rafmacalaba/datause-encoder-data huggingface dataset:
and public-sector versus private-sector employment (ILO 2018).

Our experiment took place in Jendouba, one of the poorest governorates in Tunisia.<sup>3</sup> The Jendouba governorate is administratively divided into 95 Imadas (localities), 15 of which are classified as urban, and 80 of which are classified as rural. Our RCT targeted 2,000 women in the 80 rural Imadas. These women were recruited from the treatment and control groups of another randomized experiment, which evaluated the impact of a public-works program (The World Bank 2017).<sup>4</sup> Data from the 2014 census and from our baseline survey provide information about life in rural Jendouba (our data are described in Section 4.1). In 2014, only 37% of the adult population of rural Jendouba was economically active. Illiteracy rates were around 40%, and 48% of household heads had no education. In 2016, the value of daily consumption per capita at baseline was about TND 9 (less than USD 4). For most households, the main source of income is coming from casual work, agriculture, and animal farming. The labor market in rural Jendouba is highly segregated by gender. Female workers are typically working in agriculture, either as casual laborers (harvesting, weeding, sowing) or at home (Zuccotti et al. 2018). The United Nations General Assembly (2013) describes that _“In Jendouba, agriculture is the main source of income and depends primarily on female labour, particularly older women, who are ready to accept the sector’s low wages. These older rural women are predominantly casual agricultural labourers, who are collected every morning from the streets, transported, standing in the back of trucks, to the farms, and compelled to work long hours for meagre pay.”_ Women are also in charge of small-scale agriculture and livestock farming at home (Massin et al. 2016). About 18% of households reported owning land at baseline and 49% of households were owning livestock (usually

> 3See Cocco (2013) for a powerful photo documentary on living conditions in Jendouba.

> 4Between 2012 and 2016, 40 rural Imadas took part in a project of Community Works and Local Participation (CWLP) implemented by the Ministry of Vocational Training and Employment (MFPE) with the technical support of the World Bank. The CWLP program was implemented as a cluster RCT: 40 Imadas were randomly selected to benefit from public works and 40 Imadas were placed in a pure control group. Within each Imada, local associations identified 50 households willing to participate in the cash-for-work (CfW) program. In treatment Imadas, 35 households were randomly selected to participate in the CfW program and 15 households were chosen as control. Our baseline data are the endline data of the CWLP evaluation. We built our impact evaluation on the CWLP evaluation for three reasons: facilitate sampling, minimize costs, and allow for the evaluation of the long-term effects of the CWLP. Our interventions were cross-randomized without stratifying by the CWLP treatment status. We study heterogeneous treatment effects by treatment status in the CWLP evaluation in Appendix G.

6
easier for these workers to find jobs close to home and/or to the fact that it is more expensive to live near railway stations; i.e., that proximity to rail is capitalized into land values.

# **B. Impact of Subsidies on Mode Choice**

A bus subsidy equal to 30% of fares, if removed, would likely have a significant effect on mode choice. In this section we evaluate the impact of an increase in bus and rail fares on ridership based on the commute mode choice models reported in Takeuchi, Cropper and Bento (2007).TPF<sup>17</sup> FPT Table 13 presents own and cross-price elasticities for bus and rail from the commute mode choice models estimated by Takeuchi, Cropper and Bento (2007) using data collected by Baker et al. (2005). In these models rail+bus is distinguished as a separate mode choice from taking rail or bus alone. Price elasticities differ slightly depending on the form of the mode choice model estimated. However, own price elasticities are always higher for bus than for rail, and an increase in bus fares reduces ridership more for bus-only users as opposed to users of rail+bus.

