rafmacalaba/datause-encoder-data huggingface dataset:
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Maloney, W. F. (2004). Informality Revisited. _World Development_ , 32 (7): 1159 – 1178. Melitz, M. J. (2003). The impact of trade on intra‐industry reallocations and aggregate industry productivity. _econometrica, 71_ (6), 1695-1725.

Mendi, P., & Costamagna, R. (2017). Managing Innovation Under Competitive Pressure From Informal Producers. _Technological Forecasting and Social Change_ , 114: 192 – 202.

Perry, G. E., Maloney, W. F., Arias, O. S., Fajnzylber, P., Mason, A. D., & Saavedra-Chanduvi, J. (2007). Informality: Exit and Exclusion. World Bank, Washington, DC. Pfeifer, C. (2015), “The Nexus Between Top Managers’ Human Capital and Firm Productivity,” _Applied Economics Letters_ 22 (12): 982 – 986.

Pisani, M. J. (2015). Does Informality Impact Formal Sector Firms? A Case Study From Nicaragua. _Journal of Developing Areas_ 49(2): 317-334.

Pounov, C. (2016). Corruption’s Asymmetric Impacts on Firm Innovation. _Journal of Development Economics_ , 118: 216 – 231.

Roodman, D. (2011). Fitting Fully Observed Recursive Mixed-process Models with CMP. _Stata Journal_ , 11(2): 159–206.

Rosenthal, S. S., & Strange, W. C. (2004). Chapter 49—Evidence on the Nature and Source of Agglomeration Economies. _Handbook of Regional and Urban Economics_ , 4: 2119 – 2171. Rothenberg, A.D., Gaduh, A., Burger, N.E., Chazali, C., Tjandraningsih, I., Radikun, R., Sutera, C., Weilant, S. (2016). Rethinking Indonesia’s informal sector. _World Development_ , 80: 96–113. <mark>Rozo, S., and Winkler, H. J. (2019). Is Informality Good for Business? The Impacts of IDP Inflows on Formal Firms.</mark> _<mark>Journal of Human Resources</mark>_ <mark>, forthcoming.</mark> Sabarwal, S., & Terrell, K. (2008). _Does gender matter for firm performance? Evidence from Eastern Europe and Central Asia_ : The World Bank.

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<mark>Tokman, Victor E., (1978), Competition between the informal and formal sectors in retailing: The case of Santiago,</mark> _<mark>World Development</mark>_ <mark>, 6, issue 9-10, p. 1187-1198.</mark> Wagner, J. (2007). Exports and productivity: A survey of the evidence from firm‐level data. _World Economy, 30_ (1), 60-82.

33
5

# **3 Poverty and Growth Diagnostics**

# **3.1 Decomposition Analysis of Growth and Inequality**

A useful way to take a first look at the impact of growth on poverty is by decomposing the change in the headcount rate. In general, a change in poverty can be attributed to the interaction of two processes -- growth in mean consumption and a change in consumption inequality. The formal decomposition proposed by Datt and Ravallion (1992) attributes changes in poverty to a growth effect, an inequality effect, and an interaction effect (the residual).

Results from this decomposition applied to the Tanzania data are shown in Table 3. The decomposition shows that overall growth reduced poverty in the country as whole by 4.6 percentage points. The slight increase in inequality, however, increased poverty by 1.1 percentage points. The same general pattern holds for rural areas and urban areas other than Dar es Salaam.

The results are more notable for Dar, where the substantial decline in poverty was attributable to the city’s economic growth. Holding inequality constant, growth reduced poverty by 16.3 percentage points. The growth-induced decline in poverty was partially countered by increasing inequality, which drove up poverty by 9.8 percentage points.

**Table 3: Growth and Inequality Poverty Decomposition**

||**Mainland**<br>**Tanzania**|**Dar es**<br>**Salaam**|**Other**<br>**urban**<br>**areas**|**Rural**<br>**areas**|
|---|---|---|---|---|
|**Poverty Rate in 1991/92**|38.6|28.1|28.7|40.8|
|**Poverty Rate in 2000/01**|35.6|17.6|25.8|38.6|
|**Change in Poverty**|-3.0|-10.5|-2.9|-2.2|
|**Breakdown in levels**|||||
|**Growth Component**|-4.6|-16.3|-4.4|-2.6|
|**Redistribution Component**|1.1|9.8|1.5|-0.4|
|**Residual**|0.6|-4.0|0.0|0.9|
|**Breakdown in percentages**|||||
|**Growth Component**|155%|156%|154%|119%|
|**Redistribution Component**|-35%|-93%|-53%|20%|
|**Residual**|-20%|38%|-1%|-39%|

Notes: Decompositions were calculated using the approach of Datt and Ravallion (1992). The analysis shown here uses 1991/92 as the base year for the decomposition.

