rafmacalaba/datause-encoder-data huggingface dataset:
privately-owned and state-owned) with sales above 5 million RMB (equivalent to roughly $600,000). We take the estimate for 1998 as the one characterizing the initial stationary allocation. We dene _TFPR_ and _TFPQ_ exactly as in Hsieh and Klenow (2009). The scaling parameter _ZI_ , which shapes the average distortion in the economy, has a direct mapping on the capital-output ratio in the stationary equilibrium. Hence, we calibrate its values in 1998 and 2011 to replicate China's capital-output ratio in these years. The parameter values governing the idiosyncratic distortions in the initial and terminal steady states are reported in table 3 The prot tax and the xed production cost are calibrated to match statistics of the rm size and earnings distribution. As said earlier in the text, these reducedform instruments are intended to tractably capture the various elements that hinder private entrepreneurial activity and compress the earnings distribution in a communist regime such as China's in 1998. Our strategy for their calibration is to appeal to observable outcomes on which these instruments exert a rst-order eect. To this end, we set the average rm size and the earnings share accounted for by the richest 1% of households in 1998 as empirical moments. We target an average rm size of 3.1 times the average rm size in the U.S. manufacturing sector and a top earnings share of 8% which we draw from the World Inequality Database (Piketty et al. 2019). The strategy results in a xed production cost of _fc_ = 48 _._ 1 , equivalent to 57% of the average prots gross of innovation expenses and xed costs in the initial steady state, and a prot tax _τ_ 0<sup>_π_= 0</sup><sup>_._4.Theseparametervaluesarealsoreportedintable3</sup> The pace of reversal of the distortions during the reform is disciplined as follows. For idiosyncratic distortions, we t a linear trend to the time series of regression coefcients of _log_ ( _TFPR_ ) and _log_ ( _TFPQ_ ) estimated from the rm-level data between 1998 and 2005. The linear trend allows us to project the evolution of idiosyncratic distortions beyond the estimating period into 2011, which is the last year in our aggregate data. We assume that in the terminal steady-state, idiosyncratic distortions stabilize at the level projected for 2011. The evolution of the scaling parameter _ZI,t_ is set to converge linearly between 1998 and 2011 from the initial an the terminal values. In terms of the prot taxes, we also feed a linear path of reversal disciplined to match the pace reduction of the average rm size in China during the acceleration. In this way, while the average rm size dynamics will be replicated by construction, the implied dynamics of inequality will be untargeted, and hence can be used as validation for the model's mechanisms. In terms of the xed production cost, we assume they remain at the initial steady state's level, in reection of the 25
- 1.82 -

prices. The reason Is quite shuple. This policy shifts resources away from more productive employment In cocoa<sup>production toward less effiient</sup> productlon In maize. It may be argued that this outcome<sup>Is really a consequence of the assumption of static</sup> agricultural technogy. However, until convincing evidence<sup>Is at hand that Ghanaian</sup> agriculture Is movlng to cost<sup>efficlent</sup> cost efficient higher productivity practices, a food self-suffiieWncy policy entalls<sup>heavy offilency</sup> **lsss.** On the other hand, combining food self-sufficiency with a high cocoa prie policy does yild a strong kIpact on employment of rural labor and smalholder<sup>farmer Incomes. The greater</sup> Impact on the welfare of the poor might be an<sup>acceptable tradeoff</sup> for lower static efficiency. Moreover, If It Is accepted that productivity<sup>enhanchg technh;al change In agriculture Is</sup> more likely in the context of<sup>rising agricultural employment and Incomes, then dynamic</sup> efficiency gans are more lkely<sup>under the food self-sufficincy</sup> scenario.