If the elasticities in Table 13 are weighted by the share of bus commuters taking bus-only (64%) v. rail+bus (36%), the elasticity of bus commuters with respect to the bus fare ranges from -0.36 to -0.32. This implies that removal of the bus subsidy (i.e., a 30% increase in fares) would reduce bus commuters by 10-11%. To which modes would these commuters switch? Most would switch to rail only; however, some would switch to walk only, and some would switch to a car or two-wheeler.TPF<sup>18</sup> FPT

**Table 13: Own and Cross-Price Elasticities of Rail and Bus Use for Journey to Work**

||**Incre**<br>|**ase in Rail Fa**<br>|**re on**<br>|**Incr**<br>|**ease in Bus Fa**<br>|**re on**<br>|
|---|---|---|---|---|---|---|
|**Estimation Model**|**Rail**<br>**Users**|**Rail+Bus**<br>**Users**|**Bus**<br>**Users**|**Rail**<br>**Users**|**Rail+Bus**<br>**Users**|**Bus**<br>**Users**|
|Multinomial Logit<br>(Cost/Wage)|-0.08|-0.07|0.06|0.26|-0.19|-0.45|
|Multinomial Logit<br>ln(Hicksian bundle)|-0.07|-0.07|0.05|0.25|-0.17|-0.42|
|Mixed Logit<br>ln(Hicksian bundle)|-0.07|-0.07|0.04|0.25|-0.26|-0.35|

Source: Takeuchi, Cropper and Bento (2007)

> <sup>17</sup> The money cost used in this analysis is based on the lowest fares, i.e. second-class rail fares and ordinary bus fares.

> <sup>18</sup> Specifically, the elasticity of walking with respect to bus fare ranges from 0.06 to 0.08, of rail, from 0.25 to 0.26, of two-wheeler, from 0.03-0.04.  The elasticity of car usage with respect to bus fare is 0.03.

18
In the absence of evasion, we would expect the unit value gap to be negative, as import statistics include the cost of freight and insurance, neither of which is captured by the export data. However, as indicated in Table 8, in 6 out of 10 countries the average unit value gap is positive. It is even more striking that in all countries, the average unit value gap is larger for differentiated products. This is true for both the conservative and the liberal definition of differentiated products. Further, Table 9 suggests that in all but one country (Poland) the unit value gap is larger for products with the above median tariff rate.

To test this relationship more formally, we regress the unit value gap on the tariff rate, differentiated product dummy and the interaction between the two variables. To save space, we present only the specification estimated with country-year and product fixed effects and the specification in first differences. We restrict our attention to the sample excluding agricultural products.<sup>11</sup>

**<u>Table 10: Unit value gap.</u>**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|||Levels|||First difference|s|
|Tariff|0.000|-0.001|-0.001|0.003|-0.004|-0.003|
||(0.001)|(0.001)|(0.001)*|(0.002)|(0.005)|(0.004)|
|Tariff*Conservative dummy||0.002<br>(0.001)*|||0.012<br>(0.005)**||
|Tariff*liberal dummy|||0.003<br>(0.001)**|||0.010<br>(0.005)**|
|Country-year fixed effects|Yes|Yes|Yes|No|No|No|
|Product fixed effects|Yes|Yes|Yes|No|No|No|
|Country fixed effects|No|No|No|Yes|Yes|Yes|
|Agricultural products|Excluded|Excluded|Excluded|Excluded|Excluded|Excluded|
|Observations|121963|121963|121963|94658|94658|94658|
|Adjusted R-squared|0.66|0.66|0.66|0.0001|0.0002|0.0002|

All regressions include a constant. Robust standard errors are listed in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%.

As evident in Table 10, we find no evidence of price misrepresentation (i.e., reporting unit values of imports as being lower than what they really are) being responsive to the tariff rate in general. On the contrary, in one case we find a negative and statistically significant coefficient on the tariff rate. However, we do find evidence suggesting that price misrepresentation is positively correlated with the tariff rate in the case of differentiated products. The results suggest that a one-percentage-point increase in the tariff

> 11 Including agricultural products in the sample would not change the conclusions of this study.

19
# **1. INTRODUCTION**

In the span of fifteen years since 1995, Mongolia’s transition from a centrally planned to a market-based system has proceeded successfully, and the economy has grown rapidly in real terms at an average, annual rate of 6.3 percent. The transformation involved the implementation of fundamental economic reforms including price liberalization, privatization, opening the economy to foreign competition, and establishing market institutions. Macroeconomic policies have remained prudent, debt levels have become manageable,<sup>1</sup> and a freely floating exchange rate regime has been put in place. In addition, Mongolia’s transition was largely unhindered by widespread social or political unrest, although the young democracy is still being perfected.

