rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 1: Observable Selection Effects Induced by Flexible vs Standard Offers**

||(1)<br>Flexi<br>Contract<br>Disburse|(2)<br>ble<br>Offers:<br>d Loans|(3)<br>Stan<br>Contract<br>Disburse|(4)<br>dard<br>Offers:<br>d Loans|(5)<br>p-value<br>(1)=(3)|
|---|---|---|---|---|---|
||Mean|SD|Mean|SD||
|**Panel A: Loan characteristics**||||||
|Principal (1000s COP)|1437|1008|1403|969|0.42|
|Term (months)|12.65|3.25|12.58|3.27|0.45|
|Interest rate (APR)|37|2|37|2|0.90|
|P-value of joint test|||||0.81|
|**Panel B: Socioeconomic status (SES) of clients**||||||
|Client is female|0.66|0.47|0.64|0.48|0.64|
|Age of the client (years)|39.2|13.45|40.79|14.01|0.03|
|Married or in a common-law marriage|0.69|0.46|0.68|0.47|0.59|
|Some higher education|0.37|0.48|0.34|0.47|0.23|
|Client is head of household|0.19|0.4|0.23|0.42|0.07|
|Lives in a house (omitted: apartment or room)|0.87|0.33|0.88|0.33|0.95|
|Owns home|0.32|0.47|0.33|0.47|0.37|
|Household income (1000s COP)|1502|911|1437|821|0.24|
|Household expenses (1000s COP)|825|405|809|390|0.40|
|P-value of joint test|||||0.37|
|**Panel C: Business characteristics**||||||
|Age of primary business (years)|8.95|7.36|9.10|7.84|0.57|
|Retail sector|0.64|0.48|0.62|0.48|0.90|
|Productive sector|0.17|0.37|0.16|0.36|0.49|
|Services sector|0.20|0.40|0.22|0.41|0.45|
|Sales (1000s COP)|3353|3143|3185|3112|0.31|
|Profits (1000s COP)|528|485|503|444|0.39|
|P-value of joint test|||||0.81|
|Number of observations|582||1,893|||
|P-value ofjoint test: loan,SES,and business charac|teristics||||0.79|

Notes: P-values based on regressions that control for treatment assignment probability; for additional details, see Section 4.
**Figure 3: Non-parametric regressions of yield for main crops for smallholders and estates**

<!-- Start of picture text -->
-4 -2 0 2 4 6 -4 -2 0 2 4 6<br>ln (tobacco area in ha) ln (maize area in ha)<br>95% CI lpoly smooth: ln (tobacco yield in kg) 95% CI lpoly smooth: ln (mazie yield in kg)<br>8<br>8<br>6<br>6<br>4<br>4<br>2<br>2<br>0<br><!-- End of picture text -->

**Figure 3a: Tobacco**

**Figure 3b: Maize**

<!-- Start of picture text -->
-4 -2 0 2 4 -4 -2 0 2 4<br>ln (ground nuts area in ha) ln (cassava area in ha)<br>95% CI lpoly smooth: ln (ground nuts yield in kg) 95% CI lpoly smooth: ln (cassava yield in kg)<br>7 10<br>8<br>6<br>6<br>5<br>4<br>4<br>2<br>3 0<br><!-- End of picture text -->

**Figure 3c: Ground nuts**

**Figure 3d: Cassava**

_Source:_ Own computation from 2006/07 NACAL. _Note:_ As explained in the text, both smallholder and estate samples are included.

25
result to a comprehensive set of robustness checks that account for the influence of outlier and extreme observations, for potential alternative explanations, and for various poverty measures. Section IV offers some concluding remarks.

# **II. The Model**

We now present a two-sector model with asymmetric technologies to help us understand the relation between sectoral growth and poverty alleviation. We focus on the two-sector case for simplicity, but the results are analogous for the n-sector case.

The economy is populated by two types of individuals: poor and rich. Both types are endowed with _n_ units of labor, derive utility from the consumption of a final good, and have the same discount factor ρ and instantaneous utility function _u_ ( _c_ )=log( _c_ ). However, only rich individuals have access to an asset _a_ that allows them to transfer wealth across periods. This setting implies that the income and consumption of poor individuals depend only on the real wage rate. Thus we assume that the rate of poverty reduction is related only to the growth rate of real wages. Although this is an extreme assumption, it simplifies considerably the analysis and is roughly consistent with the low saving rates observed both in poor countries and poor households within a country.<sup>1</sup> The final good, _y,_ is produced by a perfectly competitive firm using a constantreturns-to-scale technology and two intermediate goods, _y_ 1 and _y_ 2, as inputs according to The final good can be used not only for consumption but also as capital in the production of the intermediate goods. Each intermediate good is produced by a perfectly competitive firm according to the following technology with labor-augmenting technological progress,

> 1 Schmidt-Hebbel and Serven (1999) show that saving rates increase with income across countries.  In poor countries, the saving rates are below 10%.  Attanasio and Székely (1998) provide evidence on households’ saving rates at different levels of the income distribution in Mexico.  Their data show that saving rates increase strongly with income and display even negative values up to the 25th percentile of the household income distribution.

