rafmacalaba/datause-encoder-data huggingface dataset:
**_3. Develop proper means to empower the citizens_** .  Citizens can effectively assist in making service providers accountable and in promoting equity, cost control and quality in public service delivery.  Citizens can also assist in encouraging sub-national governments to implement national policies and standards.  To be able to do so, however, citizens need information and voice.

China can build on the government’s rich experience in promoting public awareness. Over the past decades, China’s implementation of family planning policies, for instance, involved a very efficient and effective information dissemination facilitated by government agencies at the local as well as central level. More recently, the Government of China has been very successful in soliciting citizens’ feedback on the reconstruction strategy after the Wenchuan Earthquake in 2008, on health system reform, tax reform, and many other reform and policy proposals. Government agencies in all sectors need to be required to actively disseminate information, facilitate information sharing and promote transparency about government policies, public resource allocation, standards, procedures, fees, and the quality of public services. Moreover, local governments could further expand recent pilots to involve citizens in decision making.

Government also needs to be active in facilitating the collection of citizens’ feedback and in ensuring that citizens’ feedback is addressed. In this respect, it is useful to consider the possible utilization of the citizens’ scorecard, targeted local household surveys, public service complaint hot line and other innovative information channels. Citizens’ feedback can usefully feed into the performance evaluation system discussed above and help in assessing the performance of service providers or local government agencies. A well-functioning performance evaluation system would thus also help address citizens’ feedback.

This study reveals that cities in China offer a tremendous experience in developing new ways to enhance public service delivery. The analysis gives confidence that China is able to harness its growing public resources and to implement governance reforms so as to make public services work well for all its citizens.

10
financial markets, all of whom may be influenced by growth forecasts.

# **5 Robustness testing and heterogeneity**

In this section we provide a variety of tests and alternative specifications to examine the robustness of our main results.

## **5.1 Discovery sequencing: First, second and multiple prior discoveries**

Next we examine, whether there is any difference between the economic effects of a first discovery within a country versus subsequent discoveries. Here, we are interested in whether the impact of a resource discovery is stronger for new producing countries or whether it persists, regardless how mature the petroleum sector may already be.<sup>11</sup> First, and first or second discoveries lead to much larger and much bigger growth differential than subsequent ones. As a result, first, and first or second discoveries result in a very large (over 1.5 and 2.5 percentage points respectively) forecasting error, which drops magnitude for subsequent (multiple) discoveries.

||(1)<br>First Dif|(2)<br>First Act|(3)<br>First Fore|(4)<br>Second dif|(5)<br>Second Act|(6)<br>Second fore|(7)<br>Multi dif|(8)<br>Multi Act|(9)<br>Multi fore|
|---|---|---|---|---|---|---|---|---|---|
|lag<br>Fdisco|-1.695<sup>_∗_</sup><br>(0.957)|0.894<br>(0.621)|0.654<br>(0.638)|||||||
|lag<br>Secdisco||||-2.579<sup>_∗_</sup>|-0.453|0.458||||
|||||(1.310)|(0.929)|(0.781)||||
|lag<br>Multidisco|||||||-0.597<sup>_∗_</sup>|-0.201|0.427|
||||||||(0.325)|(0.353)|(0.324)|
|Obs|3781|5413|4773|3781|5413|4773|3781|5413|4773|
|R-sq|0.06|0.05|0.03|0.06|0.05|0.03|0.06|0.05|0.03|
|Standard errors in|parentheses|||||||||
|_∗p <_0_._10, _∗∗p <_|0_._05, _∗∗∗p <_0|_._01||||||||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
||First Dif|First Act|First Fore|Second dif|Second Act|Second fore|Multi dif|Multi Actu|al<br>Multi fore|
|inst<br>~~l~~ag<br>~~F~~disco|-2.586<sup>_∗∗_</sup>|0.730|1.004|||||||
||(1.157)|(0.640)|(0.860)|||||||
|inst<br>~~l~~ag<br>~~S~~ecdisco||||-2.557<sup>_∗∗_</sup>|-1.464<sup>_∗_</sup>|0.449||||
|||||(1.267)|(0.827)|(0.766)||||
|inst<br>~~l~~ag<br>~~M~~ultidisco|||||||-1.001<sup>_∗∗_</sup>|-0.887<sup>_∗_</sup>|0.558|
||||||||(0.449)|(0.493)|(0.499)|
|Obs|2807|3972|3536|2807|3972|3536|2807|3972|3536|
|R-sq|0.05|0.06|0.03|0.05|0.06|0.03|0.05|0.06|0.04|
|Standard errors in pare|ntheses|||||||||
|_∗p <_0_._10, _∗∗p <_0_._05|, _∗∗∗p <_0_._01|||||||||