# **3.2 Sectoral Decomposition of Changes in Poverty**

Another way to break down the overall change in poverty is by considering the contribution of changes in poverty in each sector to the national change. Following Huppi and Ravallion (1991), the national change in the headcount rate can be attributed to a combination of changes within each sector and shifts in population between sectors. There is also a small portion of the change that is due to an interaction effect between intra-sector changes and population shifts.

Results from a decomposition for Tanzania are shown in Table 4. A somewhat surprising point is that while poverty declined much more in Dar es Salaam (from 28.1 to 17.6 percent), most of the small drop in the national poverty rate was due to a modest decline in poverty in rural areas.
17 inappropriately affect the pricing of capital goods.'' De Long and Summers (1990) point out that some of the newly industrializing countries such as Korea and Brazil have relatively low prices of investment durables<sup>relative to</sup> construction prices. They argue that this is a result of<sup>different trade and</sup> tax policies in these countries designed to promote<sup>industrialization. The</sup> effects of policy on investment prices may be particularly<sup>important for</sup> countries for which benchmark studies of prices have not<sup>been done as one may</sup> be estimating effects based on extrapolating from other countries<sup>policies.</sup> Even for benchmark countries this could be important as extrapolations<sup>from</sup> the benchmark year to other years may not fully account<sup>for policy changes.</sup> The issue of choosing ICP or own country data may be even more problematic in the case of government expenditures because there are various interpretations of what the government expenditure to GDP ratio is supposed to measure. Ram (1987) argues that ICP prices are better than own<sup>currency</sup> prices when one is trying to measure government provision of productive services because own currency prices will not account for the tendency<sup>of</sup> government services to be relatively inexpensive in low-income countries. Thus, the ratio of government expenditures to GDP computed using<sup>own currency</sup> prices may under-estimate the provision of public goods in low-income countries. On the other hand, if one is trying to measure the distortionary effects of taxation, one may prefer own country prices<sup>because these may more</sup> accurately reflect the relative size of t:he government in the economy. Statistically, the simple correlations of growth and government consumption share over the period 1960-85 are strongly<sup>negatively correlated</sup> using the data constructed by Summers-Heston and positively correlated using '<sup>3</sup> See Bradford (1987), Barro (1989), and De Long and Summers<sup>(1990).</sup>
Martínez, Claudia, Esteban Puentes and Jaime Ruiz-Tagle (2013) “Micro-Entrepreneurship Training and Asset Transfers: Short Term Impacts on the Poor”, <u>http://works.bepress.com/claudia_martinez_a/5</u> McKenzie, David (2010) “Dimensions of informality in Bangladesh”, Mimeo. World Bank.

McKenzie, David (2015) “Identifying and Spurring High-Growth Entrepreneurship: Experimental Evidence from a Business Plan Competition”, Mimeo. World Bank.

McKenzie, David and Christopher Woodruff (2014) “What are we learning from business training evaluations around the developing world?”, _World Bank Research Observer_ , 29(1): 48-82.

Taylor, Frederick (1911), _Principles of Scientific Management_ , New York and London: Harper and Brothers.

# **Appendix 1: Business Practices Measured**

The _total score_ – the composite business practice score -- ranges between 0 and 1. The total is the proportion of the following 26 business practices used by the firm: _Marketing Practices:_ coded as 1 for each of the following that the business has done in the last 3 months:

- M1: Visited at least one of its competitor’s businesses to see what prices its competitors are charging

- M2: Visited at least one of its competitor’s businesses to see what products its competitors have available for sale

- M3: Asked existing customers whether there are any other products the customers would like the business to sell or produce

- M4: Talked with at least one former customer to find out why former customers have stopped buying  from this business

- M5: Asked a supplier about which products are selling well in this business’ industry

- M6: Attracted customers with a special offer

- M7: Advertised in any form (last 6 months)

Note: M1 and M2 are coded as zero if the firm says it has no competitors. M4 is coded as zero if the firm says it has no former customers.

