rafmacalaba/datause-encoder-data huggingface dataset:
# 2 An urban land-use model with tenure insecurity (benchmark

# model)

We rst present a benchmark model based on the standard land use model of urban economics, in which we introduce tenure insecurity. This allows us to derive city structure and surplus in the absence of a property right system that makes land tenure secure.

## 2.1 Main assumptions

The urban space is represented by a line segment at the extremity of which lies a CBD where all jobs are located. Each location on this segment (denoted by its distance _x_ to the city center) has a unit mass of landowners, each endowed with one land plot.<sup>7</sup> Each landowner decides whether or not to sell his land plot to a potential migrant coming to the city, thereby extracting the migrant's willingness to pay for residence in that particular location. Because migrants will be working in the CBD, they value proximity to the city center. As migrant buyers are competing with one another, sellers sell their plots to the highest bidder. We consider an open-city model, in which buyers migrate to the city until the expected utility in the city (given by their expected disposable income) is equalized with the rural utility level _u_ .<sup>8</sup> In our model, land tenure is insecure for some plots in the sense that a buyer can lose his plot in the future with a non-zero probability. This probability may reect the numerous conicts over land ownership that are prevalent in many developing country cities, in particular those that result from unclear initial ownership of the land by the seller, which is ground for competing claims.<sup>9</sup> However, not all plots are insecure. There are two possible levels of tenure security _Q ∈{q,_ 1 _}_ , with _q <_ 1 . Insecure plots may be contested and have a probability _q_ of remaining in the hands of their buyer in the future, whereas secure plots are uncontested and have a probability 1 of remaining in the hands of their buyer. When a buyer is evicted from his plot, the plot is simply grabbed by an absentee landowner and can be resold to another migrant. We denote _π_ the exogenous proportion of secure plots before the city forms and assume it is uniform across all locations.

The key assumption in our model is the existence of an information asymmetry between sellers

> 7Our assumption of a unit of land per location makes our framework akin to Alonso (1964).

> 8We assume a linear utility function and a price of the composite good normalized to 1, so that the utility in the city dened as the consumption of the composite good is exactly equal to the expected disposable income.

> 9Other conicts may oppose heirs, customary owners and investors, private parties and public authorities. See Durand-Lasserve et al. (2015) for a full typology of urban land conicts.

6
Forbes (2002) has attempted to estimate one of the possible costs of the URR. Using a Tobin’s q framework and an Euler equation for a panel of Chilean publicly traded companies from 1988 to 2000, this author concluded that smaller traded firms were more financially constrained than larger firms as a consequence of the policy. This conclusion has been confirmed by a study by Gallego & Hernandez (2003) on the microeconomic effects of the controls, which examined the financial statements of 73 Chilean firms from 1986 to 2001. One has to acknowledge that it is evident that the URR had its costs; nonetheless, those studies do not consider that it was precisely the objective of the policy to limit the access to foreign finance (i.e. the exposure) of firms that are more likely to have unhedged foreign currency debt and are more financially fragile (subject to bankruptcy from capital flow reversals or sudden stops). Smaller firms are more likely to fall into those categories.

The econometric literature on the Colombian capital account regulations has focused its attention on their impact on the level and composition of net capital inflows and, to a smaller extent, on the effect of the regulations on the real exchange rate and domestic interest rates. In addition to the usual technical econometric difficulties in analyzing those issues, in the Colombian case, the shortcomings of the empirical analysis are compounded by the lack of appropriate high frequency balance of payments statistics covering the period during which the controls were in place.

Cardenas and Barrera (1997), using data from 1985 to 1996, analyzed the effectiveness of the controls in terms of their impact on the level and composition of capital flows. They estimated simple OLS regressions for capital flows to Colombia on a proxy of the external environment, interest rate differentials, fundamentals and measures of the tax equivalent of the deposit requirements and found that the URR has been successful in tilting the maturity structure of inflows towards long-term flows, but did not reduce the total volume of capital flows. Cardenas and Steiner (2000) updated the data set that was used by Cardenas and Barreras and they ran a simple Chow test to determine if the reserve requirements changed the composition of inflows in favor of long-term flows and concluded that the deposit requirements were effective in doing so.