Table 9: First, first or second and multiple discoveries, top panel all sample, lower panel weak institutions

> 11The ordering of discoveries is determined across the full giant oil discovery dataset, so discoveries are only recorded as first in the analysed period (1988 onwards), where there was no preceding discovery recorded in the full sample (going back to 1868).

23
**Table 11. Results of log-linear models. Youths from 18-35 years of age of ENCEL 2017. Comparison** **<u>by migratory status.</u>**

|||||Consta<br>|nt fluidity<br>|||Non-co<br>|nstant flu<br>|idity<br>|
|---|---|---|---|---|---|---|---|---|---|---|
|Type of model|||g.l.|G<sup>2</sup>|X<sup>2</sup>|BIC|g.l.|G<sup>2</sup>|X<sup>2</sup>|BIC|
|1. Independence|||50|297.51|327.44|83.67|||||
|2. Homogeneous diag|onal||49|85.32|84.48|-124.23|48|63.41|60.26|-141.87|
|3. Diverse diagonal|||47|76.6|76.1|-124.4|44|59.64|56.08|-128.53|
|4.<br>Symmetry<br>homogeneous diagon|(2p)<br>al|with|48|81.03|80.19|-124.25|46|57.08|53.65|-139.65|
|5.<br>Symmetry<br>homogeneous diagon|(3p)<br>al|with|47|81.03|80.2|-119.97|44|54.41|51.5|-133.76|
|6. Symmetry (4p)<br>diagonal|with|diverse|46|74.67|73.6|-122.06|42|54.99|50.94|-124.63|
|7. Symmetry (5p)<br>diagonal|with|diverse|45|74.65|73.61|-117.8|40|52.16|48.66|-118.9|
|8. Corners (2p) with<br>diagonal|homo|geneous|48|82.12|81.18|-123.16|46|56.85|54.13|-139.87|
|9. Corners (3p) with<br>diagonal|homo|geneous|47|79.76|79.12|-121.24|44|53.94|51.62|-134.23|
|10. Corners (4p)<br>diagonal|with|diverse|46|70.69|69.34|-126.04|42|50.93|46.9|-128.69|
|11. Corners (5p)<br>diagonal|with|diverse|45|65.68|64.76|-126.77|40|46.45|42.71|-124.62|

Note: g.1. = degree of freedom; G<sup>2</sup> = Deviance; X<sup>2</sup> = Pearson’s chi square; BIC = Bayesian Index Criteria. Source: Own elaboration from ENCEL 2017 (n=3,084).

The results of the log-multiplicative models confirm that there are differences in relative mobility between youths who remained in their localities of origin and those who migrated. The values of the φ coefficient for these theoretical models are different from 1 (between 0.23 and 0.26), with non-migrant youths as a reference. That is, migrants experienced more relative mobility and the difference between both groups seems important.

The parameters of the selected theoretical models present evidence that the difference lies mainly in the strength of intergenerational inheritance. In the case of model 2, which had the best fit, we observed that non-migrants have a 2.47 times higher probability to inherit their provider’s occupation than to have a different occupation ( _dp=2.47, p<0.001_ ), while in the case of migrants this probability is only 1.26 times and its statistical significance is marginal ( _dp=1.26, p=.08_ ). By incorporating additional parameters to the main diagonal in other theoretical models selected, the parameter of inheritance of migrants becomes statistically insignificant.

The other three models show two more differences between youths who did not migrate and migrants. Model 4 indicates that non-migrants experienced short-distance mobility from all strata of origin ( _sim=1.22, p<0.001_ ) and an interpretation of the parameters of models 8 and 9 suggests 38
local goods, and hence prices and wages, without any wealth eects on labor supply (I assume government purchases are not valued by households). Again, this is very dierent from the cross-region transfer multiplier, which is negative in NC models.

