rafmacalaba/datause-encoder-data huggingface dataset:
The above equation offers many insights regarding the drivers of growth. First, the TFP growth �� has the largest direct effect on growth where a 1 percentage point increase in TFP leads to a 1 percentage point increase in growth rate of output per capita. Second, the production function parameter � plays an important role in determining the relative importance of capital and labor growth in driving aggregate growth. The larger the labor share of income, the more responsive is the output growth to increases in participation rate, working age population to population ratio and human capital per worker. Conversely, the larger the labor share of income, lower the effect of capital accumulation in generating growth. Finally, it is important to note that keeping all else constant, the same level of investment share of output can lead to different output growth rates depending on the level of the capital‐to‐ � output ratio of the economy. Investment becomes less effective as the capital‐to‐output ~~�~~ � ~~�~~ ratio rises in the economy. This implies that the same level of investment share of output will generate lower growth if capital in the economy grows faster than the output. Hence, an investment led growth strategy in absence of reforms that focus on growth of productivity, human capital and participation will eventually run out of steam.

In the next section, I begin with the quantitative analysis and examine various growth scenarios for the country. The first step of the analysis requires the calibration of the model to the Bangladesh economy. Following calibration of the model, the long‐term growth scenarios for the country are considered by asking two related questions. First, how much growth can be achieved under various reasonable paths of growth drivers and second, what time paths of growth drivers, in particular investment, is essential to realize a given growth path.

# **3. Quantitative Analysis**

Each period of the model corresponds to a year which implies that all growth rates are annual in nature. A desirable feature of the model is that it is parsimonious with regards to parameters. There are only three parameters that need calibration – labor share of income � , �� depreciation rate � and initial capital‐to‐output ratio ~~.~~ ��

- **Labor share of income:** ���. ��. The Penn World Tables 8.1 does not contain the labor share data for the case of Bangladesh. An alternative source that provides guidance for this parameter is the Global Trade Analysis Project (GTAP). The GTAP database contains information that can be used to calculate the aggregate labor share.<sup>5</sup>

<!-- Start of picture text --> 0.60<br>0.50<br>0.40<br>0.30<br>5 See Hertel, Tsigas and Narayanan (2002) on details regarding the calculation of labor shares in the GTAP 0.20<br>database.<br>0.10<br>7<br>0.00<br>Bangladesh LMI ‐ Mean LMI ‐ Median<br>Labor Share of Income<br><!-- End of picture text -->

**Source: PWT 8.1, GTAP**
information). Doing so allows us to run regressions where we can identify the association between a firm’s ranking in a given auction and its ensuing credit growth. First, we find that winning firms experience significantly larger growth in credit relative to losing firms within the same auction. Second, we find that, again, all the effect is explained by non-collateral credit. Third, we find that the ranking itself does not seem to matter in explaining the evolution of credit. In particular, when excluding winners, we find no differential effect on credit growth between the firms that were ranked second vs. firms that were ranked third and fourth within the same auction. And fourth, we show all the previous results are not driven by the presence of pre-trends in the evolution of credit for winners vs. the rest.

**Loan applications.** Finally, in Appendix B.4, we exploit data on loan applications to see whether the acceptance probability of a credit application increases when firms get a procurement contract. In particular, we can see whether a firm has applied to a given bank and whether the loan application has been accepted or rejected throughout our sample period. We use this information to help identify an increase in firms’ borrowing capacity. To do so, we run regressions at the firmbank level and relate the probability of firms obtaining a loan to whether they have received a procurement contract. We find that the probability of receiving a bank loan conditional on having applied for it increases by approximately 2 percentage points in the quarter that a firm wins a procurement project, which is consistent with the idea that procurement represents a credit supply shock for the

# **4 The Model**

We set up a model of privately held heterogeneous firms. We build on standard models of firm dynamics with collateral constraints —as Midrigan and Xu (2014), Moll (2014), or Buera and Moll (2015)— and extend this setting to allow for (a) a public sector demanding goods from private firms, (b) earnings-based borrowing constraints with different degrees of pleadgeability for procurement and private sector sales, (c) downward-sloping demands in both the private and public sectors, and (d) a choice to compete for procurement projects. In the model, financial frictions distort the economy through different channels. First, for a given selection of firms into procurement, financial frictions lead to lower aggregate capital, misallocation of capital across firms, and misallocation of capital within firms across sectors. And second, financial frictions also distort the selection of firms into procurement, giving more value to firm net worth at the expense of firm productivity.