Ocampo and Tovar (1999) used Vector Error-Correction and State-Space models to estimate the effects of the deposit requirements on capital flows and found that the price6
sector can be explained as a result of limited competitive pressures. Finally, using data for eight countries in the region – Argentina, Brazil, Chile, Colombia, Costa Rica, El Salvador, Mexico and Peru - Levy Yeyati and Micco (2007) find that foreign bank penetration weakened competition in the region as measured by the H statistic proposed in Panzar and Rose (1987).

All in all the evidence on competition and efficiency suggests that foreign bank entry can bring potential gains in this area except in environments which limit competitive forces such as when bank concentration is high, bank activities are restricted, and bank entry and exit is difficult.

# **_(ii) Stability_**

The rise in importance of foreign banks in developing countries has led to an intense debate on the pros and cons of foreign bank participation in terms of its impact on stability. Those opposed to foreign bank participation argue that because foreign banks have weaker ties to developing nations and have more alternative business opportunities than domestic banks, they are more likely to be fickle lenders. Furthermore, there is also the potential that they could import shocks from their home countries. On the other hand, in favor of greater stability is the notion that foreign banks are typically well diversified institutions, with access to many sources of liquidity that will be less affected by shocks.

Though some studies have found that foreign banks can respond to shocks from their home countries (see Goldberg, 2002; Martinez Peria, Powell and Vladkova-Hollar, 2005), a larger number of studies have found that they tend to be more stable lenders than domestic banks, in particular during periods of crisis in developing countries. Looking at the behavior of banks in Argentina and Mexico during the 1994-95 Tequila crisis, Goldberg, Dages and Kinney (2000) find that foreign banks generally had higher loan growth rates than their domesticallyowned counterparts, with lower volatility of lending contributing to lower overall volatility of credit. Peek and Rosengren (2000) reach a similar conclusion after examining direct and crossborder foreign bank lending in Argentina, Brazil, and Mexico during 1994-1999. Using bank level data for the late 1990s for Argentina, Chile and Colombia, Crystal, Dages, and Goldberg (2001) show that foreign banks on average exhibited higher loan growth rates than domestic banks. Examining the behavior of foreign and domestic banks in Malaysia during the 1997-98 Asian crisis, Detragiache and Gupta (2006) find no evidence that foreign banks abandoned the 12
WPS3923

# **PUBLIC-PRIVATE SECTOR WAGE DIFFERENTIALS AND RETURNS TO EDUCATION IN DJIBOUTI**

by Paloma Anós Casero<sup>1</sup> and Ganesh Seshan<sup>2</sup>

## **Abstract**

Do public sector workers earn a wage premium in Djibouti and are the returns to education different across the sectors? Private and public sector wage earnings are estimated using 1996 household survey data, while controlling for selectivity using Heckman’s two stage approach. The paper finds that Djiboutian public sector employees earn a wage premium, independent of their personal attributes and human capital endowments, and are more likely to be males and have parents in the public sector. Workers in the public sector earn higher private rates of return to education than do private sector workers with post-secondary schooling. These results raise concerns about the current government’s hiring and wagesetting practices that generate distortions in the labor market and are not efficiently allocating labor and public resources.

Keywords: public/private wage differential, private returns to education, Djibouti JEL Classification: J31, J45

### World Bank Policy Research Working Paper 3923, May 2006

_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._

> 1 Paloma Anós Casero is Senior Economist at the Social and Economic Development Group (MNSED), Middle East and North Africa Region, World Bank D.C. Email: panoscasero@worldbank.org

> 2 Ganesh Seshan is a consultant (ETC) at the Social and Economic Development Group (MNSED), Middle East and North Africa Region, World Bank. D.C. Email: gseshan@worldbank.org
Table 3: Business Outcomes

