rafmacalaba/datause-encoder-data huggingface dataset:
Table 8: Estimations of manufacturing <u>production functions, organized sector</u>

|||DV: Log|output in man|ufacturing es|tablishment, 2|000 sample||
|---|---|---|---|---|---|---|---|
||Full<br>|Full<br>|Full<br>|Full<br>|Full<br>|Traditional<br>|Modern<br>|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Log employment|0.121+++<br>(0.006)|0.121+++<br>(0.006)|0.123+++<br>(0.006)|0.122+++<br>(0.006)|0.125+++<br>(0.007)|0.106+++<br>(0.008)|0.147+++<br>(0.010)|
|Log capital|0.011+++|0.011+++|0.010+++|0.010+++|0.008++|-0.001|0.028+++|
||(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|(0.005)|(0.006)|
|Log materials|0.931+++|0.930+++|0.931+++|0.930+++|0.933+++|0.943+++|0.907+++|
||(0.006)|(0.006)|(0.006)|(0.006)|(0.006)|(0.008)|(0.010)|
|(0,1) Input counts above||0.039+++||0.031+++|0.034+++|0.035+++|0.028+|
|industry median||(0.009)||(0.010)|(0.010)|(0.013)|(0.015)|
|District average of|||0.129+++|0.102+++|0.122+++|0.117+++|0.053|
|inputs counts metric|||(0.031)|(0.033)|(0.033)|(0.039)|(0.059)|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Additional urban traits|||||Yes|||
|Observations|25,106|25,106|25,106|25,106|24,139|14,749|10,357|

Notes: Estimations consider simple production functions for manufacturing establishments in India. Indian data are taken from ASI and NSS. Additional urban traits include log manufacturing employment in district, log manufacturing employment in plant's districtindustry, log share of local manufacturing employment in the unorganized sector, and log share of district-industry manufacturing employment in the unorganized sector. Estimations report robust standard errors, include industry fixed effects, and weight observations by sample weights. + significant at 10% level; ++ significant at 5% level; +++ significant at 1% level.

Table 9: Estimations of manufacturing <u>production functions, unorganized sector</u>

|||DV: Log o|utput in manu|facturing esta|blishment, 2|000 sample||
|---|---|---|---|---|---|---|---|
||Full<br>|Full<br>|Full<br>|Full<br>|Full<br>|Traditional<br>|Modern<br>|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Log employment|0.362+++|0.361+++|0.362+++|0.361+++|0.355+++|0.359+++|0.408+++|
||(0.012)|(0.012)|(0.012)|(0.012)|(0.013)|(0.013)|(0.034)|
|Log capital|0.089+++|0.089+++|0.088+++|0.089+++|0.089+++|0.089+++|0.085+++|
||(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|(0.011)|
|Log materials|0.628+++|0.626+++|0.627+++|0.626+++|0.626+++|0.626+++|0.640+++|
||(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|(0.017)|
|(0,1) Input counts above||0.029++||0.024+|0.025++|0.028++|-0.093+++|
|industry median||(0.012)||(0.012)|(0.012)|(0.013)|(0.033)|
|District average of|||0.068+|0.046|0.025|0.042|0.139|
|inputs counts metric|||(0.037)|(0.040)|(0.040)|(0.041)|(0.108)|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Additional urban traits|||||Yes|||
|Observations|120,599|120,599|120,599|120,599|120,599|113,003|7,596|

Notes: See Table 8.
impact depended on the local institutional framework, and in particular that the lack of redistribution from winners to losers was especially felt in areas where institutions, for historical reasons, favored the politically and economically well-connected agents.

Rauch (1995) showed, using data from US cities in the first two decades of the twentieth century that an educated bureaucracy and adequate formal functioning rules in terms of recruitment, tenure, etc., are instrumental in allowing the choice and successful realization of long-matured infrastructure projects. In his sense, professionalization of the bureaucracy, because to some extent it constrains politicians and limits opportunism, makes more likely a choice of infrastructure close to the optimal one, or at least that longgestation-period projects are not replaced by short term ones for electoral considerations for example. Finally, Henisz and Zelner (2000) argue, on the basis of cross-country evidence from the electricity sector, that the investment incentives of private firms are affected by a combination of the level of political constraints on politicians and the degree of interest-group intervention in the organization and regulation of the industry.