In the past ten years, Mongolia’s macroeconomic performance has been strong. Foreign direct investment – especially in mining and construction activities, and Mongolian workers’ remittances have increased significantly in recent years fuelling the observed high GDP growth. The average share of FDI in Mongolia’s GDP of 5.2 percent for the 10year period (1996-2005) was higher than the average for the East Asia region, and more than three times that for the low-income country group to which it belongs. The economy grew at a fast pace averaging 8.4 percent in the period 2004-06.

The structure of the economy has, however, evolved in a manner where the sources of real GDP growth have become concentrated, and heavily dependent on mining and livestock, and employment generation has been elusive, despite having a young and educated labor force. In 2005, the mining sector directly accounted for 18 percent of GDP, 66 percent of industrial output, almost 76 percent of export earnings and 20 percent of Government revenue. The mining sector output has the potential to increase substantially over the next decade. It is projected to double or even triple from 2003 levels by 2010 provided large projects get development approvals and are successfully commissioned. Mongolia has consequently become increasingly dependent on a narrow range of exports, and vulnerable to commodity price shocks.

Given these challenges, one may wonder whether Mongolia will be able to sustain its growth record in the coming years. _What will it take for Mongolia’s real GDP growth to remain high in the next ten years? What are the key constraints to growth that must be addressed in the short to medium term to maintain economic growth?_ These are some of the questions that motivated this study and to answer them the paper applies the growth diagnostics approach postulated by Rodrik (2004a) and outlined in Hausmann, Rodrik, and Velasco (2005). Our approach of applying the HRV methodology is useful in those cases where data limitations prevent us from estimating shadow prices. For the diagnostics, we rely on an eclectic array of macro- and micro-economic techniques including country-level and firm-level comparisons, times series analyses, sector studies and investment climate surveys.

> 1 The macroeconomic outlook however remains somewhat vulnerable to risks that need to be monitored and managed effectively.

2
financial health; and suppliers of differentiated products use trade credit to signal their willingness to invest in relationship-specific capital.

Trade credit can be especially relevant in countries/regions where the rule of law and the legal system are weak. Weak institutions would, in principle, prompt greater use of trade credit, which requires less formal contract enforcement institutions and mechanisms than bank loans. Informal institutions such as belonging to the same social networks (such as hometown connections between CEOs of borrowing firms and their suppliers) or personal knowledge about suppliers/customers have facilitated trade credit relationships, as observed in Vietnam, Eastern European countries, and China. Such countries have relied more heavily on trade credit than others, and the positive effects on firm growth tend to be stronger where industry demand for credit is greater. Furthermore, where the level of social trust is low, informal institutions such as hometown connections loom even larger in facilitating trade credit. Countries with stronger formal legal foundations—for example, the court systems in Eastern European countries in the 1990s were further along than that in transitional Vietnam—can rely on those institutions to facilitate greater provision of trade credit by suppliers (Johnson, McMillan and Woodruff 2002). Better formal institutions also enable customers to switch suppliers, which facilitates competition among suppliers and thus improves trade credit terms for borrowers. Finally, in economic systems where bank credit allocation is heavily tilted toward (inefficient) state-owned enterprises, the trade credit extended by profitable private firms could improve credit allocation in the spirit of the Coase Theorem (Coase 1960) that resources would tend to flow to usages where efficiency improvement is possible.

The patterns that emerge from evidence around the world suggest a sweet spot or ‘Goldilocks’ region where mid-size firms and those in countries at middling levels of development tend to rely relatively more heavily on trade credit as a source of finance that can help spur productivity.

An implication for further studies is that better designs of firm surveys or better combinations of complementary datasets could facilitate more direct tests of various theoretical predictions regarding trade credit. Specifically, detailed data on the relationship between suppliers and customers are crucial.

