of|8) |< whe28<&a.22\y~~ 2- > sangeet==>Increasesexmet))—adn risk of | <EEEIDNE)”fcasaww. 9X |acontamination&Increasepatient risk skin; ofof)<br>FanHint focA GSEs| Faecal-oral eeeeee (7{ ingertion’ofa” central, venous, ~)' _/Matemai| procedures& neonatal»during ' SSatSte¢ woundcare't pe«| eREetsSuial<br>{insertionv ' {rien)transmissionv | ie\_ventationv |' {intravenous‘ afteriatcathetervor | +| _ “chiabitnand/orafterv '|.Vv ll +t procedure}vEF<br>Ventilator Associated<br>Urinary Tract(UTIs)Infections GastrointestinalInfections (GSIs) System AssociatedPneumonia/(VAPIHAP) PneumoniaHospital Bloodstream(BSis)Infections Surgical(Sis)Site Infections<br><!-- End of picture text -->
income inequality, the key ingredient is that it must help expand access by the poor, as argued for example by Estache et al. (2000).<sup>3</sup> A related strand of the empirical literature focuses on the poverty effects of specific infrastructure projects using matching techniques that combine samples of beneficiaries with samples drawn from regular household surveys.<sup>4</sup> On the whole, the evidence shows that public investment in infrastructure —specifically, in the rehabilitation of rural roads— improves local community and market development. For example, rehabilitation of rural roads raises male agricultural wages and aggregate crop indices in poor villages of Bangladesh (Khandker et al. 2006). Likewise, in Vietnam the result is an increase in the availability of food, the completion rates of primary school and the wages of agricultural workers (Mu and van de Walle, 2007). In the same vein, other studies find that access to new and improved roads in rural areas enhances opportunities in non-agricultural activities in Peru (Escobal and Ponce, 2002) and in non-farm activities among women in Georgia (Lokshin and Yemtsov, 2005).<sup>5</sup> Few empirical studies have tackled directly the inequality impact of infrastructure at the macroeconomic level. Among them are those of López (2004) and Calderón and Servén (2008), both of which use cross-country panel data. López uses telephone density to proxy for infrastructure, while Calderón and Servén employ synthetic indices of infrastructure quantity and quality. In both cases, the finding is that, other things equal, infrastructure development is associated with reduced income inequality. Combined with the finding that infrastructure also appears to raise growth, the implication is that, in the right conditions, infrastructure development can be a powerful tool for poverty reduction.

> 3 We should also note that there may be two-way causality in this relationship, that is, income inequality may prevent the access of poorer people to infrastructure services. For example, Estache, Manacorda and Valletti (2002) show that income inequality adversely affects access to internet, while Alesina, Baqir and Easterly (1999) argue that more unequal societies devote less effort to the provision of public goods, including infrastructure.

> 4 This line of research compares the beneficiaries of the infrastructure project under analysis and a control group, using propensity score matching methods to eliminate the bias arising from time-invariant unobservable community characteristics that might affect the project’s outcome.

> 5 Note that in the Peruvian case, income expansion generated by the rehabilitation of rural roads is faster than consumption expansion in areas articulated product and factor markets through motorized roads. The excess income is saved since beneficiaries perceived improvements in rural roads as transitory given the record of road maintenance in the area (Escobal and Ponce, 2002).

6
18

# Table A.7: Returns to Experience and Education by College Status, Robustness

|Group:|(1) Pre|-College W|orkers|(2) C|ollege Wo|rkers|
|---|---|---|---|---|---|---|
|**_Panel A:_ Returns to Experience**|LIC|MIC|HIC|LIC|MIC|HIC|
|Baseline|1.9|2.1|4.4|3.2|2.7|4.2|
|Incl. Child Labor Exp from 15|2.2|2.4|4.8|2.9|2.7|4.2|
|Incl. Child Labor Exp from 13|2.7|2.5|4.8|2.9|2.7|4.2|
|Incl. Child Labor Expfrom 6|2.5|2.4|4.8|2.9|2.7|4.2|
|Decadal Cohort FE|1.9|2.1|4.4|3.2|2.7|4.2|
|Important Events FE|1.6|2.4|4.0|2.4|1.5|3.7|
|Excl. Self-Employed Workers|1.8|2.0|4.5|3.3|2.7|4.3|
|<br>Estimated UsingMales Only|2.0|2.3|4.8|3.2|2.8|5.0|
|Unemployment_<_7% (25p)|1.9|2.2|4.3|3.2|2.7|4.0|
|Unemployment_<_10% (50p)|1.9|2.1|4.4|3.2|2.7|4.0|
|NLF_<_35% (25p, 50p)|2.3|2.1|4.4|3.1|2.7|4.1|
|Unemployment_>_7% (25p)|2.1|2.2|4.8|2.6|2.7|4.5|
|Unemployment_>_10% (50p)|2.4|2.2|4.3|2.3|2.3|4.3|
|NLF_>_35% (25p, 50p)|1.8|2.0|4.0|3.1|2.3|4.2|
|Non-Linear: Bin 5 Only|2.7|2.8|7.9|4.6|3.5|7.3|
|Non-Linear: Bins 10-25 Only|1.4|1.6|2.9|2.5|2.1|2.8|
|**_Panel B:_ Returns to Education**|HIC|MIC|LIC|HIC|MIC|LIC|
|Baseline|5.5|8.8|6.7|13.6|11.5|13.0|
|Incl. Child Labor Exp from 15|5.7|9.1|7.6|15.2|12.2|13.0|
|Incl. Child Labor Exp from 13|6.0|9.3|7.8|15.2|12.2|13.0|
|Incl. Child Labor Expfrom 6|6.8|9.6|8.0|15.2|12.2|13.0|
|Decadal Cohort FE|5.4|8.8|6.7|13.5|11.5|13.0|
|Important Events FE|5.4|8.7|6.6|12.7|10.7|12.7|
|Excl. Self-Employed Workers|4.7|8.0|6.8|11.9|11.3|13.7|
|Estimated UsingMales Only|5.1|9.0|6.4|13.7|11.2|12.6|
|Unemployment_>_7% (25p)|5.5|9.2|6.4|13.5|11.3|13.2|
|Unemployment_<_10% (50p)|5.5|8.7|6.8|13.6|11.5|12.3|
|NLF_<_35% (25p, 50p)|7.7|8.9|6.6|20.4|11.9|13.4|
|Unemployment_>_7% (25p)|8.1|7.5|6.4|19.1|16.8|14.3|
|Unemployment_>_10% (50p)|8.6|7.8|7.3|19.3|12.9|12.1|
|NLF_>_35% (25p, 50p)|4.9|7.0|7.8|12.6|14.1|11.1|
|Non-Linear: Education|5.5|8.8|6.7|13.6|11.5|13.0|
|10 Industry FE|4.4|6.7|6.0|13.1|10.7|11.1|
|10 IndustryFE + Controls|4.0|4.3|6.0|10.7|8.5|10.5|

_Notes:_ This table shows the returns to experience and the returns to education by college status when implementing various robustness checks. See text for details. Controls: dummies for the relationship to the head of household (head, spouse, children, parents, other relatives, non-relatives), dummies for the marital status (married, never married, living together, divorced/separated, widowed), dummy if the individual lives in an urban location, and dummy if the individual is self-employed.