_Buying and Stock Control Practices:_ coded as 1 for each of the following:

- B1: Attempted to negotiate with a supplier for a lower price on raw material

- B2: Compared the prices or quality offered by alternate suppliers or sources of raw materials to the business’ current suppliers or sources of raw material

- B3: The business does not run out of stock monthly or more (coded as one if the business has no stock)

22
rates of population growth; (2) there has been no large-scale creation of jobs and other opportunities for citizens; and (3) the inequality gap is widening—and doing so rapidly. Further, a number of other key indicators, such as measures of the accumulation of physical and human capital and households’ access to basic services, suggest that Nigeria is lagging behind other countries in the region, despite its impressive GDP growth (World Bank, 2017).

At the beginning of the millennium, Nigeria had an estimated 224 trillion liters of surface water and 50 million trillion liters of groundwater, for an estimated population of about 128 million. About 6 billion liters were consumed in 2001, which suggests an abundant water resource potential (Akujieze et al., 2003). But the country faces some significant obstacles to utilizing its water potential. The available hydrogeological base maps for the country are of poor quality, and hence not of much use in helping the government develop a water exploration and extraction plan. Furthermore, poor knowledge of the Nigerian geological terrain, lack of infrastructure facilities, and the absence of a working legislature significantly hinder the exploration, exploitation, operation, control, and management of Nigeria’s abundant groundwater resources.

Nigeria’s rates of access to WASH services are below those seen in many other Sub-Saharan African countries. Fifty-seven million people in Nigeria continue to live without access to improved water. As many as 130 million people do not meet the Millennium Development Goal (MDG) standards for sanitation. Poor sanitation in its own right costs the country 455 billion naira ($2 billion) per year. These problems persist although the country has achieved the MDGs for water (World Bank, 2017).

Nigeria’s urban areas have considerably better access to improved water services than its rural areas (World Bank, 2017). However, rapid rural-urban migration is placing significant strain on urban water supply and infrastructure. For example, the Nigerian capital city of Abuja is a key destination for rural migrants seeking better employment and a safer living environment. The city administration is trying to provide water and sanitation services to a rapidly growing number of people, across disparate neighborhoods that include informal settlements (Abubakar, 2014). A 2012 study indicated that, in southwest Nigeria, Abeokuta City’s water scheme would no longer be adequate to meet the total water 4
statistically significant as 1 percent falls within the 0.45 - 0.96 percent confidence interval.<sup>34</sup> Although the IV2 estimate of the average trade intensity effect appears larger than the estimates based on IV1, the difference is not statistically significant. Based on 95% confidence intervals, IV2 places the trade intensity effect somewhere between 0.56 and 0.96 percent, while for IV1 the interval is 0.45 and 0.62 percent, confirming that both IV results are comparable. Thus, based on the confidence intervals of all the IV specifications, on average, the impact of trade intensity ranges between 0.45 and 0.96 percent.

|**Table 3. **R|esults— Theimpact oftra<br>(1)<br>OLS|deintensity onGDPper<br>(2)<br>IV1|capita<br>(3)<br>IV2|
|---|---|---|---|
|VARIABLES|<br>Log GDP/Cap|<br>Log GDP/Cap|<br>Log GDP/Cap|
|Log Adj Trade|0.227***|0.536***|0.756***|
||(0.0395)|(0.0428)|(0.0997)|
|Invest(% GDP)|0.00322|-0.00265|-0.00503*|
||(0.00253)|(0.00165)|(0.00258)|
|Human Cap(%)|0.00358***|0.00168***|0.000567|
||(0.00121)|(0.000577)|(0.000859)|
|Inflation(2010 = 100)|0.00165***<br>(0.000507)|0.00239***<br>(0.000480)|0.00311***<br>(0.000744)|
|First Stage F- test|-|36.98***|11.53***|
|Hansen J test p-value|-|0.6251|0.1640|
|Observations|4,566|4,553|3,722|
|R-squared|0.879|0.824|0.779|
|Number of id|166|163|145|
|Country FE|Yes|Yes|Yes|
|TimeFE|Yes|Yes|Yes|

**Note:** **_Adj Trade_** is adjusted trade share, known as trade intensity; **_Invest (% GDP)_** is private investment measured by gross fixed capital formation in percentage of GDP; **_Human capital (%)_** represents secondary school enrollment ratio; and **_Inflation (2010 = 100)_** is Consumer Price Index. Robust standard errors in parentheses. Robust standard errors in parentheses.  *** p< 0.01, ** p< 0.05, * p< 0.1.

