rafmacalaba/datause-encoder-data huggingface dataset:
Column 4 presents linear probability estimates of the effect of LEAP on contributions in the dichotomous public good activity. Around 64 to 65 percent of subjects contributed to the public good. There was little variation in the behavior of the poor and non-poor subjects in this case. More importantly there was no significant difference between subjects from treated and control communities, neither in the full sample nor in sub-categories of poverty. Roughly five to six percent more subjects contributed in the LEAP villages than did subjects in the control village, but this increment was not statistically significant across samples.

Estimates from the trust activity are presented in columns 5 and 6. Column 5 shows the effect of the program on trust—the amount sent by the “investor” in the activity. Column 6 shows the impact of the program on trustworthiness—the amount returned by the trustee as a percentage of the total amount available to the trustee. The total amount available to the trustee is his or her initial endowment of 3,000 riels plus triple the amount the “investor” gave him or her. The number of “investors” and “trustees” is unequal because on a few occasions an odd number of subjects arrived for the games due to attrition. Rather than turning away sure-to-be disappointed subjects, we randomly matched two trustees to one investor in those sessions. In such a case the trustees received the payoff consistent with his or her actions and the relevant investors received the payoff decided by the first trustee with whom they were randomly paired.

Focusing first on column 5, there is no significant difference in trust between subjects in treated and control villages in the full sample or in either of the subsamples (poor and nonpoor). Subjects did send more in the treated villages than in the control villages, but the difference was never statistically significant. There may be a problem with low-powered tests in this particular case, since poor subjects in treated villages sent about fifteen percent more than did poor subject in control villages. This behavior was highly variable however; the standard error was considerably larger than the coefficient so the estimated effect was not at all close to statistical significance. Still the sample size of only 165 raises concerns about low power in this case.

19
# CHAPlER 4

# MACROECONOMIC EFFECTS OF AID-SUPPORTED POLICY REFORM

## 4.1 Introduction

The conclusions to a recent volume on the role of capital inflows is development (Lele and Nabi, 1990a) deliberately avoided quantifying the contribution of these flows to growth. The reason given was that the country case studies showed domestic policies to be the primay determinant of a country's development (Lele and Nabi, 1990b) - the role of government, aid and policies is too complex to disentangle. Whilst not agreeing that we should not at least try to answer the question "can aid work without reform7?'` it is undoubtedly true that policy stance is a vita] factor in macroeconomic perfbrmance. In this chapter issues concerning the assessment of the impact of aid-supported policy reforms are discussed.

The first analyses of policy conditionality (e.g. Killick, 1984) focussed on IMP stabilisation programs and were critical of the excessive emphasis on demand restraint to restore balance of payments equilibrium. In more recent years the study of World Bank adjustment programs has led to a slightly more positive consensus. Most studies find the investment rate to suffer as a result of adjustment policies (e.g. Mosley _eta!.,_ 1991; World Bank, 1988, 1990 and 1992). The effect on growth, however, is usually positive - but often weakly (and often also insignificantly) so (e.g. Mosley _et al.,_ 1991; World Bank, 1988 and 1990 - but not 1992, which reports a significant positive impact). The effect on the balance of payments is negative (Mosley _et al,_ 1991; World Bank 1988) - largely because of the import surge following trade liberalization and the weak or slow supply response of exports (World Bank, 1990 - again the 1992 report finds a stronger export response).<sup>2</sup> However, there are a number of methodological issues that need to be addressed before these findings may be accepted.

For aid-supported policy reforms to have a beneficial impact on a country's economic performance, two conditions must be satisfied. First, the donor presence must make a difference to the reforms that are implemented. Does the presence of aid donors make for better policies or does aid, as critics maintain, allow recipient governments to pursue unsustainable and misguided policies? Second, the reforms that do take place must have a positive impact on performance. These two issues are analytically distinct.