Again, this potential policy endogeneity presents a challenge for econometric studies. Indeed, if infrastructure investment decisions are the result of an endogenous policy process, the expected signs may not be the obvious ones, a point forcefully made in Rodrik (2005) for the cross-country growth literature. A simple example would be the fact that if countries deciding to increase their infrastructure investment are precisely the ones where the existing stock is deficient, and the resulting improvement to the infrastructure stock is only partial still leaving this group of countries with lower average stocks, the expected sign would then be negative despite the fact that the effect of this investment might be positive for the specific group of countries. This therefore requires specification of the underlying model and may be addressed by one of the IV strategies discussed above (reverse causality or unobserved effects) or by the use of simultaneousequation models, as in Cadot et al. (2006).

36
8 necessary to avoid underestimation of the total loss, as the proportion of on-farm PHL increases cumulatively over time.<sup>12</sup> It makes all households comparable in their PHL, as if they were all interviewed just before the new harvest of the new agricultural season (see technical appendix A1 for more details on the adjustment procedure as well as a graphical presentation).

Both rural and urban households were included and through the application of the household sampling weights (or more specifically, their inverse to obtain the expansion factor), nationally representative estimates of the incidence and proportion of PHL (as well as other PHL related indicators) were obtained. To correct for differences in total maize output per household in obtaining national maize PHL estimates per household, household expansion factors were further multiplied by each household’s total maize production (in kg). Application of the household expansion factor alone would give the average maize PHLs for a nationally-representative household as opposed to the national maize PHL per household on average. It would overemphasize the losses of small versus big producers. Both estimates are reported (Table 1).

The proportion of maize harvest lost on-farm among households who have finished their maize harvest<sup>13</sup> and who have strictly positive harvested quantities, ranges on average between 1.4 percent in Malawi and 5.9 percent in Uganda. In Tanzania, it fluctuates between 4.4 percent during the 2008-9 survey and 2.9 percent during the 2010-11 survey. When expressed in terms of the representative household, the on-farm PHL proportion of the maize harvest is lower, ranging between 1.9 (Tanzania) and 3.9 percent (Uganda), except for Malawi where it amounts to 2.9%.

Clearly, this is substantially less than the average 8 percent on-farm handling and storage post-harvest loss (in weight) for cereals reported by FAO (2011) for SSA or the post-harvest maize weight losses reported by APHLIS for those three countries in those particular years. The latter range between 17 (Tanzania) and 21 percent (Malawi) when including the losses during all stages of

> 12 On-farm PHL for maize tends to increase rapidly three to four months after the harvest, when insect pests really start to cause havoc (Stathers, Lamboll, and Mvumi, 2013), while additional losses gradually decline thereafter (mainly because stocks are increasingly depleted towards the beginning of the new harvest). 13 Among the maize farmers in Uganda, Malawi and Tanzania, almost all reported strictly positive harvested quantities (99.2 percent, 95.4 percent and 94.3 percent respectively).
8

- iv) develop regular quantitative and qualitative feedback about performance;

- v) have the capacity to vary their delivery strategies, package of interventions, and IEC strategies for the needs of particular client groups;

- vi) develop a package of financial and/or non-financial performance incentives for field staff; and

- vii) make use of NGO, private sector, or community-based service delivery channels as complements or alternatives to the public sector.

# 3. REVIEW OF **UANK** WORK IN PHN

This section reviews Bank work in the PHN sector from the perspectives developed in the previous section. The main focus is on the content of the Bank's PHN projects, with briefer discussions of the PHN sector work program and policy and research work progtam, and of institutional factors in the Bank which shape the content and emphasis of the overall PKN work program. Generalizations about the Bank's PHN projects are based on a desk review of 47 projects appraised between 1981, the year after the Bank's PHN department was founded, and mid-1987, the time that the PHN department was disbanded and dispersed among the regional departments of the Bank. A list of the projects by year is given in Annex A together with the results of the quantitative analysis of project content on the basis of which much of this section is written.