||||Business|Performan|ce|||Cl|ient Co|mpositio|n||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Business<br>Survival<br>(1)|Total<br>Clients<br>(2)|Revenue<br>Last<br>Week<br>(3)|Revenue<br>Last<br>Month<br>(4)|Proft<br>Last<br>month<br>(5)|Performance<br>Index<br>(1)-(5)<br>(6)|Sells to<br>Strangers<br>(7)|Online<br>Clients<br>(8)|Store<br>credit<br>value<br>(9)|Store<br>credit<br>clients<br>(10)|Store<br>credit<br>Percent<br>(11)|Ofers<br>delivery<br>(12)|
|Treatment|0.07<sup>_∗∗∗_</sup>|1.68<sup>_∗∗_</sup>|11.26<sup>_∗_</sup>|27.82|9.15|0.13<sup>_∗∗_</sup>|0.08<sup>_∗∗∗_</sup>|0.76<sup>_∗∗∗_</sup>|-1.25|-0.56<sup>_∗_</sup>|-2.45|0.05<sup>_∗_</sup>|
||(0.03)|(0.80)|(5.85)|(16.95)|(8.32)|(0.05)|(0.03)|(0.23)|(4.37)|(0.34)|(1.66)|(0.03)|
|RI p-values|.02|.05|.05|.11|.24|.02|0|0|.77|.04|.11|.06|
|Control Mean|.75|8.64|60.11|213.53|98.41|-.06|.31|1.38|37.81|3.6|20.66|.31|
|Obs|983|985|961|952|947|985|914|985|960|913|957|915|

_Notes_ : This table estimates specification (1) in the paper. Outcomes (2) to (12) are coded as zero for closed businesses; table A.7 reports on the outcomes conditional on business survival. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust standard errors in parentheses. All regressions have strata fixed effects and control for variables chosen using post-double-selection lasso procedures and baseline level of the outcome when available. RI p-values are from a randomization inference procedure that randomly permutes treatment assignment. Business performance outcomes are defined as follows: (1) is a binary variable for whether the business is still active; (2) is the distinct number of clients who made a purchase last month; (3) and (4) are revenue in USD last week and last month, respectively, winsorized at the 95th percentile; (5) is profit in USD last month, winsorized at the 95th percentile; and (6) is a business performance index that averages the z-scores from outcomes (1) to (5). Client composition outcomes are defined as follows: (7) is a binary variable for whether the respondent reports that at least some of her clients are not a part of her family or friends; (8) is the number of distinct clients who made a purchase last month and who made the order via online channels such as Facebook or Instagram; (9) is the value in USD of last month’s sales made on store credit, winsorized at the 95th percentile; (10) is the number of distinct clients that currently owe store credit; (11) is the reported percent of last month’s revenue coming through store credit; and (12) is a binary variable for whether the respondent offers delivery services. Outcomes in USD assume a conversion rate of 1.41 USD for 1 JD
migration condition may be written as Conditional on a given level of home country experience, the probability to migrate increases the higher the US return to home country experience relative to the home country return, holding all other wage determinants constant. Under the assumption that v0j and v1j have a bivariate normal distribution with zero mean, standard deviations �0 and �1 and correlation coe¢ cient �, the substitution of the migration condition into the wage equation yields where �j = E(vjjvj > zj) = �(zj)=(1 � �(zj), vj = <u>(v1j</u> � v0j)=�vj, zj=(�0j � �1j + cj � (�1j � �0j)Xj)=�vj and �vj = q�<sup>2</sup> vj�<sup>�0j�1j:�jiscalled</sup> the inverse Mills ratio. 1 � �(zj) is the probability to migrate. (Heckman (1979)) The fact that the truncation of the error term depends on Xj implies that the expected value of the estimated return to home country experience of US immigrants di¤ers from the US return to home country experience of the average home country resident if �j is not controlled for, as where �j =<sup>�0</sup> �<sup><u>j</u></sup> vj<sup>�1j(</sup> �<sup>�</sup> 0<sup>1</sup> j<sup><u>j</u>��j):</sup> Equation (7) shows that the expected value of the return to home country experience estimated using the sample of US immigrants E(�<sup>b</sup> j) is composed of two terms. There is a direct e¤ect of home country experience on log well as migration costs determine the decision to migrate. US immigration laws matter as well. Furthermore, the decision to migrate to the US is not exclusively based on economic gains, but also on family ties and political reasons. Census data does not allow to control for these factors as it neither provides information about the legal status of the immigrant nor the reason for immigration. The Roy model further assumes that migration decisions are irreversible and hence ignores the issue of self-selection induced by selective return migration. Last, self-selection may also occur when immigrants decide in which US division to reside. The regional variation in demand for skills, however, seems to be a less important determinant for the settlement of immigrants across regions than it is for native borns (see Bratsberg and Terrell (2000)).