3
# **Table C.5: Summary statistics**

## **Panel A: Promotion analysis**

||N|Mean|SD|Min|Max|
|---|---|---|---|---|---|
|Age|198|25_._80|3_._53|19_._00|38_._00|
|Married|198|0_._84|0_._36|0_._00|1_._00|
|Household members|198|3_._27|1_._57|1_._00|10_._00|
|Household head|198|0_._12|0_._33|0_._00|1_._00|
|Migrant|198|0_._66|0_._47|0_._00|1_._00|
|Education years|198|8_._33|1_._76|0_._00|15_._00|
|Nr Children (if ever married)|180|1_._04|0_._79|0_._00|5_._00|
|Combined income of spouses (if married)|167|23630_._19|6556_._26|9500_._00|44760_._00|
|Education difference of spouses (if married)|167|_−_0_._31|3_._05|_−_10_._00|9_._00|
|Age gap of spouses (if married)|166|_−_4_._64|2_._75|_−_14_._00|4_._00|
|Husband works in garment industry (if married)|167|0_._66|0_._47|0_._00|1_._00|
|Woman’s share in spouses’ income(if married)|167|0_._46|0_._14|0_._09|1_._00|

**Panel B: Exposure analysis**

||N|Mean|SD|Min|Max|
|---|---|---|---|---|---|
|Age|238|26_._20|4_._16|18_._00|40_._00|
|Married|238|0_._89|0_._31|0_._00|1_._00|
|Household members|238|3_._09|1_._42|2_._00|8_._00|
|Household head|238|0_._01|0_._11|0_._00|1_._00|
|Migrant|238|0_._63|0_._48|0_._00|1_._00|
|Education years|238|6_._08|2_._48|0_._00|10_._00|
|Nr Children (if ever married)|224|1_._16|0_._84|0_._00|4_._00|
|childoutsidehh|238|0_._42|0_._49|0_._00|1_._00|
|Total household income|238|21166_._26|7325_._44|6500_._00|43000_._00|
|Education difference of spouses (if married)|208|_−_0_._35|3_._74|_−_10_._00|9_._00|
|Age gap of spouses (if married)|208|_−_5_._24|3_._25|_−_23_._00|_−_1_._00|
|Husband works in garment industry (if married)|208|0_._66|0_._47|0_._00|1_._00|
|Woman’s share in household income|238|0_._48|0_._18|0_._00|1_._00|

Notes: Panel A uses data from the baseline survey of the promotion program, conducted in the factory. Because there is no baseline for the exposure analysis, Panel B uses data from the household surveys only from the non-exposed comparison group.

73
test the robustness of the result to a more powerful first stage. As discussed in Section 3.2, many of the restrictions in the NIL apply only to foreign investors, notably the limitations on foreign equity shares. Hence, in column 2 we replace total entry with entry of foreign plants.<sup>40</sup> Using this specification, the value of the F-statistic increases up to 12.2, which is above the standard threshold of 10 (Stock et al., 2005). As in column 1, the coefficient is negative and significant, but double in size. Along with the results of the first stage, this implies a similar -0.25 elasticity of wage markdown with respect to product market liberalization. This evidence is consistent with foreign entrants driving the impact of product liberalization on labor market power.

Table 2: 2SLS estimates: Impact of product market entry on plant-level wage markdowns.

||(1)<br>Markdown|(2)<br>Markdown|
|---|---|---|
|Entry (log-# of entrants, 5-dig product market)|-0.990**<br>(0.438)||
|Entry (log-# of foreign entrants, 5-dig product market)||-2.303**<br>(1.158)|
|Observations|34,269|34,269|
|Base-year covariates-year FE|yes|yes|
|Plant FE|yes|yes|
|Industry-year FE|2-dig|2-dig|
|Commuting zone-year FE|regency|regency|
|SE clustering|5-dig|5-dig|
|First stage coefcient|-0.259**|-0.111***|
||(0.105)|(0.032)|
|First stage F|6.068|12.20|