It should be noted that quantitative generalizations about the portfolio are based on features that were or were not included in the original project lesigns. This study therefore does not take account of changes in design made during implementation. In addition to generalizations about the portfolio, particular features, components or projects are described in the text or the case studies which follow it, where these seem to be useful for replication. But it should also be noted that in most cases, judgments about effectiveness can only be made on an interim basis, since most projects are not yet completed and evaluated.

# A. Lending

# Outreach

The vast majority of the Bank's PHN projects (about 95%) have included as one of their objectives extension of the coverage and/or quality of population, health, or nutrition services to poorly served populations. The main exceptions are a small number of projects focusing narrowly on manpower training and/or the institutional development of a Ministry of Healt, at the central ievel. Several indicators show that there is a consistent effort to include outreach activities in the Bank's PHN projects. No less than 69% of the projects involve the use of community health workers
<!-- Start of picture text --> on =Global factor = Group factor<br>30<br>50<br>10<br>0<br>All Advanced EMDEs<br>economies<br><!-- End of picture text --> <!-- Start of picture text --> Percent mMedian Mean<br>40<br>20 > ©<br>° °<br>a)<br>© Oo KR]}®B 9 RIB oO RK<br>P OP Fl? fF FT} RP 2 F&F<br>© @ co oO ice} co oO © -<br>5S- &- BISN 7 8- 8/5N 7 8- 8N<br>All Advanced EMDEs<br>economies<br><!-- End of picture text --> <!-- Start of picture text --> Feteent = & Median ¢ Mean<br>30<br>20 .<br>10 © ° ><br>0<br>All AEs EMDEs All AEs EMDEs<br>Global factor Group factor<br><!-- End of picture text --> <!-- Start of picture text --> Number of countries<br>80<br>—1970-85 —1986-2000 -—2001-17<br>60 \ .<br>40 \ \<br>20 ~TMN ---<br>~ ~<a .<br>~‘<br>9 SS ——<br>Variance20 share explained40 by global60 factor, 80<br>percent<br><!-- End of picture text -->
<!-- Start of picture text --> LSMS(-ISA) surveys Some LSMS-type surveys LFS<br>Stylized questions on:<br>** WageHousehold employmententerprises List. of wage activitieswae Screeningstylized- questions6 quequestion,on:followed. b'Y<br>* Farming activities Wage employment<br>: ‘i nest 2 hland cmployment * wage employment .: i one4s 2 howellnd employment<br>e ° searcl fava ability * Job search/availability ° searcl fava ability<br>* HoursEarningspr worky! in wage *+ HoursEarnings worked by activityos .* employmentEarningsours worked in wage<br>' Optional questions: unpaid work, casual work, control over earnings, labor | Optional questions: retrospective questions, !<br>' underutilization, unpaid care work, informality ' ' labor underutilization, informality '<br><!-- End of picture text -->
occupation and crop production as a secondary occupation contrast with the main finding of Ruiz and Vargas-Silva (2015) wherein refugee inflow does not have an effect on traditional employment activities.

Table 4: 2SLS estimation of the impact of refugee inflow on the number of household members engage in each livelihood activities.

||(1)<br>IHS<br>(Salaried<br>employment|(2)<br>IHS<br>(Housework<br>)|(3)<br>IHS<br>(Commerce or<br>other business)|(4)<br>IHS (Crop<br>production<br>)|(5)<br>IHS<br>(Livestock<br>production|(6)<br>IHS<br>(Other<br>activity|
|---|---|---|---|---|---|---|
||)|<br>**_Panel A: as a_**|<br>**_primary activity_**||)|)|
|IHS (refugee|6.024***|0.236|1.451|-2.708***|7.033**|-2.038|
|inflow)|(2.208)|(0.960)|(1.216)|(0.775)|(3.046)|(3.498)|
|||<br>**_Panel B: as a_**|<br>**_secondary activity_**||||
|IHS (refugee|24.770|6.096***|0.650|7.145**|3.897|1.497|
|inflow)|(24.742)|(2.146)|(0.559)|(3.290)|(2.376)|(1.955)|
|N|3375|3375|3375|3375|3375|3375|
|Kebele fixed<br>effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Nearest region|Yes|Yes|Yes|Yes|Yes|Yes|
|fixed effects|||||||
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: ***significant at 1%, **significant at 5%, *significant at 10%; village level clustered standard errors in parenthesis; other activity includes causal labor, mining, religious leader, and other activities than listed above; IHS stands for inverse hyperbolic sine transformation; the summary statistics of first-stage regressions for each model are as presented in Appendix C as we used the same sample and set of covariates.