## **4.1 Technology**

Time is discrete and we omit the subscript _t_ unless it is strictly needed. The economy is populated by a continuum of size 1 of heterogeneous infinitely-lived households indexed by _i_ . Each household is also an entrepreneur running a firm that produces a differentiated intermediate good _yi_ . There 12
**2**

# **THE** NATIONAL AC.OUNTS

# Accounting Identities

4. The RMSM uses a two-gap accounting framework. It ensures consistency between proiections of the Balance of Payments and the National Accounts via the resource gap which both accounts share in common. The accounting identities are :<sup>4</sup> GDP - C + I + X - M 1) GDP - C + S 2) These identities are expressed in real terms where gross domestic product (GDP) is the sum of %onsumption (C), investment (I) and exports (X) less imports (M).<sup>5</sup> Alternatively, GDP is the sum of consumption plus saving (S). Identity 3 states that tha two gaps between investment and savings and between imports and exports are the same. If the level of savings is inadequate to finance a targeted level of investment, then investment must be financed from abroad in the form of net imports. As shown in Section III below, this will necessitate external borrowing.

# Exogenous Variables

5. In the RMSM, real GDP growth is exogenous and must be targeted by the user.<sup>6</sup> There are two common stratsgies. The first is to choose a rate which will ensure a constant or increasing GDP per capita growth rate in order to find out what sort of investment and external borrowing will be needed. The second is to choose a level of growth that results in levels of investment and borrowing that are consistent with ones' expectations of ability and availability. This latter approach is a parallel to the "availabilities" method and usually ends up as an iterative process as growth rates are adjusted to produce the expected feasible levels of borrowing and investment.

4. Please note that many variables have been aggregated for the purpose of this paper. The mnemonic rules for var'able names have, however, been retained to make the transition to or from the actual model as painless as possible. Appendix B illustrates the translation in detail.

5. The national accounts can be expressed either in local currency or in US dollars. The assumption used here is that local currency will be used.

6. GDP is disaggregated into three categories: agriculture, industry and all other goods. Separate growth rates are applied to each.
structured according to the needs of the study<sup>8</sup> . In this analysis we have used the data for the year 2004-05. All the financial variables are expressed in 2004-05 prices<sup>9</sup> .

# **Expenditures**

For expenditures we concentrate on the service wise break up in each ULB on the five major services for comparisons across UAs. A considerable proportion of the total expenditures are spent on account of other expenditures which we have considered later in the analysis. It is to be noted that for Delhi, Chennai and Hyderabad, water supply and sewerage boards share the responsibilities with the ULBs. The manner in which the responsibilities are divided between the municipal government and the parastatal agency, however, is not the same in all the UAs.

The Delhi Jal Board (DJB) covers both capital and O&M expenditures but only for the central city; the smaller ULBs purchase in bulk from the DJB. Hyderabad Metropolitan Water Supply and Sewerage Board (HMWSSB) covers capital and O&M expenditures for 4 ULBs including the central city; other ULBs purchase in bulk from the HMWSSB. Planning, execution, management of network is done by the ULBs themselves. However there is a proposal for full coverage of capital and O&M expenditures of the entire area of the GHMC by the HMWSSB. Chennai Metropolitan Water Supply and Sewerage Board (CMWSSB) covers O&M and capital expenditures for the central city; the other ULBs cover their capital expenditure themselves. However they transfer resources on account of augmentation or new capital work to the Board and the planning, execution and management of new capital work is done by the CMWSSB. All these Boards recover costs by borrowing funds from state and financial institutions, as they incur perpetual losses<sup>10</sup> . All these UAs have mostly metered connections, private and government tankers are also available for additional supply.