70
about three times the weight of LPG and consumption of the latter is about 170 kg/year. So emissions saved from a switch to biogas from LPG are about 0.5 tCO2e as compared to 1.6 tCO2e when the switch is from firewood. With fewer than 10% of biogas adopters coming from LPG‐using households it follows that our estimate will need to adjusted downward by at most 0.1 tCO2e to 1.5 tCO2e. This is not large enough to make a material difference to our conclusions.

We should also take into account the fact that a few households may be induced to increase their livestock holdings (or to retain livestock they would otherwise have stopped keeping) if the subsidy to biogas were to increase. However, most households that adopt biogas are likely to be those for whom this decision would not affect their livestock holdings. Since the effect of additional livestock on firewood consumption is not very high (Table 2), once again this effect, while reducing the value of emission reductions from biogas plants will not be large enough to make a material difference to our conclusions above.

Since any offset program for biogas stoves will be very small relative to global emissions, we may suppose that its effect will be linear with respect to climate change. Hence, expected utility maximization would suggest that the expected value of the saving should be used. Despite the remaining uncertainty, there is, therefore, a strong case for using the central estimates and designing offset programs to link existing carbon markets with capital subsidies to biogas in Nepal and similarly situated countries.

In sum, we conclude that an offset program for biogas can be a very attractive option for low‐cost mitigation of climate change from the point of view of the designers of carbon markets. It can be equally, if not more attractive, for the governments of Nepal and other similarly placed countries as a means of finance for tackling the issues of energy access, pollution, health, and forest degradation.

# **5. Conclusions**

In this paper we demonstrate the important positive external effects private adoption of biogas can have in developing countries. International carbon markets 15
# ANNEX 3: COUNTRY TYPOLOGY OF FISCAL DURESS

The following countries are categorized as experiencing "fiscal duress during the 1980s if they met two of three criteria: sustained improvement in fiscal balance, sustained reductions in the share of government spending in GDP, and sustained reductions in real government spending.

Edii,.tion spending I (% of govermment lbdget)

||Total b<br>1975 -79|udget<br>1980-85|Disri<br> <br>1975-|onary<br>79|budget<br>1980-85|
|---|---|---|---|---|---|
|Africa|15.3|15.3|16.1||16.9|
|Africa duressed|13.9|15.2|14.6||17.1|
|Asia|12.4|12.4|13.6||14.2|
|Asia duressed|17.6|16.7|18.8||18.6|
|EMENA|11.6|11.7|12.1||13.4|
|EMENA duressed|14.2|13.7|15.9||17.3|
|LAC|16.6|14.5|17.4||15.7|
|LAC duressed|18.2|16.1|18.6||16.4|
|P<br>|Gross enro<br>rimary scho<br>|llment rates<br>gls<br>|(%)<br>Seg.<br>|Qnd= s<br>|chools<br>|
|1975-77|1980-82|1984-85|1975-77|1980-8|2 1984-8.|
|Africa<br>61|71|71|13|16|18|
|Africa duressed<br>60|71|67|9|14|12|
|Asia<br>_55_|66|68|33|34|36|
|Asia duressed<br>56|57|52|44|47|49|
|EMENA<br>69|77|77|33|42|49|
|EMENA duressed<br>86|99|102|30|38|44|
|LAC<br>100|103|104|39|46|50|
|LAC duressed<br>104|109|110|40|48|53|

56
3. _Secondary outcomes among refugees:_ These include a broader set of variables that were grouped into 14 broad families in the PAP including: (1) dwelling characteristics and household structure, (2) consumption and expenditure, (3) financial participation, (4) earnings, labor, and occupational choice, (5) migration, (6) physical, mental health, and sleep, (7) marriage and fertility, (8) child outcomes, (9) social capital, (10) political attitudes, (11) time use, (12) education and cognition, (13) behavioral games and preferences, and finally, (14) specific COVID-19 related outcomes. The three primary outcomes noted above are a subset of these measures. Certain outcomes were collected in each of the three survey rounds, for instance, the food security measures (the number of meals eaten and the frequency of going to bed hungry). Respondents also reported information on child school attendance, and completion of learning activities when schools were closed due to the COVID-19 pandemic. In the midline survey collected in 2020 (during the pandemic), the physical health outcomes included additional questions focused specifically on COVID-19 symptoms and treatment but these were dropped in later rounds as the pandemic had eased.