19
Table 7: Impact of adding cash to therapy on mental health.

||(1)<br>**Prim**<br>_Minimal d_|(2)<br>**ary**<br>_epression_|(3)|(4)<br><br>_D_|(5)<br>**Secondary**<br>_iscrete scor_|(6)<br><br>_es_|(7)|
|---|---|---|---|---|---|---|---|
||=1 if<br>PHQ8_≤_4|=1 if<br>GHQ12_<_3|PHQ8|GHQ12|Self-<br>Esteem|Resilience|Locus of<br>control|
|_Panel A: Midline (12 mont_|_hs follow-up_|_)_||||||
|IPT-G+|-0.101|-0.095|0.992|0.587|-|-|-|
||(0.020)|(0.024)|(0.228)|(0.176)||||
|IPT-G mean|0.231|0.312|8.938|4.831|-|-|-|
|IPT-G SD|0.422|0.464|5.462|3.331||||
|Observations|1052|1052|1052|1052||||
|H0: IPT-G+=0 p-values|0.000|0.000|0.000|0.001|-|-|-|
|FDR adj. q-values|0.000|0.000|0.000|0.001||||
|_Panel B: Endline (24 mont_|_hs follow-up_|_)_||||||
|IPT-G+|-0.068|-0.071|0.458|0.226|-0.095|-0.946|-0.019|
||(0.028)|(0.031)|(0.451)|(0.249)|(0.229)|(0.368)|(0.143)|
|IPT-G mean|0.244|0.303|9.144|5.102|13.109|43.399|5.937|
|IPT-G SD|0.430|0.460|5.799|3.641|3.485|5.507|2.683|
|Observations|964|953|964|953|940|903|950|
|H0: IPT-G+=0 p-values|0.017|0.025|0.314|0.367|0.680|0.012|0.896|
|FDR adj. q-values|0.025|0.025|0.611|0.611|0.851|0.062|0.896|

Notes: This table shows ITT estimates of the impact of adding Cash to IPT-G on the primary and secondary mental health outcomes, comparing the IPT-G only group to IPT-G+ group; All regression models include randomization block fixed effects centered and fully interacted with treatment; Standard errors clustered at the club level in parentheses; Estimates at endline are weighted to account for the probability of selection for intensive tracking; Columns 1-2 show results for the primary outcomes, indicators for whether the PHQ-8 score is _≤_ 4 or the GHQ-12 score is _<_ 3; Columns 3-7 show results for the secondary outcomes, the PHQ-8 and GHQ-12 raw scores, the Rosenberg Self-Esteem scale, the Child and Youth Resilience Measure-Revised, and Locus of Control, all in terms of their raw scores; The rows marked as _H0:..._ show the p-value for a t- test of the indicated null hypothesis on the regression coefficients and the corresponding q-values adjusting for the false discovery rate within primary and secondary outcomes and round, as per Benjamini and Hochberg (1995); Sample size differences across outcomes are due to shorter tracking questionnaires being administered to difficult-to-reach subsets of CRs.

37
purchased input use between the pre-and post-2006 period together with a drop in the number of workers (from 4.1 to 2.3 per ha – Table 4) suggests a shift towards a more input-intensive mode of production, a notion supported by the fact that daily wages more than doubled (from US$ 3.3 to 7.3). In the first period, profits for farms below 1500 ha were indeed negative and very small. By contrast, they increased markedly, partly due to higher oilseed prices, ranging from US$ 47.3 for those below 500 ha to US$ 85.0 for farms above 5000 ha in the period after 2006.

Reductions in employment and intensity of labor use narrowed the gap in labor intensity between the largest and the smallest farms: while the smallest farms used 6.71 workers per 100 ha at a wage of US$ 3.28 (vs. 2.67 at a wage of US$ 4.28 on the largest farms) in 2001, this decreased to 3.3 and 1.8 workers, respectively (at wages of US$ 5.1 and 8.6) by 2011. This was accompanied by a decrease of hired labor’s cost share from 17.9% to 12.7 and a decline in the cost share of land from 17.0 to 11.4% (Table 3C). Both before and after 2006, profit per ha was lowest in the West and highest in the South (US$ -16 and 48 and 11 and 83, respectively).