_Notes:_ this table presents 2SLS estimates of the impact of the log-number of entrants in a product market, defined as a 5-digit industry, on plant-level log-wage markdown. An inverse hyperbolic sine transformation is applied to the independent variable in order to deal with zero entry. The log-number of entrants is instrumented with a dummy variable quantifying investment restrictions. Base-year covariates are: i) a Herfindahl–Hirschman index based on plants’ sales to account for product market concentration; ii) the share of blue collar workers to control for government’ s reluctance in reforming an industry; iii) the share of industry output produced by state-owned enterprises (SOE); iv) the product average import tariff rate; v) a dummy equal to one if a product market is subject to at least one non-tariff measure, and vi) the market share of politically connected plants. Errors are clustered at the product market-level. The coefficients with<sup>_⋆⋆⋆_</sup> are significant at the 1% level, with<sup>_⋆⋆_</sup> are significant at the 5% level, and with<sup>_⋆_</sup> are significant at the 10% level.

# **6.3 The Role of Minimum Wages**

The presence of monopsony and oligopsony power often justifies the use of labor market policies to reduce it, notably minimum wages (Manning, 2021). Thus, we conclude the analysis by examining whether product market regulation affects markdowns less when

> 40 Foreign entrants are defined as plants with age zero and a positive share of foreign capital. Results— available upon request—are similar when using shares of foreign capital larger than 50% and 90%.

27
and sweets. The commodities for which the measure of pro-poorness is greater than one are those with higher budget shares for low-income households. Of the 11 food items identified, cereals, roots and grains have the highest value for the indicator.

The envelope approach to policy impact analysis has some limitations because one can only capture the static effects of the policy reform. Furthermore, the fact that the envelope theorem is valid only in the neighborhood of the initial optimum makes the method inappropriate for the study of large price changes or in situations in which the household is out of equilibrium due to restrictions such as rationing. These cases require an estimation of complete demand and supply systems.

Table 4.1. Guinea (1994): Distributional Characteristics of Components of Food Expenditure

||PovertyGap|Squared PovertyGap|Watts|
|---|---|---|---|
|Beef|0.65|0.56|0.61|
|Palm Oil|1.22|1.16|1.19|
|Bread|0.78|0.72|0.75|
|Fresh Fish|0.53|0.45|0.49|
|Smoked Fish|1.10|1.14|1.12|
|Local Rice|1.33|1.30|1.30|
|Imported Rice|1.17|1.23|1.30|
|Sugar|1.10|1.07|1.09|
|Cereals|1.57|1.57|1.57|
|Roots|1.53|1.56|1.55|
|Grains|1.56|1.61|1.59|
|Fruits|0.99|0.91|0.95|
|Vegetables|1.26|1.28|1.28|
|Meats|0.83|0.76|0.79|
|Livestock|0.94|0.95|0.95|
|Beverages|0.57|0.47|0.52|
|Oils|1.14|1.08|1.10|
|Sweets|1.04|1.01|1.03|
|Canned Food|0.42|0.37|0.40|

Source : Essama-Nssah(2007)

# **Discrete Choice Methods**

A discrete choice model can be characterized in terms of two fundamental elements: the _choice set_ and the _decision process_ (or the decision rule). As noted earlier, the choice set must be discrete and exhaustive, and the included options must be mutually exclusive. In the case of discrete models of labor supply, for instance, the choice set can be represented by a few options such as not working, working part-time and working full 45
WPS4168

# **Mobility and Earnings in Ethiopia’s Urban Labor Markets: 1994-2004**

Arne Bigsten<sup>♦</sup> Taye Mengistae<sup>∗</sup> Abebe Shimeles<sup>♦</sup>

## **Abstract**

An analysis of panel data on individuals in a random selection of urban households in Ethiopia reveals large, sustained, and unexplained earnings gaps between public and private, and formal and informal sectors over the period 1994-2004. We have no formal evidence whether these gaps reflect segmentation of the labor market along either of these divides. In other words, we cannot show whether they are at least in part due to impediments to entry to the higher wage sector. However, we do have evidence that, if segmentation explains any part of the observed earnings gaps, then it could only have weakened over the survey decade. We find, first, that the rate of mobility increased between the two pairs of sectors. Sample transition rates grew across survey waves, while state dependence in sector choice decreased. Second, the sensitivity of sector choice to earnings gaps increased over the same period. In particular, the role of comparative earnings in selection into the informal sector was evident through out the survey decade and increased in magnitude over the second half of the period.

**JEL Classification** : J42, J62, J31, J45, O17

**Key Words** : Segmented labor markets, labor market mobility, wage differentials, informal sector, public sector labor markets

## World Bank Policy Research Working Paper 4168, March 2007

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> ♦ University of Gothenburg, Gothenburg, Sweden.