The difference between the results of this study versus earlier studies somewhat depends on differences in the measurement of trade and growth as well as the methodology used, making comparisons difficult. For example, Frankel and Romer (1999) used the trade intensity measure, finding a positive impact of trade share on GDP per capita of approximately 2 percent. Feyrer (2018) relied on trade volume and found coefficients that range between 0.5 and 0.75 percent (impact of trade volume on GDP per capita). Brueckner and Lederman (2015) relied on the ratio of trade to GDP and found a short-term coefficient of approximately 0.5 percent and a cumulative long run effect of 2 percent. Because the current study relies on adjusted trade intensity, our findings are different from those of Frankel and Romer (1999), Feyrer (2018), and Brueckner and Lederman (2015) even though Feyrer’s and Brueckner and Lederman’s coefficients are similar.

> 34 Confidence intervals (95%) are calculated as follows: CI= Coefficient ± 2 × Standard Error. In this case, for IV1, CI1 = 0.536 ±

> 2 × 0.0428 giving a ,  CI1 = [0.45, 0.62], while for IV2 ,  CI2 = 0.756 ± 2 × 0.0997 with CI2 = [0.56, 0.96].

16
1/(ν−1) In other terms, _p_ * = 1 −  1  ν

**Table 2.1.  Cut-off Rank as a Function of the Aversion Parameter** .

|υ|1.1|1.2|1.5|2.0|3|6|10|20|30|50|100|200|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|p*|0.62|0.60|0.56|0.50|0.42|0.30|0.23|0.15|0.11|0.08|0.05|0.03|

_Source_ : Computed according to equation (2.14) These results suggest that the cut-off rank is a decreasing function of the aversion parameter. As this parameter increases, the focus is shifted to the lower end of the distribution. Thus evaluation will attach more weight to the situation of the most deserving group. For instance, when the aversion parameter is equal to 100, the criterion attaches more weight to the lowest 5 percent of the distribution. An aversion level of 200 shifts the focus of the criterion to the poorest 3 percent of the population. These results also make it obvious that _the aversion parameter allows one to calibrate the poverty focus of the evaluation_ .

We can now use the structure of the impact indicator defined by expression (2.2) to derive _a measure of the rate of pro-poor growth_ as a weighted sum of points on the growth incidence curve<sup>8</sup> . This measure, θ **(** ν **)** , is a function of the focal parameter, ν.

Using the definition of the covariance between two variables, we can write the rate of pro-poor growth in a form analogous to the expressions presented above for points on the GIC or the PGC. It is equal to the average growth rate γ* plus _a covariance-based distribution correction factor_ .

(2.16) θ(ν) = γ * +ν cov[∆ ln _x_ 1(, − _p_ )ν−1]

> 8 As indicated earlier, Chenery et al. (1974) propose a similar indicator on the basis of a semi-logarithmic

_n_ social evaluation function of the form: _W_ ( _x_ ) = ∑β _k_ log _xk_ . The associated impact indicator is thus a _k_ =1 weighted sum of individual growth rates. They suggest the following rules for the specification of evaluative weights. Assign weights either in proportion to the number of people in each group (“one man one vote” principle) or inversely proportional to their initial incomes (“poverty weights”).

12
suggest that “there is growing evidence that the impact on the poor has been more acute than for the non-poor” (p.419). Huang et al. (2011) find that the crisis led to a very sharp drop in rural off-farm employment in China between October 2008 and April 2009, followed by an equally fast recovery by August 2009.

In the context of Thailand, Paitoonpong and Akkarakul (2009) argue that women “are among the most vulnerable to the downturn” (p.11), mainly due to their relatively high representation in the less-stable jobs in export-oriented manufacturing firms. Chirathivat and Mallikamas (2011) show that while food consumption was maintained during the crisis period, and spending on services rose, the amount devoted to buying durable goods fell sharply. They do not, however, disaggregate the effects to the level of household groups. The World Bank (2010, p.69) contends that in Thailand, “vulnerable households lost ground in 2009”.