Table **LC.1** 10 Cocoa Supply Estbates ('000 Metric Tons)

|Horizon and Scenario|Cr<br>120|OOBjoINPvlc<br>140|(1987 cOiM<br>160|180|200|
|---|---|---|---|---|---|
|Ten-Year Horizon||||||
|Population Growti Only<br>Population, Income &<br>Wago Growth<br>Population &income Growth<br>(wliF no food imporb)|338<br>248<br>216|527<br>468<br>216|**643**<br>548<br>296|687<br>an6<br>440|728<br>6/6<br>530|
|Fort-vYew Hortzon||||||
|Population Growth Or<br>Populton.<br>bnom &<br>**Wa**<br>o<br>Population &ncom Growth<br>(no food<br>mo-)|1062|1449<br>314|1578<br>636<br>282|1697<br>1191<br>689|1781<br>1346<br>1179|
# **4. Poverty trends: Official and Revised**

## **4.1 Revised poverty-line construction**

In a recent assessment of possible alternatives to the official poverty figures, van den Boom (2010) compares the official figures with figures obtained by averaging the region-specific poverty lines and creating a unique aggregate national poverty line. The simulation leads to striking changes in poverty patterns in Mozambique. Under the aggregate PL, poverty appears significantly higher among the rural population (with a 65 percent poverty rate for 2008/09 compared to the official 57 percent), while the urban population appears considerably less poor (39 percent for 2008/09 compared to the official rate of just under 50 percent). These poverty dynamics would seem to be more consistent with other data sources, including regional economic growth rates and anthropometric indicators, both at the regional and national levels. In addition, van den Boom’s methodology produces dramatic improvements in the stability of poverty trends over time, as well as the near-complete elimination of the puzzling povertyrate swings described above.

**1.** Using the information contained in the 1996/97 survey we construct a single-basket poverty line:

### **Equation 4**

where<sup>is a bundle typically consumed by the poorest segment of the population in 1996/97 (see</sup> Appendix for a list of items and food shares). This bundle is developed by taking the population weighted average of bundle consumed by household below a certain consumption threshold (in our case the 60<sup>th</sup> percentile) and scaled up to the standard threshold of 2150 calories per capita per day. The bundle<sup>is then multiplied by a vector of prices</sup>

### **Equation 5**

Combining Equation 4 and Equation 5 and adding to account for the non-food share and , a spatial Paasche index for the 13 regions, we obtain

### **Equation 6**

19
||I|nfows|O|utfows|
|---|---|---|---|---|
||Total|Explained|Total|Explained|
|PER|3.56|1.46(1.06)|3.56|1.85(0.74)|
|URY|5.39|2.77(1.33)|6.28|0.91(1.28)|
|VEN|3.32|0.94(0.93)|5.68|2.32(1.33)|
|CYP|51.3|16.2(22.3)|49.6|14.8(23.3)|
|ISR|4.47|2.73(1.05)|4.51|3.18(0.77)|
|JOR|8.36|3.52(2.23)|7.89|0.61(1.76)|
|KWT|7.84|2.76(2.24)|45.51|16.79(19.15)|
|OMN|4.17|2.22(1.12)|9.48|5.69(2.46)|
|SAU|2.86|1.42(0.83)|15.18|8.91(5.33)|
|EGY|5.72|1.92(1.87)|4.47|0.70(1.36)|
|IND|2.07|1.76(0.20)|1.99|1.63(0.20)|
|KOR|4.08|2.88(0.66)|3.32|2.12(0.80)|
|MYS|6.21|3.54(1.29)|7.58|4.49(1.33)|
|PAK|2.20|0.82(0.73)|1.81|0.03(0.39)|
|PHL|4.26|3.27(0.69)|3.71|1.86(0.84)|
|SGP|35.77|19.39(9.90)|40.69|21.53(12.57)|
|THA|5.85|2.42(1.98)|4.24|2.69(0.92)|
|MAR|3.29|1.81(0.95)|2.52|0.77(0.81)|
|CHN|2.55|1.51(0.66)|4.40|3.91(0.30)|
|POL|3.64|2.62(0.62)|2.38|1.29(0.53)|
|ROM|6.51|4.82(1.20)|2.63|1.41(0.69)|

Table 6: Standard deviation of flows, as percentage of GDP, and fraction explained by the factors. Two-standard errors shown in brackets.