We also find evidence that refugee presence improved households’ access to market, measured by the distance to markets in kilometers and minutes. Specifically, a 1% increase in refugee presence led to a decrease in distance to markets in minutes and kilometers by 2.6% and 3%, respectively. This can potentially be attributed to the creation of new markets (mainly driven by demand) due to refugee inflow, as observed by Vemuru et al. (2020), which reduced distance to markets and improved access to markets. This led to commercialization of activities through sale of livestock products as our main analysis revealed.

Table 5: 2SLS estimation of the impact of refugee inflow on market access measured in distance to market in minutes.

||(1)<br>IHS (distance to market, in<br>kilometers)|(2)<br>IHS (distance to market, in<br>minutes)|
|---|---|---|
|IHS (refugee inflow)|-2.644***<br>(0.924)|-3.092***<br>(0.989)|
|Kebele fixed effects|3375|3375|
|Nearest Region fixed|Yes|Yes|
|effects|||
|Controls|Yes|Yes|
|Kebele fixed effects|Yes|Yes|

Notes: ***significant at 1%, **significant at 5%, *significant at 10%; village level clustered standard errors in parenthesis; IHS stands for inverse hyperbolic sine transformation.

# **7. Summary and conclusion**

This paper aims to assess the impact of refugee inflow on livelihood strategies in host communities. The study considers two major livelihood strategies: livelihood diversification 19
# - W -- s-m~~AN9 _I_ Description gf Property- Tax Administr'tion Programs in Brazil and the PhiliRRines

1. For purposes of this study, two detailed case studies of property tax intervention were evaluated. While both have received World Bank funding, neither represents a Bank-led initiative. The first is the Philippines Real Property Tax Administration Pro4ect (RPTA), financed by USAID; the second, the Brazilian Program for Municipal Technical-Administrative Improvement (CIATA), financed by the Brazilian government.

2. Both programs are ambitious: they aim to reform property tax administration on a national scale, in countries where both valuation and collection are local responsibilities. Both aim to have lasting impacts, by introducing procedural change. As targets for evaluation, the two projects have an additional virtue: they are longstanding, providing the opportunity to evaluate their impacts ex post facto, and to benefit from modifications made in response to early problems in implementation.

# Proiect Description

3. RPTA and CIATA were both initiated in response to a similar diagnosis of the problem. Local governments were perceived as having inadequate revenues. This was attributed to a failure to exploit the property tax, which was in turn attributed to problems in administration of the tax--specifically the failure of existing procedures to produce comprehensive, accurate information on the physical characteristics of the tax base. 4. The design of both programs responded to this diagnosis. Both aimed to change the way property is discovered and identified. Both jump-started the procedural reform by producing: (1) a new set of parcel maps, incorporating all properties in the jurisdiction; (2) new property records, based on field surveys of all properties in the jurisdiction, and (3) a new set of billing records, incorporating the revised property data. Transmission of knowledge was ensured by involvement of TA recipients in the production of these products.1' 5. The two programs are executed in the form of a standardized package of technical assistance, delivered over a fixed time period to preselected groups of municipalities. In the Philippines, the program is managed by the Office of Local Government Finance in the Ministry of Finance. In Brazil, the program is sponsored by the equivalent unit in the Ministry of Finance but is executed by staff of the government data-processing parastatal.

1/ The details of the programs differed slightly in response to differing preproject conditions: The Brazilian program aimed at resuscitating an existing map-based inventory system that had fallen into disrepair in smaller municipalities. The Philippines program aimed at a fundamental change in the system of discovery; replacing a system based on owner-declarations with one using map-based government inventories.
In fact, when looking at its concentration, almost 15 percent of the total spending on tertiary education in Chile goes to the tenth decile, which is twice the share received by the bottom 10 percent of the population: 6.7 percent (Figure 10.B). In terms of its incidence, when social spending on tertiary education is analyzed as share of income in each decile, such share is higher for the first decile (4.8 percent) than for the tenth decile (0.4 percent), a result that is consistent with the positive marginal contribution to inequality (0.003) found previously, indicating that this component of social spending exerts a slightly equalizing effect.