# **3.2 Measures of social cohesion among neighbors**

We pre-specified four primary outcomes regarding the attitudes of Jordanian neighbors:

1. _Interpersonal social attitudes and perceptions:_ This was assessed using an index derived from questions about social ties between the Jordanian respondents and Syrian refugees, attitudes regarding social proximity, and opinions on refugees’ contributions to society.

2. _Economic attitudes and perceptions:_ Focused on Jordanians’ perceptions of the impact of Syrian refugees on the Jordanian economy, based on survey questions.

3. _Altruism:_ Measured through a dictator game, this outcome gauged altruistic behavior towards refugees, with real monetary incentives provided for a random subset of the sample to 13
# **3. Data**

Data for this study come from two sources. The first is the 2010-11 Census of Manufacturing Industries conducted by Pakistan Bureau of Statistics. The second data source is the distribution company performance evaluation report by National Electric Power Regulatory Authority (NEPRA). We describe these in turn.

# **3.1 Firm Data**

The Census of Manufacturing Industries provides a thorough annual overview of firmlevel activities, including detailed information on a variety of input costs, including labor, capital and electricity, as well as revenue data. The 2010-2011 census mainly covers firms in Punjab province.<sup>3</sup> Punjab in Pakistan has the largest economy of all provinces and the majority of the manufacturing activity is concentrated in this area.

The census covers 4,499 firms in 23 sectors at the 2-digit level of Pakistan Standard Industrial Classification (PSIC).<sup>4</sup> The distribution of firms by the 2-digit classification are shown in Table 1. Of the 23 divisions, number 13 (Manufacturing of Textiles) covers roughly 28 percent of our sample, division 10 (Manufacturing of Food Products) accounts for 15 percent, and division 32 (Other Manufacturing) is the third-largest with 11 percent of the sample. The 23 sectors can be further broken down into 236 subcategories at the 5-digit level of PSIC.

Firm-level characteristics are described in Table 2. The mean total product value at the firm level is about 523,000 rupees<sup>5</sup> , whereas the mean value-added is 114,000 rupees. Average total employment costs are just over 17,000 rupees, whereas the average cost of raw materials is over 300,000 rupees. Firms have an average value of fixed assets of 150,000 rupees, which is notably half the value of the raw material costs for the average firm and less than a third of the value of the product output. The median number of employees at a firm in the census is 22 people, with an average of 103 and a standard deviation of 569.

> 3 There are four firms in the data set located outside Punjab province, including one in Karachi and three in Islamabad. Excluding these firms from the sample does not change the estimation results. 4 A description of the PSIC and the relationship to ISIC is available at the following <u>http://www.pbs.gov.pk/sites/default/files/other/PSIC_2010.pdf</u>

> 5 The Rupee-USD exchange rate was roughly 85:1 in June 2011.