42
37 TALE 4.11 Sanegak A pag MoBe and Todal CG _pauo By SkM _Cates_ in 17 FE, I66

|Category|Compe<br>Baso|nsation<br>Toad|Prcent<br>Com<br>Com<br>Bonuses|of TotU<br>pnaton<br>prised of<br>Wagr<br>Supplement|
|---|---|---|---|---|
|High-level<br>manager|126,593|520,09|18.85%|<br>44.9%|
|Upper-level<br>techniians<br>and middle<br>level managers|100,181|J45,134|203%|15.3%|
|Middle-level<br>tehnicans<br>and lower<br>level managers|78,289|160,198|25.8%|16.5%|
|Sled<br>employees|58,292|110,906|293%|,11.0%|
|Skled workers<br>and unskied<br>employees|*46469|77,642|26.5%|7.9%|
|Laborers and<br>unskilled workers|38,258|56,963|22.7%|6.2%|

Source: Authors' _rvq_ of workers in 17 firms laborer's and unskilled worker's compensation.4 **ln contrast, the** variance in bonus percentages across skil categories is rather small-18 percent to 29 percent. Bonuses are, however, an increasingly important component of total compensation for the lower **skill** groups, rising up to one-third, leveling off, and then falling for the upper two skill groups.

4. Incidentally, the wage supplement is also creatng a larger wedge between the average earnings of the top and the low level sklm categories. Whereas the ratio of the base salary of high level managers to laborer and unskilled workers is 3.3, the same ratio using total compsion is 9.1.
# Chart **11.** Trade Integration and Transfer Purchasing Power

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18
simultaneously.<sup>15</sup> Since the STRI focuses mainly on the set of measures which discriminate against foreign services and providers, the greatest level of openness is associated with a value of zero. However, since the STRI does not adequately cover complementary areas of nondiscriminatory prudential and pro-competitive regulation, and since it is likely that the results of liberalization depend on the state of these types of complementary regulation, we cannot say that a zero level of STRI is necessarily immediately desirable from a broader welfare or development perspective.

**Annex Table A1: How STRI scores are assigned**

|**Overall policy**<br>**description**|**5-point**<br>**scale**|**Policy summary examples for ASEAN Member States**|
|---|---|---|
|Open without<br>restrictions|0|**Cambodia: Retail bank loan – mode 1**<br>“No restrictions.”|
|Virtually open|0.25|**Vietnam: Life insurance –mode 3**<br>“Entry as a branch is not allowed. No restrictions on foreign ownership<br>in Greenfield subsidiary or acquisition of existing entity.”|
|Existence of<br>major/non-trivial<br>restrictions|0.50|**Thailand: Air passenger domestic – mode 3**<br>“The limit on foreign ownership is 49 percent, with effective control by<br>Thai nationals. At least 40% of Board of Directors must be national.”|
|Virtually closed<br>a.|0.75<br>1|**Malaysia: Reinsurance–mode 1**<br>“Reinsurance companies must demonstrate domestic unavailability in<br>both Malaysia and Labuan before obtaining services abroad.”<br>**Philippines: Architecture services –mode 3**|
|Completely<br>closed||“Commercial presence is not allowed.”|

Notes: As is apparent from the examples shown, most subsector-mode combinations are characterized by multiple provisions, in which case the regime assignment reflects the overall restrictiveness of all applicable measures.

Once a score has been attached to each regime, STRI values can be aggregated across sectors and modes of supply. Let _s jmc_ denote the basic scores on a 5-point scale per sub-sector and mode of supply as described in Table 2. In order to arrive at an aggregate STRI of country _c_ , _STRIc_ , we

> 15  The Database Guide (Borchert, Gootiiz and Mattoo, 2012a) contains in its Section 4 three examples—from Burundi, Thailand and India—that illustrate how a portfolio of several measures is being assigned to one of the five basic scores.  In principle, policy measures can be divided into two tiers.  The first tier measures include those that affect market entry decisions most significantly, such as a limit on foreign ownership and the number of licenses. The second tier measures are those that affect operations of service providers, such as restrictions on the repatriation of earnings.  The second tier measures do not contribute to overall restrictiveness when first tier measures are prohibitive.  In contrast, if the first tier measures are not prohibitive then second tier measures are also considered in determining the overall restrictiveness score.