While social spending on tertiary education is slightly equalizing, this intervention is not pro-poor as indicated by its positive concentration coefficient (Figure 11). In fact, most of the interventions through public spending analyzed in this paper are equalizing (positive marginal contributions). Among them, the most pro-poor (negative concentration coefficients) are direct transfers followed by primary education, pre-school and secondary education. The water subsidy and social spending on health are also somewhat pro-poor. In the case of the subsidy to public transportation it is slightly equalizing but not pro-poor, whereas the subsidy to gas exerts a neutral effect on inequality (zero marginal contribution) and it is also not pro-poor<sup>30</sup> —the latter is not surprising given that the gas subsidy uses a geographical targeting and it does not consider the poverty status of the population—.

**Figure 11: Concentration coefficients of social spending and public spending on subsidies**

<!-- Start of picture text --> Gas subsidy (Magallanes region) 0.316<br>Tertiary education 0.125<br>Public transportation subsidy 0.019<br>All indirect subsidies -0.003<br>Health -0.099<br>Water subsidy -0.198<br>Secondary education -0.215<br>Pre-school -0.246<br>Primary education -0.270<br>Other direct transfers -0.282<br>Non-contributory pensions -0.305<br>All direct transfers -0.331<br>Ingreso ético familiar -0.464<br>Chile Solidario -0.503<br>-0.60 -0.50 -0.40 -0.30 -0.20 -0.10 0.00 0.10 0.20 0.30 0.40<br><!-- End of picture text --> _Source_ : Author’s elaboration based on Martinez-Aguilar and Ortiz-Juarez (2016).

# **4.2 Fiscal redistribution in Chile: A comparative perspective**

The redistributive effect of direct transfers, measured as the percent change in the Gini coefficient from market income plus pensions to disposable income, is particularly larger in Chile (5.4 percent) than in other Latin American countries with a comparable fiscal incidence analysis: it is well above the average, and between 2.3 and 4 times larger than the effect found in Dominican Republic, Guatemala, and the Andean countries. A salient result is that although spending on direct transfers as a share of

> 30 The concentration coefficients for all fiscal interventions analyzed is shown in the appendix Table A1.

21
# Economic Integration, Industrial Structure, and Catch-up Growth: Firm-Level Evidence from Poland<sup>_∗†_</sup>

Paulo Bastos<sup>_‡_</sup> Stefania Lovo<sup>_§_</sup> Gonzalo Varela<sup>_¶_</sup> Jan Hagemejer<sup>_∥_</sup>

- _Keywords:_ Economic integration, industrial structure, foreign direct investment, catch-up growth, firm performance.

_JEL Classification_ : F11; F14; F15; F23; F63; L25; O47

> _∗_ We thank several anonymous referees for very helpful comments. We are also grateful to participants at various conferences and seminars for helpful comments and suggestions. Research for this paper was supported in part by the World Bank’s Multidonor Trust Fund for Trade and Development and through the Strategic Research Program on Economic Development. The views expressed herein are those of the authors only and not those of the World Bank. Bastos is also affiliated with the CEPR and REM. We remain responsible for any errors.

> _†_ Data Availability Statement. The data that support the findings of this study are available from the from the Central Statistical Office of Poland and the Orbis database of Bureau Van Dijk. Restrictions apply to the availability of these data, which were used under license for this study. The data from the Central Statistical Office of Poland are confidential, but are available to other researchers. The data from Bureau Van Dijk require a subscription. We stand ready to provide interested researchers with the necessary information on how to obtain the data from the Central Statistical Office of Poland and Bureau Van Dijk; as well as the programs, and details for the computations necessary for replication.

> _‡_ Development Research Group, World Bank. E-mail: pbastos@worldbank.org

> _§_ Department of Economics, University of Reading. E-mail: s.lovo@reading.ac.uk

> _¶_ World Bank. E-mail: gvarela@worldbank.org

> _∥_ University of Warsaw and CASE–Center for Social and Economic Research. Email: j.hagemejer@uw.edu.pl