> 8 The surveys were conducted for the Project titled ‘Improving Fiscal Health of Indian Cities’ funded jointly by the World Bank. The contribution of the research team of the Project for their inputs in designing the questionnaires, communicating with the ULB officials and conducting these surveys is gratefully acknowledged. Some further clarifications and additions in the dataset were made for this particular analysis. However, the methodology, particularly for the estimation of expenditure needs is entirely different than that used in the project. See NIPFP (2007)(a),(b),(c), NIPFP 2008(a),(b).

> 9 For Kolkata the most recent data available is for 2003-04, so we have expressed the figures in constant 2004-05 prices with the standard sector specific deflators available for water supply gas and electricity (used for water supply and street lighting), roads (used for roads) and other services (used for sewerage and solid waste management).

> 10 CMWSSB is the only Board which has earned profits in the past for some years.

15
## **Table 7. Structural Equations for Revenues and Spending of State Governments**

<u>(z-statistic in parenthesis)</u>

|_Variable_|_Revenues_|_Spending_|
|---|---|---|
||0.053||
|Real state income per capita|(5.39)|1.03|
|Revenues||(16.26)<br>–0.12|
|Growth||(–0.29)|
||–0.01|0.001|
|Total population|(–4.30)|(0.72)|
||0.36||
|Revenues (lag 1)|(6.99)||
||–0.02|–0.41|
|Planning Commission Grants (lag 1)|(–0.11)|(–3.14)|
||–0.49|0.03|
|Finance Commission Transfers (lag 1)|(–2.64)|(0.20)|
||0.02|0.27|
|Deficit (lag 1)|(0.29)|(5.02)|
|Political affiliation|2.70|40.86|
|(=1 if center and state govt. belong to same political party)|(0.09)|(2.01)|
||–21.84|–44.67|
|Affiliation * Seats|(–0.69)|(–2.04)|
||–0.16|3.78|
|(1- Affiliation) * Seats|(–0.01)|(0.18)|
|Coalition government|–25.74|–31.59|
|(=1 if state executive consists of a coalition govt.)|(–1.25)|(–2.18)|
||N=332|N=332|
||R-sq=0.94|R-sq=0.98|

_Note:_ State fixed effects and year effects included; 3SLS regression.

# **4. Conclusion**

This paper conducts a simple test of the common-pool game in a federation where subnational governments are more likely to have higher deficits because they do not internalize the macroeconomic effects of fiscal profligacy. Specifically, we test two predictions implied by this model that subnational governments that belong to the same political party as the central government should have lower fiscal deficits because they are more likely to internalize the effect of increasing deficits; and that states that are more dependent on intergovernmental transfers should have higher fiscal deficits because of the greater disconnect between revenues and spending. We find that in 15 major states of India over the period 1972-95, states in fact have substantially higher deficits (about 10 percent of the sample average) when their government belongs to the same party governing at the center. The additional deficit of affiliated states is financed almost entirely by additional loans from the central government (as opposed to the market) leading to our interpretation that political 25
|Land ownership/lease/rent|X|X<br>X|
|---|---|---|
|Location characteristics|X|X|
|Community characteristics|X||
|Access to remittances||X|

Table A2. Estimated PMT eligible share and distribution of beneficiaries by actual per capita consumption deciles for different PMT cutoff scores for Models 2 and 3

|Quantiles of the true consumption distribution=>|Bottom<br>Decile|Bottom<br>Quintile|Top<br>Quintile|Top<br>Decile|All|
|---|---|---|---|---|---|
||**Model 2**|||||
|_Eligible share_||||||
|Existing Samurdhi beneficiaries|40.2|37.1|3.8|2.2|18.8|
|PMT cutoff score*||||||
|Score: 878 (20th percentile)|49.3|38.6|0.2|0.2|11.6|
|Score: 895 (30th percentile)|75.2|65.0|1.0|0.5|23.5|
|Score: 909 (40th percentile)|89.3|82.2|3.2|1.6|36.1|
|_Distribution of beneficiaries_||||||
|Existing Samurdhi beneficiaries|21.4|39.5|4.1|1.2|100|
|PMT cutoff score*||||||
|Score: 878 (20th percentile)|42.5|66.5|0.4|0.1|100|
|Score: 895 (30th percentile)|32.1|55.4|0.8|0.2|100|
|Score: 909 (40th percentile)|24.7|45.5|1.8|0.5|100|