4
Lastly, ME runs the National Committee for Productivity and Technological Innovation ( _Comité Nacional de Productividad e Innovación Tecnológica_ —COMPITE), which provides SME consulting and training services. The objective of the program is to help SME to be more competitive by increasing productivity and improving quality. COMPITE consultants use best-practice business development and management models to improve the management of firms. Between 2001 and 2006, COMPITE supported close to 50,000 firms, which had to pay part of the cost of the services.

**Table 6 COMPITE: Main Results 2001-2006**

||**2001**|**2002**|**2003**|**2004**|**2005**|**2006**|**Total**|
|---|---|---|---|---|---|---|---|
|**Services**|12,949|13,226|14,192|12,512|16,901|1,338|71,118|
|**SME**|6,585|8,550|10,184|9,168|13,746|778|49,011|
|**Hours**|82,804|76,003|204,450|238,912|291,097|28,426|921,692|
|**Participants**|42,005|35,394|62,876|57,195|65,881|14,390|277,741|
|**SME with ISO 9000**<br>**certifications**|307|238|277|227|257|195|1,501|

* Data from 2001 to June 2006. **Source:** Secretaría de Economía (2007c)

# _Bancomext_

Banco Nacional de Comercio Exterior (Bancomext) is the country’s export-import development bank. Bancomext supports the participation of Mexican firms in global markets, providing financing, training and technical assistance. Bancomext targets SMEs with export sales of up to US$20 million per year. The bank’s major support mechanism is direct medium and long-term credit to exporting firms. In addition, the bank offers direct and Internet-based information services about foreign markets and exporting procedures and requirements for Mexican firms. The bank also promotes the participation of Mexican SMEs in international trade fairs to promote foreign investment in Mexico.

**Table 7 Bancomext: Main Results 2001-2006**

|**Y**|**Resource**|<br>**s mobilized (million **|**USD)**|**Firms ben**|**efited**|
|---|---|---|---|---|---|
|**ear**||**nsurance an**||||
||**Loans**|<br>**guarantees**|**Total**|**SME**|**Total**|
|2001|4,893|619|5,512|1,628|1,716|
|2002|5,442|1,551|6,993|1,807|1,866|
|2003|5,435|742|6,177|1,202|1,257|
|2004|4,986|437|5,423|751|826|
|2005|5,254|138|5,392|533|584|
|2006|4,865|87|4,952|N/A|N/A|
|Total|30,875|3,574|34,449|5,921|6,249|

**Source:** Bancomext (2006a and 2006b)

Bancomext has two main programs: Crediexporta and the Technical Assistance Program ( _Programa de Asistencia Técnica_ —PAT). Crediexporta is Bancomext’s most important financing program mainly targeted to SMEs. The program has five different modalities: (i) 7
**<u>Table 2: Main income source of households leasing out before and after land transfer</u>**

|**After**|Agric.production|**Bef**<br>Local non-farm|**ore**<br>Migration|Total|
|---|---|---|---|---|
|Agric. production|15.91%|0.63%|0.18%|16.73%|
|Local non-farm|8.32%|19.80%|0.45%|28.57%|
|Migration|32.91%|2.62%|19.17%|54.70%|
|Total|57.14%|23.06%|19.80%|100.00%|