# 5. Quantitative Cross-Region Transfer Multipliers

In this section, I investigate the extent to which standard NK or NC models can rationalize the multipliers estimated in the data. The full models used here are similar to the analytical models presented at the start of Section 4, but the full NK model has Calvo sticky prices/wages rather than rigid prices/wages. The population share of the home region is calibrated to represent a typical US state ( _n_ = 1 _/_ 50 rather than _n →_ 0 as in the analytical model). Other parameters are taken from NS, who present a very similar model except for parameters relating to two features not in their model: the fraction of handto-mouth household ( _ω_ = 1 _/_ 3 ; based on evidence from Kaplan, Violante, and Weidner 2014) and sticky wages that adjust once a year, on average ( _θw_ = 0 _._ 75 ). See Appendix Table 11 for a full list of parameters.

Before comparing model-based and empirical multipliers, it is important to ensure that these multipliers are calculated in the same way. As there are no ocial state-level price data, empirical estimates in Section 3 are produced by deating nominal labor income or nominal GDP by national ination.<sup>40</sup> I call these measured multipliers to distinguish them from the actual multipliers in Proposition 1 (actual multipliers are quantities, which are nominal values deated by local (state-level) producer prices). Measured and actual impact multipliers are very similar in NK models (due to sticky prices), but in NC models with exible prices and persistent shocks, demand shocks increase

> 40Using my own constructed proxy of quarterly state-level prices, I nd little robust evidence that the transfers studied above increased local ination. Regressions of my quarterly state ination proxy on transfers are always insignicant in the 2000s, and over 19521974 transfers are always negative and insignicant once I control for the state-specic eects of oil price shocks. See Appendix 3.2 for further details. NS nd that there is very little movement in the local CPI in response to local military purchases and (consequently) get similar results when they deate by either national or state annual CPIs.

34
# Factor Market Failures and the Adoption of Irrigation in Rwanda<sup>∗</sup>

Maria Jones<sup>†</sup> Florence Kondylis<sup>†</sup> John Loeser<sup>†</sup> Jeremy Magruder<sup>‡</sup> _Updated_ January 2021 JEL Classification Codes: O1, O12, O13, Q12, Q15 Keywords: Technology adoption, Irrigation, Factor markets ∗ This draft benefited from comments from Abhijit Banerjee, Chris Barrett, Paul Christian, Alain de Janvry, Simeon Djankov, Esther Duflo, Andrew Foster, Doug Gollin, Saahil Karpe, Elisabeth Sadoulet, John Strauss, Duncan Thomas, Chris Udry, and seminar audiences at Cornell University, Georgetown University, Georgia State University, Harvard University/MIT, Michigan State University, North Carolina State University, Northwestern University, University of California, Berkeley, University of Pittsburgh, University of Southern California, and the World Bank. We thank the European Union, the Global Agriculture and Food Security Program (GAFSP), the World Bank Rwanda Country Management Unit, the World Bank i2i fund, 3ie, and IGC for generous research funding. Emanuele Brancati, Anna Kasimatis, Roshni Khincha, Christophe Ndahimana, and Shardul Oza provided excellent research assistance. Finally, we thank the technical staff at MINAGRI, the staff of the LWH project implementation unit, and the World Bank management and operational teams in Rwanda for being outstanding research partners. We are particularly indebted to Esdras Byiringiro, Jolly Dusabe, Hon. Dr. Gerardine Mukeshimana, and Innocent Musabyimana for sharing their deep knowledge of Rwandan agriculture with us. Magruder acknowledges support from NIFA. This study was approved by UC Berkeley IRB (2016-06-8861), the Rwanda National Ethics Committee, and IPA IRB (10951), and is registered in the AEA RCT Registry (AEARCTR0001323). The views expressed in this manuscript do not reflect the views of the World Bank. All errors are our own.

> †Development Impact Evaluation, World Bank

> ‡UC Berkeley, NBER
agriculture) wage gaps represent important drivers of inequality, this set‐up allows to explain changes in distributions.

For the goods markets, equilibrium for domestically produced goods sold domestically is assumed through market clearing prices. And the small country assumption is assumed for export and import prices and thus they are exogenous, i.e. export levels do not influence international prices and import demand does not influence (CIF) import prices. For the factor markets, wages equate demand and supply of the various segments (agriculture and non‐agriculture) of labor markets with the assumption of full employment, i.e. a vertical labor supply. Capital supply is assumed to be fixed and mobile across sectors and a market clearing rental rate is calculated by the model. Finally, a sector‐specific factor, representing natural resources, is employed exclusively in production in the oil and gas sector; in the current version its supply is sensitive to the international price of oil, therefore its reduction triggers a reduction of the supply of the natural resource and some contraction of the output of the oil sector.