> ∗ World Bank Group, Washington DC, USA.
in one sitting prior to any plot visits. The only contact with these households was made during the time that the field teams returned to the EA for the post-harvest visit to the treatment households. At the conclusion of each interview, one maize plot was then selected at random, and the enumerator accompanied the farmer to the selected maize plot, whose area and plot outline was obtained using the handheld GPS device. This protocol mirrors the current interview flow in the household surveys that have been supported by the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) initiative, including the IHS5 for Malawi. Table A1 provides an overview of the fieldwork implementation timeline, and the distribution of interviews with treatment and control households over time.

# **3. NCME as a Window on Behavioral Anomalies**

NCME in self-reported plot areas has been widely reported (e.g., Carletto et al., 2013; Carletto et al., 2015; Dillon et al., 2019; Abay et al., 2019; Gourlay et al., 2019, Abay et al. 2021). This analysis corroborates prior findings and allows us to make more nuanced observations and link these patterns to behavioral anomalies observed in the broader economics literature. These findings lay the foundation for section 4's exploration of the causal impacts of providing objectively verifiable information that might resolve NCME.

Table 1 reports the descriptive statistics for sampled households and plots. Most households are male-headed and literate with about three-quarters of them relying on farming as main source of livelihood. On average, the plots are half an acre while the average farm size is roughly 1.5 acres. Table 1 shows that the randomization worked. Most of the observable characteristics are balanced across the treatment and control groups, and we cannot reject the null hypothesis of jointly zero coefficients associated with the regressors in Table 1.<sup>9</sup> Most importantly, pre-treatment self-reported plot area appears to be statistically similar across the control and treatment group plots.

> 9 The F-test statistic for the regression of the treatment indicator variable on the characteristics listed in Table 1 equals 1.14, with p-value=0.29.

7
33 Table 4: Tests of the parallel trend assumption

||Random tre<br>Generalized<br>test on e<br>|nd model<br>Hausman<br> qn(5)<br>|Differential<br>Non-linea<br>test on<br>|trend model<br>r restriction<br>eqn(10)<br>|
|---|---|---|---|---|
|Dependent variable|chi-square|p-value|_F_|p-value|
|Tot. health exp. per capita|2.450|0.118|0.290|0.595|
|PYLL cancer of colon|0.720|0.395|1.280|0.270|
|PYLL breast cancer|0.790|0.375|1.350|0.257|
|PYLL cervical cancer|0.570|0.450|0.060|0.809|
|PYLL diabetes|0.000|0.966|0.090|0.771|
|PYLL ischemic heart disease|_1.090_|_0.296_|_0.650_|_0.428_|
|PYLL cerebrovascular disease|0.190|0.664|0.470|0.501|
|PYLL influenza pneumonia|3.720|0.054|0.170|0.683|
|PYLL pregnancy|2.360|0.125|8.220|0.009|
|PYLL perinatal causes|2.170|0.141|0.120|0.728|
|Formal sector employment|_3.170_|_0.075_|_5.550_|_0.028_|
|Employment|1.130|0.289|0.100|0.756|

Note: Italicized numbers indicate estimates that are to be ignored because diagnostic tests have shown them to be irrelevant or unreliable.

Table 5: DID estimates of SHI effects on health and labor outcomes

||D<br>mo<br>|ID<br>del<br>|Rando<br>mo<br>|m trend<br>del<br>|Differen<br>mo<br>|tial trend<br>del<br>|
|---|---|---|---|---|---|---|
|Dependent variable|Coef|p-value|Coef|p-value|Coef|p-value|
|Tot. health exp. per capita|0.035|0.001|0.040|0.001|0.037|0.261|
|PYLL cancer of colon|-0.034|0.412|-0.029|0.468|-0.037|0.525|
|PYLL breast cancer|0.055|0.095|0.059|0.082|0.065|0.245|
|PYLL cervical cancer|0.020|0.805|0.027|0.738|0.005|0.949|
|PYLL diabetes|0.000|0.996|0.007|0.927|-0.118|0.376|
|PYLL ischemic heart disease|_0.009_|_0.760_|_0.013_|_0.635_|_0.131_|_0.139_|
|PYLL cerebrovascular disease|-0.125|0.175|-0.124|0.187|-0.065|0.125|
|PYLL influenza pneumonia|0.075|0.339|0.068|0.403|0.050|0.619|
|PYLL pregnancy|-0.031|0.767|-0.043|0.690|-0.135|0.361|
|PYLL perinatal causes|0.012|0.599|0.010|0.690|0.098|0.396|
|Formal sector employment|_0.000_|_0.944_|_0.002_|_0.699_|_0.006_|_0.788_|
|Employment|-0.003|0.407|-0.003|0.370|0.025|0.153|

Notes: In all models, the natural logarithm of the dependent variable is used. Italicized numbers indicate estimates that are to be ignored because diagnostic tests have shown them to be irrelevant or unreliable.
- [32] Xu, Xiaohe and Martin Whyte. 1990. " Love Matches and Arranged Marriages: A Chinese Replication," Journal of Marriage and the Family 52(3), 709-722.