There was divergence in the extent to which fertilizer and machinery were used across farm size classes: in the first period, the mean cost share for fertilizer was 12.0% for the smallest vs. 10.3% for the largest, increasing to 12.6% on average in the second period (11.7% vs. 14.9% for smallest vs. largest). Very large farms seem to have become more energy efficient, reducing use of this input in the second period to 14.5% (vs. 19.4% for the smallest group).

Kernel-weighted nonparametric regressions illustrate the evolution of output values and costs with farm size. Figure 2 presents these for log of crop value and cost per ha (indicated by solid and dashed lines, respectively), together with the 95% confidence interval for each. Noting that profits are the vertical distance between output and input cost, we note that, if all periods are lumped together, ‘small’ farms up to almost 2,000 ha made losses. Data on cost and output per ha for pre- and the post-2006 periods in figures 2.1 and 2.2 suggests that in the first period, profitability was limited, most farms lost money, and few super-large farms existed. After 2006, profitability improved and some entities cultivated more than 150,000 ha. Output, costs and profit per ha are precisely estimated up to a farm size of 10,000 ha but confidence bands widen thereafter. Profits are maximized at farm sizes of 2,000 to 3,000 ha, although output per ha peaks at 30,000 to 50,000 ha. This casts doubt on the notion that superior productive performance was the main driver of the expansion of super-large farms.

# **3.3 Entry and exit as main drivers of productive performance**

Table 3 shows that attrition was high; more than 80% of sample farms either exited (42%) or entered (40%; 28% of stayers and 12% of churners) during the period and only 17.3% remained in the sample 10
Table 7c: District-industry-year regressions for squared deviation in actual loans and expected loans

|||Ui dii|Panel es|Ui<br>timation with dist|Addi<br>rict-industry,st|ate-year and indu|Milli<br>stry-year FE|Milli||
|---|---|---|---|---|---|---|---|---|---|
|||sng strct||sng state-|ng||saocaton|saocaton||
||Baseline<br>estimation|<br>covariates<br>instead of FE|Dropping<br>weights|<br>industry-year<br>FE|<br>employment<br>control|Using balanced<br>panel|without industry<br>aggregation step|using OLS<br>TFP metric|Using average<br>deviation|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
||||A|. Using land and|labor misallocat|ion in organized|sector|||
|Land misallocation index|0.034++|0.023++|0.031+|0.032+|0.036++|0.034|0.049++|0.045++|0.025|
||(0.017)|(0.011)|(0.016)|(0.019)|(0.017)|(0.022)|(0.019)|(0.019)|(0.017)|
|Labor misallocation index|-0.021|-0.029++|-0.018|-0.019|-0.027|-0.047++|-0.021|-0.017|-0.011|
||(0.019)|(0.013)|(0.018)|(0.022)|(0.019)|(0.023)|(0.022)|(0.022)|(0.020)|
|Log employment in|||||0.080+++|||||
|district-industry-year|||||(0.012)|||||
|Observations|14839|14839|14839|14839|14839|6546|14839|14839|14839|
|Adjusted R-squared|0.099|0.036|0.108|0.099|0.106|0.132|0.099|0.099|0.090|
||||B. Usi|ng land and labor|misallocation i|n total manufactu|ring sector|||
|Land misallocation index|0.041+|0.037++|0.039|0.040|0.036|0.049+|0.051++|0.036+|0.026|
||(0.024)|(0.015)|(0.024)|(0.026)|(0.024)|(0.025)|(0.023)|(0.022)|(0.026)|
|Labor misallocation index|-0.034+|-0.037+++|-0.037+|-0.033|-0.030|-0.023|-0.011|0.016|-0.016|
||(0.020)|(0.013)|(0.020)|(0.022)|(0.020)|(0.021)|(0.021)|(0.022)|(0.020)|
|Log employment in|||||0.079+++|||||
|district-industry-year|||||(0.012)|||||
|Observations|14839|14839|14839|14839|14839|6546|14839|14839|14839|
|Adjusted R-squared|0.099|0.036|0.108|0.099|0.106|0.132|0.099|0.099|0.090|

Notes: See Table 6a. Misallocation variables and loan deviations are expressed in unit standard deviations.