The version of the CGE model used here has a 2011 base year and relies on the Social Accounting Matrix for the Russian Federation and on bilateral trade flows from the Global Trade Analysis Project (GTAP) 9 database to calibrate initial parameters.

The scenario of the oil shock is implemented by reducing the exogenous international price of oil by 50 percent and a new equilibrium is calculated.<sup>7</sup> The results of the model, in terms of changes between the initial equilibrium and the oil shock one of: (i) prices (for food and non‐food items), (ii) wages (for the four labor market segments), (iii) unskilled labor migration from agriculture to non‐agricultural activities, and (iv) per capita consumption are passed to the household survey data and, in the microsimulation, a new hypothetical global income distribution is generated for the oil shock equilibrium.

# _The microsimulation model_

The ultimate focus of analysis is household welfare, and household real per capita income is here assumed as its indicator:

> 7 The model is solved in a comparative statics approach. This means that the model assumes no changes in the demographic structure of the population nor in its skill endowment. Likewise, the supply of capital and land is assumed fixed. The only variable factor of production is natural resources that, as mentioned in the main text, responds to changes in the international price of oil. The economy is also assumed to operate in full employment.

8
- 4) Microfinance institutions – financial institutions whose primary business model is to lend to (and possibly take deposits from) the poor.

- 5) Other – other institutions providing deposit or retail lending services which don’t fall into the above categories.

These categories were chosen in part to facilitate the disaggregation of some of the institutions which are typically more active in serving financial inclusion policy goals (such as SSFIs) or which tend to target middle class or low income clients with basic banking services (MOFIs and MFIs). The descriptions of the various categories as they were presented to the survey respondents are provided in Table 2. The regulators were asked to use their best judgment to choose only one category in which each type of institution fits. In a few cases, regulators could not separate data for two types that likely would have fallen into different categories (e.g. credit union data could not be separated from commercial bank data in Switzerland) and were asked to put them into the category which was dominant, or to leave both categories as missing and provide only the system wide total. An example of how the form was filled by a regulator is shown in Table 3.

The MFI category is somewhat problematic in that many institutions which would likely be categorized by regulators as falling into the other categories consider themselves to be MFIs. For example, deposit taking MFIs are registered as banks in many countries small rural cooperatives conduct micro-lending exclusively. Since our main focus is the use of financial services by the poor, we decided to keep the MFI category and recognize that in many countries where no data was provided, MFIs are grouped in with one of the other categories.

A second part of the survey contains questions on regulations and policies relating to access to financial services, including:

- Financial services provided though the post offices

- The use of agents and correspondents

- Bank account management

8
# **When the Rain Stops Falling: Effects of Droughts on the Tunisian Labor Market**<sup>∗</sup>

Federica Alfani<sup>†</sup> , Giacomo Pallante<sup>§</sup> , Alessandro Palma<sup>¶</sup> , Abdelkader Talhaoui<sup>#</sup>

**J.E.L. codes:** Q1, Q54, J21

**Keywords:** drought, agriculture, employment, gender gap, Tunisia

* The authors want to thank the National Institute of Statistics (INS) of Tunisia for the ongoing collaboration and Gabriele Restelli for excellent assistance with data preparation. We also acknowledge participants to the authors’ Workshop of the MENA Chief Economist's Research Programs on Gender and Labor Markets and the SITES 2023 conference in Naples for useful comments.

> † World Bank, E-mail: falfani@worldbank.org

> # Tunisian National Institute of Statistics. Email: talhaoui.abdelkader@ins.tn

> § School of International Studies and DSRS, University of Trento, E-mail: giacomo.pallante@unitn.it

> ¶ Gran Sasso Science Institute (GSSI), E-mail: alessandro.palma@gssi.it
<!-- Start of picture text --> Figure 6: Sub-national revenue as % of GDP Transition<br>Developing<br>10<br>9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999<br><!-- End of picture text --> Source: IMF Government Finance Statistics (Various years) and World Bank Fiscal Indicators <u>(http://www1.worldbank.org/publicsector/decentralization/fiscalindicators.htm)</u> 20
**Figure 3: Tonga Employer assessment of the most important and most lacking staff characteristics, %**