5
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
||OP 2|stage|OP|LP|FE|Dif|BB|
||1st stage|2nd stage||||||
|Serv. Lib.||0.130***|0.314***|0.284***|0.100***|0.075***|0.049***|
|||(0.020)|(0.018)|(0.017)|(0.016)|(0.014)|(0.008)|
|Exporter||0.075***|0.117***|0.097***|0.097***|0.095***|0.085***|
|||(0.012)|(0.013)|(0.011)|(0.011)|(0.010)|(0.010)|
|Serv. Lib _×_||0.011|0.032|0.148***|0.040|0.029|0.034**|
|Exporter||(0.025)|(0.031)|(0.029)|(0.024)|(0.020)|(0.012)|
||||Revenue|function pa|rameters|||
|ln(_K_)|0.054***||0.050***|0.134***|0.027***|0.035***|0.029***|
||(0.008)||(0.014)|(0.020)|(0.007)|(0.008)|(0.005)|
|ln(_L_)|0.272***||0.229***|0.242***|0.398***|0.414***|0.378***|
||(0.009)||(0.010)|(0.011)|(0.017)|(0.017)|(0.010)|
|ln(_M_)|0.635***||0.688***|0.569***|0.560***|0.505***|0.502***|
||(0.009)||(0.009)|(0.047)|(0.013)|(0.011)|(0.004)|
|ln(_Y_)|0.024*||0.022|0.023|0.048***|0.033***|0.065***|
||(0.011)||(0.015)|(0.013)|(0.012)|(0.009)|(0.012)|
|ln(_ri,t−_1)|||||||0.064***<br>(0.014)|
|Time|Yes|Yes|Yes|Yes|Yes|Yes||
|Industry|Yes||Yes|Yes|Yes|Yes||
|Time _×_ Industry||Yes||||||
|Firm||Yes|||Yes|||
|Firms|9411|11057|9411||11057|||
|Obs.|41127|40440|41127|40440|40440|29041|32306|

Notes: * p<0.05, ** p<0.01, *** p<0.001. Table reports point estimates of regression of ln( _TFP_ ) (column (2)) or ln( _Sales_ ) (columns (3)-(7)) on services liberalization and export status, as well as revenue function parameters for Ukrainian manufacturing firms for 2001-2007. Production function in all models is estimated for all manufacturing industries pooled in one regression. Columns (1) and (2) are estimated by Olley-Pakes two-stage procedure. Column (3) is estimated by Olley-Pakes one-stage procedure. Column (4) is estimated by Levinsohn-Petrin procedure. Column (5) is estimated by OLS with firm fixed effects. Column (6) is estimated by OLS in first differences. Column (7) is estimated by Blundell-Bond. Standard errors, reported in parentheses, are either bootstrapped for OP and LP methods, or robust for all other methods.

## Table 9: Alternative methods

# **5 Conclusions**

This paper finds that liberalization of services has a positive effect on productivity of manufacturing firms. We consider an episode of a limited liberalization in Ukraine, which primarily targeted the services sector as a prerequisite to the WTO accession. These particular features of the episode allow to separate the effect of services liberalization from the effects of 33
Policy Research Working Paper 5279

# **Abstract**

The allocation of production across firms is a potentially important explanation of the productivity gap between rich and poor economies. Reforms to trade policy and the domestic financial sector are often both key elements of policy packages aimed at reducing productive distortions. However, the impact of each reform in reallocating production within an economy is usually analyzed independently. This paper asks how do such general equilibrium effects of trade and domestic financial sector reforms interact in terms of their effects on productivity, wages and utility. Motivated by recent firm-level studies, I add two-way linkages between firms’ production and exporting decisions and their financial constraints to a general equilibrium heterogeneous firm trade model. The interaction effects between reforms appear qualitatively important. Trade and domestic financial sector reforms have complementary effects on the average productivity and size of domestic producers. However, if much reallocative work has already been done through a wellfunctioning financial sector, the marginal benefits of trade liberalisation for wages and household utility are reduced. Improvements in the ability to use exports as pledgeable collateral enhance both the wage and productivity effects of trade reforms. The model also highlights the potential for financial sector reforms in one economy to be exported via the trade channel, affecting decisions to produce or export in the foreign economy and putting downward pressure on foreign real wages.