_Source:_ Own calculation based on NBS/World Bank survey (tenants’ assessment for 1106 land rental contracts)

|**Table 3: Per capit**<br>|**a income househo**<br>|**lds leasing inand le**<br>**Household**<br>|**asing out before and**<br>**s leasing out**<br>**Before**<br>|**after transfer**<br>||
|---|---|---|---|---|---|
|**After**|<1500 Y|1500-3000 Y|3000-5000 Y|>5000 Y|Total|
|<1500 Y|9.84%|0.63%|0.09%|0.00%|10.56%|
|1500-3000 Y|17.42%|24.82%|0.09%|0.00%|42.33%|
|3000-5000 Y|1.81%|15.97%|12.82%|0.09%|30.69%|
|>5000 Y|1.08%|1.99%|6.95%|6.41%|16.43%|
|Total|30.14%|43.41%|19.95%|6.50%|100.00%|
|||**Househol**<br>|**ds leasing in**<br>**Before**<br>|||
|**After**|<1500 Y|1500-3000 Y|3000-5000 Y|>5000 Y|Total|
|<1500 Y|10.57%|0.00%|0.00%|0.00%|10.57%|
|1500-3000 Y|10.34%|36.78%|0.69%|0.00%|47.82%|
|3000-5000 Y|0.00%|15.17%|17.01%|0.69%|32.87%|
|>5000 Y|0.00%|0.00%|4.60%|4.14%|8.74%|
|Total|20.92%|51.95%|22.30%|4.83%|100.00%|

_Source:_ Own calculation based on NBS/World Bank survey (landlords’ and tenants’ assessments for 1106 contracts).

18
Benjamini, Yoav and Yosef Hochberg (1995) “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing”, _Journal of the Royal Statistical Society Series B_ , 57(1): 289-300.

Betcherman, Gordon, Karina Olivas, and Amit Dar (2004) “Impacts of Active Labor Market Programs: New Evidence from Evaluations with Particular Attention to Developing and Transition Countries.” World Bank Social Protection Discussion Paper Series 0402.

Blattman, Christopher, Nathan Fiala and Sebastian Martinez (2013) “Generating skilled self-employment in developing countries: Experimental evidence from Uganda””, _Quarterly Journal of Economics_ , forthcoming.

Bruhn, Miriam and David McKenzie (2009), “In Pursuit of balance: Randomization in Practice in Development Field Experiments,” _American Economic Journal: Applied Economics_ 1(4): 200-232.

Burbidge, John, Lonnie Magee and A. Leslie Robb (1988). "Alternative Transformations to Handle Extreme Values of the Dependent Variable" _Journal of the American Statistical Association_ 83(401): 123-127 Card, David, Pablo Ibarraran, Ferdinando Regalia, David Rosas-Shady, and Yuri Soares (2011) “The Labor Market Impacts of Youth Training in the Dominican Republic”, _Journal of Labor Economics_ 29(2): 267-300.

Casey, Katherine, Rachel Glennerster and Edward Miguel (2012) “Reshaping Institutions: Evidence on Aid Impacts using a Pre-analysis plan”, _Quarterly Journal of Economics_ 127(4): 1755-1812.

Cho, Yoonyoung, Davie Kalomba, Mushfiq Mobarak and Victor Orozco (2013) “Gender Differences in the Effects of Vocational Training: Constraints on Women and Drop-Out Behavior”, _IZA Discussion Paper no. 7408._ Crépon, Bruno, Esther Duflo, Marc Gurgand, Roland Rathelot, and Philippe Zamora (2013) “Do Labor Market Policies have Displacement Effects? Evidence from a Clustered Randomized Experiment”, _Quarterly Journal of Economics_ 128(2): 531-80.

Delavande, Adeline, Xavier Gine and David McKenzie (2010) “Measuring Subjective Expectations in Developing Countries: A Critical Review and New Evidence”, _Journal of Development Economics_ , 94(2): 151-63 Fink, Günther, Margaret McConnell and Sebatian Vollmer (2012) “Testing for Heterogeneous Treatment Effects in Experimental Data: False Discovery Risks and Correction Procedures”, Mimeo. Harvard School of Public Health.

Ganzeboom, Harry and Donald Treiman (1996) “Internationally Comparable Measures of Occupational Status for the 1988 International Standard Classification of Occupations”, _Social Science Research_ 25(1): 201-39.