<!-- Start of picture text --> practical knowledge of job<br>theoretical knowledge of job<br>literacy and numeracy<br>degree achieved/educational institution...<br>computer<br>management/leadership<br>time management/organizational skills<br>foreign language (English)<br>independence/problem solving<br>commitment/desire to learn<br>customer skills<br>teamwork/communications<br>punctuality/attendance<br>honesty<br>0 10 20 30<br>most lacking characteristics in staff most important characteristic in staff<br><!-- End of picture text --> Question: what are the most important characteristics in your staff? Which are the biggest skills gaps? _Source: Adapted from Figures 14 and 15, pages 24 and 25, in TCCI 2010._

**Figure 4: Indonesian firms ranking of “most important” skills, by sector**

<!-- Start of picture text --> Managers<br>Skilled workers<br>80<br>80<br>70<br>70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>manufacturing service manufacturing service<br>Question: share of firms rating each skill as “very important<br>Source: diGropello (2011), adapted from Figure 2.22 on page 81.<br><!-- End of picture text --> 43
- 30 -

difference in preference, we must assume that Japan has different tastes and displays an abnormal bias for home products." 1/ 59. One issue with the results of the Lawrence study is an apparent identification problem. As Saxonhouse and Stern (1988) point out, comparing the results of Lawrence's analysis of 1970 export shares with his analysis of 1983 export shares would lead us to conclude that Japanese protection for manufacturing increased substantially between 1970 and 1983. Saxonhouse and Stern ask if it is really plausible, because "this is precisely the period when virtually all formal Japanese barriers to the import of manufactured goods were eliminated."2/ Saxonhouse and Stern, therefore, would conclude: "If the Japanese trade structure did become more distinctive between 1970 and i983, this can be more properly attributed to increasing foreign barriers against Japanese exports. Japan's import shares of manufactures may well be a better index of Japanese competitiveness rather than its export shares." 3/ X. Conclusions 60. The purpose of this note is to review the recent major efforts of econometric investigation into two related but separate questions; namely (i) whether Japan imports less of all products than it should, 1/ Lawrence (1987), pp. 536-37.

2/ Saxonhouse and Stern (1988), p. 31.

3/ Saxonhouse and Stern (1988), p.<sup>31</sup> .
Appleton (1999). The average poverty line for rural areas was 15.6 U.S. dollars per month (21,790 Ugandan Shillings), or 0.52 cents per day. The national poverty rate in 1999 was 35 percent.

In Africa, poverty is widespread and it is important to understand its determinants and ways to eradicate it. Looking at poverty is more revealing than looking at household expenditure or income. While it is possible to test whether trade affects the income of rich Ugandan households, our analysis is tailored to explore impacts among the most vulnerable group of the population.

Households do not export directly but we can infer their export-related activities from the crops they grow. We divide crops into two groups, export crops and food crops. The division is based on aggregate data on Ugandan exports, reported in Table 2. Major export commodities are coffee, cotton, tea, and tobacco, and other non-traditional products such as fish, fruits, and flowers. In the crop questionnaire of the Uganda National Household Survey, prevailing export crops are coffee, tea, cotton, pineapples, and passion fruits.<sup>10</sup> Food crops include matooke (banana), maize, sweet potatoes, sorghum. Food crops are mostly destined to home consumption.

In our analysis, we aggregate all the export crops into one export crop activity. While there may be differences across crops that can be worth exploring, our data are not rich enough to conduct a detailed disaggregated analysis. In the empirical work, we exploit regional disparities in export adoption, poverty, and marketing costs as identifying variation. Figure 3 gives an overview of regional disparities in export crops in Uganda based on information from the UNHS and FAO. The figure includes four panels, each one of them depicting the prevalence of different crops in different regions. The top-left map reveals that coffee is mostly produced in the center of the country and in mountainous regions in the Southwest, West, Northwest, and East. Tea (top-right map) is produced in the South, the Center, and in some Northern regions. Cotton (bottom-left panel) is the major crop of the Northern provinces; there is some cotton production in the Southwest and Southeast as well. Finally, fruits, like passion fruits and pineapples, are grown in the Center, the Northwest

> 10Other export products, like vanilla, fish or flowers, are not significant in the 1999/2000 data.

10