This paper—a product of the Poverty Reduction & Economic Management Sector Department, East Asia and Pacific Region—is part of a larger effort in the department to examining regional growth and integration. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at ataylor2@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Table 6: Loss of state revenue through under-valuation of property**

# **Case I:** Laderpally village, Sambalpur district

0.6 acre of land transacted @ Rs. 60,000 per acre Sale price negotiated between transactors (P1): Rs. 36,000 Price disclosed for deed registration (P2): Rs. 27,000

||||(Rs.)|
|---|---|---|---|
|**Item**|**Expendit**<br>|**ure under**<br>|**Loss to the state**|
||**P1**|**P2**||
|Stamp duty @4.2%|1,512|1,134|378|
|Stamp duty surcharge@10.7%|3,852|2,889|963|
|Registration fee @2%|720|540|180|
|Mutation fee|8|8||
|Writing of sale deed|50|50||
|Endorsement fee|5|5||
|Incidental expenses|2|2||
|TOTAL|6,149|4,628|1,521 (25%)|

Note: * The figure in parenthesis is the percentage loss of revenue to the state.

# **Case II:** Badaverna village, Khurda district

0.65 acre of land transacted @ Rs. 40,000 per acre Sale price negotiated between transactors (P1): Rs. 26,000 Price disclosed for deed registration (P2): Rs. 19,500

||||(Rs.)|
|---|---|---|---|
|**Item**|**Expen**|**diture**|**Loss to the state**|
||**Actual**|**Shown**||
|Stamp duty @4.2%|1,092|819|273|
|Stamp duty surcharge*|2,782|1,892|890|
|Registration fee @2%|520|390|130|
|Mutation fee|8|8||
|Writing of sale deed|50|50|_|
|Endorsement fee|5|5||
|Incidental expenses|2|2||
|TOTAL|4,459|3,166|1,293 (29%)**|

Note: * Stamp duty surcharge, in rural areas, on land value more than Rs. 25,000 is 10.7% and for less than Rs. 25,000 it is 9.7%

** The figure in parenthesis is the percentage loss of revenue to the state.

34
Sharing a common language would presumably mitigate the cost of travel as well, both pecuniary and non‐pecuniary costs. Finding information about a particular destination might be easier and less time‐ consuming for travelers who speak the local language. A common language would also reduce the potential apprehension of visiting a foreign country. A similar argument may be made with regards to countries that share a colonial relationship, as visitors may feel greater cultural affinity to the destination country. In addition, colonial ties, past or present, may also induce travel between the two countries because of existing cross‐border family bonds. It is important to note that countries speaking a common language or sharing a colonial relationship have deeper commercial ties —such countries trade more goods with one another, a constant result of all gravity studies— which should result in increased business travel. Since our dependent variable is the number of visitors, our empirical results may capture other reasons for traveling, in addition to tourism.

The discussion in section 2 suggests that the increased use of digital platforms would affect the cost elements behind the term τ�� . The reduction in search cost for air travel would make the distance and the common border terms less binding. Similarly, digital planning tools would help mitigate language barriers —digital platforms offer services in several languages— and reduce the cost of finding suitable accommodations as well as other travelers’ opinions about a given destination. Digital tools could also help bridge cultural gaps between a given origin‐destination pair —making the colonial relationship less of an advantage when visiting a country.

# **3.2 Data**

Bilateral tourism flows come from the United Nations World Tourism Organization (UNWTO, 2019) and cover the period 1995 to 2017, the last year for which such data are available. It should be emphasized that, contrary to trade data, reported tourism data vary considerably across countries. For the purpose of this paper, we focused on arrivals of non‐resident tourists or visitors at national borders as our proxy for the demand of tourist services. To the best of our knowledge, no data exist on bilateral tourism services trade expressed as monetary receipts or expenditures. For some countries, the UNWTO reports, in addition or in place of arrivals at the border, overnight stays of non‐resident tourists at hotels or similar accommodations.<sup>9</sup> Thus, compared to standard trade databases used in estimating trade gravity equations, the bilateral tourism database used in this paper does not offer a comprehensive account of

> 9 For more details, see UNWTO (2018a).

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