Groh, Matthew, Nandini Krishnan, David McKenzie and Tara Vishwanath (2012) “Soft skills or hard cash? The impact of training and wage subsidy programs on female youth employment in Jordan”, _World Bank Policy Research Working Paper_ no. 6141.

29
# _Growth of Manufacturing Bases in Districts_

Moving from the productivity regressions, Table 9 considers the potential link between specialization and growth. The topic of specialization/diversity and city growth has been often studied over the past two decades (Duranton, 2013a), and we seek to provide early evidence on this link within Indian manufacturing. We estimate a cross-sectional growth equation at the district level of the form: _Growthd_ = β⋅ _Specializationd_ , _t_ 0 + δ⋅ _Diversityd_ , _t_ 0 + χ _s_ +φ⋅ _Z d_ + ε _d_ .

Our core regressors are again the specialization and diversity measures that utilize a ten-point scale. The _t0_ subscript signifies that we measure these attributes at the start of the sample using the 1989-1994 data. The outcome variable is the log employment growth in manufacturing for the district from 1989 to 2010. Regressions include a vector of state fixed effects χ _s_ to account for differences in regional growth in manufacturing for India across the period. The vector _Zd_ contains the additional district-level controls that we modeled in Table 7. We weight districts by their log population in 2001 and report robust standard errors.

The first column shows a large partial correlation for the specialization index. An increase of one decile in the specialization index is associated with 9% higher employment growth (e.g., moving from a growth rate of 2.00% to 2.18%). This is a quite substantial boost that Columns 2 and 3 show is not present with the diversity levels of districts. Similar elasticities are evident without the state fixed effects, too.

Column 4 further shows that the results are robust to including district-level covariates taken from the 2001 Census discussed earlier. These covariates tend to have weak multivariate explanatory power, with the main exception being a robust relationship between education shares and higher growth. Regardless, the inclusion of these controls does not impact the specialization index’s connection to growth. Columns 5 and 6 show similar results when we do not weight observations or when we exclude districts with less than one million people in population. The latter check is important given that the exclusion of small districts guards against cases where our manufacturing data start to become thin for measuring the index values, and it is noteworthy that the standard errors remain consistent despite the reduction in sample size. Column 7 shows 20
- 14 -

Finally, the subnational poverty rates are regressed on the RAI and other relevant indicators. There is no doubt that poverty is relevant to the RAI (table 5). The coefficient is estimated at -0.103.

Among the RAI determinants, road density is found to be significant, but road quality is not correlated significantly with poverty. These results seem to be consistent with the abovementioned twin challenges in Africa. African countries need not only road rehabilitation, but also network expansion. The evidence reconfirms the particular importance of road network expansion to reduce poverty in rural areas. The poverty regression is only focused on three countries in Africa: Kenya, Mozambique, and Uganda. Therefore, the results can be interpreted to mean that many of the rural poor in these countries live beyond the current road networks. Thus, significant efforts would likely be required to provide them better access to the road network.

**<u>Table 5. OLS Regression of the Poverty Rate</u>**

|Variable|Coef.|Std.Err.||Coef.|Std.Err.||
|---|---|---|---|---|---|---|
|ln_RAI_|-0.103|(0.020)|***||||
|ln_RdDen_||||-0.133|(0.033)|***|
|ln_Quality_||||-0.011|(0.015)||
|ln_PopDen_||||0.020|(0.021)||
|ln_PopConc_||||-0.075|(0.037)|**|
|ln_Urban_||||-0.014|(0.002)|***|
|_v_(_Mozambique_)|-0.008|(0.035)||-0.354|(0.076)|***|
|_v_(_Uganda_)|-0.076|(0.026)|***|-0.155|(0.031)|***|
|Constant|0.399|(0.025)|***|0.235|(0.121)|*|
|Obs.|285|||281|||
|R squared|0.3215|||0.4418|||
|Fstatistics|35.66|||51.77|||

_Note:_ The dependent variable is the poverty rate. Robust standard errors are shown in parentheses. *, **, and *** indicate statistical significance at 10, 5, and 1 percent, respectively.