25
# **Table 6: WLS Estimates of Continuous Treatment Effects of Exports and Related Exports** **<u>on Employment and Wages</u>**

||Change|in logemp|loyment|Change i|n logwage f|or stayers|Differ|ential wage|change|
|---|---|---|---|---|---|---|---|---|---|
|Export shock * 2007:2|-0.01||-0.01|-0.01||-0.01|-0.02**||-0.02*|
||(0.016)||(0.016)|(0.006)||(0.007)|(0.010)||(0.010)|
|Export shock * 2007:3|-0.02||-0.02|-0.02**||-0.02*|-0.02*||-0.02|
||(0.017)||(0.020)|(0.008)||(0.008)|(0.010)||(0.011)|
|Export shock * 2007:4|0.00||0.01|-0.00||-0.00|0.01||0.01|
||(0.020)||(0.028)|(0.009)||(0.010)|(0.006)||(0.008)|
|Export shock * 2008:1|0.06||0.04|-0.00||0.01|0.00||0.00|
||(0.041)||(0.038)|(0.007)||(0.007)|(0.012)||(0.012)|
|Export shock * 2008:2|-0.03||-0.05*|-0.01||-0.01*|-0.01||-0.01|
||(0.029)||(0.029)|(0.005)||(0.007)|(0.013)||(0.013)|
|Export shock * 2008:3|0.03||0.01|-0.01||-0.01|-0.01||-0.01|
||(0.035)||(0.029)|(0.011)||(0.011)|(0.008)||(0.008)|
|Export shock * 2008:4|0.08***||0.06**|0.02||0.02|0.04***||0.03***|
||(0.021)||(0.025)|(0.014)||(0.014)|(0.010)||(0.011)|
|Export shock * 2009:1|<br>0.10||<br>0.04|<br>0.04***||<br>0.03**|<br>0.00||<br>-0.01|
||(0.067)||(0.057)|(0.010)||(0.012)|(0.006)||(0.010)|
|Export shock * 2008:2|-0.05||-0.05|-0.01||-0.00|-0.01**||-0.01|
||(0.045)||(0.039)|(0.005)||(0.006)|(0.005)||(0.007)|
|Export shock * 2009:3|<br>0.03||<br>0.04*|<br>-0.03***||<br>-0.03***|<br>-0.03||<br>-0.02|
||(0.026)||(0.026)|(0.008)||(0.009)|(0.022)||(0.019)|
|Export shock * 2009:4|0.01||0.04|-0.01||0.00|0.00||0.02*|
||(0.024)||(0.025)|(0.007)||(0.007)|(0.008)||(0.009)|
|Rel.exp. shock * 2007:2||-0.06|-0.04||-0.03|-0.02||-0.10*|-0.06|
|||(0.082)|(0.082)||(0.045)|(0.048)||(0.060)|(0.048)|
|Rel.exp. shock * 2007:3||<br>0.00|<br>0.05||<br>-0.05|<br>-0.01||<br>-0.07|<br>-0.03|
|||(0.074)|(0.080)||(0.052)|(0.054)||(0.059)|(0.054)|
|Rel.exp. shock * 2007:4||-0.08|-0.10||-0.03|-0.03||-0.01|-0.03|
|||(0.091)|(0.133)||(0.038)|(0.039)||(0.033)|(0.041)|
|Rel.exp. shock * 2008:1||0.29|0.20||-0.08**|-0.09**||0.03|0.02|
|||(0.203)|(0.162)||(0.037)|(0.036)||(0.058)|(0.052)|
|Rel.exp. shock * 2008:2||<br>0.10|<br>0.21||<br>0.05|<br>0.08*||<br>0.01|<br>0.03|
|||(0.175)|(0.172)||(0.047)|(0.044)||(0.063)|(0.055)|
|Rl hk * 20083||030|028||003|001||004|002|
|e.exp. soc :||.|.||-.|.||-.|-.|
|||(0.218)|(0.212)||(0.066)|(0.072)||(0.049)|(0.048)|
|Rel.exp. shock * 2008:4||0.31*|0.17||0.03|-0.00||0.18**|0.12|
|||(0.180)|(0.177)||(0.033)|(0.030)||(0.081)|(0.070)|
|Rel.exp. shock * 2009:1||1.00***|0.92***||0.29***|0.24***||0.14|0.16|
|||(0.368)|(0.337)||(0.082)|(0.078)||(0.086)|(0.106)|
|Rel.exp. shock * 2008:2||-0.14|-0.04||-0.05|-0.04||-0.07**|-0.05|
|||(0.232)|(0.179)||(0.038)|(0.043)||(0.035)|(0.039)|
|Rel.exp. shock * 2009:3||-0.08|-0.18||-0.17**|-0.11*||-0.27*|-0.23|
|||(0.167)|(0.172)||(0.066)|(0.066)||(0.146)|(0.144)|
|Rel.exp. shock * 2009:4||-0.33**|-0.42**||-0.15**|-0.15*||-0.12*|-0.16**|
|||(0.136)|(0.160)||(0.070)|(0.082)||(0.064)|(0.080)|