- [33] Zeng, Yi and Deqing Wu. 2000. " Regional Analysis of Divorce in China since 1980," Demography 37(2), 215-219.

- [34] Zhang, Junsen and William Chan. 1999. " Dowry and Wife's Welfare: A Theoretical and Empirical Analysis," Journal of Political Economy 107(4), 786-808.

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# Appendix A: Proof of Propositions

The optimal values of _α_<sup>_∗_</sup> and _h_<sup>_∗_</sup> _f_<sup>arejointlydeterminedby(1)and(2).Notethat</sup><sup>_α∗_canbe</sup> solved from condition (1) as a function of _h_<sup>_∗_</sup> _f_<sup>,whichcanthenbepluggedin(2)tosolve</sup><sup>_h∗_</sup> _f_<sup>.</sup> The optimal values of _α_<sup>_∗∗_</sup> and _h_<sup>_∗∗_</sup> _f_<sup>arejointlydeterminedby(3)and(4),basedonwhichwe</sup> get _∂α_<sup>_∗∗_</sup> _/∂hp >_ 0 and _∂h_<sup>_∗∗_</sup> _f_<sup>_/∂hp>_0byCramer'srule:</sup> where _|H|_ is the determinant of Hessian matrix

and _|H| >_ 0 is assumed for the existence of optimal solutions. We can also get _∂α_<sup>_∗∗_</sup> _/∂γ >_ 0 and _∂h_<sup>_∗∗_</sup> _f_<sup>_/∂γ_inasimilarway.Weassume</sup><sup>_δf_1</sup><sup>_−ηps_12</sup><sup>_≥_0,whichessentiallymeansthat</sup>

30
Providers of legal services believe that the market for legal services is competitive but also that great importance is attached to quality and reputation. Therefore, clients are likely to remain with a law office even if the law office raises the prices for the services provided. Some of the stakeholders in the sector believe that it is difficult for a new law office to establish successfully in Mozambique since the main clients for legal services (the government, international tenders, large and/or foreign firms) tend to always turn to the same “reputable” law offices for legal services.

Finally, note that our focus is on the private practice of law and on legal services demanded by and supplied to the private entrepreneurial sector. We do not discuss the demand for legal services by individuals nor the provision of legal services by the public judicial sector, which is discussed in detail in Levy (2002).

# **3.3 Engineering Consulting Services**

The current number of graduates in engineering in Mozambique seems to be well below the number needed for achieving the country’s growth objectives. Shortages are particularly evident for railroad engineers and navy engineers, given the needs of Mozambique’s transport system, and for mining engineers, given the recent and forthcoming investments in mega-projects in mining. Also, there are very few engineers or engineer technicians with knowledge of road maintenance, although they are in great need.

The definition of engineering services is more nebulous than that of the previous two types of services given the partial overlap between engineering consulting services and construction services, on which we do not focus. The main providers of engineering consulting services in Mozambique are 8-10 firms. These firms employ a total of about 90 Mozambican engineers, 11 foreign engineers, as well as 18 technical engineers (with medium-level training). Based on the enterprise census data shown in Table 5, as of 2002 there were 41 firms providing engineering and architectural consulting services in Mozambique. Assuming that many of those firms continue to operate today, this implies that there are at least another 30 firms providing engineering consulting services in Mozambique (plus any new law offices that opened between 2002 and 2008). The majority of engineering consulting firms are Mozambican-owned but our survey of engineering consulting firms in Mozambique identified 4 firms with some degree of foreign ownership. The Mozambican Guild of Engineers has a current membership of 1100 engineers. Note however that the most important employers for engineers in Mozambique are not engineering consulting firms but rather telecommunications firms (Mcel, Vodacom, and TDM), mining firms (MOZAL), and oil firms (Petromoc).

There is no information on the market structure in engineering consulting services. Based on our survey in Maputo, 2 large firms account for more than 65% of the market, followed by another player with about 22% of the market, and the rest of the market is accounted for by smaller firms.

21