# **Model 3**

|_Eligible share_||||||
|---|---|---|---|---|---|
|Existing Samurdhi beneficiaries|40.2|37.1|3.8|2.2|18.8|
|PMT cutoff score*||||||
|Score: 878 (20th percentile)|46.8|36.9|0.3|0.2|11.1|
|Score: 895 (30th percentile)|74.5|63.9|0.9|0.4|23.2|
|Score: 909 (40th percentile)|89.2|82.0|3.5|1.8|36.4|
|_Distribution of beneficiaries_||||||
|Existing Samurdhi beneficiaries|21.4|39.5|4.1|1.2|100|
|PMT cutoff score*||||||
|Score: 878 (20th percentile)|42.1|66.4|0.5|0.2|100|
|Score: 895 (30th percentile)|32.1|55.0|0.8|0.2|100|
|Score: 909 (40th percentile)|24.5|45.0|1.9|0.5|100|

See notes for Table 2.

25
# 4. Data, classification, and descriptive statistics

## 4.1. Brazil Consumer Expenditure Survey

Our research relies on the comprehensive Consumer Expenditure Survey (Pesquisa de Orçamento Familiar, POF) released in 2020, encompassing consumption data collected during 2017-18. The POF is a comprehensive survey conducted by the Brazilian Institute of Geography and Statistics (IBGE) that collects data on the purchasing habits and dietary intake of households across the country. It classifies food items into different groups based on their primary purpose and intended use, such as staple foods, beverages, dairy products, meats, and processed foods. Notably, the POF dataset offers extensive disaggregated data on a wide array of products, including over 8,000 food items, facilitating our identification of food groups based on the NOVA classification. In addition, this survey collects information on various aspects of household living standards, including consumption patterns, non-food expenditures, income, and socioeconomic variables.

To extract consumption data, we employed two distinct questionnaires from the POF. The first one is the " _Caderneta coletiva_ " questionnaire, which captures shared household consumption and provides information on total expenditure and quantities consumed. The second one is the " _Despesa Individual_ " database, which focuses on individual consumption, provides data on total expenditure for a limited number of items and does not include information on quantity. Food consumption is only included in the former. However, as we are also interested in the total consumption, we merge the two databases to obtain total household consumption, adding the shared component with the individual consumption of each household member.

To present the profile of the individuals in the database, Table 1 presents demographic statistics by consumption decile. These variables are important for the analysis, as they are included as controls in the estimation of price elasticity, described in section 5.1. The demographic statistics of POF are aligned with other household surveys, such as the Continuous National Household Sample Survey ( _PNAD Contínua_ ), showing that the higher the consumption decile, the higher the incidence of educated white men as heads of household.

Table 1 - Demographic statistics

|Consumption<br>Decile|Woman<br>head<br>of household|White head of<br>household|Literate head of<br>household|Urban household|Years of schooling of<br>head of household|
|---|---|---|---|---|---|
|1|49%|26%|81%|76%|6.25|
|2|47%|32%|85%|80%|6.78|
|3|43%|34%|86%|81%|7.31|
|4|42%|37%|88%|84%|7.69|
|5|41%|41%|92%|87%|8.29|
|6|42%|44%|92%|88%|8.78|
|7|39%|48%|94%|90%|9.37|
|8|39%|53%|96%|91%|10.1|
|9|38%|60%|97%|93%|11.4|
|10|37%|69%|99%|96%|13.3|
|Total|42%|44%|91%|86%|8.93|

Source: POF 2017/2018 9
period 1990-2004. The country scores are averages across a larger number of service sector activities (see Annex). The data used in figure 1 are averages of the country scores over time at the group level. They are subject to limitations in that they do not cover all service sectors and are somewhat arbitrary due to a certain amount of subjectivity associated with the EBRD assessment of sectoral policies (these are based on small sample surveys of practitioners in each country). They do, however, give a reasonable impression of the reform agenda put in place in these countries. In terms of policy, group (a) has consistently performed best over the whole transition period, followed by groups (b), (c), and (d), in that order.