# _The Question of Interest_

Figure 1 shows that during the great recession economic growth in Thailand followed the same pattern as the world as a whole, with a 2.3% drop in real GDP in 2009, the worst contraction in Southeast Asia (Swee-Hock 2011, p.10).

In this paper we address a seemingly straightforward question: Who in Thailand was actually hurt by the recession of 2009? Was it the poor, or the ultra-poor, or urban populations, or farmers, or women, or wage workers, or large households, or the self-employed, or young children, or school children, or pensioners, or those in the informal sector? Only once we have answered this question can we begin to determine whether the government response to the recession was well targeted, and of an appropriate magnitude. This work also fits within the growing body of research on how households cope in times of recession; UNESC (2009) presents a recent example.

An important reason for focusing on Thailand is the availability of excellent and timely household survey data. The National Statistics Office conducts its Socio-Economic Survey on an on-going basis, interviewing about 3,800 households nationwide every month, and collecting detailed information on consumption as well as many other variables. In principle this allows one to track the actual effects of global recession on different groups in Thailand, with a lag (to allow for data cleaning and processing) of just a month or two. We are also interested in Losers and Winners in Recession: Thailand 2008-09 4
Table 5: Household-level Returns to Education for Father and Children

||**Father’s G**|**eneration**|**Children’s**|**Generation**|
|---|---|---|---|---|
|Years of Education|25.977***|25.581***|18.327***|18.934***|
||(2.146)|(2.086)|(2.665)|(2.685)|
|Rural (=1)|-1.882|8.019|15.654|19.741|
||(16.644)|(16.186)|(26.260)|(26.147)|
|Years of Education_×_Rural|-14.628***|-14.272***|-5.494*|-5.787*|
||(2.446)|(2.377)|(2.942)|(2.943)|
|Number of Working Age Adults||35.157***||12.029***|
|||(3.293)||(2.586)|
|Constant|141.880***|46.038***|85.695***|49.451*|
||(14.893)|(16.752)|(24.199)|(25.510)|
|Observations|3993|3993|3396|3396|

Notes: Standard errors are clustered at the community level (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). Unit of observation is household in both father’s generation and children’s generation. If reported father year’s education varied within a household, an average of father’s years of education is taken. For column (1) and (2), the dependent variable is the inflation adjusted household expenditure in first three waves ( wave 1 (1993), wave 2 (1997), and wave 3 (2000), for column (3) and (4), the dependent variable is inflation adjusted household expenditure in last two waves (wave 4 (2007) and wave 5 (2014)). Inflation rates are calculated using price of rice separately for rural and urban areas. For father rural and urban are defined based on the location of household in 1993. The fathers income sample is restricted to fathers who are between 25 and 56 in 1993. In father’s sample, the number of working age adults is the total number of individuals who are between age 18 to 60 within the household in 1993. For children rural and urban defined based on the location of birth. If the child was born in a big city or town, it was considered as Urban, and if the respondent was born in a village, it was considered Rural. The children sample is restricted to individuals who are 18 to 40 in 2007 and also a household head. In child’s sample, the number of working age adults is the total number of individuals who are between 18 to 60 within the household in 2007.

37
# The Macroeconomics of the Public Sector Deficit: The Case of Thailand

by Virabongsa Ramangkura and Bhanupongse Nidhiprabha

# **Table of Contents**

|1.|Introduction|1|
|---|---|---|
|2.|Historical Background|1|
||2.1<br>The Macroeconomic Performance of the Thai Economy<br>2.2<br>Fiscal Structure<br>2.3<br>Regulations on Public Deficit|1<br>2<br>4|
|3.|Decomposition of the Public Deficit|5|
|4.|Implication of Domestic Financing the Deficit|7|
|5.|Effects of Public Deficit on Private Investment<br>and Consumption|8|
|6.|Impacts of Public Deficit on Real Exchange Rate and Trade<br>Deficit|9|
|7.|A Macroeconomic Model and Estimation Results|10|
|8.|Implication of Public Deficit|19|
||8.1<br>Historical Simulation<br>8.2<br>Counterfactual Simulation of the Effects of Public<br>Deficit|20<br>20|
|9.|Conclusion|**22**|
|Refer|ences|24|
|Figu|res|**26**|
|Table|s|40|
|Appe|ndices|55|