Robust standard errors in parentheses with 1,089 observations. *** p<0.001, ** p<0.01, * p<0.05. Trade shock calculated from October 2008 to November 2008.

52
gasoline, which fuels passenger cars and (privately operated) microbuses. Another option is to introduce some form of tolling by the mile for passenger cars though, as discussed below, implementing this would require overcoming significant challenges. Whether microbuses should be taxed or subsidized on a per mile basis is not immediately clear even in principle; while they deter private use of automobiles, they themselves contribute to congestion and pollution. A further possibility is to adjust fares charged to users of the public bus and subway system.

**Table 1. Ranking of Selected Megacities Based on Ambient Air Quality and Road Travel Speed**

|Megacities in 2000|Total suspended<br>particulates (μg m<br>-3)<br>[Rank]|Sulfur dioxide<br>(μg m<br>-3)<br>[Rank]|Nitrogen dioxide<br>(μg m<br>-3)<br>[Rank]|Multi-pollutant<br>index<br>[Rank]|Average road speed<br>miles per hour<br>[Rank]|
|---|---|---|---|---|---|
|Tokyo|40 [15]|19 [9]|55 [11]|-0.27 [16]|16.2 [9]|
|Mexico City|201 [10]|47 [4]|56 [10]|0.52 [10]|14.0 [6]|
|New York - Newark|27 [18]|22 [7]|63 [7]|-0.23 [14]|23.9 [14]|
|São Paulo|53 [14]|18 [10]|47 [12]|-0.29 [17]|15.0 [8]|
|Mumbai (Bombay)|243 [9]|19 [9]|43 [14]|0.39 [11]|13.8 [5]|
|Kolkata (Calcutta)|312 [6]|19 [9]|37 [15]|0.59 [9]|na|
|Shanghai|246 [8]|53 [3]|73 [5]|0.87 [8]|12.4 [4]|
|Buenos Aires|185 [11]|20 [8]|20 [18]|-0.01 [13]|18.6 [10]|
|Delhi|405 [4]|18 [10]|36 [16]|0.92 [7]|14.4 [7]|
|Los Angeles - Long Beach - Santa Ana|39 [16]|9 [13]|66 [6]|-0.25 [15]|29.5 [15]|
|Osaka - Kobe|34 [17]|19 [9]|45 [13]|-0.37 [18]|20.5 [13]|
|Jakarta|271 [7]|35 [6]|120 [3]|1.24 [5]|11.6 [2]|
|Beijing|377 [5]|90 [2]|122 [2]|2.01 [2]|11.1 [1]|
|Rio de Janeiro|139 [13]|15 [11]|60 [8]|0.11 [12]|18.6 [10]|
|Cairo|593 [2]|37 [5]|59 [9]|1.93 [3]|12.4 [3]|
|Dhaka|516 [3]|120 [1]|83 [4]|2.40 [1]|na|
|Moscow|150 [12]|15 [11]|170 [1]|1.07 [6]|18.6 [10]|
|Karachi|668 [1]|13 [12]|30 [17]|1.81 [4]|na|

# Notes

Pollution figures are for year 2000, while road speed is for 2005. μg m-3 is micrograms per cubic meter. The multi pollutant index combines pollutants in the first three columns. Road speed is averaged across all passenger vehicles.