**Figure 1. The Time Path of Service Sector Reform in Transition Economies**

<!-- Start of picture text --> Service Sector Reform Index<br>1990-2004 Groups (a)-(d)<br>4.50<br>4.00<br>3.50<br>3.00<br>2.50 Group (a)<br>2.00<br>Group (b)<br>1.50<br>Group (c)<br>1.00<br>Group (d)<br>0.50<br>0.00<br>1990 1995 2000<br><!-- End of picture text --> _Source_ : EBRD (2004).

If, however, we rank the country groups on the basis of GATS commitments, the ranking of groups (a) and (d) is reversed, while the ranking of groups (b) and (c) remains unchanged (Table 5). That is, the non-Baltic ex-Soviet Union members in the sample, despite having made among the deepest GATS commitments, are the countries that score the lowest in terms of the quality of actual service sector policies. This finding also holds if we do not use the average EBRD index of actual policy – which may be “biased” insofar as these are countries where reform may have started late, and, as noted above, they acceded to the WTO towards the end of the 1990s or later. If we use the 2004 EBRD index instead of the average over time, the same finding obtains. Comparing the averages with the most recent EBRD scores reveals that there is 12
<u>Table 18. Innovation by City Size (% of all firms in each category)</u>

|**Country**|**City Size**|**Product**|**Process**|**Product or Process**|**Organizational**|
|---|---|---|---|---|---|
|Bangladesh|Less than 1 mm|17%|12%|24%|18%|
||Over 1 mm|41%|62%|80%|24%|
|Pakistan|Less than 1 mm|0.2%|2%|2%|6%|
||Over 1 mm|10%|10%|18%|22%|
|India|Less than 1 mm|51%|69%|86%|51%|
||Over 1 mm|57%|52%|81%|51%|
|Nepal|Less than 1 mm|11%|11%|20%|59%|
||Over 1 mm|14%|9%|23%|18%|
|ECA|Less than 1 mm|19%|12%|26%|23%|
||Over 1 mm|17%|12%|24%|18%|
|Africa|Less than 1 mm|23%|28%|41%|32%|
||Over 1 mm|32%|29%|51%|39%|

Source: Author’s elaboration from Enterprise Survey (2014) In line with the behavior observed in the ECA and Africa regions, all countries of the South Asia region except Bangladesh display higher product innovation rates in main business cities compared to other cities (Table 19). By contrast, all countries except Pakistan show higher process innovation rates in other cities. Organizational innovation predominates in main business cities following international patterns.

<u>Table 19. Innovation by Business City (% of all firms in each category)</u>

|**Country**|**Business City**|**Product**|**Process**|**Product & Process**|**Organizational**|
|---|---|---|---|---|---|
|Bangladesh|Main Business City|43%|61%|79%|26%|
||Other City|45%|64%|78%|24%|
|Pakistan|Main Business City|8%|8%|15%|17%|
||Other City|3%|2%|3%|11%|
|India|Main Business City|56%|59%|83%|53%|
||Other City|42%|73%|87%|43%|
|Nepal|Main Business City|13%|10%|21%|34%|
||Other City|10%|14%|21%|27%|
|ECA|Main Business City|23%|14%|30%|26%|
||Other City|18%|12%|25%|21%|
|Africa|Main Business City|27%|31%|47%|39%|
||Other City|22%|29%|43%|32%|

Source: Author’s elaboration from Enterprise Survey (2014) Innovation activity in South Asia appears to be geographically concentrated only when considering the novelty of innovation. To explore the impact of agglomeration and following the literature, Table 20 compares employment shares across provinces with innovation activities shares and their concentration measured with the Herfindahl index. Larger concentration of innovation activities in relation to employment implies that there may be some agglomeration effects of innovation activity, as suggested in the literature. In general, however, the table shows that innovation activities tend to be less concentrated than employment, except in Pakistan, and more concentrated when considering higher degrees of innovation 24