Sources: Gurjar et al. (2007) and MCD (2007).

In previous literature there is little in the way of a conceptual and empirical framework for assessing the benefits and costs of these pricing options for Mexico City. Such assessment in important for understanding which pricing reforms yield the biggest welfare gains, an important element for policymakers to gauge the extent to which transportation prices should be reformed. Other criteria, which are beyond our scope, would also be important to consider, including distributional impacts, political feasibility, ease of monitoring and enforcement of policy reforms.

3
# **Policy Research Working Paper Series**

||**Title**|**Author**|**Date**|**Contact**<br>**forpaper**|
|---|---|---|---|---|
|WPS2952|The EffectsofaFee-WaiverProgram<br>onHealthCareUtilization among the<br>Poor:EvidencefromArmenia|NazmulChaudhury<br>JeffreyHammer<br>EdmundoMurrugarra|January2003|N.Chaudhury<br>84230|
|WPS2953|HealthFacility Surveys.An<br>Introduction|Magnus Lindel6w<br>AdamWagstaff|January 2003|HSladovich<br>37698|
|WPS2954|Never TooLateto GetTogether<br>AgainTurning the CzechandSlovak<br>CustomsUnionintoaSteppingStone<br>toEUIntegration|BartlomiejKaminski<br>BeataSmarzynska|January 2003|P.Flewitt<br>32724|
there are many undernourished women and children one tends to find them more widely spread across the household wealth distribution. From a policy perspective, these results suggest that targeting relatively poor households will tend to work less well in reaching vulnerable women and children in countries where the overall problem of undernutrition is greater.<sup>29</sup>

**_Covariates of the conditional probabilities:_** We examine the correlations with three variables, GDP per capita, the female literacy rate (Table 2) and the wealth-index effect (Table 7). We can think of these as shift parameters of the joint probability density of wealth and nutrition.  GDP and the FLR are of obvious interest. The wealth effect is less obvious. In this context, the wealth-index effect can be interpreted as a measure of the extent of nutritional inequality by wealth, and the expectation is that a steeper wealth effect would be associated with a greater concentration of undernutrition in wealth-poor households.

Table 11 gives the correlation coefficients among the conditional probabilities as well as those with the other social and economic indicators from Table 4. The conditional probabilities are positively correlated with the relevant wealth effects. For underweight women, we find that r= 0.64 and 0.71 for 20% and 40% respectively. For stunting, the corresponding correlations with the wealth effects for height-for-age are 0.39 and 0.44, while for wasting they are 0.47 and 0.64. The positive correlation of the conditional probabilities with the wealth effects is also found on using regressions to control for the other summary statistics in Table 4, and these partial correlations are statistically significant in most cases.<sup>30</sup> We find that the shares of stunted children found in wealth-poor households are quite strongly positively correlated with GDP per capita (r=0.77 for the poorest 20% and r=0.67 for 40%), but this is not the case for underweight women (r=-0.01 and r=-0.17 respectively) or wasted children (r=-0.20 and r=-0.21 respectively). All six measures of the shares of nutritionally vulnerable women and children are positively correlated with the female literacy rate, though not all are statistically significant; r=0.31 and 0.30 for underweight women and the poorest 20% and 40% respectively, while r=0.42 and 0.34 for stunted children and r=0.20 and 0.15 for wasted children. There are no significant correlations with access to water and sanitation. Nor did the regressions reveal any sign of significant partial correlations

> 29 This is also evident in the data for stunting in Africa assembled by Bredenkamp et al. (2014) (see the Africa data points in their Figure 1), although across all developing countries Bredenkamp et al. find that inequalities in stunting are greater in countries where stunting is more prevalent. Evidently Africa is different in this respect, though the reason is unclear. 30 For the shares of underweight women and stunted children in the poorest 20% the partial correlations are only significant at about the 10% level, while they are significant at the 5% level in all other cases.

19